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PEER INFLUENCE IN RELATION TO ACADEMIC PERFORMANCE AND SOCIALIZATION AMONG ADOLESCENTS: A LITERATURE REVIEW

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Recent Findings on Peer Group Influences on Adolescent Smoking

  • Original Paper
  • Published: 08 July 2010
  • Volume 31 , pages 191–208, ( 2010 )

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  • Bruce G. Simons-Morton 1 &
  • Tilda Farhat 1  

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This review addresses peer group influences on adolescent smoking with a particular focus on recently published longitudinal studies that have investigated the topic. Specifically, we examine the theoretical explanations for how social influence works with respect to adolescent smoking; discuss the association between peer and adolescent smoking; consider socialization and selection processes with respect to smoking; investigate the relative influence of best friends, close friends, and crowd affiliations; and examine parenting behaviors that could buffer the effects of peer influence. Our review indicates the following with respect to adolescent smoking: (a) substantial peer group homogeneity of smoking behavior; (b) support for both socialization and selection effects, although evidence is somewhat stronger for selection; (c) an interactive influence of best friends, peer groups, and crowd affiliation; and (d) an indirect protective effect of positive parenting practices against the uptake of adolescent smoking. We conclude with implications for research and prevention programs.

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This research was supported in part by the intramural research program of the NIH, Eunice Kennedy Shriver National Institute of Child Health and Human Development.

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Simons-Morton, B.G., Farhat, T. Recent Findings on Peer Group Influences on Adolescent Smoking. J Primary Prevent 31 , 191–208 (2010). https://doi.org/10.1007/s10935-010-0220-x

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Published : 08 July 2010

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DOI : https://doi.org/10.1007/s10935-010-0220-x

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Recent Findings on Peer Group Influences on Adolescent Substance Use

This review addresses peer group influences on adolescent smoking with a particular focus on recently published longitudinal studies that have investigated the topic. Specifically, we examine the theoretical explanations for how social influence works with respect to adolescent smoking, discuss the association between peer and adolescent smoking; consider socialization and selection processes with respect to smoking; investigate the relative influence of best friends, close friends, and crowd affiliations; and examine parenting behaviors that could buffer the effects of peer influence. Our review indicates the following with respect to adolescent smoking: (1) substantial peer group homogeneity of smoking behavior; (2) support for both socialization and selection effects, although evidence is somewhat stronger for selection; (3) an interactive influence of best friends, peer groups and crowd affiliation; and (4) an indirect protective effect of positive parenting practices against the uptake of adolescent smoking. We conclude with implications for research and prevention programs.

Introduction

Adolescent smoking.

The prevalence of smoking increases dramatically during adolescence ( Johnston, O'Malley, Bachman, and Schulenberg 2007 ). While not all experimental users increase their uptake over time ( Abroms, Simons-Morton, Haynie, and Chen 2005 ; Tucker, Klein, and Elliott 2004 ), early initiation increases the likelihood of habituation, leading to a host of negative outcomes ( Pierce and Gilpin 1995 ). Therefore, prevention of initiation and progression is an important national health objective ( U.S.Department of Health and Human Services 2000 ). The development of effective prevention programs depends on a firm understanding of the factors associated with adolescent smoking.

Social influences are among the most consistent and important factors associated with adolescent smoking ( Kobus 2003 ). Social influences are important with respect to a wide range of health behaviors, including medication taking ( Berkman 2000 ), diet ( Larson, Neumark-Sztainer, Hannan, and Story 2007 ), sexual intercourse ( Henry, Schoeny, Deptula, and Slavick 2007 ), and substance use ( Kobus 2003 ). Adolescents may be particularly susceptible to social influences given their developmental stage and the importance of school and peer groups in adolescent life ( Steinberg and Monahan 2007 ). Moreover, there may be uniquely social aspects of adolescent smoking and other substance use, in that other adolescents provide access, opportunity, and reinforcement ( Kirke 2004 ; O'Loughlin, Paradis, Renaud, and Gomez 1998 ). Therefore, it should not be surprising that adolescent substance use and peer use are highly associated. While the effects of peer groups on adolescent substance use have been widely documented, much remains to be learned, especially regarding the mechanisms of peer influence ( Kobus 2003 ).

The purpose of this paper is to review and summarize the literature on peer group influences on adolescent smoking, building on the several recent reviews of the topic ( Hoffman, Monge, Chou, and Valente 2007 ; Kobus 2003 ; Tyas and Pederson 1998 ), and focusing on the recent publications on smoking. We conducted Internet searches with Web of Science and other search engines using key words such as “adolescent smoking,” “adolescent substance use,” “longitudinal studies,” “peer influence,” “socialization,” and “selection.” To be included in this review, studies had to have been published in 1999 or more recently; be longitudinal; include adolescent smoking as an outcome (either separately, or investigated within the context of adolescent substance use); and include measures of peer smoking at a minimum of two time points.

To provide a useful framework for the discussion of social influence, in general, and peer influence, in particular, on smoking, the paper is organized around the following key questions: What is social influence? What are the theoretical explanations for how social influence works? To what extent does peer smoking predict adolescent smoking? Are adolescents influenced by their friends (socialization) or do adolescents select friends with similar interests (selection) with respect to smoking? Are best friends, close friends, or crowd affiliations more important? Do positive parenting behaviors buffer the effects of peer influence?

Conceptual and theoretical perspectives on social influences on behavior

What is social influence.

Social influence is the effect others have on individual and group attitudes and behavior ( Berkman 2000 ). A conceptualization of multi-level social influences on adolescent smoking is presented in Figure 1 . The conceptualization suggests that social influences on adolescent smoking are exerted through social context, social networks, and group membership that operate mainly on social norms. Details of these constructs and of the relationships between them are presented in the following paragraphs.

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Conceptual model for social influences on adolescent smoking

Social norms are the patterns of acceptable beliefs, attitudes, and behaviors ( Axelrod 1984 ; Kameda, Takezawa, and Hastie 2005 ). Because human development occurs very slowly, individuals are socialized over time by family, school, and community and religious institutions according to the prevailing social norms. Social norms are influenced by – but also influence – social context, group membership, and social networks. The social influence processes that facilitate these reciprocal relationships between social norms and social structures are socialization and selection. Briefly, socialization is the tendency for individuals’ norms and behaviors to be influenced by the norms and behaviors of one’s group and conforming to them. Selection, however, refers to the tendency of individuals to seek-out peers with similar norms and behaviors ( Simons-Morton 2007 ).

Social context refers to the opportunities for interaction and the contexts within which individual interaction occurs ( Webster, Freeman, and Aufdemberg 2001 ). Social context determines the breadth, extent and nature of interpersonal interaction and therefore shapes the interpretation of social norms. As noted, humans are social creatures who live in families, reside in neighborhoods, belong to religious organizations, attend school, and go to work, all social enterprises through which most social interactions occur and which define the social context. Direct and primary social influence is thought to occur mainly within individuals’ proximal social context, which includes the family and peer groups ( Dawkins 1989 ). Our experiences and the information we gain in these settings shape our understanding of what is normative and acceptable behavior and train us in social relations ( Dawkins 1989 ).

Social context determines opportunity for social interaction through social network formation. In its simplest form, a social network is a map of all of the relevant ties between individuals and groups ( Valente, Gallaher, and Mouttapa 2004 ). One’s social network consists of all the people and groups with whom one has contact and the nature and extent of social interactions. The formation of each person’s social network is largely determined by shared social context such as neighborhood, school, church, and family ( Wilcox 2003 ). Social networks are important because connected people share information and shape each other’s perceptions of social norms. However, it is not just who individuals’ know or how often they spend time with them, but the nature of relationships (closeness, reciprocity, frequency of contact) that also contributes to social influence ( Valente, Gallaher, and Mouttapa 2004 ).

