nested case control study is prospective or retrospective

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• Published: 14 May 2020

Application of the matched nested case-control design to the secondary analysis of trial data

• Christopher Partlett   ORCID: orcid.org/0000-0001-5139-3412 1 , 2 ,
• Nigel J. Hall 3 ,
• Alison Leaf 4 , 2 ,
• Edmund Juszczak 2 &
• Louise Linsell 2  

BMC Medical Research Methodology volume  20 , Article number:  117 ( 2020 ) Cite this article

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A nested case-control study is an efficient design that can be embedded within an existing cohort study or randomised trial. It has a number of advantages compared to the conventional case-control design, and has the potential to answer important research questions using untapped prospectively collected data.

We demonstrate the utility of the matched nested case-control design by applying it to a secondary analysis of the Abnormal Doppler Enteral Prescription Trial. We investigated the role of milk feed type and changes in milk feed type in the development of necrotising enterocolitis in a group of 398 high risk growth-restricted preterm infants.

Using matching, we were able to generate a comparable sample of controls selected from the same population as the cases. In contrast to the standard case-control design, exposure status was ascertained prior to the outcome event occurring and the comparison between the cases and matched controls could be made at the point at which the event occurred. This enabled us to reliably investigate the temporal relationship between feed type and necrotising enterocolitis.

Conclusions

A matched nested case-control study can be used to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised, and has several advantages over a standard case-control design. This method offers the potential to make reliable inferences in scenarios where it would be unethical or impractical to perform a randomised clinical trial.

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Key messages

A matched nested case-control design provides an efficient way to investigate causal relationships using untapped data from prospective cohort studies and randomised controlled trials

This method has several advantages over a standard case-control design, particularly when studying time-dependent exposures on rare outcomes

It offers the potential to make reliable inferences in scenarios where unethical or practical issues preclude the use of a randomised controlled trial

Randomised controlled trials (RCTs) are regarded as the gold standard in evidence based medicine, due to their prospective design and the minimisation of important sources of bias through the use of randomisation, allocation concealment and blinding. However, RCTs are not always appropriate due to ethical or practical issues, particularly when investigating risk factors for an outcome. If beliefs about the causal role of a risk factor are already embedded within a clinical community, based on concrete evidence or otherwise, then it is not possible to conduct an RCT due to lack of equipoise. It is often not feasible to randomise potential risk factors, for example, if they are biological or genetic or if there is a strong element of patient preference involved. In such scenarios, the main alternative is to conduct an observational study; either a prospective cohort study which can be complicated and costly, or a retrospective case-control study with methodological shortcomings.

The nested case-control study design employs case-control methodology within an established prospective cohort study [ 1 ]. It first emerged in the 1970–80s and was typically used when it was expensive or difficult to obtain data on a particular exposure for all members of the cohort; instead a subset of controls would be selected at random [ 2 ]. This method with the use of matching has been shown to be an efficient design that can be used to provide unbiased estimates of relative risk with considerable cost savings [ 3 , 4 , 5 ]. Cases who develop the outcome of interest at a given point in time are matched to a random subset of members of the cohort who have not experienced the outcome at that time. These controls may develop the outcome later and become a case themselves, and they may also act as a control for other cases [ 6 , 7 ]. This approach has a number of advantages compared to the standard case-control design: (1) cases and controls are sampled from the same population, (2) exposures are measured prior to the outcome occurring, and (3) cases can be matched to controls at the time (e.g. age) of the outcome event.

More recently, the nested case-control design has been used within RCTs to investigate the causative role of risk factors in the development of trial outcomes [ 8 , 9 , 10 ]. In this paper we investigate the utility of the matched nested case-control design in a secondary analysis of the ADEPT: Abnormal Doppler Enteral Prescription Trial (ISRCTN87351483) data, to investigate the role of different types of milk feed (and changes in types of milk feed) in the development of necrotising enterocolitis. We illustrate the use of this methodology and explore issues relating to its implementation. We also discuss and appraise the value of this methodology in answering similar challenging research questions using clinical trial data more generally.

ADEPT: Abnormal Doppler Enteral Prescription Trial (ISRCTN87351483) was funded by Action Medical Research (SP4006) and investigated whether early (24–48 h after birth) or late (120–144 h after birth) introduction of milk feeds was a risk factor for necrotising enterocolitis (NEC) in a population of 404 infants born preterm and growth-restricted, following abnormal antenatal Doppler blood flow velocities [ 11 ]. Consent and randomisation occurred in the first 2 days after birth. There was no difference found in the incidence of NEC between the two groups, however there was interest in the association between feed type (formula/fortifier or exclusive mother/donor breast milk) and the development of NEC. Breast milk is one of few factors believed to reduce the risk of NEC that has been widely adopted into clinical practice, despite a paucity of high quality population based data [ 12 , 13 ]. However, due to lack of equipoise it would not be ethical or feasible to conduct a trial randomising newborn infants to formula or breast milk.

With additional funding from Action Medical Research (GN2506), the authors used a matched nested case-control design to investigate the association between feed type and the development of severe NEC, defined as Bell’s staging Stage II or III [ 14 ], using detailed daily feed log data from the ADEPT trial. The feed type and quantity of feed was recorded daily until an infant had reached full feeds and had ceased parenteral nutrition, or until 28 days after birth, whichever was longest. Using this information, infants were classified according to the following predefined exposures:

Exposure to formula milk or fortifier in the first 14 days of life

Exposure to formula milk or fortifier in the first 28 days of life

Any prior exposure to formula milk or fortifier

Change in feed type (between formula, fortifier or breast milk) within the previous 7 days.

In the remainder of the methods section we discuss the challenges of conducting this analysis and practical issues encountered in applying the matched nested case-control methodology. In the results section we present data from different aspects of the analysis, to illustrate the utility of this approach in answering the research question.

Cohort time axis

For the main trial analysis, time of randomisation was defined as time zero, which is the conventional approach given that events occurring prior to randomisation cannot be influenced by the intervention under investigation. However, for the nested case-control analysis, time zero was defined as day of delivery because age in days was considered easier to interpret, and also it was possible for an outcome event to occur prior to randomisation. Infants were followed up until their exit time, which was defined by the first occurrence of NEC, death or the last daily feed log record.

Case definition

An infant was defined as a case at their first recorded incidence of severe NEC, defined as Bell’s staging Stage II or III [ 14 ]. Infants could only be included as a case once; subsequent episodes of NEC in the same infant were not counted. Once an infant had been identified as a case, they could not be included in any future risk sets for other cases, even if the NEC episode had been resolved.

Risk set definition

One of the major challenges was identifying an appropriate risk set from which controls could be sampled, whilst also allowing the analysis to incorporate the time dependent feed log data and adjust for known confounders. A diagnosis of NEC has a crucial impact on the subsequent feeding of an infant, therefore it was essential that the analysis only included exposure to non-breast milk feeds prior to the onset of NEC. A standard case-control analysis would have produced misleading results in this context, as infants would have been defined as a cases if they had experienced NEC prior to the end of the study period, regardless of the timing of the event in relation to exposure to non-breast milk. Using a matched nested case-control design allowed us to match an infant with a diagnosis of NEC (case) at a given point in time (days from delivery) to infants with similar characteristics (with respect to other important confounding factors), who had not experienced NEC at the failure time of the case. Figure  1 is a schematic diagram of this process. Each time an outcome event occurred (case), infants that were still at risk were eligible to be selected as a control (risk set). A matching algorithm was used to select a sample of controls with similar characteristics from this risk set. Infants selected as controls could go on to become a case themselves, and could also be included in the risk sets for other cases.

Schematic diagram illustrating the selection of controls from each risk set. Three days following delivery, an infant develops NEC. At this point, there are 11 infants left in the risk set. Four controls with the closest matching are selected, including one infant that becomes a future case on day 18

Selection of matching factors

An important consideration was the appropriate selection of matching factors as well as identifying the optimum mechanism for matching. Sex, gestational age and birth weight were considered to be clear candidates for matching factors, as they are all associated with the development NEC. Gestational age and birth weight in particular are both likely to impact the infant’s feeding and thus their exposure to non-breast milk feeds. Both gestational age and birth weight were matched simultaneously, because of the strong collinearity between gestational age and birth weight, illustrated in Fig.  2 . This was achieved by minimising the Mahalanobis distance from the case to prospective controls of the same sex [ 15 ]. That is, selecting the control closest in gestational age and birth weight to the case while taking into account the correlation between these characteristics.

Scatterplot of birth weight versus gestational age for infants with NEC (cases) and those without (controls)

Typically, treatment allocation would be incorporated as a matching factor since in a secondary analysis it is a nuisance factor imposed by the trial design, which should be accounted for. However, in this example, the ADEPT allocation is associated with likelihood of exposure, since it directly influences the feeding regime. For example, an infant randomised to receive early introduction of feeds is more likely to be exposed to non-breast milk feeds in the first 14 days (44%) than an infant randomised to late introduction of feeds (23%). The main trial results also demonstrated no evidence of association with the outcome (NEC) and therefore there was a concern about the potential for overmatching. Overmatching is caused by inappropriate selection of matching factors (i.e. factors which are not associated with the outcome of interest), which may harm the statistical efficiency of the analysis [ 16 ]. Therefore, we did not include the ADEPT allocation as a matching factor, but we conduct an unadjusted and adjusted analysis by trial arm, to examine its impact on the results.

Selection of controls

Another important consideration was the method used to randomly select controls from each risk set for each case. This can be performed with or without replacement and including or excluding the case in the risk set. We chose the recommended option of sampling without replacement and excluding the case from the risk set, which produces the optimal unbiased estimate of relative risk, with greater statistical efficiency [ 17 , 18 ]. However, infants could be included in multiple risk sets and be selected more than once as a control. We also included future cases of NEC as controls in earlier risk sets, as their exclusion can also lead to biased estimates of relative risk [ 19 ].

Number of controls

In standard case-control studies it has been shown that there is little statistical efficiency gained from having more than four matched controls relative to each case [ 20 , 21 ]. Using five controls is only 4% more efficient than using four, therefore there is no added benefit in using additional controls if a cost is attached, for example taking extra biological samples in a prospective cohort setting. However gains in statistical efficiency are possible by using more than four controls if the probability of exposure among controls is low (< 0.1) [ 4 , 5 ]. Neither of these were issues for this particular analysis, as there were no additional costs involved in using more controls and prevalence of the defined exposures to non-breast milk was over 20% among infants without a diagnosis of NEC. However, there was a concern that including additional controls with increasing distance from the gestational age and birth weight of the case may undermine the matching algorithm. Also, increasing the number of controls sampled per case would lead to an increase in repeated sampling, resulting in larger number of duplicates present in the overall matched control population. This was a particular concern as control duplication was most likely to occur for infants with the lowest birth weight and gestational ages, from which there is a much smaller pool of control infants to sample from. This would have resulted in a small number of infants (with low birth weight and gestational age) being sampled multiple times and having disproportionate weighting in the matched control sample. Therefore, we limited the number of matched controls to four per case.

Statistical analysis

The baseline characteristics of infants with NEC, the matched control group, and all infants with no diagnosis of NEC (non-cases) were compared. Numbers (with percentages) were presented for binary and categorical variables, and means (and standard deviations) or medians (with interquartile range and/or range) for continuous variables. Cases were matched to four controls with the same sex and smallest Mahalanobis distance based on gestational age and birth weight. Conditional logistic regression was used to calculate the odds ratio of developing NEC for cases compared matched controls for each predefined exposure with 95% confidence intervals. Unadjusted odds ratios were calculated, along with estimates adjusting for ADEPT allocation.

The results of the full analysis, including the application of this method to explore the relationship between feed type and other clinically relevant outcomes, are reported in a separate clinical paper (in preparation). Of the 404 infants randomised to ADEPT, 398 were included in this analysis (1 infant was randomised in error, 1 set of parents withdrew consent, 3 infants had no daily feed log data and for 1 infant the severity of NEC was unknown). There were 35 cases of severe NEC and 363 infants without a diagnosis of severe NEC (non-cases). Of the 140 matched controls randomly sampled from the risk set, 109 were unique, 31 were sampled more than once, and 8 had a subsequent diagnosis of severe NEC.

The baseline characteristics of infants with severe NEC (cases) and their matched controls are shown in Table  1 , alongside the characteristics of infants without a diagnosis of severe NEC (non-cases). The matching algorithm successfully produced a well matched collection of controls, based on the majority of these characteristics. There were, however, a slightly higher proportion of infants with the lowest birthweights (< 750 g) among the cases compared to the matched controls (49% vs 38%). The only other factors to show a noticeable difference between cases and matched controls are maternal hypertension (37% vs 49%) and ventilation at trial entry (6% vs 21%), neither of which have been previously identified as risk factors for NEC. Figure  3 shows scatter plots of birth weight and gestational age for the 35 individual cases of NEC and their matched controls, which provides a visual representation of the matching.

Scatterplots showing the matched cases and controls for each case of severe NEC. Each panel contains a separate case of NEC and the matched controls

The main results of the adjusted analysis are presented in Fig.  4 . Unadjusted analyses are included in Table A 1 in the supplementary material, alongside a post-hoc sensitivity analysis that additionally includes covariate adjustment for gestational age and birthweight. While the study did not identify any significant trends between feed-type and severe NEC the findings were consistent with the a priori hypothesis, that exposure to non-breast milk feeds is associated with an increased risk of NEC. In addition, the study identified some potential trends in the association of feed-type with other important outcomes, worthy of further investigation.

Forest plot showing the adjusted odds ratio comparing severe NEC to exposures. Odds ratios are adjusted for sex, gestational age and birthweight (via matching) and trial arm (via covariate adjustment). a Odds ratio and 95% confidence interval. b 109 unique controls

Employing a matched nested case-control design for this secondary analysis of clinical trial data overcame many of the limitations of a standard case-control analysis. We were able to select controls from the same population as the cases thus avoiding selection bias. Using matching, we were able to create a comparable sample of controls with respect to important clinical characteristics and confounding factors. This method allowed us to reliably investigate the temporal relationship between feed type and severe NEC since the exposure data was collected prospectively prior to the outcome occurring. We were also able to successfully investigate the relationship between feed type and several other important outcomes such as sepsis. A standard case-control analysis is typically based on recall or retrospective data collection once the outcome is known, which can introduce recall bias. If we had performed a simple comparison between cases and non-cases of NEC without taking into account the timing of the exposure, this would have produced misleading results. Another advantage of the matched nested case-control design was that we were able to match cases to controls at the time of the outcome event so that they were of comparable ages. The methodology is especially powerful when the timing of the exposure is of importance, particularly for time-dependent exposures such as the one studied here.

While the efficient use of existing trial data has a number of benefits, there are of course disadvantages to using data that were collected for another primary purpose. For instance, it is possible that such data are less robustly collected and checked. As a result, researchers may be more likely to encounter participants with either invalid or missing data.

For instance, the some of the additional feed log data collected in ADEPT were never intended to be used to answer clinical research questions, rather, their purpose was to monitor the adherence of participants to the intervention or provide added background information. In this study, it was necessary to make assumptions about missing data to fill small gaps in the daily feed logs. Researchers should take care that such assumptions are fully documented in the statistical analysis plan in advance and determined blinded to the outcome. Another option is to plan these sub-studies at the design phase, however, there needs to be a balance between the potential burden of additional data collection and having a streamlined trial that is able to answer the primary research question.

Another limitation of the methodology is that it is only possible to match on known confounders. This is in contrast to a randomised controlled trial, in which it is possible to balance on unknown and unmeasured baseline characteristics. As a consequence, particular care must be given to select important matching factors, but also to avoid overmatching.

The methodology allows for participants to be selected as controls multiple times, so there is the possibility that systematic duplication of a specific subset of participants (e.g. infants with a lower birthweight and smaller gestational age) could lead to a small number of participants disproportionately influencing the results. Within this study, we conducted sensitivity analyses with fewer controls, and were able to demonstrate that this had a minimal impact on the findings.

We have demonstrated how a matched nested case-control design can be embedded within an RCT to identify credible associations in a secondary analysis of clinical trial data where the exposure of interest was not randomised. We planned this study after the clinical trial data had already been collected, but it could have been built in seamlessly as a SWAT (Study Within A Trial) during the trial design phase, to ensure that all relevant data were collected in advance with minimal effort. This method has several advantages over a standard case-control design and offers the potential to make reliable inferences in scenarios where unethical or practical issues preclude the use of an RCT. Moreover, because of the flexibility of the methodology in terms of the design and analysis, the matched nested case-control design could reasonably be applied to a wide range of challenging research questions. There is an abundance of high quality large prospective studies and clinical trials with well characterised cohorts, in which this methodology could be applied to investigate causal relationships, adding considerable value for money to the original studies.

Availability of data and materials

ADEPT trial data are available upon reasonable request, subject to the NPEU Data Sharing Policy.

Abbreviations

Abnormal Doppler Enteral Prescription Trial

• Randomised controlled trial

Necrotising enterocolitis

Continuous positive airway pressure

Umbilical artery catheter

Umbilical venous catheter

Study within a trial

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Acknowledgements

This work was presented at the International Clinical Trials Methodology Conference (ICTMC) in 2019 and the abstract is published within Trials [ 22 ].

This work was supported by Action Medical Research [Grant number GN2506]. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Christopher Partlett

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Christopher Partlett, Alison Leaf, Edmund Juszczak & Louise Linsell

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Nigel J. Hall

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NH, AL, EJ and LL conceived the project. CP performed the statistical analyses under the supervision of LL and EJ. CP and LL drafted the manuscript and EJ, AL and NH critically reviewed it. All authors were involved in the interpretation of results. The author(s) read and approved the final manuscript.

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Correspondence to Christopher Partlett .

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Supplementary information

Additional file 1..

Table A1 Association between exposures and the development of Severe NEC. Each case is matched to 4 controls with the same sex and the smallest distance in terms of the Malhalanobis distance based on gestational age and birthweight.

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Partlett, C., Hall, N.J., Leaf, A. et al. Application of the matched nested case-control design to the secondary analysis of trial data. BMC Med Res Methodol 20 , 117 (2020). https://doi.org/10.1186/s12874-020-01007-w

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Nested case-control studies: advantages and disadvantages

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• Philip Sedgwick , reader in medical statistics and medical education 1
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Researchers investigated whether antipsychotic drugs were associated with venous thromboembolism. A population based nested case-control study design was used. Data were taken from the UK QResearch primary care database consisting of 7 267 673 patients. Cases were adult patients with a first ever record of venous thromboembolism between 1 January 1996 and 1 July 2007. For each case, up to four controls were identified, matched by age, calendar time, sex, and practice. Exposure to antipsychotic drugs was assessed on the basis of prescriptions on, or during the 24 months before, the index date. 1

There were 25 532 eligible cases (15 975 with deep vein thrombosis and 9557 with pulmonary embolism) and 89 491 matched controls. The primary outcome was the odds ratios for venous thromboembolism associated with antipsychotic drugs adjusted for comorbidity and concomitant drug exposure. When adjusted using logistic regression to control for potential confounding, prescription of antipsychotic drugs in the previous 24 months was significantly associated with an increased occurrence of venous thromboembolism compared with non-use (odds ratio 1.32, 95% confidence interval 1.23 to 1.42). The researchers concluded that prescription of antipsychotic drugs was associated with venous thromboembolism in a large primary care population.

Which of the following statements, if any, are true?

a) The nested case-control study is a retrospective design

b) The study design minimised selection bias compared with a case-control study

c) Recall bias was minimised compared with a case-control study

d) Causality could be inferred from the association between prescription of antipsychotic drugs and venous thromboembolism

Statements a , b , and c are true, whereas d is false.

The aim of the study was to investigate whether prescription of antipsychotic drugs was associated with venous thromboembolism. A nested case-control study design was used. The study design was an observational one that incorporated the concept of the traditional case-control study within an established cohort. This design overcomes some of the disadvantages associated with case-control studies, 2 while incorporating some of the advantages of cohort studies. 3 4

Data for the study above were extracted from the UK QResearch primary care database, a computerised register of anonymised longitudinal medical records for patients registered at more than 500 UK general practices. Patient data were recorded prospectively, the database having been updated regularly as patients visited their GP. Cases were all adult patients in the register with a first ever record of venous thromboembolism between 1 January 1996 and 1 July 2007. There were 25 532 cases in total. For each case, up to four controls were identified from the register, matched by age, calendar time, sex, and practice. In total, 89 491 matched controls were obtained. Data relating to prescriptions for antipsychotic drugs on, or during the 24 months before, the index date were extracted for the cases and controls. The index date was the date in the register when venous thromboembolism was recorded for the case. The cases and controls were compared to ascertain whether exposure to prescription of antipsychotic drugs was more common in one group than in the other. Despite the data for the cases and controls being collected prospectively, the nested case-control study is described as retrospective ( a is true) because it involved looking back at events that had already taken place and been recorded in the register.

Selection bias is of particular concern in the traditional case-control study. Described in a previous question, 5 selection bias is the systematic difference between the study participants and the population they are meant to represent with respect to their characteristics, including demographics and morbidity. Cases and controls are often selected through convenience sampling. Cases are typically recruited from hospitals or general practices because they are convenient and easily accessible to researchers. Controls are often recruited from the same hospital clinics or general practices as the cases. Therefore, the selected cases may not be representative of the population of all cases. Equally, the controls might not be representative of otherwise healthy members of the population. The above nested case-control study was population based, with the QResearch primary care database incorporating a large proportion of the UK population. The cases and controls were selected from the database and therefore should be more representative of the population than those in a traditional case-control study. Hence, selection bias was minimised by using the nested case-control study design ( b is true).

The traditional case-control study involves participants recalling information about past exposure to risk factors after identification as a case or control. The study design is prone to recall bias, as described in a previous question. 6 Recall bias is the systematic difference between cases and controls in the accuracy of information recalled. Recall bias will exist if participants have selective preconceptions about the association between the disease and past exposure to the risk factor(s). Cases may, for example, recall information more accurately than controls, possibly because of an association with the disease or outcome. Although in the study above the cases and controls were identified retrospectively, the data for the QResearch primary care database were collected prospectively. Therefore, there was no reason for any systematic differences between groups of study participants in the accuracy of the information collected. Therefore, recall bias was minimised compared with a traditional case-control study ( c is true).

Not all of the patient records in the UK QResearch primary care database were used to explore the association between prescription of antipsychotic drugs and development of venous thromboembolism. A nested case-control study was used instead, with cases and controls matched on age, calendar time, sex, and practice. This was because it was statistically more efficient to control for the effects of age, calendar time, sex, and practice by matching cases and controls on these variables at the design stage, rather than controlling for their potential confounding effects when the data were analysed. The matching variables were considered to be important factors that could potentially confound the association between prescription of antipsychotic drugs and venous thromboembolism, but they were not of interest as potential risk factors in themselves. Matching in case-control studies has been described in a previous question. 7

Unlike a traditional case-control study, the data in the example above were recorded prospectively. Therefore, it was possible to determine whether prescription of antipsychotic drugs preceded the occurrence of venous thromboembolism. Nonetheless, only association, and not causation, can be inferred from the results of the above nested case-control study ( d is false)—that is, those people who were exposed to prescribed antipsychotic drugs were more likely to have developed venous thromboembolism. This is because the observed association between prescribed antipsychotic drugs and occurrence of venous thromboembolism may have been due to confounding. In particular, it was not possible to measure and then control for, through statistical analysis, all factors that may have affected the occurrence of venous thromboembolism.

The example above is typical of a nested case-control study; the health records for a group of patients that have already been collected and stored in an electronic database are used to explore the association between one or more risk factors and a disease or condition. The management of such databases means it is possible for a variety of studies to be undertaken, each investigating the risk factors associated with different diseases or outcomes. Nested case-control studies are therefore relatively inexpensive to perform. However, the major disadvantage of nested case-control studies is that not all pertinent risk factors are likely to have been recorded. Furthermore, because many different healthcare professionals will be involved in patient care, risk factors and outcome(s) will probably not have been measured with the same accuracy and consistency throughout. It may also be problematic if the diagnosis of the disease or outcome changes with time.

Cite this as: BMJ 2014;348:g1532

Competing interests: None declared.

• ↵ Parker C, Coupland C, Hippisley-Cox J. Antipsychotic drugs and risk of venous thromboembolism: nested case-control study. BMJ 2010 ; 341 : c4245 . OpenUrl Abstract / FREE Full Text
• ↵ Sedgwick P. Case-control studies: advantages and disadvantages. BMJ 2014 ; 348 : f7707 . OpenUrl CrossRef
• ↵ Sedgwick P. Prospective cohort studies: advantages and disadvantages. BMJ 2013 ; 347 : f6726 . OpenUrl FREE Full Text
• ↵ Sedgwick P. Retrospective cohort studies: advantages and disadvantages. BMJ 2014 ; 348 : g1072 . OpenUrl FREE Full Text
• ↵ Sedgwick P. Selection bias versus allocation bias. BMJ 2013 ; 346 : f3345 . OpenUrl FREE Full Text
• ↵ Sedgwick P. What is recall bias? BMJ 2012 ; 344 : e3519 . OpenUrl FREE Full Text
• ↵ Sedgwick P. Why match in case-control studies? BMJ 2012 ; 344 : e691 . OpenUrl FREE Full Text

Case-Control Studies

• Introduction
• Learning Objectives
• Overview of Case-Control Design
• A Nested Case-Control Study
• Retrospective and Prospective Case-Control Studies
• When is a Case-Control Study Desirable?
• The DES Case-Control Study
• Selecting & Defining Cases and Controls
• The "Case" Definition
• Sources of Cases
• Selection of the Controls
• Sources for "Controls"
• Population Controls:
• Hospital or Clinic Controls:
• Friend, Neighbor, Spouse, and Relative Controls:
• How Many Controls?
• Methods of Control Sampling
• The Rare Outcome Assumption
• More on Selection Bias
• Analysis of Case-Control Studies
• Advantages and Disadvantages of Case-Control Studies

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Content ©2016. All Rights Reserved. Date last modified: February 9, 2016. Wayne W. LaMorte, MD, PhD, MPH

• En español – ExME
• Em português – EME

Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

• Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .  Plastic and Reconstructive Surgery.  2010 Dec;126(6):2234-2242.
• Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .  Biometrics . 1975 Sep;31(3):643–649.
• von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative.  The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies.   Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

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No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

very helpful information provided here

really thanks for wonderful information because i doing my bachelor degree research by survival model

Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

Thank you this was so helpful amazing

Apreciated the information provided above.

So clear and perfect. The language is simple and superb.I am recommending this to all budding epidemiology students. Thanks a lot.

Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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Observational Studies: Cohort and Case-Control Studies

Jae w. song.

1 Research Fellow, Section of Plastic Surgery, Department of Surgery The University of Michigan Health System; Ann Arbor, MI

Kevin C. Chung

2 Professor of Surgery, Section of Plastic Surgery, Department of Surgery The University of Michigan Health System; Ann Arbor, MI

Observational studies are an important category of study designs. To address some investigative questions in plastic surgery, randomized controlled trials are not always indicated or ethical to conduct. Instead, observational studies may be the next best method to address these types of questions. Well-designed observational studies have been shown to provide results similar to randomized controlled trials, challenging the belief that observational studies are second-rate. Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, we describe these study designs, methodological issues, and provide examples from the plastic surgery literature.

Because of the innovative nature of the specialty, plastic surgeons are frequently confronted with a spectrum of clinical questions by patients who inquire about “best practices.” It is thus essential that plastic surgeons know how to critically appraise the literature to understand and practice evidence-based medicine (EBM) and also contribute to the effort by carrying out high-quality investigations. 1 Well-designed randomized controlled trials (RCTs) have held the pre-eminent position in the hierarchy of EBM as level I evidence ( Table 1 ). However, RCT methodology, which was first developed for drug trials, can be difficult to conduct for surgical investigations. 3 Instead, well-designed observational studies, recognized as level II or III evidence, can play an important role in deriving evidence for plastic surgery. Results from observational studies are often criticized for being vulnerable to influences by unpredictable confounding factors. However, recent work has challenged this notion, showing comparable results between observational studies and RCTs. 4 , 5 Observational studies can also complement RCTs in hypothesis generation, establishing questions for future RCTs, and defining clinical conditions.

Levels of Evidence Based Medicine

From REF 1 .