Group membership (e.g., family, religious, school, peer) is a particularly powerful socializing experience and people often change their perceptions, opinions, and behavior to be consistent with standards or expectations (norms) of the group ( Forgas and Williams 2001 ; Kameda, Takezawa, and Hastie 2005 ). Peer group affiliation becomes particularly important and influential during adolescence ( Brown 1989 ). Being a friend or part of a larger group, such as a clique, classroom, grade, school, club, or activity; or loosely affiliating with an amorphous crowd with similar interests (e.g., sports, music, drugs) provides great benefits of acceptance, friendship, and identity, but can also demand conformity ( Brown 1989 ). Group members tend to share common attitudes and behavior and this is particularly true for adolescent peer groups ( Eiser, Morgan, Gammage, Brooks, and Kirby 1991 ). Substance use is one factor about which friends and groups of adolescents tend to come to agreement, leading to group homogeneity ( Kandel 1978 ), although there may be periods of adolescence when peer influence is greatest ( Eckhardt, Woodruff, and Edler 1994 ; Steinberg and Monahan 2007 ). Susceptibility to peer influences may vary by gender and race (reviewed in Hoffman, Monge, Chou, and Valente 2007 ).

In summary, adolescents experience a range of social influences that may provide some direct effects on the likelihood of substance use, including smoking, but mainly provide indirect effects through social norms. In this section, we have presented social context, social networks, and group membership as discrete sources of influence; however, they are highly overlapping and interactive. As proposed by Bronfenbrenner (1979) , it may be useful to think of the strength of various social influences as depending on proximity and frequency of contact, where the closest circles of influence include the people with whom adolescents associate most of the time (family and peers) and whose influence on their behavior, particularly smoking, is likely to be the greatest.

What are the theoretical explanations of how social influence contributes to adolescent smoking?

No one theory fully explains social influence, but many theories emphasize that people learn through social interaction. A substantial discussion of theory is beyond the scope of the present review, and other papers have presented excellent overviews of theory relating to adolescent smoking uptake ( Hoffman, Monge, Chou, and Valente 2007 ; Kobus 2003 ). However, it may be useful here to point out the centrality of social norms in the prominent theories typically used to design research and explain findings on peer group effects. Social cognitive theory ( Bandura 1996 ) emphasizes the importance of cognitive representations in the form of expectations about social norms that arise from observational and experiential learning. Reasoned action ( Fishbein and Ajzen 1975 ) emphasizes the importance of perceived social (subjective) norms on intentions. Primary socialization ( Oetting and Donnermeyer 1998 ) and social bonding theories ( Hirschi 1969 ) suggest that adolescent peer group effects will be stronger in the absence of strong social bonds with family and school. Social identity theory ( Terry, Hogg, and White 2000 ) suggests that adolescents try on various identities and adopt the norms that are central to the social identity of the peer group to remain in good standing. Similarly, social exchange theory ( Kelley and Thibaut 1985 ) argues that friendships and group membership requires fair exchanges (reciprocity), leading to conformity of behavior between friends and group members. Of course, the nature of the relationships of group members greatly influences the nature of this reciprocity ( Plickert, Cote, and Wellman 2007 ). Social network theory suggests that social norms are shaped by information shared among members of a social system ( Scott 2000 ; Valente 1995 ). Norms also figure prominently in the literature on persuasion and social marketing ( Hastings and Saren 2003 ). Indeed, social influence is the basis for two-stage communication strategies in which persuasive communications are directed not at the ultimate target, but at opinion leaders whose attitudes and behavior influence others in their social groups (Rogers, 2003). Urberg et al. (2003) described the two-stage model of social influence as it applies to adolescent substance use.

Each of these theories shares the perspective that close (proximal) relationships provide a primary social influence, while the media and other aspects of culture provide important but secondary influences. Close relationships are most important because they are persistent, valued, and emotional. Individuals interact more often and spend more time with close relationships, and time spent together provides opportunities for influence. Each of these theories also recognizes that adolescents develop perceptions about social norms from information sharing (via interaction or observation) with people and groups in their social environment. In brief, social influence is implicit or explicit in many psycho-social theories and is one of the most consistently considered phenomenon in social psychology and persuasion ( Terry and Hogg 2000 ).

Peer group homogeneity with respect to adolescent smoking

To what extent does peer group smoking predict adolescent smoking.

The tendency for adolescent peer group members to share common characteristics such as smoking, termed alternatively as peer group clustering or homogeneity, has been well described ( Andrews, Tildesley, Hops, and Li 2002 ; McPherson, Smith-Lovin, and Cook 2001 ; Alexander, Piazza, Mekos, and Valente, 2001 ). Good evidence of this association comes from studies using prospective research designs, which allow the researcher to determine if peer use predicts future adolescent use, thereby providing stronger evidence of causality than cross-sectional associations. Indeed, research using prospective designs assess adolescent and peer substance use at baseline (Time 1) and adolescent substance use at follow up (Time 2 or at multiple time points), providing a test of the extent to which peer substance use predicts eventual adolescent use, while controlling for adolescent baseline use. Through standard literature review procedures (as discussed in the introduction), we identified 40 prospective studies published since 1999 linking peer group smoking or measures of substance use that include smoking, to future adolescent use.

Despite a wide range of differences in methods and populations studied, all but one of the papers reviewed reported positive associations between peer use at Time 1 and adolescent smoking at follow-up, including the following: (a) 23 of 24 papers that examined the relationship of friend smoking or smoking as part of a measure of substance use at Time 1 and smoking or substance use at follow-up; (b) all nine papers that examined the relationship between grade-level prevalence at Time 1 and smoking at follow up ( Bricker, Andersen, Rajan, Sarason, and Peterson 2007 ; Eisenberg and Forster 2003 ; Ellickson, Bird, Orlando, Klein, and Mccaffrey 2003 ; Ellickson, Perlman, and Klein 2003 ; Epstein, Griffin, and Botvin 2000 ; Mccabe, Schulenberg, Johnston, O'Malley, Bachman, and Kloska 2005 ; Rodriguez, Romer, and Audrain-McGovern 2007 ; Spijkerman, van den Eijnden, and Engels 2005 ); (c ) all five papers that reported both friend and grade level prevalence ( Epstein, Bang, and Botvin 2007 ; Gritz, Prokhorov, Hudmon, Jones, Rosenblum, Chang, Chamberlain, Taylor, Johnston, and De Moor 2003 ; Simons-Morton and Haynie 2003b ; Simons-Morton 2002 ; Smet, Maes, De Clercq, Haryanti, and Winarno 1999 ); (d) and all three papers that examined the influence of friend use at Time 1 on adolescent smoking trajectory groups ( Abroms, Simons-Morton, Haynie, and Chen 2005 ; Vitaro, Wanner, Brendgen, Gosselin, and Gendreau 2004 ; Wills, Resko, Ainette, and Mendoza 2004 ). All previous articles examined smoking as a distinct outcome, with the exception of the article by Wills et al (2004) , which considered smoking as part of a substance use composite score. To better illustrate the influence of peer smoking on adolescent smoking, we describe select findings in the subsequent paragraphs.

Does peer group influence on adolescent smoking vary by adolescent subgroups?