Observational studies fall under the category of analytic study designs and are further sub-classified as observational or experimental study designs ( Figure 1 ). The goal of analytic studies is to identify and evaluate causes or risk factors of diseases or health-related events. The differentiating characteristic between observational and experimental study designs is that in the latter, the presence or absence of undergoing an intervention defines the groups. By contrast, in an observational study, the investigator does not intervene and rather simply “observes” and assesses the strength of the relationship between an exposure and disease variable. 6 Three types of observational studies include cohort studies, case-control studies, and cross-sectional studies ( Figure 1 ). Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e. prospective or retrospective study design). Cross-sectional studies, also known as prevalence studies, examine the data on disease and exposure at one particular time point ( Figure 2 ). 6 Because the temporal relationship between disease occurrence and exposure cannot be established, cross-sectional studies cannot assess the cause and effect relationship. In this review, we will primarily discuss cohort and case-control study designs and related methodologic issues.

Analytic Study Designs. Adapted with permission from Joseph Eisenberg, Ph.D.

Temporal Design of Observational Studies: Cross-sectional studies are known as prevalence studies and do not have an inherent temporal dimension. These studies evaluate subjects at one point in time, the present time. By contrast, cohort studies can be either retrospective (latin derived prefix, “retro” meaning “back, behind”) or prospective (greek derived prefix, “pro” meaning “before, in front of”). Retrospective studies “look back” in time contrasting with prospective studies, which “look ahead” to examine causal associations. Case-control study designs are also retrospective and assess the history of the subject for the presence or absence of an exposure.

COHORT STUDY

The term “cohort” is derived from the Latin word cohors . Roman legions were composed of ten cohorts. During battle each cohort, or military unit, consisting of a specific number of warriors and commanding centurions, were traceable. The word “cohort” has been adopted into epidemiology to define a set of people followed over a period of time. W.H. Frost, an epidemiologist from the early 1900s, was the first to use the word “cohort” in his 1935 publication assessing age-specific mortality rates and tuberculosis. 7 The modern epidemiological definition of the word now means a “group of people with defined characteristics who are followed up to determine incidence of, or mortality from, some specific disease, all causes of death, or some other outcome.” 7

Study Design

A well-designed cohort study can provide powerful results. In a cohort study, an outcome or disease-free study population is first identified by the exposure or event of interest and followed in time until the disease or outcome of interest occurs ( Figure 3A ). Because exposure is identified before the outcome, cohort studies have a temporal framework to assess causality and thus have the potential to provide the strongest scientific evidence. 8 Advantages and disadvantages of a cohort study are listed in Table 2 . 2 , 9 Cohort studies are particularly advantageous for examining rare exposures because subjects are selected by their exposure status. Additionally, the investigator can examine multiple outcomes simultaneously. Disadvantages include the need for a large sample size and the potentially long follow-up duration of the study design resulting in a costly endeavor.

Cohort and Case-Control Study Designs

Advantages and Disadvantages of the Cohort Study

Cohort studies can be prospective or retrospective ( Figure 2 ). Prospective studies are carried out from the present time into the future. Because prospective studies are designed with specific data collection methods, it has the advantage of being tailored to collect specific exposure data and may be more complete. The disadvantage of a prospective cohort study may be the long follow-up period while waiting for events or diseases to occur. Thus, this study design is inefficient for investigating diseases with long latency periods and is vulnerable to a high loss to follow-up rate. Although prospective cohort studies are invaluable as exemplified by the landmark Framingham Heart Study, started in 1948 and still ongoing, 10 in the plastic surgery literature this study design is generally seen to be inefficient and impractical. Instead, retrospective cohort studies are better indicated given the timeliness and inexpensive nature of the study design.

Retrospective cohort studies, also known as historical cohort studies, are carried out at the present time and look to the past to examine medical events or outcomes. In other words, a cohort of subjects selected based on exposure status is chosen at the present time, and outcome data (i.e. disease status, event status), which was measured in the past, are reconstructed for analysis. The primary disadvantage of this study design is the limited control the investigator has over data collection. The existing data may be incomplete, inaccurate, or inconsistently measured between subjects. 2 However, because of the immediate availability of the data, this study design is comparatively less costly and shorter than prospective cohort studies. For example, Spear and colleagues examined the effect of obesity and complication rates after undergoing the pedicled TRAM flap reconstruction by retrospectively reviewing 224 pedicled TRAM flaps in 200 patients over a 10-year period. 11 In this example, subjects who underwent the pedicled TRAM flap reconstruction were selected and categorized into cohorts by their exposure status: normal/underweight, overweight, or obese. The outcomes of interest were various flap and donor site complications. The findings revealed that obese patients had a significantly higher incidence of donor site complications, multiple flap complications, and partial flap necrosis than normal or overweight patients. An advantage of the retrospective study design analysis is the immediate access to the data. A disadvantage is the limited control over the data collection because data was gathered retrospectively over 10-years; for example, a limitation reported by the authors is that mastectomy flap necrosis was not uniformly recorded for all subjects. 11

An important distinction lies between cohort studies and case-series. The distinguishing feature between these two types of studies is the presence of a control, or unexposed, group. Contrasting with epidemiological cohort studies, case-series are descriptive studies following one small group of subjects. In essence, they are extensions of case reports. Usually the cases are obtained from the authors' experiences, generally involve a small number of patients, and more importantly, lack a control group. 12 There is often confusion in designating studies as “cohort studies” when only one group of subjects is examined. Yet, unless a second comparative group serving as a control is present, these studies are defined as case-series. The next step in strengthening an observation from a case-series is selecting appropriate control groups to conduct a cohort or case-control study, the latter which is discussed in the following section about case-control studies. 9

Methodological Issues

Selection of subjects in cohort studies.

The hallmark of a cohort study is defining the selected group of subjects by exposure status at the start of the investigation. A critical characteristic of subject selection is to have both the exposed and unexposed groups be selected from the same source population ( Figure 4 ). 9 Subjects who are not at risk for developing the outcome should be excluded from the study. The source population is determined by practical considerations, such as sampling. Subjects may be effectively sampled from the hospital, be members of a community, or from a doctor's individual practice. A subset of these subjects will be eligible for the study.

Levels of Subject Selection. Adapted from Ref 9 .

Attrition Bias (Loss to follow-up)

Because prospective cohort studies may require long follow-up periods, it is important to minimize loss to follow-up. Loss to follow-up is a situation in which the investigator loses contact with the subject, resulting in missing data. If too many subjects are loss to follow-up, the internal validity of the study is reduced. A general rule of thumb requires that the loss to follow-up rate not exceed 20% of the sample. 6 Any systematic differences related to the outcome or exposure of risk factors between those who drop out and those who stay in the study must be examined, if possible, by comparing individuals who remain in the study and those who were loss to follow-up or dropped out. It is therefore important to select subjects who can be followed for the entire duration of the cohort study. Methods to minimize loss to follow-up are listed in Table 3 .

Methods to Minimize Loss to Follow-Up

Adapted from REF 2 .

CASE-CONTROL STUDIES

Case-control studies were historically borne out of interest in disease etiology. The conceptual basis of the case-control study is similar to taking a history and physical; the diseased patient is questioned and examined, and elements from this history taking are knitted together to reveal characteristics or factors that predisposed the patient to the disease. In fact, the practice of interviewing patients about behaviors and conditions preceding illness dates back to the Hippocratic writings of the 4 th century B.C. 7

Reasons of practicality and feasibility inherent in the study design typically dictate whether a cohort study or case-control study is appropriate. This study design was first recognized in Janet Lane-Claypon's study of breast cancer in 1926, revealing the finding that low fertility rate raises the risk of breast cancer. 13 , 14 In the ensuing decades, case-control study methodology crystallized with the landmark publication linking smoking and lung cancer in the 1950s. 15 Since that time, retrospective case-control studies have become more prominent in the biomedical literature with more rigorous methodological advances in design, execution, and analysis.

Case-control studies identify subjects by outcome status at the outset of the investigation. Outcomes of interest may be whether the subject has undergone a specific type of surgery, experienced a complication, or is diagnosed with a disease ( Figure 3B ). Once outcome status is identified and subjects are categorized as cases, controls (subjects without the outcome but from the same source population) are selected. Data about exposure to a risk factor or several risk factors are then collected retrospectively, typically by interview, abstraction from records, or survey. Case-control studies are well suited to investigate rare outcomes or outcomes with a long latency period because subjects are selected from the outset by their outcome status. Thus in comparison to cohort studies, case-control studies are quick, relatively inexpensive to implement, require comparatively fewer subjects, and allow for multiple exposures or risk factors to be assessed for one outcome ( Table 4 ). 2 , 9

Advantages and Disadvantages of the Case-Control Study

An example of a case-control investigation is by Zhang and colleagues who examined the association of environmental and genetic factors associated with rare congenital microtia, 16 which has an estimated prevalence of 0.83 to 17.4 in 10,000. 17 They selected 121 congenital microtia cases based on clinical phenotype, and 152 unaffected controls, matched by age and sex in the same hospital and same period. Controls were of Hans Chinese origin from Jiangsu, China, the same area from where the cases were selected. This allowed both the controls and cases to have the same genetic background, important to note given the investigated association between genetic factors and congenital microtia. To examine environmental factors, a questionnaire was administered to the mothers of both cases and controls. The authors concluded that adverse maternal health was among the main risk factors for congenital microtia, specifically maternal disease during pregnancy (OR 5.89, 95% CI 2.36-14.72), maternal toxicity exposure during pregnancy (OR 4.76, 95% CI 1.66-13.68), and resident area, such as living near industries associated with air pollution (OR 7.00, 95% CI 2.09-23.47). 16 A case-control study design is most efficient for this investigation, given the rarity of the disease outcome. Because congenital microtia is thought to have multifactorial causes, an additional advantage of the case-control study design in this example is the ability to examine multiple exposures and risk factors.

Selection of Cases

Sampling in a case-control study design begins with selecting the cases. In a case-control study, it is imperative that the investigator has explicitly defined inclusion and exclusion criteria prior to the selection of cases. For example, if the outcome is having a disease, specific diagnostic criteria, disease subtype, stage of disease, or degree of severity should be defined. Such criteria ensure that all the cases are homogenous. Second, cases may be selected from a variety of sources, including hospital patients, clinic patients, or community subjects. Many communities maintain registries of patients with certain diseases and can serve as a valuable source of cases. However, despite the methodologic convenience of this method, validity issues may arise. For example, if cases are selected from one hospital, identified risk factors may be unique to that single hospital. This methodological choice may weaken the generalizability of the study findings. Another example is choosing cases from the hospital versus the community; most likely cases from the hospital sample will represent a more severe form of the disease than those in the community. 2 Finally, it is also important to select cases that are representative of cases in the target population to strengthen the study's external validity ( Figure 4 ). Potential reasons why cases from the original target population eventually filter through and are available as cases (study participants) for a case-control study are illustrated in Figure 5 .

Levels of Case Selection. Adapted from Ref 2 .

Selection of Controls

Selecting the appropriate group of controls can be one of the most demanding aspects of a case-control study. An important principle is that the distribution of exposure should be the same among cases and controls; in other words, both cases and controls should stem from the same source population. The investigator may also consider the control group to be an at-risk population, with the potential to develop the outcome. Because the validity of the study depends upon the comparability of these two groups, cases and controls should otherwise meet the same inclusion criteria in the study.

A case-control study design that exemplifies this methodological feature is by Chung and colleagues, who examined maternal cigarette smoking during pregnancy and the risk of newborns developing cleft lip/palate. 18 A salient feature of this study is the use of the 1996 U.S. Natality database, a population database, from which both cases and controls were selected. This database provides a large sample size to assess newborn development of cleft lip/palate (outcome), which has a reported incidence of 1 in 1000 live births, 19 and also enabled the investigators to choose controls (i.e., healthy newborns) that were generalizable to the general population to strengthen the study's external validity. A significant relationship with maternal cigarette smoking and cleft lip/palate in the newborn was reported in this study (adjusted OR 1.34, 95% CI 1.36-1.76). 18

Matching is a method used in an attempt to ensure comparability between cases and controls and reduces variability and systematic differences due to background variables that are not of interest to the investigator. 8 Each case is typically individually paired with a control subject with respect to the background variables. The exposure to the risk factor of interest is then compared between the cases and the controls. This matching strategy is called individual matching. Age, sex, and race are often used to match cases and controls because they are typically strong confounders of disease. 20 Confounders are variables associated with the risk factor and may potentially be a cause of the outcome. 8 Table 5 lists several advantages and disadvantages with a matching design.

Advantages and Disadvantages for Using a Matching Strategy

Multiple Controls

Investigations examining rare outcomes may have a limited number of cases to select from, whereas the source population from which controls can be selected is much larger. In such scenarios, the study may be able to provide more information if multiple controls per case are selected. This method increases the “statistical power” of the investigation by increasing the sample size. The precision of the findings may improve by having up to about three or four controls per case. 21 - 23

Bias in Case-Control Studies

Evaluating exposure status can be the Achilles heel of case-control studies. Because information about exposure is typically collected by self-report, interview, or from recorded information, it is susceptible to recall bias, interviewer bias, or will rely on the completeness or accuracy of recorded information, respectively. These biases decrease the internal validity of the investigation and should be carefully addressed and reduced in the study design. Recall bias occurs when a differential response between cases and controls occurs. The common scenario is when a subject with disease (case) will unconsciously recall and report an exposure with better clarity due to the disease experience. Interviewer bias occurs when the interviewer asks leading questions or has an inconsistent interview approach between cases and controls. A good study design will implement a standardized interview in a non-judgemental atmosphere with well-trained interviewers to reduce interviewer bias. 9

The STROBE Statement: The Strengthening the Reporting of Observational Studies in Epidemiology Statement

In 2004, the first meeting of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) group took place in Bristol, UK. 24 The aim of the group was to establish guidelines on reporting observational research to improve the transparency of the methods, thereby facilitating the critical appraisal of a study's findings. A well-designed but poorly reported study is disadvantaged in contributing to the literature because the results and generalizability of the findings may be difficult to assess. Thus a 22-item checklist was generated to enhance the reporting of observational studies across disciplines. 25 , 26 This checklist is also located at the following website: www.strobe-statement.org . This statement is applicable to cohort studies, case-control studies, and cross-sectional studies. In fact, 18 of the checklist items are common to all three types of observational studies, and 4 items are specific to each of the 3 specific study designs. In an effort to provide specific guidance to go along with this checklist, an “explanation and elaboration” article was published for users to better appreciate each item on the checklist. 27 Plastic surgery investigators should peruse this checklist prior to designing their study and when they are writing up the report for publication. In fact, some journals now require authors to follow the STROBE Statement. A list of participating journals can be found on this website: http://www.strobe-statement.org./index.php?id=strobe-endorsement .

Due to the limitations in carrying out RCTs in surgical investigations, observational studies are becoming more popular to investigate the relationship between exposures, such as risk factors or surgical interventions, and outcomes, such as disease states or complications. Recognizing that well-designed observational studies can provide valid results is important among the plastic surgery community, so that investigators can both critically appraise and appropriately design observational studies to address important clinical research questions. The investigator planning an observational study can certainly use the STROBE statement as a tool to outline key features of a study as well as coming back to it again at the end to enhance transparency in methodology reporting.

Acknowledgments

Supported in part by a Midcareer Investigator Award in Patient-Oriented Research (K24 AR053120) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (to Dr. Kevin C. Chung).

None of the authors has a financial interest in any of the products, devices, or drugs mentioned in this manuscript.

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Clinical course of poststroke epilepsy: a retrospective nested case-control study

Affiliation.

• 1 Department of Neuroscience, Uppsala University 751 85, Uppsala, Sweden.
• PMID: 26445704
• PMCID: PMC4589812
• DOI: 10.1002/brb3.366

Introduction: Recently, several epidemiological studies have demonstrated that epilepsy develops after approximately 10% of all cerebrovascular lesions. With an aging population, poststroke epilepsy is likely to be of increasing relevance to neurologists and more knowledge on the condition is needed. Patients with poststroke epilepsy are likely to differ from other epilepsy patient populations regarding age, side-effect tolerability, comorbidities, and life expectancy, all of which are important aspects when counselling newly diagnosed patients to make informed treatment decisions.

Method: We have here performed a nested case-control study on 36 patients with poststroke epilepsy and 55 controls that suffered stroke but did not develop epilepsy. The average follow-up time was between 3 and 4 years.

Results: In our material, two-thirds of patients achieved seizure freedom and 25% experienced a prolonged seizure (status epilepticus) during the follow-up period. Cases consumed more health care following their stroke, but did not suffer more traumatic injuries. Interestingly, the mortality among cases and controls did not differ significantly. This observation needs to be confirmed in larger prospective studies, but indicate that poststroke epilepsy might not infer additional mortality in this patient group with considerable comorbidities.

Conclusions: The observations presented can be of value in the counselling of patients, reducing the psychosocial impact of the diagnosis, and planning of future research on poststroke epilepsy.

Keywords: Cerebrovascular diseases; epilepsy; treatment.

Publication types

• Research Support, Non-U.S. Gov't
• Case-Control Studies
• Comorbidity
• Epilepsy / epidemiology*
• Middle Aged
• Prospective Studies
• Retrospective Studies
• Seizures / epidemiology
• Stroke / epidemiology*

• Open access
• Published: 04 November 2023

Prediction of incidence of neurological disorders in HIV-infected persons in Taiwan: a nested case–control study

• Ya-Wei Weng 1 , 2 ,
• Susan Shin-Jung Lee 2 , 3 , 4 ,
• Hung-Chin Tsai 2 , 3 , 4 , 5 ,
• Chih-Hui Hsu 6 &
• Sheng-Hsiang Lin 1 , 6 , 7  

BMC Infectious Diseases volume  23 , Article number:  759 ( 2023 ) Cite this article

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Neurological disorders are still prevalent in HIV-infected people. We aimed to determine the prevalence of neurological disorders and identify their risk factors in HIV-infected persons in Taiwan.

We identified 30,101 HIV-infected people between 2002 and 2016 from the National Health Insurance Research Database in Taiwan, and analyzed the incidence of neurological disorders. We applied a retrospective, nested case–control study design. The individuals with (case group) and without (control group) a neurological disorder were then matched by age, sex and time. Factors associated with neurological disorders were analyzed using a conditional logistic regression model, and a nomogram was generated to estimate the risk of developing a neurological disorder.

The incidence of neurological disorders was 13.67 per 1000 person-years. The incidence remained stable during the observation period despite the use of early treatment and more tolerable modern anti-retroviral therapy. The conditional logistic regression model identified nine clinical factors and comorbidities that were associated with neurological disorders, namely age, substance use, traumatic brain injury, psychiatric illness, HIV-associated opportunistic infections, frequency of emergency department visits, cART adherence, urbanization, and monthly income. These factors were used to establish the nomogram.

Neurological disorders are still prevalent in HIV-infected people in Taiwan. To efficiently identify those at risk, we established a nomogram with nine risk factors. This nomogram could prompt clinicians to initiate further evaluations and management of neurological disorders in this population.

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Due to the widespread use of combination antiretroviral therapy (cART), the life expectancy of individuals infected with human immunodeficiency virus (HIV) has improved and even approaches that of the general population [ 1 ]. However, a gap remains in comorbidity-free years between HIV-infected individuals and the general population [ 2 ]. In addition to comorbidities including cardiovascular diseases, cancers, diabetes, dyslipidemia and chronic renal diseases, which are prevalent in people living with HIV (PLWH)[ 3 , 4 ], neuropsychiatric conditions are also common in PLWH [ 5 ]. The neurological complications of HIV are quite diverse, and in the early stages of infection can include meningitis, encephalitis and Bell's palsy. Late-stage symptoms include HIV-associated neurocognitive disorders, toxoplasma encephalitis, tuberculous meningitis, cryptococcal meningitis and neurosyphilis [ 6 ]. As with the other HIV-associated comorbidities, HIV-associated neurocognitive disorders are still prevalent in the modern cART era, with an overall prevalence rate of around 45% [ 7 , 8 ]. These disorders can affect the quality of life and contribute to mortality in PLWH [ 9 ]. The pattern of HIV-associated neurocognitive disorders has changed in the recent two decades [ 10 ], and the prevalence may be underestimated due to a lack of awareness [ 11 ].

HIV also affects the central nervous system early in infection [ 12 ], and blood–brain barrier disruption has been demonstrated early in the course of primary HIV infection [ 13 ]. Thus, central nervous system infection caused by primary HIV infection or other pathogens (virus, bacteria, fungi) is also a common neurological complication in HIV-infected patients. However, there are limited data about neurological disorders in PLWH in the Asia–Pacific region [ 14 , 15 ].

In Taiwan, cART has been provided free of charge since 1997. However, guidelines for the diagnosis and treatment of HIV/AIDS in Taiwan have recommended initiating cART according to different CD4 cell levels at different times: < 200 cells/mm 3 in 2006, < 350 cells/mm 3 in 2010, < 500 cells/mm 3 in 2013, and "treat all" since 2016. Improvement in treatment coverage for PLWH was also implemented in other countries due to new scientific evidence around HIV treatment during this period of time [ 16 ]. Several studies have reported that CD4 nadir and CD4 count are predictors of HIV neurological disorders in the era of modern cART [ 17 , 18 , 19 ]. Thus, there may have been dynamic changes or even improvements in neurological disorders in PLWH in Taiwan during this time.

Several clinical factors and comorbidities have been reported to contribute to cognitive impairment in PLWH, including advanced HIV disease [ 17 ], duration of HIV infection [ 20 , 21 ], obesity and diabetes [ 22 ], increased age [ 23 ], and hepatitis C infection [ 23 ]. In addition, alcohol use, substance abuse, traumatic brain injury, sleep disorders and psychiatric illnesses may also predispose to cognitive disorders in PLWH [ 24 ].

In the present study, we aimed to determine the dynamic changes in neurological disorders from 2002 to 2017, and to identify risk factors for neurological disorders in HIV-infected persons even under different treatment strategies in Taiwan.

Study population and study design

This was a retrospective, population-based, nested case–control study using clinical data retrieved from the Taiwan National Health Insurance Research Database (NHIRD). Patients with a diagnosis of HIV infection during the period from 1 January 2002 to 31 December 2016 were identified in the NHIRD. HIV infection is a notifiable disease in Taiwan and the cost of copayments for medical services for patients with HIV infection can be waived, and this can help to ensure the accuracy of the diagnosis of these patients.

Data source

By using the incidence of neurological disorders in HIV patients as the outcome variable, we excluded individuals with missing age or sex data and neurological disorders before the diagnosis of HIV infection. To estimate the effects of potential covariates on the risk of neurological disorders, a nested case–control study design with age, sex and time matching was applied in this study (Fig.  1 ). The primary outcome was the incidence of a first diagnosis of a neurological disorder after a diagnosis of HIV. Neurological disorders included neurocognitive disorders and central nervous system infections. The covariates were dyslipidemia, hepatitis C infection, substance use, alcoholism, traumatic brain injury, sleep apnea, sexually transmitted diseases, diabetes mellitus, psychiatric illnesses and HIV-associated opportunistic infections. These covariates were defined as the diagnoses recorded once or more during inpatient care or twice or more during ambulatory care within 1 year before the index date. Demographic profile (including sex, birth date, urbanization and monthly income), frequency of emergency department (ED) visits, and cART adherence were also extracted as covariates. The frequency of ED visits was analyzed because a previous study showed that ED visits were primarily driven by disease severity in people with HIV infection [ 25 ]. Adherence to cART was calculated as the proportion of days covered by dividing the number of days of ART coverage during the measurement period by the length of the measurement period [ 26 ]. Urbanization level was classified into urban, suburban and rural categories based on five aspects: population density, percentage of residents who were agricultural workers, the number of physicians per 100,000 people, percentage of residents with college or higher education, and percentage of residents aged 65 years or older [ 27 ].

Flow chart of the HIV cohort for evaluating the risk of neurological disorders

Diagnoses in the NHIRD are coded based on International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Edition (ICD-10) codes. ICD-9 codes were used between 2002 and 2014, and ICD-10 codes were used between 2015 and 2017. The ICD-9 and ICD-10 codes for the outcomes and covariates are provided in the Supplementary Table 1 . The end of the observation period was defined as the occurrence of a neurological disorder, the end of 2017, or withdrawal from the National Health Insurance program.

This study was conducted after approval by the Institutional Review Board (IRB) of the National Cheng Kung University Hospital (B-EX-109–026). Since personal identification information is encrypted before releasing the data to researchers, informed consent was able to be waived from the IRB of the institute.

Statistical analysis

Incidence rates were expressed per 1000 prospective person-years of observation from 2002 through 2017. Continuous variables were compared using the Student's t test, and categorical variables were compared using the chi-square test or Fisher's exact test. Variables significantly associated with the risk of neurological disorders in univariate conditional logistic regression analysis were then selected to construct the final multivariate logistic regression model. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). A p value < 0.05 was considered to be statistically significant.

A nomogram is a two-dimensional diagram used to represent a mathematical function involving several predictors [ 28 ]. The variables significantly associated with the risk of neurological disorders in the multivariate logistic regression analysis were used to generate a nomogram.

Demographic and clinical characteristics

A total of 30,101 HIV-infected people were identified from 2002 to 2016, of whom 24,239 were used for further matching. A total of 2132 (8.8%) individuals were diagnosed with neurological disorders during the follow-up period. Of the 2132 HIV-infected people with neurological disorders, 87.27% were male and the mean age (± standard deviation) at diagnosis was 38.5 ± 14.7 years. About 65.45% of individuals received cART therapy. Among these 2132 individuals, 1168 (54.8%) individuals have central nervous system infections, and 997 (46.8%) individuals have neurocognitive disorders. Half of the neurological disorders were identified before the initiation of cART. The proportion of central nervous system infections and neurocognitive disorders were quite similar before and after starting cART. The overall incidence of neurological disorders was 13.67 per 1000 person-years (Fig.  2 ). The incidence of central nervous system infections was 7.49 per 1000 person-years, and the incidence of neurocognitive disorders was 6.40 per 1000 person-years. The median time from the index date to a diagnosis of a neurological disorder was 3.6 years. The individuals with (case group) and without (control group) a neurological disorder were then matched by age, sex and time. The cases and controls were selected at a 1:4 ratio (Fig.  1 ). Table 1 shows the demographic and clinical characteristics of the case ( n  = 1655) and control ( n  = 6620) groups.

Incidence rate (per 1000 person-years) of neurological disorders among HIV-infected persons in Taiwan from 2002–2017

Factors associated with neurological disorders in the HIV-infected persons

Risk factors included in conditional logistic regression analysis were age at HIV diagnosis, dyslipidemia, hepatitis C infection, substance use, alcoholism, traumatic brain injury, sleep apnea, sexually transmitted diseases, diabetes mellitus, psychiatric illnesses, HIV-associated opportunistic infections, frequency of ED visits, cART adherence, urbanization level and monthly income. Odds ratios, adjusted odds ratios and their corresponding upper and lower 95% confidence intervals are presented in Table 2 . In the univariate analysis, older age, hepatitis C infection, substance use, alcoholism, traumatic brain injury, sexually transmitted diseases, psychiatric illnesses, HIV-associated opportunistic infections, frequency of ED visits, cART adherence, urbanization and monthly income were associated with neurological disorders. Dyslipidemia, sleep apnea and diabetes were not associated with neurological disorders. In the multivariate analysis, hepatitis C infection, alcoholism and sexually transmitted diseases were no longer significant. Due to concerns about confounding by age, we then performed subgroup analyses of only younger subjects (arbitrarily defined as less than 40 years of age) and only older subjects (40 years or older). The results are shown in Table 3 .

According to the multivariate analysis results, a nomogram was generated to estimate the risk of developing a neurological disorder as shown in Fig.  3 . By summing the risk score for each factor as shown in the nomogram, the risk of developing a neurological disorder for each individual can be assessed.

Nomogram for predicting the development of neurological disorders in HIV-infected persons

In this retrospective nested case–control study, we found several risk factors for neurological disorders in HIV-infected people and then developed a simple risk scoring system to identify those at risk. To the best of our knowledge, this scoring system is the first to be specifically designed for identifying neurological disorders in people infected with HIV. Several clinical factors and comorbidities have been reported to be associated with neurological disorders in HIV-infected people, including the frequency of ED visits [ 29 ], cART adherence [ 30 , 31 ], advanced HIV disease [ 17 ], duration of HIV infection [ 20 , 21 ], and older age [ 23 ]. Comorbidities including obesity, diabetes [ 22 ], hepatitis C infection [ 23 ], alcohol use, substance abuse, traumatic brain injury, sleep disorders and psychiatric illnesses [ 24 ] have also been associated with neurological disorders in HIV-infected people. The large number of factors which can contribute to the development of neurological disorders in this population makes it more complex to predict. Through the proposed nomogram with some basic clinical information, clinicians can identify those at risk and initiate further screening for comorbidities, drug compliance education, or even cognitive function evaluations. This nomogram may serve as a screening tool for identifying risk populations.