A main finding emerging from this literature points to the variation of peer influence on adolescents’ smoking by socio-demographic characteristics. While gender differences are well established, with girls shown to be more strongly influenced by peer smoking than boys ( Griffin et al., 1999 ), age differences were less clear. For example, Vitaro et al. (2004) found that friend use predicted adolescent smoking progression in the peer 12–13 and 13–14 year old groups, but not in the 11–12 year old groups. Conversely, Abrams and colleagues (2005) found that 6 th graders (age=11 years) with friends who smoke were more likely over time to become intenders, experimenters, or regular smokers.

This literature also provides valuable information on peer group effects in minority populations. Several studies found that African-American youth with friends who smoke were more likely to initiate smoking over time than those with no such friends ( Brook, Pahl, and Ning 2006 ; White, Violette, Metzger, and Stouthamer-Loeber 2007 ). Similarly, positive associations between friends’ smoking and adolescent smoking were observed among Latino ( Livaudais et al., 2007 ) and Chinese ( Chen et al., 2006 ) adolescents. A comparison of peer influence by race/ethnicity yields conflicting findings, with studies showing less effect of peer smoking on adolescent smoking among African-American than White adolescents ( Ellickson, Perlman, and Klein 2003 ; Robinson, Murray, Alfano, Zbikowski, Blitstein, and Klesges 2006 ); while others reporting similar peer group influence for White, Black, and Hispanic students ( Gritz, 2003 ). The different findings could be due to differences in samples by age or geographic location.

Peer group influence also varies by individual characteristics including genetics, which could influence exposure to substance-using friends ( Cleveland, Wiebe, Rowe, 2005 ); and personal attributes such as competency skills ( Epstein et al., 2007 ), or perceptions of personal harm due to smoking ( Rodriguez et al.,2007 ). Finally, peer influences on smoking may be moderated by strong social bonds to school and family ( Ellickson, Perlman, Klein, 2003 ).

Overall, this literature is surprisingly consistent in reporting positive associations between peer smoking and future adolescent smoking, and provides evidence that peer behavior affects initiation, progression, and trajectories. It also documents the influence of peer use on adolescent use among adolescents of various race and ethnicity groups, and shows that this influence may be mediated or moderated by cognitions, gender, and maturation. This research provides substantial evidence that smoking among friends predicts adolescent future smoking, but modest evidence that general prevalence, for example, within a particular grade or school, predicts future smoking, with the exception though, of cases where a higher general prevalence of smoking among senior students is related to an increase in smoking among lower-grade students ( Leatherdale, Cameron, Brown, Jolin Kroeker, 2006 ). However, while this literature bettered our understanding of peer influence on adolescent smoking, it does not address how peer group influences actually work.

The research on peer influence is limited by the fact that it is not possible to determine the extent to which friendships in existence at study initiation were formed due to selection or socialization processes. These friendships that are already in place at the beginning of a study would have been influenced by past socialization and selection processes that would be difficult or impossible to determine ( Cohen and Syme 1985 ). However, beyond that caveat, it can reasonably be assumed that associations between friends who smoke and smoking uptake are evidence of socialization and associations between smoking status and increases in the number of smoking friends is evidence of selection.

Are adolescents influenced (socialized) by their friends or do adolescents select friends with similar interests (selection) with respect to smoking?

The processes by which peer influence leads to peer group homogeneity of behavior are socialization and selection. Socialization is the tendency for attitudes and behavior to be influenced by the actual or perceived attitudes and behavior (e.g., norms) of ones’ friends and the conforming properties of group membership. Selection, on the other hand, is the tendency to affiliate and develop friendships with those who have similar attitudes and common interests ( Simons-Morton 2007 ).

Peer socialization

Peer socialization is the effect of existing social relationships on the formation of social norms. With socialization, the group accepts an adolescent based on shared characteristics. To be accepted, the adolescent takes on the attitudes and behaviors of the group ( Evans, Powers, Hersey, and Renaud 2006 ). Peer socialization can be overt, as in peer pressure, or perceived, where the adolescent accepts or changes attitudes and behavior based on perceived group norms that may or may not be actual. Socializing processes that facilitate the uptake of adolescent smoking can also discourage use ( Stanton, Lowe, and Gillespie 1996 ).

Peer socialization is often referred to as peer pressure, a term that suggests that adolescents directly persuade their friends to conform to their behavior. However, peer pressure is only one aspect of socialization. While there is evidence that adolescents do offer their friends cigarettes and that smoking is typically initiated in the context of peers ( Kirke 2004 ; Lucas and Lloyd 1999 ; Robinson, Dalton, and Nicholson 2006 ), most of the evidence indicates that socialization is mainly a normative process and not one of overt peer pressure. In surveys, youth report that overt peer pressure is not a factor for their smoking, but report that they sometimes experience internal pressure to smoke in the presence of other adolescents who are smoking, an evidence for the influence of perceived social norms rather than overt peer pressure ( Nichter, Nichter, Vuckovic, Quintero, and Ritenbaugh 1997 ). These findings suggest that perceived social norms exert a socializing effect.

Social norms need only be perceived to influence behavior. It has been shown that adolescents sometimes perceive that the prevalence of smoking is higher among their peers than they are in actuality ( Bauman and Ennett 1996 ; Iannotti, Bush, and Weinfurt 1996 ), which may be due to several possible factors. Adolescents may psychologically project their own smoking behavior onto others, thereby overestimating smoking prevalence ( Miller, Monin, and Prentice 2000 ). Adolescents may also develop a false consensus that one’s attitudes and behavior are normative when they are not ( Berkowitz 2004 ).

Overall, it seems that socialization occurs mainly through indirect pressure to conform through actual or perceived social norms. Although direct and overt peer pressure almost certainly operates, there is substantially less empirical evidence of its importance compared with the indirect influence on social norms.

Peer selection

Unlike socialization, where the person conforms to group norms, selection occurs when an individual seeks or affiliates with a friend or group with common attitudes, behaviors, or other characteristics. Selection processes include de-selection. When some members of a peer group begin smoking or experimenting with other substances, other members of the peer group can respond by dropping out of the group (de-selection), conforming to the new group norm (socialization), risking group disapproval, or living with the dissonance between their norms and the group’s ( Andrews, Tildesley, Hops, and Li 2002 ).

Selection may be abstract and internal, when a person affiliates with others by identifying with them or with what they represent, rather than affiliating on the basis of observable behaviors. For example, adolescents may identify with groups according to musical preferences, reputation, or interests ( ter Bogt, Engels, and Dubas 2006 ). Such affiliations may be highly transient among adolescents. Selection also involves actual affiliation and, within the limits of their social network, people gravitate toward individuals or groups who share their interests and values, and provide a supportive context for their own views and behavior ( Urberg, Degirmencioglu, and Tolson 1998 ). Adolescents who are interested in smoking, for example, may select as friends adolescents with similar interests in smoking ( Ennett and Bauman 1994 ), although smoking may be just one manifestation of a constellation of social norms leading to social selection.

Recent evidence regarding effects of selection and socialization on smoking

While selection and socialization processes can operate independently, they may also be interactive. Previous reviews have noted that some studies have found support for selection, some for socialization, and some for both with respect to adolescent smoking uptake ( Hoffman, Monge, Chou, and Valente 2007 ; Kobus 2003 ). However, there has been considerable disagreement about the relative importance of these two processes ( Arnett 2007 ; Bauman and Ennett 1996 ; Ennett and Bauman 1994 ).

To examine the latest findings on the topic, we reviewed published studies not included in previous reviews, using the methodology outlined in the introduction. Of the 13 papers reviewed (several papers were unique analyses of separate questions asked of the same data), seven used structural equation, general linear equation, or latent growth modeling; two used cross-lagged auto-regressive analyses to evaluate adolescent and peer substance use relationships from year to year; and four studies employed social network methods. All these methods are particularly useful for sorting out the effects of socialization and selection.