Educational attainment [ 32 ], tobacco use [ 33 ], and cART regimen [ 34 , 35 ] can also influence neurocognitive function. Since educational attainment is closely related to the level of income [ 36 , 37 ] and monthly income could be extracted from the NHIRD, we used monthly income as a covariate instead of educational attainment as data on educational attainment are not available in the NHIRD. However, more research is needed to evaluate whether adding more parameters (clinical factors and/or biomarkers) could better predict the development of neurological complications in HIV-infected people.

The incidence of neurological disorders in HIV-infected persons was stable from 2006 to 2017 (13.67 per 1000 person-years) even though early treatment and even a "treat all" policy was applied during this period and more tolerable modern cART was used. This finding is consistent with previous studies in which neurological complications were still prevalent in HIV-infected persons due to it being neuroinvasive, neurotropic and neurovirulent [ 38 , 39 ]. Thus, neurological manifestations are an important concern among people with HIV infection.

In the subgroup analyses of only younger subjects and only older subjects, substance use was significantly associated with neurological disorders in the younger subjects(adjusted HR = 1.45, p  = 0.003), but not in the older subjects(adjusted HR = 1.01, p  = 0.963). This may be because substance use is typically higher in adolescents and young adults, and the neurological complications of substance use can occur in both acute and early HIV infection [ 40 ]. This should raise awareness of neurological disorders in young HIV-infected people with substance use disorders.

The key strength of this study is the application of a nationwide database to identify predictors of neurological disorders. The high coverage, easy accessibility, and low copayments result in high adherence of beneficiaries to the National Health Insurance program, which minimizes potential selection and information biases.

Some limitations should also be addressed. First, some risk factors for neurological disorders such as low CD4 cell count, high blood viral load, low educational attainment, tobacco use and cART regimen are not included in the NHIRD and could not be incorporated into the scoring system. Both CD4 cell count and blood viral load are important predictors of outcomes in HIV-infected persons [ 17 , 41 ]. In addition, we used HIV-associated opportunistic infections as a proxy for advanced HIV status. Second, the diagnosis of neurological disorders and comorbidities depended on claims data from the NHIRD, and physicians who cared for these patients were not neurologists, which may have led to underestimation of the proportion of neurological disorders. Third, cART adherence was calculated by the proportion of days covered, and the actual adherence rate may have been lower, especially in those with neurological disorders [ 42 , 43 ].

In conclusion, neurological disorders are still prevalent in HIV-infected persons. To efficiently identify those at risk, we established a nomogram with nine risk factors. This nomogram could prompt clinicians to initiate further evaluations and management of neurological disorders.

Availability of data and materials

The de-linked datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. The data are not publicly available because the use of the National Health Insurance Research Database is limited to research purposes only.

Abbreviations

Combination antiretroviral therapy

Emergency department

Human immunodeficiency virus

International Classification of Diseases, Ninth/Tenth Edition

National Health Insurance Research Database

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Acknowledgements

We are grateful to all research assistants for providing the statistical consulting services from the Biostatistics Consulting Center, Clinical Medicine Research Center, National Cheng Kung University Hospital.

This work was supported by Kaohsiung Veterans General Hospital (KSVGH110-D08-1 to YWW) and Veterans Affairs Council, Republic of China (VAC112-001).

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Ya-Wei Weng & Sheng-Hsiang Lin

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Ya-Wei Weng, Susan Shin-Jung Lee & Hung-Chin Tsai

Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

Susan Shin-Jung Lee & Hung-Chin Tsai

School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan

Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung, Taiwan

Hung-Chin Tsai

Biostatistics Consulting Center, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan

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YWW, SJL, HCT and SHL conceived the study and designed the protocol. YWW, CHH and SHL performed the data management and analyses. YWW drafted the paper. SJL and HCT revised the manuscript. SHL provided critical revisions and supervised the paper. All authors contributed to and approved the final paper.

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This study was conducted after approval by the Institutional Review Board (IRB) of the National Cheng Kung University Hospital (B-EX-109–026). Since personal identification information is encrypted before releasing the data to researchers, informed consent was able to be waived from the Institutional Review Board (IRB) of the National Cheng Kung University Hospital (B-EX-109–026). And all methods were carried out in accordance with relevant guidelines and regulations.

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Supplementary Information

Additional file 1: supplementary table 1..

ICD-9 and ICD 10 codes used for neurological disorders and covariates.

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Weng, YW., Lee, S.SJ., Tsai, HC. et al. Prediction of incidence of neurological disorders in HIV-infected persons in Taiwan: a nested case–control study. BMC Infect Dis 23 , 759 (2023). https://doi.org/10.1186/s12879-023-08761-4

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Cardiovascular disease (CVD) is a major complication in type 1 diabetes mellitus (T1D) patients. Previous studies have suggested that statins may be helpful for prevention of CVD in T1D, but there are limited data on the role of statins in T1D. We investigated the relationship between statin treatment and cardiovascular risk in T1D patients using a population-based cohort.

We conducted a retrospective cohort study using the Korean nationwide health insurance database from January 2007 to December 2017. This study included 11,009 T1D patients aged ≥ 20 years without a prior history of CVD. The primary outcome was a composite development of stroke or myocardial infarction. Statin use during follow-up was treated as a time-varying variable. We performed a multivariable time-dependent Cox regression analysis adjusting for sex, age, type of insurance, hypertension, renal disease, and use of antiplatelets and renin–angiotensin–aldosterone system inhibitors.

During the mean follow-up of 9.9 ± 3.7 years of follow-up, 931 T1D patients (8.5%) suffered primary outcome. Statin treatment was associated with a reduced risk of the primary outcome (adjusted hazard ratio, 0.76; 95% confidence interval 0.66–0.88; p < 0.001). Statin use led to decreased risks of ischemic stroke and myocardial infarction, but was not related to hemorrhagic stroke. We also found that the risk of cardiovascular events decreased as the cumulative exposure duration of statins increased.

Conclusions

Statin use was associated with a lower risk of cardiovascular events in T1D patients. Further prospective studies are needed to confirm the potential role of statins in prevention of CVD in patients with T1D.

Introduction

Type 1 diabetes mellitus (T1D) is a chronic metabolic disease precipitated by an immune-associated destruction of insulin-producing β-pancreatic cells [ 1 ]. Abnormally high blood glucose level in T1D affects major organs and can lead to a variety of complications over time. Typically, people with T1D have a greater risk of cardiovascular events than the general population, and cardiovascular disease (CVD) is a major cause of morbidity and mortality in those with T1D [ 2 ]. Therefore, lifelong control of the cardiovascular risk profile is essential in the management of individuals with T1D.

Statins are a class of lipid-lowering medications and one of the most widely used drugs worldwide. With good efficacy and well-established safety, statins remain the cornerstone in the prevention and treatment of CVD [ 3 , 4 ]. In type 2 diabetes mellitus (T2D), statin treatments have beneficial effects on the prevention of CVD and mortality [ 5 ]. Contrary to the cumulative evidence supporting the use of statins in people with T2D for cardiovascular prevention, there are very limited data concerning the use of statins in T1D [ 6 ]. Regarding the high CVD risk in T1D, guidelines recommend statin treatment for primary prevention in T1D patients > 40 years of age [ 7 , 8 ]. However, these recommendations are mainly derived from studies on patients with T2D, and the effect of statins on cardiovascular risk in T1D patients is not well established. Especially, knowledge of the role of statins in Asian patients with T1D is lacking [ 9 ]. In the current study, we investigated the association between statin use and the development of CVD in T1D using a population-based cohort from the Korean nationwide healthcare claims database.

Data source

This study is a retrospective analysis of a population-based T1D cohort from a nationwide health insurance claims database in Korea. Korea has a public single-payer health insurance system that covers the entire nation, and the Health Insurance Review and Assessment Service (HIRA) is a government agency specializing in reviewing medical claims from health care providers and quality assessment of health care services [ 10 ]. For the purpose of political and academic research with an appropriate review process, HIRA provides health care claims data to researchers. The HIRA database contains health care information from each patient visit to a medical institution (primary-care clinics, public health centers, general hospitals, and tertiary referral hospitals), diagnoses, prescriptions, medical procedures, and demographic data. In the claims data, the diagnoses at each hospital visit are recorded according to the 10th edition of the International Statistical Classification of Diseases (ICD-10) coding scheme, and prescription records include drug name, dose, prescription date, and duration. The provided claims dataset is anonymized and does not contain any identifiable information. This study was approved by the Institutional Review Board of Yongin Severance Hospital, Yonsei University College of Medicine (9-2021-0119). The requirement for informed consent in this study was waived because of its retrospective nature, and analyses were performed using fully anonymized data.

Study cohort with T1D

Using population-based healthcare claims data from the HIRA, we selected patients who received an insulin prescription with a diagnostic code of T1D (ICD-10 code of “E10”) between 2007 and 2017. Because T1D is often confused with other types of diabetes mellitus, such as T2D, we tried to identify T1D patients using strict criteria from a previous study of T1D in Korea [ 11 ]. According to the criteria, we only included patients with ≥ 3 claims for prescription of insulin. Patients without an additional insulin regimen established between 1–2 years after the first insulin treatment were excluded. Patients who had a diagnosis of another type of diabetes (“E11–14”) within 2 years of the first insulin prescription were also excluded. We also excluded patients < 20 years of age in whom the effect of statins is uncertain. Patients with pancreatic cancer or who underwent total or partial pancreatectomy were excluded. Additionally, those who had CVD prior to T1D diagnosis (ischemic heart diseases: “I20–25”, stroke: “I60–64, I69”, carotid artery stent, carotid endarterectomy, coronary stent insertion, coronary artery bypass graft) and those with a follow-up period < 90 days were excluded. Figure  1 demonstrates the inclusion and exclusion of study participants.

Flow chart of patient enrollment

Follow-up and study outcomes

The index date and start date of follow-up were defined as the initial date of insulin prescription with the diagnostic code of “E10” for T1D in each patient. Patients with T1D included in the study were followed up to the occurrence of the primary outcome; censoring; or until December 31, 2020 (the study end date). The primary outcome was development of a cardiovascular event and was a composite of stroke and myocardial infarction (MI)—whichever occurred first. Stroke was determined by admission with the primary diagnosis of “I60–63” with brain computed tomography or magnetic resonance imaging performed during the admission [ 12 ]. MI was defined by admission with a primary diagnosis of “I21”. Diagnostic accuracies for stroke and MI based on the health claims data in Korea have been reported as sufficient [ 13 , 14 ]. The secondary outcome was ischemic stroke (“I63”), hemorrhagic stroke (“I60–62”), or MI (“I21”), which are components of the primary outcome. In the analysis for secondary outcomes, individual outcomes were treated as competing events, and patients experiencing competing events were censored at the time the event occurred.

We collected data on demographics, such as age, sex, type of insurance (national health insurance and medical aid from the government), and presence of hypertension and renal disease from the HIRA database. The public health care system in Korea is a two-tiered system of national health insurance and medical aid. The Korean medical aid program provides free or reduced-cost care for low-income families and individuals. The remaining proportion of the population is covered by national health insurance. The presence of hypertension was considered if the patient received anti-hypertensive agents and had the corresponding diagnostic codes of hypertension (ICD-10 codes “I10–13”, “I15”) [ 15 ]. Renal disease was identified by the presence of relevant diagnostic codes (ICD-10 codes “N17–19”, “E10.2” or “I12–13”) or claims of hemodialysis, peritoneal dialysis, and/or procedures related to renal disease [ 12 , 16 ].

Use of statins, antiplatelets, and RAAS inhibitors

During the study period, we collected prescription data (drug name, dose, and duration) from the HIRA database for statins (atorvastatin, fluvastatin, lovastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) and oral antiplatelets (aspirin, clopidogrel, ticlopidine, ticagrelor, prasugrel, triflusal, dipyridamole, and cilostazol) in each patient. Because medication intake varies over time, treatments involving these medications during the follow-up period have a time-varying feature. Additionally, we assessed the use of renin–angiotensin–aldosterone system (RAAS) inhibitors, such as angiotensin-converting enzyme inhibitors and angiotensin receptor blockers, in each patient throughout the follow-up period. On each day of the follow-up period, the use of medications was determined by prescription coverage (Fig.  2 ). In the analyses, the use of medications (statins, antiplatelets, and RAAS inhibitors) was treated as a time-dependent variable.

An example of determining the prescription of a statin as a time-dependent variable

Statistical analyses

Characteristics are expressed as mean ± standard deviation values for continuous variables and number (%) for categorical variables. To evaluate whether treatment with statins was associated with the occurrence of a subsequent stroke or MI, we calculated hazard ratio (HR) and 95% confidence interval (CI) values based on a time-dependent Cox proportional hazards regression model for the development of cardiovascular events, which included the use of statins as a time-dependent variable. Adjustments were performed for sex, age, type of insurance, presence of hypertension and renal disease, use of antiplatelets and RAAS inhibitors. The assumption of proportional hazards for use of statins in the Cox regression model was tested by calculating the Schoenfeld residuals using the “ cox.zph ” function in the R package of “ survival ” and was satisfactory. To evaluate the potential interactions with statin treatment, we performed subgroup analyses according to sex, age group (20–39 or ≥ 40 years), and enrollment period.