The findings of the first seven studies in Table 1 used latent growth modeling or similar analyses. All studies examined adolescent smoking as a distinct outcome, with the exception of Wills and Cleary’s study (1999) , where smoking was part of a substance use composite score. Evidence of socialization or selection is based on the longitudinal relationships between peer and adolescent substance use: Peer smoking at Time 1 predicting an increase in adolescent smoking over time, would be evidence of socialization, whereas adolescent smoking at Time 1 predicting peer smoking over time would be evidence of selection. However, when viewed from the perspective of adolescents’ influence on peer smoking, rather than the reverse, an increase over time in peer smoking would be socialization. The findings were mixed, with one study reporting effects only for socialization, five studies reporting effects for selection only, and three studies reporting effects of both socialization and selection. Wills and Cleary (1999) found effects of socialization and not selection on a combined measure of smoking, drinking, and marijuana use. DeVries et al. (2003) , Simons-Morton et al. (2004) , DeVries et al. (2006) , and Hoffman et al. (2007) found evidence of selection, but not of socialization on smoking progression. Urberg et al. (2003) found effects of both socialization and selection on smoking and drinking, Mercken et al. (2007) found effects of both processes on smoking, and Audrain-McGovern et al. (2006) found a direct effect on smoking progression of socialization and an indirect effect of selection through growth over time in friends who smoke.

Review of recent studies of peer socialization and selection on adolescent smoking * §

Two studies used auto-regressive analyses where the cross-lagged relationship between adolescent and peer smoking ( Tucker et al., 2008 ) or substance use ( Simons-Morton and Chen, 2006 ) at each time point was examined. Both studies found evidence of reciprocal effects of socialization and selection. Tucker et al. (2008) found evidence for both selection and socialization on smoking, with stronger effects for selection than socialization. Simons-Morton and Chen (2006) found similar magnitude of effects, but a more consistent effect of selection than socialization on a combined measure of adolescent and peer substance use.

The four social network studies found effects of socialization and the three that assessed selection also found evidence of selection. Urberg et al. (2003) reported effects of both selection and socialization on adolescent substance use. Maxwell (2002) reported effects of both socialization and selection on smoking, drinking, and chewing tobacco. Kirke (2004) reported that Irish adolescents tended to have common substance use behaviors over time, with selection a somewhat stronger effect than socialization. Hall and Valente (2007) reported direct effects of selection and indirect effects of socialization on smoking.

Findings of these studies with advanced study designs suggest that both socialization and selection processes contribute to peer group homogeneity with respect to smoking, probably in some sort of syncopation ( Urberg, Luo, Pilgrim, and Degirmencioglu 2003 ), with rather stronger evidence for selection than socialization. Effects were found for a variety of populations and varying measures of both peer and adolescent substance use. These modern designs and methods provide stronger evidence and richer findings than the traditional prospective analyses, where future adolescent substance use is predicted by current peer use.

Methodologies for investigating socialization processes: Comparative assessment

Growth modeling provides an elegant test of the relationship of peer use at Time 1 to the growth in adolescent use (socialization) and adolescent use at time 1 on peer use over time (selection), and these studies provided stronger support for selection than socialization. The findings of the two studies that used autoregressive approaches indicated that the magnitude of the effect of selection is relatively consistent but the effect of socialization varies over time, which suggests that these processes may be interactive and may vary by age or friendship dynamics.

Social network analyses are informative because they follow the same adolescents and peers over time, thus overcoming the objection that growth model analyses may over-estimate selection effects to the extent that adolescents’ reports of their friends’ substance use may be projections rather than true measures of friend use ( Arnett 2007 ; Bauman and Ennett 1996 ; Iannotti, Bush, and Weinfurt 1996 ). It is particularly interesting that the social network studies reviewed consistently demonstrated effects of both socialization and selection (where measured), similar to the findings of previous social network studies ( Ennett, Bauman, and Koch 1994 ). Social network studies can also provide unique information about the nature of peer influence that cannot be learned from other designs. For example, Urberg et al. (2003) reported that reciprocal friendships provided greater influence than non-reciprocal friendships, consistent with theory ( Plickert, Cote, and Wellman 2007 ) and other research ( Terry, Hogg, and White 2000 ). Also, Kirke (2004) demonstrated among Irish adolescents that adolescent smoking is a highly social activity in that adolescents smoke in groups and offer and borrow cigarettes.

Collectively, the studies reviewed provide strong evidence for peer influence effects on adolescent smoking, suggest that selection is at least as important as socialization, and that these two processes are probably interactive. However, more can be learned about the nature of peer influence processes and how they might vary by age, gender, race, and friendship qualities and what factors mediate the relationship between adolescent and peer smoking.

Are best friends, close friends, or crowd affiliations more important?

While substantial information exists on the independent influences of best friends and peer groups on adolescent smoking, few studies have examined the differential impact of these relationships. Establishing a close relationship with one friend and belonging to a peer group are thought to be more or less equally important for adolescents and both types of relationships may facilitate essential developmental tasks such as the building of social skills, identity formation, and social support ( Giordano 2003 ). Yet, best friends and peer groups may not equally influence adolescents’ behavior. If influence results from wanting to please friends, then best friends would be expected to be more influential. However, if influence derives from the desire to conform to the group norms, then peer group influence would be expected to supersede the influence of one close friend ( Urberg, Degirmencioglu, and Pilgrim 1997 ).

Only four studies were identified that examined whether best friendships and peer groups function differently to affect adolescent smoking and other substance use. Several findings emerged from these studies. First, the influence of a best friend as compared to the influence of a group of friends varied depending on the behavior under consideration (best friend’s influence was greatest for behaviors that are illegal), and the stage of use (best friends predicted initation whereas the peer group predicted transition to current use) ( Urberg, Degirmencioglu, and Pilgrim 1997 ). Second, best friendships and peer groups interacted to better predict adolescent use ( Hussong 2002 ). For example, adolescents with substance-using best friends showed a decreased risk for substance use if they had other close friends who were not high substance-users. However, the influence of a best friend was shown to be independent of peer groups in another investigation ( Alexander, Piazza, Mekos, and Valente, 2001 ). Finally, adolescents with reciprocal friendships within a group were less influenced by the overall level of smoking among the group than adolescents with no reciprocal friendships ( Aloise-Young, Graham, and Hansen 1994 ).

Crowd affiliation has been identified as another source of influence on adolescent smoking ( Engels, Scholte, van Lieshout, de Kemp, and Overbeek 2006 ; Michell 1997 ; Michell and Amos 1997 ; Urberg, Shyu, and Liang 1990 ). Each crowd has a reputation that allows adolescents to recognize youth who share similar beliefs, attitudes and behaviors. As adolescents affiliate with specific crowds, they tend to embrace the behaviors of the crowd, perhaps as a result of their perceptions of the crowd’s reputation, rather than direct peer pressure from crowd members ( Kobus 2003 ).

The prevalence of smoking varies considerably between youth crowds. Crowds that are perceived as “deviant” or unconventional, are likely to have the highest smoking rates ( La Greca, Prinstein, and Fetter 2001 ; Schofield, Pattison, Hill, and Borland 2003 ; Verkooijen, de Vries, and Nielsen 2007 ). Reasons for smoking also vary across crowds, and can range from the maintenance of high social status to the need to climb up in the hierarchy ( Michell and Amos 1997 ). The association between crowd membership and smoking can best be explained by social identity theory, which emphasizes the importance of group membership for adolescents’ self-identity. Accordingly, adolescents affiliated with a crowd are likely to be influenced by the crowd’s norms and will tend to adopt the crowd’s normative behaviors ( Verkooijen, de Vries, and Nielsen 2007 ).

In summary, best friends, peer groups and social crowds all appear to affect adolescents’ smoking and other substance use. While few studies have examined whether their effects are independent or interactive, results suggest that effects are dependent on (1) the specific substance used; (2) the stage of use; and (3) relationship characteristics (e.g., adolescent is member of the group but not central to it). More research is needed to clarify the mechanisms through which these influence processes occur, particularly using national samples, to allow for the simultaneous evaluation of the effects of best friends, peer groups and social crowds across a range of substances and for different demographic subgroups.

Do positive parenting behaviors buffer the effects of peer influence?

Parent influence has frequently been found to be associated with adolescent smoking. However, associations have generally been modest ( Avenevoli and Merikangas 2003 ). Household smoking has been identified as a modest predictor of adolescent smoking ( Hoffman, Monge, Chou, and Valente 2007 ; Kobus 2003 ). But it is not clear if this effect is due to increased availability of cigarettes, modeling, or parenting practices. Prospective studies have shown protective effects of a variety of positive parenting practices ( Simons-Morton and Haynie 2003a ), including setting expectations ( Abroms, Simons-Morton, Haynie, and Chen 2005 ; Forrester, Biglan, Severson, and Smolkowski 2007 ; Simons-Morton 2004 ; Tucker, Klein, and Elliott 2004 ) parent support ( Simons-Morton 2007 ; Simons-Morton 2004 ; Wills, Resko, Ainette, and Mendoza 2004 ) and parental monitoring ( Dishion and Andrews 1995 ; Mounts and Steinberg 1995 ; Simons-Morton 2007 ; Simons-Morton, Chen, Abroms, and Haynie 2004 ). The effect of positive parenting practices may be influenced by the strength of family ties ( Urberg, Luo, Pilgrim, and Degirmencioglu 2003 ) Parents and peers appear to provide independent effects on smoking ( Simons-Morton and Haynie 2003a ). However, of the few studies that have examined both peer and parent effects, most indicate that peers provide greater influences on adolescent smoking than parents ( Hoffman, Monge, Chou, and Valente 2007 ).

One mechanism by which parents can protect their children from smoking and other undesired behaviors is to discourage their association with friends who engage in these behaviors, provide bad examples, and otherwise exert negative socializing influences, as indicated in Figure 1 . Several studies have demonstrated that parent influence on adolescent smoking occurs indirectly by preventing friendship formation with smoking peers ( Avenevoli and Merikangas 2003 ; Simons-Morton, Haynie, Crump, Eitel, and Saylor 2001 ), moderating the effects of friend influence ( Dielman, Butchart, and Shope 1993 ), or moderating affiliation with smoking peers ( Engels and van der Vorst 2003 ). Urberg (2003) reported that teens who value their parents are less likely to select substance-using friends. Several recent studies reported that positive parenting practices and parent-teen relationship factors reduce likelihood of adolescents forming friendships with substance using peers, providing indirect protective effects on adolescent smoking ( Simons-Morton 2004 ; Tucker, Martínez, Ellickson, and Edelen 2008 ).

Limitations of existing literature

While there are many papers on peer influences on adolescent smoking and other substance use, a limited number of papers have reported prospective findings in which both peer and adolescent smoking were assessed. For example, few such papers have compared the relative effects of best friend, close friends, or general peer group. There is also a paucity of research on social influences among ethnic groups. Further, while current studies examining the effects of socialization and selection suggest that an increase in smoking uptake at Time 2 by the number of friends who smoked at Time 1 is evidence of socialization, and that an increase in friends who smoke at Time 2 among adolescents who smoke at Time 1 provides evidence of selection, the two processes may not be that distinct and are actually interactive. More information is, however, needed regarding the circumstances surrounding socialization and selection. For example, a smoker at Times 1 and 2 with non-smoking friends at Time 1 but with friends who smoke at Time 2 may illustrate selection (choosing new friends) or socialization (influencing Time 1 friends to smoke) processes, that could only be disentangled through gathering more information about group composition and dynamics over time. Finally, many studies have used a measure of substance use that includes smoking and other substance use, usually drinking, sometimes marijuana use. The main advantage of this convention is it allows for the configuration of a continuous or ordinal measure, with many analytic advantages over nominal measures of smoking. However, this convention makes it impossible to know the relative influences on smoking compared with overall substance use.

In this manuscript, we provided a conceptual model showing social influence on adolescent smoking occurring at multiple levels. Within this context we discussed the literature on proximal social influences on adolescent smoking, including peer and parent influences. Based on this review we offer the following tentative conclusions.

  • There is substantial peer group homogeneity with respect to adolescent smoking and other substance use. This is to say that adolescents with friends who smoke are likely to smoke themselves or to take up smoking over time. The reverse is also the case that adolescents without friends who smoke are less likely to take up smoking than adolescents with friends who smoke.
  • Both socialization and selection appear to provide important influence on adolescent smoking. They also appear to be interactive. The evidence from studies based on advanced research designs is somewhat stronger for selection than socialization effects.
  • Best friends appear to provide the greatest peer influence on adolescent smoking; peer groups (close friends) provide independent influence, but their influence may also interact with that of the best friend. Crowd affiliation is another friendship dimension that appears in limited research to be associated with adolescent substance use. It is modestly associated with adolescents’ smoking and may interact with peer group influence. Few studies have examined the relative influence of best friends, peer groups and crowd affiliations and a more research is needed.
  • Parenting appears to remain an important influence on adolescent smoking during adolescence, with parental smoking increasing the likelihood of adolescent smoking and protective parenting practices that are maintained over time providing both direct and indirect (by reducing the number or influence of smoking friends) protective effects against the uptake of adolescent smoking.

Implications and future directions

We believe the rich literature on the effects of peer and parent influences on adolescent smoking, while incomplete, provides a strong basis for the development of next generation prevention programs. Based on the literature, interventions might be designed that focus on cognitive factors that might mitigate the effects of peer group influences, as some social skills-oriented programs have emphasized ( Haegerich, Tolan, 2008 ), or they might be directed at the peer group and designed to alter social norms, or they could be directed at facilitating protective parenting practices.

Future research on peer influences on adolescent smoking would benefit from further examination of the relative effects of best friend, close friends and general peer group, especially among adolescent subgroups (for e.g., by gender, age, race/ethnicity). Further, examining the effects of socialization and selection deserves continued attention, as methodological advances (e.g., social network analyses software) and more refined study designs (e.g., longitudinal studies following-up adolescents and their peer group) facilitate the differentiation of these two processes.

Acknowledgments

This research was supported in part by the intramural research program of the Eunice Kennedy Shriver National Institute of Child Health and Human Development

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  • Open access
  • Published: 22 February 2024

The impact of the cost-of-living crisis on population health in the UK: rapid evidence review

  • Jade Meadows 1 ,
  • Miranda Montano 2 ,
  • Abdelrahman J. K. Alfar 1 , 3 ,
  • Ömer Yetkin Başkan 1 ,
  • Caroline De Brún 4 ,
  • Jennifer Hill 4 ,
  • Rachael McClatchey 5 , 6 ,
  • Nevila Kallfa 1 &
  • Gwen Sascha Fernandes 1 , 7  

BMC Public Health volume  24 , Article number:  561 ( 2024 ) Cite this article

Metrics details

In the UK, unique and unforeseen factors, including COVID-19, Brexit, and Ukraine-Russia war, have resulted in an unprecedented cost of living crisis, creating a second health emergency. We present, one of the first rapid reviews with the aim of examining the impact of this current crisis, at a population level. We reviewed published literature, as well as grey literature, examining a broad range of physical and mental impacts on health in the short, mid, and long term, identifying those most at risk, impacts on system partners, including emergency services and the third sector, as well as mitigation strategies.