As an additional sensitivity analysis, we conducted a nested case–control study with the T1D cohort [ 17 ]. In this design, cases are patients who experienced the primary outcome during the follow-up period. For each case, we matched three controls with replacements from the cohort (1:3 matching) who were free from the event at the time of the primary outcome in their matched case by incidence density sampling. Cases and controls were matched for all collected variables except the use of statins (sex, age [± 1 year], type of insurance, presence of hypertension, renal disease, use of antiplatelets and RAAS inhibitors). Conditional logistic regression analysis was performed with the matched case–control groups to estimate the odds ratio (OR) and 95% CI for the primary outcome according to statin treatment. We also evaluated the risk for the primary outcome according to cumulative exposure duration to statins instead of the use of statins. The cumulative exposure duration to statins was calculated as the sum of days of statin treatment between the index date and the time of the primary outcome, which was subdivided into three categories of < 1 year, 1–3 years, and > 3 years. We used the group of cumulative exposure duration to statins < 1 year as the reference category to investigate the results for each duration group. Statistical analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC, USA) and R software (version 3.3.3; The R Foundation for Statistical Computing, Vienna, Austria; http://www.R-project.org/ ). P values were two-sided, and P  < 0.05 was considered statistically significant.

Characteristics of the included T1D patients

According to the inclusion and exclusion criteria, 11,009 patients with T1D aged ≥ 20 years were included in this study (Fig.  1 ). Among the included T1D patients, 6785 (61.6%) were men, and the mean age at enrollment was 50.7 ± 15.2 years. For the type of health insurance, 805 patients (7.3%) were eligible for medical aid from the government, while 10,204 patients (92.7%) were covered by the National Health Insurance Service. Hypertension and renal disease were present in 4548 (41.3%) and 1508 (13.7%) patients, respectively (Table 1 ). At the index date, 7% of patients were taking statins, while 17% were doing so at 90 days. However, although this number increased over time, it did not reach 50% even 10 years after T1D diagnosis (Table 2 ).

Associations between statin treatment and cardiovascular events

During the mean follow-up period of 9.9 ± 3.7 years, 931 patients (8.5%) experienced the primary outcome of cardiovascular event. As a first cardiovascular event, 669 (71.9%) patients suffered stroke (ischemic stroke: 553 patients; hemorrhagic stroke: 116 patients), and 262 (28.1%) suffered MI. Among patients who experienced the primary outcome, 32.3% were taking statins at the time of the event. In multivariable time-dependent Cox analysis, statin treatment was significantly associated with decreased risk of cardiovascular events (adjusted HR, 0.76; 95% CI 0.66–0.88) (Table 3 ). During the secondary outcome analyses to identify the effects of statin on individual outcomes, statin use was significantly associated with low risk of ischemic stroke (adjusted HR, 0.74; 95% CI 0.61–0.89) and MI (adjusted HR, 0.74; 95% CI 0.56–0.96), whereas there was no association between statin use and hemorrhagic stroke (Table 4 ).

In subgroup analyses (Fig.  3 ), the beneficial effect of statins was present in both male (adjusted HR, 0.88; 95% CI 0.74–1.06) and female (adjusted HR, 0.60; 95% CI 0.48–0.76), but the statistical significance was only found in female. Both the 20–39-year-old (adjusted HR, 0.75; 95% CI 0.40–1.40) and the ≥ 40-year-old (adjusted HR, 0.71; 95% CI 0.61–0.82) groups showed a tendency to be associated with a low rate of the primary outcome on statins, but statistical significance was present only in those ≥ 40-year-old. There was no significant interaction between the association of statin and low cardiovascular events according to the type of health insurance or enrolled year.

Subgroup analysis of cardiovascular disease occurrence according to statin treatment

Nested case–control study

In the nested case–control analysis performed as a sensitivity analysis, 675 cases (patients with the primary outcome) were matched to 2025 controls without the primary outcome using 1:3 incidence density sampling (Table 5 ). The cases and controls were fully matched according to baseline characteristics, the use of antiplatelets and RAAS inhibitors. The proportion of those taking statins was lower in the case group than in the control group (32.4% in the case group vs. 38.4% in the control group). In the conditional logistic regression analysis, statin treatment was significantly associated with a lower risk of cardiovascular events (OR, 0.73; 95% CI 0.59–0.89). When we evaluated the risk according to the duration of cumulative exposure to statins, as the amount of cumulative exposure to statins increased, the risk of cardiovascular events decreased (1–3 years: OR, 0.73 [95% CI 0.57–0.94] and > 3 years: OR, 0.60 [95% CI 0.47–0.77] compared to < 1 year). The dose–response association between longer cumulative exposure to statins and lower risk was seen for ischemic stroke and myocardial infarction, but not for hemorrhagic stroke (Table 6 ).

In this population-based T1D cohort study, we evaluated the risk of CVD according to statin treatment. The number of 11,483 adult T1D patients is in line with the estimate of the registry study conducted in Korea [ 11 ]. In patients with T1D, treatment with statins was associated with a 24% lower risk of CVD. The association between statin use and fewer cardiovascular events was consistent in sensitivity analysis with a nested case–control design, and we also observed that fewer cardiovascular events occurred over a longer period of statin treatment.

It is well known that cardiovascular risk is increased in T1D patients [ 18 ]. Indeed, in our cohort study with 11,009 T1D patients, approximately 1 in 12 without previous CVD experienced a stroke or MI during the 10 year follow-up period. Several studies have demonstrated that development of cardiovascular complications is common in T1D patients, and the risk of CVD in T1D is greater than that in T2D patients [ 19 , 20 ]. A cohort study conducted in the United Kingdom reported a 3.6- to 7.7-fold increase in major CVD in T1D patients compared to the general population [ 21 ]. Cardiovascular mortality in T1D patients is higher than that both in the general population and in T2D patients [ 19 , 22 ]. Currently, CVD remains the leading cause of morbidity and mortality in T1D patients [ 23 , 24 , 25 ]. Considering the relatively early onset of T1D patients compared to T2D patients, development of CVD in T1D patients leads to more life-years lost [ 26 ].

The mechanism of high CVD risk in T1D is not fully understood, but long-term exposure to hyperglycemia, oxidative stress, and low-grade inflammation are characteristics of T1D and can contribute to the development and progression of vascular complications [ 27 ]. T1D is associated with a higher prevalence and more rapid progression of coronary atherosclerosis [ 28 , 29 ]. Furthermore, the presence of both traditional and non-traditional cardiovascular risk factors is frequently confirmed in T1D patients, and metabolic syndrome is also commonly observed [ 30 , 31 ]. Hyperglycemia due to a defect in insulin secretion in T1D also contributes to an increased risk of cardiovascular events [ 32 ].

Statins have been established to be beneficial for preventing cardiovascular events, which are major complications in T2D patients [ 33 ]. Based on the cumulative evidence, statin therapy is recommended for primary and secondary prevention of CVD in diabetic patients who are at greater risk [ 7 ]. Evidence from multiple large-scale randomized controlled trials of statin treatment suggests that the beneficial effect of statins on CVD is largely attributable to decrease of low-density lipoprotein cholesterol (LDL-C) [ 34 ]. In a study using Swedish national diabetes registry data, LDL-C was a significant predictor of death and CVD in patients with T1D [ 32 ]. For each 1 mmol/L increase of LDL-C level in T1D, there was a 35–50% greater CVD risk. Meanwhile, a low level of LDL-C in T1D was negatively associated with coronary atherosclerosis [ 35 ]. The relationship between the LDL-C–lowering effect of statins and a proportional reduction in CVD events is consistent between patients with T1D or T2D and non-diabetic individuals [ 36 , 37 ]. In addition to lowering the LDL-C level, statins have multiple pleiotropic effects such as improving endothelial dysfunction, increasing nitric oxide bioavailability, inhibiting inflammatory responses, and stabilizing atherosclerotic plaques [ 38 ]. T1D patients have elevated levels of plasma markers, which reflect inflammation and endothelial dysfunction even before the clinical manifestation of macroangiopathy [ 39 ]. Elevated markers of inflammation and endothelial dysfunction are associated with a high risk of CVD in T1D patients [ 40 , 41 ]. Administration of statins reduces the levels of inflammatory markers and improves endothelial dysfunction, although it is unclear whether statins have a similar effect in T1D [ 42 ].

There is a concern that the use of statins in T1D patients may adversely affect diabetes itself [ 43 ]. Concerns about impaired glycemic control and increased risk of diabetes with statin treatments are major discourages of adherent use of statins in clinical practice. In a study of T1D patients, statin use was associated with an increased level of HbA1c, reflecting the presence of impaired glycemic control [ 44 ]. A report also suggests that statins deteriorate insulin sensitivity in T1D patients [ 45 ]. Therefore, it is unclear whether regular statin use for the primary prevention of CVD is beneficial for T1D patients. In the current study, we demonstrated that statin use, particularly longer cumulative use, is associated with a lower risk of CVD. Our study suggests that the use of statins would assist with primary cardiovascular prevention in T1D patients at high risk. Whether the use of statins can promote hemorrhagic stroke is also a concern that inhibits statin use [ 46 ]. However, we did not find a significant relationship between the use of statins and hemorrhagic stroke in T1D patients.

In the subgroup analysis of the current study, the cardiovascular preventive effect of statins in T1D was more prominent in females than males. Currently, we did not have a clear answer whether this finding is coincidental or whether there is a notable sex difference in the effect of statins on T1D. One hypothesis is that the more prevalent risk factors, unhealthy lifestyles, and poor drug adherence in males might interrupt the beneficial effect of statins. Further study is needed for this topic. Statin treatment led to fewer cardiovascular events in both those ≥ 40 years and 20–39 years of age. However, this relationship was only significant in the ≥ 40 years age group. We suppose that this trend is due to a lack of statistical power in the younger age group, as most cardiovascular events occurred in participants ≥ 40 years of age. The current guideline for statin use in T1D patients is in accordance with the guidelines established for T2D patients, and it is recommended to use statins in T1D patients > 40 years of age and selectively use statins in those 20–39 years old according to cardiovascular risk [ 47 ]. However, the evaluation of individual cardiovascular risk is challenging [ 48 , 49 ]. The role of statins for primary prevention in T1D patients aged 20–39 years is unclear; further studies are needed to establish whether statin therapy is beneficial in this patient group. In the current study, although the use of statins in T1D patients has increased over time, only one-third of patients were receiving statins at the time of the cardiovascular event. In patients with T1D, the use of statins was substantially less common than the guidelines suggested, and the difference is greater in view of primary prevention compared to secondary prevention for CVD [ 50 , 51 ]. Given this low statin usage rate, clinicians need to more actively consider the use of statins for T1D patients and increase patient adherence to statins.

Our study has several limitations. First, because this was a retrospective study, there may be bias. Also, this study used a cohort derived from a single ethnic group. Since the characteristics of CVD and T1D may vary by country or ethnicity, caution is needed in generalizing the results. The use of health care claims data also produces limitations. We could not get clinical data such as the degree of control of diabetes (including HbA1c and glucose level) or the lipid profile of individual patients. We also did not know the indications for statin use; there is a possibility of statins being used only in patients with poor lipid profiles, but this could not be verified. Although strict criteria were used to accurately identify T1D patients, our dataset may include misdiagnosis or inadequate information due to the inherent limitation of health claims data. Based on the claims data and utilizing several criteria, patients with T1D were identified and an index date was established. However, this index date may differ from the onset of T1D. Finally, there might be a difference between the prescription records issued by physicians and the patients’ actual medication intake. However, several strengths highlight the significance of this study. Unlike many Western countries, Korea has a very low prevalence of T1D patients [ 11 ]. Therefore, we had to conduct this study using nationwide healthcare claims data. Using a population-based cohort, we were able to include a relatively large number of patients with T1D and evaluate long-term data to reveal the relationship between the development of CVD and statin treatment in T1D in real-life practice. In addition, to increase the strength of the research results, we reconfirmed the association between statin use and CVD in T1D patients by performing additional sensitivity analysis using a nested case–control study. We also identified a trend toward reduced CVD risk in T1D patients with a longer duration of statin treatment. In addition, we performed a subgroup analysis according to insurance status, which can indirectly reflect economic status, and confirmed that statin use is related to CVD risk regardless of insurance status. Our research data from an Asian T1D population consistently showed that statin treatment could contribute to CVD risk reduction in the high-risk group. In addition to the existing evidence that statin administration in T1D patients can contribute to CVD risk reduction [ 9 ], the present study provides supporting evidence for the current guideline recommending statin administration in T1D patients.

In this nationwide T1D cohort study, the use of statins was associated with an ~ 25% reduction in CVD. Also, statins were being used less frequently than recommended in the guidelines. As the actual use of statins was not sufficient, more aggressive use of statins for CVD prevention in T1D patients should be considered. Further prospective studies are needed to confirm the results of this study.

Availability of data and materials

The dataset supporting the results of this study is accessible from HIRA in Korea, but with restrictions to data availability. The use of the dataset is restricted to the current research under license; therefore, public access of the dataset is not available. Researchers are only access the data upon reasonable request with approval from the inquiry committee of research support in HIRA ( https://opendata.hira.or.kr/or/orb/useGdInfo.do ).

Abbreviations

• Type 1 diabetes mellitus
• Cardiovascular disease

Type 2 diabetes mellitus

Health insurance review and assessment service

International statistical classification of diseases

Myocardial infarction

Hazard ratio

Confidence interval

Low-density lipoprotein cholesterol

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Acknowledgements

This research utilized data obtained from Health Insurance Review and Assessment Service (M20210330196). The research results are not related to the Health Insurance Review and Assessment Service and the Ministry of Health and Welfare in Korea.

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (NRF-2020R1I1A1A01060447 to JK and NRF-2021R1I1A1A0104944111 to JY).

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JY and JK designed the study. JJ and JK were responsible for the data acquisition; JJ and JK analyzed the data. JY and JK wrote the first draft. JY, MB, SOS, and JK critically reviewed the manuscript. All authors read and approved the final manuscript.

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Yoo, J., Jeon, J., Baek, M. et al. Impact of statin treatment on cardiovascular risk in patients with type 1 diabetes: a population-based cohort study. J Transl Med 21 , 806 (2023). https://doi.org/10.1186/s12967-023-04691-6

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Melanoma and CLL co-occurrence and survival: role of KC history

• Yayi Zhao 1 ,
• Rossybelle P. Amorrortu 1 ,
• Sandra C. Stewart 2 ,
• Kavita M. Ghia 3 ,
• Vonetta L. Williams 3 ,
• Vernon K. Sondak 4 ,
• Kenneth Y. Tsai 5 ,
• Javier Pinilla-Ibarz 6 ,
• Julio C. Chavez 6 &
• Dana E. Rollison 1  

BMC Cancer volume  23 , Article number:  1084 ( 2023 ) Cite this article

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Survival following melanoma and chronic lymphocytic leukemia (CLL) have both been individually associated with previous history of non-melanoma skin cancers (specifically keratinocyte carcinomas [KC]). Furthermore, melanoma and CLL have been reported to occur within the same patients. The survival experience of patients with both cancers is understudied, and the role of history of KC is unknown. Additional research is needed to tease apart the independent associations between KC and CLL survival, KC and melanoma survival, and the co-occurrence of all three cancers.

A retrospective cohort study was conducted among patients who were diagnosed with melanoma and/or CLL at a comprehensive cancer center between 2008 and 2020. Multivariable Cox regression models were used to examine the association between history of KC and survival following melanoma and/or CLL with careful consideration of calendar year of diagnosis, treatment regimens and other risk factors. A nested case–control study comparing patients with both CLL and melanoma to those with only CLL or only melanoma was conducted to compare blood parameters across the three groups.

A time-dependent association was observed between history of KC and favorable melanoma survival within 4 years following diagnosis and poorer survival post 7 years after melanoma diagnosis. History of KC was not significantly associated with survival following the diagnosis of CLL, after adjustment for clinical factors including historical/concurrent melanoma. Patients with co-occurring melanoma and CLL tended to be diagnosed with melanoma first and had elevated blood parameters including white blood cell and lymphocyte counts as compared with patients who were diagnosed with only melanoma.

Conclusions

History of KC was an independent predictor of survival following melanoma but not of CLL. Additional studies are needed to determine if blood parameters obtained at the time of melanoma diagnosis could be used as a cost-effective way to identify those at high risk of asymptomatic CLL for the promotion of earlier CLL diagnosis.

Peer Review reports

Previous studies have reported the co-occurrence of melanoma and chronic lymphocytic leukemia (CLL) within the same individuals [ 1 , 2 , 3 , 4 , 5 , 6 ] possibly due to shared risk factors. For example, a history of keratinocyte carcinomas (KC), which includes cutaneous squamous cell carcinoma (cuSCC) and basal cell carcinoma (BCC), has been hypothesized as a shared risk factor [ 7 , 8 , 9 , 10 , 11 , 12 ]. BCC and cuSCC are both associated with ultraviolet radiation (UVR) [ 13 , 14 ] with BCC linked to intermittent and childhood UVR exposure and cuSCC linked to chronic sun exposure and immunosuppression [ 15 , 16 ]. In our previous work, we observed that a history of KC occurred more often among patients with melanoma or CLL compared to patients with other malignancies including cancers of the breast, prostate, lung, colon and non-Hodgkin lymphoma [ 17 ]. Furthermore, a history of KC was associated with reduced survival following a melanoma or CLL diagnosis in this same study population [ 17 ]. Elucidation of prognostic markers can be useful for risk stratification and surveillance of subsequent melanoma and CLL. However, no previous studies have been able to tease apart independent effects of KC on melanoma and CLL survival due to lack of detailed information on cancer treatments and other clinical parameters.

We conducted a retrospective cohort study of patients treated for melanoma and CLL at Moffitt Cancer Center (MCC) in 2008–2020, to more fully characterize the associations between KC history, melanoma, and CLL. Factors including calendar year of diagnosis, tumor histology, specific treatment regimens, and other cancer risk factors were explored as both potential confounders and effect modifiers of the association between history of KC and survival following melanoma/CLL. In addition, a nested case–control analysis was conducted to compare blood parameters between patients with both melanoma and CLL to those with only one of these cancers.

Study design and population

Methods of this retrospective cohort study have been previously reported in detail [ 17 ]. Briefly, data from MCC patients who were diagnosed with melanoma or CLL at age 18 or older were identified to examine the association between KC history and the survival following either cancer type. The melanoma and CLL cohorts were defined using site codes from International Classification of Diseases for Oncology, 3 rd edition (ICD-O3) [ 18 ]. Patients were considered eligible for the study if they 1) were diagnosed and/or treated at MCC for melanoma or CLL between December 2008-April 2020, 2) completed the Electronic Patient Questionnaire (EPQ), a questionnaire administered to all patients new to MCC, and 3) had a non-missing response to the personal history of cancer question in the EPQ. In total 5,511 and 571 patients were included in the melanoma and CLL cohorts, respectively.

An exploratory case–control analysis nested within the larger cohort was conducted to further investigate the co-occurrence of melanoma and CLL. The subgroup of patients diagnosed with both cancers who had available blood parameters ( n  = 17) were identified and compared with two matched control groups: a) patients who were diagnosed with only melanoma (melanoma controls), and b) patients who were diagnosed with only CLL (CLL controls). The comparisons of blood parameters were limited to a subset of 13 cases with blood parameters available within ± 60 days of their initial cancer diagnosis date, and the matched controls including: 26 melanoma controls (1:2 ratio) and 13 CLL controls (1:1 ratio, as it was not possible to identify more than 1 matched control for each case).

Data collection

Patient characteristics were extracted from the MCC Cancer Registry and the electronic health record (EHR) including a) clinical factors: date of diagnosis, tumor histology, tumor stage at diagnosis, first course treatments including chemotherapy (both cytotoxic chemotherapy and molecularly targeted agents), immunotherapy and radiation, therapeutic drugs used as first course treatment, Breslow thickness (melanoma cohort only), historical and concurrent melanoma (identified using ICD diagnosis codes), and b) sociodemographic and other factors: age, sex, race, ethnicity, payment methods, date of last contact, and associated vital status. Self-reported data on smoking, alcohol use, history of autoimmune disease, history of KC, family history of melanoma, and aspirin use in the last year were obtained from the EPQ. Among the sub-cohort of patients who were included in the nested case–control analysis, blood parameters closest to the corresponding cancer diagnosis were extracted from EHR. The following blood parameters were included in the analysis based on their diagnostic potential for CLL and role as markers of systemic inflammation (white blood cell [WBC] count, platelet count, hematocrit, hemoglobin, lymphocyte count, monocyte count, neutrophil count, neutrophil band, and polyploid neutrophil) [ 19 ].

Statistical analysis

Patient demographic and clinical characteristics were presented separately for the melanoma and CLL cohorts. Venn diagrams were used to describe the combinations of first course treatment and 5-year survival rates for melanoma including radiation, chemotherapy, and immunotherapy with respect to stage at diagnosis and calendar year of diagnosis. Similar methods were used to visualize the combination of treatments received by the CLL cohort, except for radiation therapy due to its infrequent use.

Based on the previously observed time-varying association between history of KC and survival following melanoma [ 17 ], a time-dependent Cox regression model with a backward elimination process was used to derive a final model. This modeling process started with a full list of covariates including age at diagnosis, sex, race, ethnicity, year of diagnosis, stage at diagnosis, smoking status, alcohol use, family history of melanoma, first course treatment of radiation, immunotherapy and chemotherapy, history of autoimmune disease, aspirin use, Breslow thickness, and insurance status. Then, the backward elimination procedure dropped the factor with the largest p-value at each run until all factors remaining in the model showed p-values smaller than 0.05. Subsequently, the final melanoma model was stratified by key factors that could potentially modify the association with KC history, including calendar year of diagnosis, tumor histology, stage at diagnosis, and first course treatment.

A similar modeling process was used to estimate the association between KC history and survival following a CLL diagnosis without a time-varying association. The following variables were included in the CLL multivariate model: age at diagnosis, sex, smoking status, historical and concurrent melanoma, first course treatment of radiation, immunotherapy and chemotherapy, insurance status, and self-reported history of melanoma. The analysis was stratified by calendar year of diagnosis and first course treatment.

To better understand whether specific medications affect the association between history of KC and survival following melanoma, the most frequently used chemotherapy and immunotherapy medications were analyzed. Unadjusted Cox regression models were used to estimate the association within each subgroup of patients who received each specific type of medication given the small sample size. A similar approach was used to evaluate potential effect modification by chemotherapy and immunotherapy medications among CLL patients.

In the nested case–control analysis, matched random sampling was performed to identify two groups of controls with respect to age at diagnosis in decades, sex, calendar year at diagnosis, smoking status, and Breslow thickness and stage at diagnosis (for melanoma only). Patient demographic and clinical characteristics were reported for the case group and the two matched control groups. The blood parameters were compared between the case group and the matched control groups using paired Wilcoxon sign-rank test with adjustment for multiple comparison using the false discovery rate method. Subsequently, a sensitivity analysis was conducted to exclude 3 cases (and their corresponding matched controls) whose CLL was diagnosed prior to their melanoma diagnosis.

Among patients with melanoma, the mean age at diagnosis was 62 years, 60% of the patients were male, 99% were White race, and 98% were non-Hispanic. CLL patients had a mean age of 63 years, 63% were male, 93% were White race, and 95% were non-Hispanic. Prevalence of KC history was 29% among melanoma patients and 20% among CLL patients. Further, history of KC was most prevalent among those who were diagnosed with melanoma between 2012–2020 as compared to those who were diagnosed with melanoma prior to 2012, with a similar pattern observed among CLL patients. Approximately 40% of the patients diagnosed with desmoplastic or lentigo melanoma reported a history of KC, while approximately 25% of melanoma patients diagnosed with other histologic types reported a history of KC (Table 1 ).

Changes in treatment were observed by stage and calendar year for patients diagnosed with melanoma. For example, among those diagnosed with stage 3 or 4 melanoma, the use of chemotherapy alone decreased from 22 to 8% while the use of only immunotherapy increased from 35 to 65% between 2009–2020 (Additional file 1 ). For patients diagnosed with CLL, a shift was observed from using both chemotherapy and immunotherapy to using only immunotherapy (Additional file 2 ).

The associations between history of KC and survival following melanoma/CLL were examined in the context of sociodemographic and clinical factors. KC history had a time-dependent effect on survival following a melanoma diagnosis, after controlling for age at diagnosis, sex, calendar year of diagnosis, stage at diagnosis, history of smoking, alcohol use, chemotherapy treatment, history of autoimmune disease, Breslow thickness, and insurance status (Table 2 ). Melanoma patients with a history of KC experienced favorable survival within 4 years of a melanoma diagnosis (hazard ratio [HR] [95% confidence interval (CI)] = 0.75 [0.62–0.91]), no differences in survival between 5–7 years following a melanoma diagnosis (HR [95%CI] = 1.04 [0.73–1.47]), and poorer survival following 7 or more years since the melanoma diagnosis (HR [95% CI] = 2.79 [1.44–5.41]) as compared to patients without a history of KC (Table 2 ). A similar time-dependent association between history of KC and survival following a melanoma diagnosis was consistently observed between subgroups of patients as defined by calendar year at diagnosis, tumor histology, and first course treatment (Additional file 3 ). No differences were observed in the association between history of KC and survival following the first 4 years of melanoma diagnosis by the type of chemotherapy (Additional file 4 ). However, some inconsistent patterns were found by specific types of immunotherapies (Additional file 4 ).

The risk of death following CLL was elevated among patients who reported a history of KC (HR = 1.53, 95% CI = 0.95–2.45), after adjustment for age at diagnosis, smoking status, any melanoma diagnosis, and chemotherapy (Table 3 ). This pattern remained consistent across subgroups of patients who received immunotherapy or chemotherapy but showed directional difference when investigating specific medications/regimens (Additional file 5 and 6 ). The association between KC and survival following CLL was also inconsistent across subgroups of patients by calendar year of diagnosis (Additional file 5 ).

Patients with both melanoma and CLL were more likely to have a history of KC (41%) compared to patients diagnosed with CLL who did not have melanoma (19%) or patients diagnosed with melanoma who did not have CLL (29%) (Fig.  1 ). In addition, patients diagnosed with CLL without skin cancer had a higher 5-year survival rate (87.2%) than CLL patients with skin cancer (~ 60%) regardless of whether the skin cancer was melanoma or KC. Given these results, we conducted an exploratory nested case–control study to investigate the co-occurrence of melanoma and CLL. Among the 52 patients included in the analysis, the mean age at the time of cancer diagnosis was 75, with males accounting for 92% of the sub-sample (Table 4 ). Ten of the 13 patients diagnosed with both cancers had a melanoma diagnosed prior to the CLL, among which 9 patients had CLL diagnosed within 60 days after the melanoma diagnosis. The WBC counts were significantly higher among cases (mean [SD] = 15.67 [12.33]) as compared with melanoma controls (mean [SD] = 9.57 [7.52]) (Table 4 ) with similar findings observed for lymphocyte counts (Δmean = 6.46) and percent lymphocytes (Δmean = 25.08). These comparisons remained significant in a sensitivity analysis where patients diagnosed with CLL prior to the melanoma were excluded. No other blood parameters differed between the cases and melanoma controls. Further, no significant differences were found when comparing the blood parameters between the cases and the CLL controls (Table 5 ).

Diagnosis of melanoma and chronic lymphocytic leukemia (CLL), and history of keratinocyte carcinoma (KC). Venn diagram of groups of patients as defined by history of keratinocyte carcinoma (KC) and diagnosis of melanoma and/or chronic lymphocytic leukemia (CLL). Patients with both melanoma and CLL were more likely to have a history of KC (41%) compared to patients diagnosed with only CLL (19%) or patients diagnosed with only melanoma (29%)

After adjustment for multiple patient factors, we observed a significant inverse association between history of KC and melanoma survival within the first 4 years since diagnosis. However, patients with a history of KC were found to have worse overall survival post 7 years of a melanoma diagnosis. This time-dependent association is consistent with our previous observations in an age- and stage-adjusted model, [ 17 ] suggesting that the association between history of KC and melanoma survival is independent of clinical factors including melanoma stage, Breslow thickness, history of autoimmune disease, and first course treatment. In addition, the current analysis found no difference in the association between history of KC and melanoma when stratifying by types of first course treatment. There is no clear biological explanation for the observed time-dependent associations, and if they are similarly observed in other study populations, additional research into underlying mechanism is warranted.