We conducted a rapid review by searching PubMed, Embase, MEDLINE, and HMIC (2020 to 2023). We searched for grey literature on Google and hand-searched the reports of relevant public health organisations. We included interventional and observational studies that reported outcomes of interventions aimed at mitigating against the impacts of cost of living at a population level.

We found that the strongest evidence was for the impact of cold and mouldy homes on respiratory-related infections and respiratory conditions. Those at an increased risk were young children (0–4 years), the elderly (aged 75 and over), as well as those already vulnerable, including those with long-term multimorbidity. Further short-term impacts include an increased risk of physical pain including musculoskeletal and chest pain, and increased risk of enteric infections and malnutrition. In the mid-term, we could see increases in hypertension, transient ischaemic attacks, and myocardial infarctions, and respiratory illnesses. In the long term we could see an increase in mortality and morbidity rates from respiratory and cardiovascular disease, as well as increase rates of suicide and self-harm and infectious disease outcomes. Changes in behaviour are likely particularly around changes in food buying patterns and the ability to heat a home. System partners are also impacted, with voluntary sectors seeing fewer volunteers, an increase in petty crime and theft, alternative heating appliances causing fires, and an increase in burns and burn-related admissions. To mitigate against these impacts, support should be provided, to the most vulnerable, to help increase disposable income, reduce energy bills, and encourage home improvements linked with energy efficiency. Stronger links to bridge voluntary, community, charity and faith groups are needed to help provide additional aid and support.

Although the CoL crisis affects the entire population, the impacts are exacerbated in those that are most vulnerable, particularly young children, single parents, multigenerational families. More can be done at a community and societal level to support the most vulnerable, and those living with long-term multimorbidity. This review consolidates the current evidence on the impacts of the cost of living crisis and may enable decision makers to target limited resources more effectively.

Peer Review reports

Introduction

In the UK, the COVID -19 pandemic and subsequent unforeseen geopolitical factors (e.g., Brexit & Ukraine-Russia War) resulted in a severe economic downturn with gross domestic product (GDP) decreasing by 11.0% in 2020, the sharpest drop since records began and unprecedented in modern times (Fig.  1 , panel a). Since March 2020, whilst GDP has increased , it has remained below 2020 pre-pandemic levels through to July 2022, accompanied by rising inflation rates due to endogenous and exogenous shocks (Fig.  1 , panel b) [ 1 ]. Internal shocks include supply chain challenges or labour shortages which affected the supply side, and pandemic-associated changes in consumer purchasing patterns which affected the demand side. These internal shocks have resulted in imbalances between the supply and demand of different markets including the goods and services market, the labour market, and the money market. Exogenous shocks occur due to non-economic interventions such as war (e.g., the Ukraine-Russia war) and the ongoing global pandemics (e.g., COVID-19). This combination of economic shocks is unprecedented, therefore the resulting impacts on inflation and the cost of living (CoL) are unique, both in terms of provenance and consequences for population health and wellbeing. This is further exacerbated by COVID-19 consequences and recovery from the pandemic which has been estimated to take 10–15 years [ 2 ]. The cost of living crisis is often regarded as the ‘second health emergency’ after the COVID-19 pandemic [ 3 ].

figure 1

Panel a : Annual % change in UK GDP since records began; Panel b : % change in GDP compared to Feb 2020 (pre-pandemic levels) [ 1 ].

Recent evidence from the World Health Organisation (WHO) shows that economic shocks and austerity exacerbate poverty, vulnerability, marginalisation, as well as socioeconomic and health inequalities [ 4 , 5 , 6 , 7 ], with serious implications for health [ 2 , 8 ],. In the UK, rising inflation has contributed to the rising CoL, which has made the population poorer and driven 1 in 5 into relative poverty [ 9 ].

The UK Office for National Statistics (ONS) checks the prices of a whole range of items in a standard ‘basket’ of goods and services and the price of that basket determines the overall price level, otherwise known as the Consumer Prices Index (CPI). Inflation is a term used to describe rising prices and how quickly prices go up is called the rate of inflation. To calculate the inflation rate, the cost of the basket or level of CPI is compared with the previous year, and this change in price level over the year is the rate of inflation [ 10 , 11 ]. The inflation rate in the UK (February 2023) was 10.5% against a usual target of 2% [ 10 ]. The inflation rate is projected to worsen through 2023 and subsequently decrease by Q1 of 2024 as depicted in Fig.  2 [ 10 ], a trend confirmed in recent months with inflation now at 4.2% (Jan 2024).

figure 2

CPI inflation Q1 2008 to Q1 2028, including successive Bank of England and Office for Budget Responsibility forecasts. Source: Institute for Government

According to the official economic projections, the expected UK economic recovery is slower than the G7 countries, a full recovery and return to pre-pandemic peak is expected in the end of 2024. The average annual growth rate is less than 1% (per year), from the start of the pandemic till 2028. Prior to this period, the growth rate was 2.75%. In addition to this, the real disposable income per person is expected to fall by 5.7 cumulative percent by the end of the next March 2024 [ 12 ].

Meanwhile the consumer’s wages and benefit payments are not in tandem with rising living costs, and in particular, the cost of housing, food, energy, and fuel [ 13 ]. For example, evidence suggests that 14.4% of households in the UK (approximately 3.53 million) will be living in fuel poverty by January 2023 [ 14 ]. Those at risk include large families, lone parents, and pensioner couples [ 15 ], with the elderly and children being most vulnerable to an increased risk of physical illness such as respiratory infections [ 16 ]. In addition, studies have found that economic hardships increase the prevalence of mental illness, including feelings of anxiety and depression [ 17 ]. The modest economic growth, the decline in real disposable income, and the slow recovery expectations will put more pressure on hospital admission from respiratory and mental illnesses.

To better understand the current cost of living crisis, the associated triggers, drivers, consequences and potential solutions, a rapid evidence review was undertaken between November 2022 and March 2023. This was a cross-organisation, multidisciplinary endeavour that brought together academics, researchers, clinicians, public health practitioners, and policymakers to develop a review that would be informative, useful and of value to the wider health and care system and their stakeholders.

Aims and objectives

The review aimed to provide a first narrative review of evidence relevant to the current CoL crisis and impact on our population health, wellbeing and related services.

The objectives of this evidence review were:

To explain the provenance of the current CoL crisis from a UK perspective including the causes, triggers, drivers, and consequences.

To map the short (1 – 2 months), mid- (3 – 6 months) and long- term (6 months plus) implications of the current CoL crisis on population health and wellbeing. These would be themed by physical health, mental health, wellbeing, education, environment, workforce, and wider health and care system pressures. It would also involve the identification of those most vulnerable to the cost-of-living crisis.

To describe how people change their behaviour or cope/respond to the current CoL crisis, how it impacts on their self-care (e.g., attending regular health checks or screening visits) and the longer-term implications of this behaviour change.

To describe some of the mitigation strategies or interventions that have been deployed to counter the negative impacts of the current CoL/economic crisis and their effects in the UK and key European counterparts, where available.

To describe the breadth of impacts on health and care system partners of the current CoL crisis.

Methodology

Search strategy.

The search strategy was based on the objectives of this review and cross-referenced with the search strategy adopted by UKHSA’s Library Services on CoL and poverty evidence reviews, conducted since November 2022. Studies published from 2020, which was the start of the coronavirus pandemic and unique set of circumstances culminating in the cost of living crisis, and up to and including the 2023 were reviewed.

Key themes were discussed with experts, such as respiratory consultants, to finalise the search strategy to ensure the search included key phrases.

The definition of the CoL crisis has been taken from the WHO which describes CoL as the decrease in real disposable income that people have been experiencing since late 2021. The key causes are high inflation overriding income and benefit increases, and, have been worsened by the COVID-19 pandemic, the war in Ukraine, disruption to global supply chains and the food and energy crises [ 2 ].

The keywords included: cost of living, population impacts, living cost, fuel poverty, poverty, mental health, physical health, wellbeing, education, work environment, ability to work, and ability to care. Full details of search strategy are included in Appendix 1 .

Selection methodology

Evidence identification, screening and extraction.

The selection criteria were structured around PICOS structure [ 18 , 19 ]:

All age groups impacted by the CoL crisis in the UK.

Intervention

Any strategies or interventions that have been used to mitigate the impacts of a CoL crisis to be presented by theme/topic, individual level e.g., reducing energy bills at home or changing food preparation patterns; community level e.g., provision of warm drinks at community centres for the elderly, economic/societal level e.g., energy payments for all households, etc. Could also present these at local/sub-regional, national levels.

All age groups that are used to compare with and against those most impacted by CoL (those most protected or least vulnerable to a financial crisis).

The primary outcomes includes a comprehensive tally of physical health (e.g., admissions, morbidity, mortality), Mental Health (e.g., anxiety, depression, injuries, suicide and self-harm), Wellbeing (e.g., social and economic insecurity, working additional jobs, reduced leisure time), educational attainment/school absenteeism, Environmental (e.g., pollution, housing conditions, infrastructure disruptions), workforce (inability to work due to sickness inability to care for family members) and Service pressures or impacts (e.g., lack of staff, lack of community based care or service provision).

Study design

Due to the specific nature of this CoL crisis and time restrictions, we reviewed all review study designs.

Inclusion criteria

UK-based studies only, due to the unique set of circumstances political policies and populations pertaining to the UK; review studies from the 1st of January 2020 up to include the 24th of February 2023. Including grey literature, such as reports from think tanks and charities, such as The Kings Fund [ 3 ] and the Joseph Rowntree Foundation [ 9 , 20 ].

Exclusion criteria

Non-UK based studies, specific study formats including letters to Editors.

Article review

From the search, excluding grey literature, 1,256 records were identified. The team extracted these titles/abstracts into Rayyan, an online software programme used for systematic literature reviews. The study team were then given individual access, with the ‘blind’ feature activated, allowing for independent assessments to be made of each article and corresponding eligibility for inclusion into this evidence review. The initial abstract review was undertaken by two members of the study team, with 50% of articles assessed by at least two members of the team – any discrepancies were discussed and a mutual decision made. Following abstract review, the full text screen was divided into topic areas where two team members reviewed each paper for suitability. As previously, any discrepancies were discussed and a mutual decision made. Each extracted article, regardless of relevance or quality, underwent initial screening to determine relevance to the review topic. Figure  3 details the process for inclusion of studies.

figure 3

PRISMA flowchart showing inclusion of studies into the study review process through Rayyan including identification, screening and final, included articles

Quality assessment methodology

Forty-one studies were included in the full text review. Suitable studies (with interventions) were appraised for quality by a primary reviewer and, to ensure robustness, 50% of these were appraised by a second reviewer. Study quality (cross-sectional, case–control, cohort or qualitative) was assessed using the Newcastle Ottawa Scale (NOS) adapted to consider key areas: selection (representativeness of the sample, sample size, non-responders and exposure details); comparability (is confounding considered) and outcome (blinding, recording and statistical test used). Whilst there are a myriad range of assessment tools available, the NOS has been endorsed by the Cochrane Collaboration to assess quality of research studies [ 18 ].

Three studies were eligible for scoring, the results are detailed in Table  1 . Eligibility for scoring was based on whether an intervention was evaluated as part of the study – those that described an intervention were considered for scoring.

The results are presented by the five objectives outlined in the methodology. Table 2 summarises the 26 papers identified as part of our review process and Table  3 describes the grey literature used.

Current CoL crisis

We have addressed objective 1 of the evidence review (describe the current CoL, including the causes, triggers, and drivers of economic instability by internal and external shock factors) as part of the introduction of this report and cited findings from the World Health Organisation [ 2 ], the Bank of England [ 11 ] and the Institute for Health Equity [ 14 ]. Six (out of twenty-six) peer-reviewed papers from the evidence review were also used to gather evidence and establish a baseline understanding of the current CoL crisis.

Short, mid and long term implications

The review mapped health and wellbeing outcomes using evidence published from six (out of twenty-six) peer-reviewed papers from the evidence review and findings from the World Health Organisation [ 2 ], the King’s Fund [ 3 ], the Joseph Rowntree Foundation [ 9 , 20 ] and Public Health Wales [ 13 ]. A summary of the findings from our reading across papers and grey literature, as well as discussions with our expert advisory panel, on relatable health outcomes is shown in Table  4 and are themed by type of impact (environmental impacts, mental health, physical health and service pressures/impact areas), by time (immediate 1–2 months, intermediate 3–6 months and longer-term 6 months and over) and populations at risk.

The review found that the strongest evidence for the impact of a CoL crisis, particularly from living in cold homes, was on acute hospital admissions due to respiratory distress or illnesses and in particular, affected the very young (children aged 0–4 years) or the elderly (75 + years) [ 13 , 16 , 29 , 33 ]. It is also anticipated that in 2023, a further 500,000 children across England will fall below the UK poverty line [ 33 ] with families struggling to buy essential items like food and clothing [ 34 ]. Increases in the cost of energy and food would result in families choosing between energy dense foods vs. more costly healthier food options [ 30 ]. The CoL crisis could widen socioeconomic inequalities in obesity by affecting disadvantaged families and communities at an existing risk of obesity [ 30 ]. Specifically concerning in younger children are issues related to living in a cold home – including unsafe sleep practices for children, reduced ventilation to keep ‘the heat in’, and living in areas where it is unsafe to open windows [ 35 ].

Behaviour change

Objective 3 of the evidence review was on the impact of the CoL crisis on health behaviours, how people cope or respond to a CoL crisis, and how it impacts on their self-care e.g., attending regular health checks or screening visits. Evidence from 3 (out of 26) peer-reviewed papers [ 32 , 36 , 37 ] from the evidence review and findings from Public Health Wales [ 13 ] informed our results. These were:

Reducing transport related costs associated with attending screening services and appointments may be effective as cost was a barrier for people accessing health and care services. Missing or delaying medical appointments will exacerbate physical and mental health illnesses and delay treatment.

Economic crisis was associated with a lower probability of drinking alcohol frequently and lower probability of being physically inactive.

Economic austerity was associated with increasing child poverty and poorer access and quality of services provided, particularly to children with physical disabilities.

Screening could be offered in acute care and community-based settings to address the social needs of vulnerable patients and families.

The Food Foundation has tracked food insecurity [ 38 ] and found that:

17.7% of households experienced food insecurity (moderate or severe) in January 2023, with 24.4% of households with children experiencing food insecurity.

3.2 million adults (6.1% of households) reported not eating for a whole day because they couldn’t afford food.

Key workers are more likely to be experiencing food insecurity.

Half of households on Universal Credit experienced food insecurity

Disability exacerbates food insecurity.

Non – white people more likely to experience food insecurity.

Mitigation strategies

Objective 4 of the evidence review was on potential interventions to address the CoL crisis at a population level. Nine (out of 26) peer-reviewed papers [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ] were included alongside evidence from the grey literature sources including the King’s Fund [ 3 ], Joseph Rowntree Foundation [ 9 ] and Public Health Wales [ 13 ].