A previous study reported an association between history of KC and worse survival following CLL diagnosis [ 8 ]. However, a history of KC was not significantly associated with survival following CLL in this analysis after adjustment for demographic and clinical factors. Additionally, the corresponding hazard ratio in the current study (HR = 1.53) was lower than the age- and sex-adjusted model (HR = 1.73) reported in our previous analysis [ 17 ] but larger than a previous study that examined CLL survival (HR = 1.29) [ 8 ] and two studies that examined non-Hodgkin lymphoma/CLL survival (HRs = 1.33/1.32) [ 20 , 21 ]. The loss of statistical significance in our current analysis could be due to limited sample size or the adjustment for additional factors (e.g., historical/concurrent melanoma), which potentially serve as confounders in the association between history of KC and CLL survival. Since KC and melanoma share common risk factors including age, Fitzpatrick skin phototypes 1–3 (e.g., light skin/hair) [ 10 ], exposure to ultraviolet radiation, and immunosuppression [ 11 , 12 ], it is likely that adjustment for melanoma reduced the effect estimates for the association between KC history and CLL survival. In fact, in a sensitivity analysis, a history of KC was significantly associated with CLL survival when the melanoma covariable was removed from the original multivariable model (HR = 1.70, 95% CI = 1.08–2.69). Together, these results suggest that the association between KC history and survival following CLL was confounded by melanoma at a magnitude of 11.1%. Collectively, KC and melanoma may predict worse survival following CLL through a shared biologic pathway such as immune dysregulation and/or DNA repair [ 22 , 23 , 24 , 25 ].

Patients diagnosed with both melanoma and CLL had a higher prevalence of KC history as compared to patients diagnosed with only melanoma or CLL. CLL is known to increase the susceptibility and incidence of cutaneous malignancies including KC and melanoma [ 2 , 3 , 26 , 27 , 28 , 29 , 30 ]. However, in our study, most patients diagnosed with both cancers were diagnosed with melanoma prior to CLL. Although the biological mechanism is not fully understood, the severe T-cells immunosuppression induced by the CLL cells maybe one of the key aspects to this increase in incidence [ 29 , 30 , 31 ]. Such immunosuppression occurs even in the earliest phases of the disease, before the CLL is clinically identified – which frequently occurs during the evaluation and treatment for melanoma [ 1 , 19 ]. Within the nested case–control analysis, we found that WBC and lymphocyte counts were elevated among patients diagnosed with both cancers (cases) as compared to their melanoma controls. These findings suggest that CLL was likely an incidental finding during the workup of melanoma. The elevated blood parameters thus pointed to undiagnosed CLL which is consistent with previous studies [ 1 , 19 ]. Furthermore, when comparing the cases with CLL controls, no differences in the blood parameters were found, suggesting that the cases were not biologically distinct from the controls who were diagnosed with CLL alone.

Our study has several strengths worth noting. This study utilized patient data synthesized across multiple sources including EPQ, Cancer Registry, and the EHR to describe treatment, survival, demographic, and clinical factors for patients diagnosed with melanoma or CLL and to estimate the association between KC history and survival following these cancers. In addition, our study was the first to explore and confirm that the time-dependent effect of KC history on melanoma survival was consistent across calendar year of diagnosis, tumor histology, and first course treatment. Specifically, our results confirmed that a significant protective association exists between history of KC and survival within 4 years of melanoma diagnosis which is important as survival within a shorter time-period following diagnosis is more likely to approximate cancer-specific survival. Incorporating data for both cancers also allowed us to identify a unique sub-population that showed a higher prevalence of KC history. Finally, findings from the nested case–control analysis point to the importance of blood workup among melanoma patients, as specific blood parameters such as WBC and lymphocyte counts could serve as indicators of asymptomatic CLL, potentially leading to earlier diagnosis and risk stratification that may improve overall outcomes [ 32 ].

The study is not without limitations. First, as an observational study, our analysis was unable to directly establish causality [ 33 , 34 ] and did not investigate biological mechanism for the observed associations. Due to the incomplete data on cause of death, we were unable to assess cancer-specific survival to examine the long-term effect from KC. Further, given that KC is not a reportable cancer to cancer registries, we were unable to ascertain KC occurring after the diagnosis of the CLL/melanoma and could not investigate the use of newly diagnosed KC as a potential method of risk stratification, although previous studies have found that patients with CLL have a up to a fivefold skin cancer risk following diagnosis of CLL [ 35 ]. The exploratory case–control analysis was limited by sample size and the availability of data on the blood parameters. However, sensitivity analyses indicated there were no differences in demographic or cancer characteristics between the patients with and without blood parameters, minimizing the possibility of selection bias for the case–control analyses. Future studies should seek to replicate these findings in larger study populations, possible through multi-center collaborations, given the rarity of melanoma and CLL co-occurrence [ 36 ]. Such future studies should consider investigating additional parameters such as lymphocyte subsets (CD4 T-cells and B-cells) and immunoglobulin G levels, which are more specific to immunosuppression for the purpose of identifying asymptomatic CLL among melanoma patients [ 31 , 37 ].

In summary, after adjustment for demographic and relevant clinical factors, a significant time-dependent association was found between history of KC and survival following diagnosis of melanoma. The overall survival following a CLL diagnosis was poorer among those with a history of KC, though not statistically significant. The nested case–control findings highlight the importance of blood workup among melanoma patients as specific blood parameters such as WBC and lymphocyte counts may signal asymptomatic CLL among melanoma patients to promote earlier diagnosis of CLL. Although an earlier diagnosis of CLL is not associated with better CLL-specific outcomes and, in most cases, won’t require any therapeutic intervention at diagnosis [ 36 , 38 ], it may be helpful to counsel patients regarding the increased risk of subsequent malignancies and infections due to cancer-related immunosuppression. Future studies are needed to investigate whether our findings reflect a true impact of immunosuppression from CLL, or if they are driven by the increased presence of neoantigens from UVR exposure.

Availability of data and materials

The datasets presented in this article are not readily available in order to protect participant confidentiality and privacy. Requests to access the datasets should be directed to the corresponding author.

Abbreviations

American Joint Committee on Cancer

Basal cell carcinoma

Confidence interval

• Chronic lymphocytic leukemia

Cutaneous squamous cell carcinoma

Electronic Health Record

Electronic Patient Questionnaire

Hazard ratio

International Classification of Diseases for Oncology, 3rd edition

• Keratinocyte carcinoma

Moffitt Cancer Center

Non-Hodgkin lymphoma

Standard deviation

Surveillance, Epidemiology and End Results

Ultraviolet radiation

White blood cell

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Acknowledgements

Not applicable.

This work has been supported in part by the Collaborative Data Services Core at the H. Lee Moffitt Cancer Center & Research Institute, a comprehensive cancer center designated by the National Cancer Institute and funded in part by Moffitt’s Cancer Center Support Grant (P30-CA076292).

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Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, USA

Yayi Zhao, Rossybelle P. Amorrortu & Dana E. Rollison

Department of Cancer Registry, Moffitt Cancer Center, Tampa, FL, USA

Sandra C. Stewart

Collaborative Data Services Core, Moffitt Cancer Center, Tampa, FL, USA

Kavita M. Ghia & Vonetta L. Williams

Department of Cutaneous Oncology, Moffitt Cancer Center, Tampa, FL, USA

Vernon K. Sondak

Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL, USA

Kenneth Y. Tsai

Department of Malignant Hematology, Moffitt Cancer Center, Tampa, FL, USA

Javier Pinilla-Ibarz & Julio C. Chavez

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Contributions

Dr. Dana E. Rollison led the study conceptualization and all authors contributed to the design. Data were obtained from Moffitt’s Collaborative Data Services Core through Kavita M. Ghia. Data analysis was performed by Yayi Zhao. Material preparation was performed by Yayi Zhao and Rossybelle P. Amorrortu. The first draft of the manuscript was written by Yayi Zhao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dana E. Rollison .

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The Moffitt Scientific Review Committee reviewed the study protocol (MCC#22268) and determined that the study met the definition of non-human subjects research and did not require IRB approval. All methods were performed in accordance with the relevant guidelines and regulations.

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Competing interests.

Financial interests: Rossybelle P. Amorrortu, Yayi Zhao, Sandra C. Stewart, Kavita M. Ghia, Dr. Vonetta L. Williams, and Dr. Ken Tsai declare they have no financial interests. Dr. Vernon K. Sondak is a compensated consultant for Merck, Novartis, Regeneron, and Iovance.

Non-financial interests: Dr. Dana Rollison serves on the Board of Directors for NanoString Technologies, Inc. Dr. Vernon K. Sondak serves on the Advisory Boards for BMS, Novartis and Eisai. Dr. Javier Pinilla-Ibarz is a compensated consultant for Janssen, AbbVie, AstraZeneca, Takeda, Novartis, TG Therapeutics, MEI, and BeiGene. Dr. Julio Chavez is a paid consultant for Morphosys, Kite Pharma, Amgen, AbbVie, BeiGene, AstraZeneca, ADC Therapeutics, TG Therapeutics, and Novartis Pharmaceutical Corp.

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Supplementary Information

Additional file 1. .

Radiation, chemotherapy, and immunotherapy as first course treatments received by melanoma patients, stratified by stage and calendar year at diagnosis. This file describes the combinations of treatment modalities received by melanoma patients who were diagnosed with earlier stage (stage 1 or 2) or later stage (stages 3 or 4) melanoma in 2009-2011, in 2012-2015, or in 2016-2020. 

Additional file 2. 

Chemotherapy and immunotherapy as first course treatments received by chronic lymphocytic leukemia (CLL) patients, stratified by calendar year at diagnosis. This file describes the combinations of treatment modalities received by patients diagnosed with chronic lymphocytic leukemia in 2009-2012, in 2013-2015, or in 2016-2020.

Additional file 3. 

Association between history of keratinocyte carcinoma (KC) and survival following diagnosis of melanoma, stratified by patient characteristics.This figure depicts the association between history of KC and survival following melanoma among different patient groups as defined by calendar year of diagnosis, melanoma histology, stage of melanoma, and types of first course treatment.

Additional file 4. 

Association between history of keratinocyte carcinoma (KC) and survival following melanoma diagnosis among patients receiving specific chemotherapy/immunotherapy medications. This figure depicts the association between history of KC and survival following melanoma among groups of patients as defined by the type of chemotherapy or the type of immunotherapy used as a part of first course treatment. 

Additional file 5. 

Association between history of keratinocyte carcinoma (KC) and survival following diagnosis of chronic lymphocytic leukemia (CLL), stratified by patient characteristics. This figure depicts the association between history of KC and survival following chronic lymphocytic leukemia among groups of patients as defined by calendar year of diagnosis and type of first course treatment.

Additional file 6. 

Association between history of keratinocyte carcinoma (KC) and survival following chronic lymphocytic leukemia (CLL) diagnosis among patients receiving specific chemotherapy/immunotherapy medications. This figure depicts the association between history of KC and survival following chronic lymphocytic leukemia among groups of patients as defined by the type of chemotherapy or the type of immunotherapy used as a part of first course treatment.

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Zhao, Y., Amorrortu, R.P., Stewart, S.C. et al. Melanoma and CLL co-occurrence and survival: role of KC history. BMC Cancer 23 , 1084 (2023). https://doi.org/10.1186/s12885-023-11573-z

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Received : 12 May 2023

Accepted : 27 October 2023

Published : 09 November 2023

DOI : https://doi.org/10.1186/s12885-023-11573-z

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Associations of maternal exposure to multiple plasma metals with the risk of fetal congenital heart defects: a prospective nested case-control study

Funding Acknowledgements: Type of funding sources: Public Institution(s). Main funding source(s): Key Area R&D Program of Guangdong Province (No.2019B020227005)

Guangdong Provincial Clinical Research Center for Cardiovascular disease (2020B1111170011)

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Z Nie, Y A N Q I U Ou, Associations of maternal exposure to multiple plasma metals with the risk of fetal congenital heart defects: a prospective nested case-control study, European Heart Journal , Volume 44, Issue Supplement_2, November 2023, ehad655.1901, https://doi.org/10.1093/eurheartj/ehad655.1901

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Congenital heart defects (CHDs) are the most common congenital anomalies with complex etiologies. There is knowledge gap with respect to the association between single and combined exposures to multiple heavy metals during pregnancy and the risk of CHDs in the fetus. Thus, we aimed to examine the association between maternal plasma heavy metal concentrations in mid-pregnancy and the risk of CHDs in the fetus.

A prospective nested case-control study was conducted in a cohort of 11,578 newborns. Exposure odds ratios (ORs) were compared between 164 CHD cases and 164 non-malformed controls delivered at the same hospital, individually matched by maternal age (±5 years) and parity. Concentrations of 21 metals were determined in maternal peripheral blood plasma via an ultra-performance liquid chromatography inductively coupled to mass spectrometry (ICP-MS). Single- and multiple-metal logistic regressions, the adaptive least absolute shrinkage and selection operator (LASSO) penalized regression analysis and restricted cubic spline (RCS) analysis were applied to explore the associations and dose-response relationships of plasma metals with CHD. We also applied Bayesian Kernel Machine Regression (BKMR) model to evaluate the cumulative effect of the exposure metals.

Median (interquartile range, IQR) Vanadium (V) [0.3 (0.3-0.4) vs. 0.3 (0.3-0.4) μg/L, P = 0.027] and cadmium (Cd) [0.1 (0.0-0.1) vs. 0.1 (0.0-0.1) μg/L, P = 0.036] levels were different between the CHDs and control groups. Compared with the first quartile, the Directed acyclic graph(DAG) model-adjusted ORs (95% CI) were 3.12 (1.72-5.64) for kalium (K), 2.21 (1.23-3.94) for manganese (Mn), 0.55 (0.33-0.94) for cerium (Ce), 1.96 (1.06-3.64) for lead (Pb) in the second quartile, and ORs (95%CIs) were 1.88 (1.08-3.26) for V, 0.53 (0.30-0.93) for Cr, 1.92 (1.10-3.35) for copper (Cu), 2.09 (1.18-3.70) for Cd, 2.14 (1.24-3.70) for stannum (Sn) and 3.05 (1.69-5.49) for Pb in the third quartile. In the multi-metal model, compared to the lowest quartile, the ORs for Sn and Pb in the second quartile were 1.32 (95% CI: 0.69-2.55) and 1.94 (95% CI: 0.98-3.84), respectively, and the ORs for both metals in the third quartiles were 2.15 (95% CI: 1.16-3.98) and 3.32 (OR=0.66, 95% CI: 1.68-6.55), suggesting that Sn and Pb had relatively stable positive results with a linear dose progression. BKMR models with 2 cluster for 9 metals showed that the overall effect of metal mixture was not statistically significant at or below 35th percentile and at or above the 70th percentile, compared to their median levels. Pb was the major contributor to the combined effect.

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