Where possible, scoring of studies with an intervention were undertaken. Three studies were scored using the Newcastle Ottawa Scale (NOS) as detailed in Table  1 :

A summary of interventions that could be deployed to mitigate the effects of the CoL crisis is presented in Appendix 2 with most peer-reviewed publications focussing on mitigating the negative impacts on mental health. By theme and strength of evidence (where grading was possible), the key findings are:

Mental health

Strong evidence (based on systematic review and meta-analysis of results) to support the use of short-stay crisis units for people experiencing mental health crises.

Evidence showed that suicide risk assessment tools are deployed variably across 85 NHS mental health organisations with limited staff training. Study outcomes recommend standardisation of assessment tools and bespoke staff training in use and implementation, to help improve service and care provision for people attempting suicide or serious self-harm.

Despite strong study design, there was a lack of evidence for the effectiveness of specialist mental health day units compared to crisis teams which support people in crisis at home.

Similarly, there was weak evidence for the use of mental health units which act independently of emergency departments with some units experiencing lengthy stays in a setting which was designed for short stays.

Public Health Wales 2023 [ 13 ] related commissioned review recommends focussing on mental health and wellbeing support including suicide prevention campaigns (short term) and linking people with community support including voluntary and community sector (long term).

Physical health

Providing financial help and home improvements to those at risk of living in a cold home (increasing the warmth and/or energy efficiency of a home)

Preventing falls through exercise (strength training) and home safety assessments

Preventing the spread of respiratory viral infections maximising vaccination uptakes e.g., influenza vaccines, employers encouraging sick employees to stay at home, and providing handwashing advice.

Helping vulnerable individuals keep warm, particularly those experiencing homelessness.

Environmental (income support, energy relief, housing, food)

Public Health Wales recommend providing targeted support on energy bills and extending the Winter Fuel Support Scheme for all households and focusing on the elderly, the very young and people with disabilities or long-term health conditions.

Advising on modifying home energy use in the community e.g., best time of day to use appliances or monitoring use with a smart meter.

Extension of the council tax reduction scheme for tenants and households experiencing hardship

Provision of meal allowances and free school meals to all primary school children.

Supporting emergency schemes such as food banks and community groups which provide essentials of daily living (extend to community/faith groups).

Voluntary & community sector

Weak evidence for the use of individualised/bespoke advice on facilitating energy tariff switching, particularly in vulnerable communities (BAME, elderly over 75 years, and families with young children). Young families most likely to switch, elderly least likely due to apathy, lack of knowledge and scepticism.

Impact on system partners

The last objective of the evidence review aimed to summarise the impact of the CoL crisis on other partners in the health and care systems, such as the voluntary and community sector, fire and rescue service, military, police, and ambulance service. The results of the review (predominantly grey literature as well as discussions with our expert advisory panel) found the following impacts by different sectors as presented here:

Voluntary and community sector

There has been a reduction in voluntary services and community groups, particularly in deprived areas where the level of need is higher. This is largely driven by an increase in energy prices, consumable such as food, and an increase in fuel prices, and more expensive labour [ 48 ].

Reductions in charitable donations and volunteer time have been seen in charity settings with charities now responding more to crisis planning, including welfare and wellbeing support for people.

A decrease in the volume of food donated to food banks has resulted in limited supplies of food provisions against rising demand in the community [ 49 ].

The rate of closures and number of closures of charities was significantly higher in 2022 than in 2021, and simultaneous reduction in the resilience of the voluntary and charity sector in April 2023 compared with previous years [ 50 ]. This includes operating losses for large front-line charities.

There has been a reduction in number of volunteers as part time jobs [ 50 ].

Successes were seen, with services being continued, in charities providing hot meals to elderly residents and a scheme for free school meals provision during school holidays.

Providing safe, warm spaces by making use of local amenities have been successfully deployed in some areas (Wiltshire Community Foundation).

Increases in fuel theft and shoplifting (crime/policing) were noted, however, the Office for National Statistics reports that this increase could be due to improved recording processes and practices by police staff and expansion of recorded crime figures to include new offences [ 51 ].

Fire & rescue

Increased risk of fires as people try to heat their homes or find alternative and cheaper ways to light or heat their homes, for example, wood burner fires linked to chimneys not being swept [ 52 ].

Increased emergency admissions in A & E from burns as a result of alternative heating mechanisms used or unsafe practices, for example, plugging in an electric heater too close to flammable materials.

Data from the House of Commons [ 53 ] shows that a series of measures have been introduced to mitigate against the CoL increase for defence people, veterans and service families including subsidised accommodation charges at 1%, freezing food charges, increasing travel allowance, and providing additional wraparound childcare services.

This is one of the first narrative reviews of the published and grey literature from 2020–2023 to describe the breadth of impact of the current and unique CoL crisis on population health in the UK. The main findings of this report refer to the immediate impacts on population health and well-being, including physical, mental, and financial health. This paper sequentially assesses the literature to present mapped population impacts, individual and population behavioural responses to the cost of living crisis, and the system wide implications of these impacts. Since conducting our review Broadbent et al. [ 54 ] and Richardson et al. [ 55 ], have published work which also investigates the impact of the cost-of-living crisis on population health, which corroborate our findings. Our review was produced to help stakeholders and partners in the health and care sector to rapidly assimilate key information, knowledge, and evidence on the impacts of the cost of living and use it to inform, challenge, change, and drive local policies and practices on tackling this crisis.

The unique provenance of the cost of living

The COVID-19 pandemic, Brexit, and the Ukraine-Russia war have resulted in unique economic shocks in the UK with steep declines in GDP, reduction in disposable incomes, and increases in inflation from 2020 to 2023. The situation has been further compounded by public sector strikes, political instability, changing fiscal and energy policies, climbing interest rates, findings further corroborated by Broadbent and colleagues [ 54 ]. At a population level, the evidence reflects a reduction in spending power due to the rising costs of essentials such as food and medication, and basic utilities such as heating, electricity, and council tax [ 56 ]. Evidence from the grey literature, the Office of National Statistics, reports that from 17 to 29 May 2023, 7 in 10 adults reported an increase in their cost of living compared to the previous year, largely driven by food bills (95%), gas or electricity bills (73%) and fuel prices (39%) [ 31 ]. A reduction in a household’s spending power or income, is likely to have marked effects on health, as other longitudinal analyses and reports have purported [ 55 ].Our review highlights the unique provenance of the current cost of living crisis, and why it is markedly different and more serious compared to previous economic crises, both at a UK and global level.

The short, mid- and long-term impacts

Our review captures a breadth of physical and mental health conditions which can be affected by the CoL crisis in the short, mid and long term. In the short term, the strongest evidence was for the impact of CoL on housing, resulting in cold, damp, or mouldy homes, and the subsequent effect on the rate of respiratory conditions. These impacts are also more likely to affect those already vulnerable, such as those with chronic conditions, lone parents, multigeneration families, and children [ 13 , 16 , 29 , 33 , 57 ]. Another group vulnerable to the CoL impacts include key workers, those on universal credit, disabled and people from non-white ethnicities [ 16 , 21 , 31 ]. The July 2023 labour market figures form the ONS show that over 410,000 people were not actively seeking employment due to long-term sick leave [ 58 ]. In the longer term, increased morbidity rates and mortality rates from all-causes and cause-specific, such as respiratory and cardiovascular diseases, are expected with recent publications citing an increase in premature mortality by up to 6.4% and life expectancy to decrease by 0.9% [ 55 ].This has been demonstrated with official mortality rates in March 2023, which were 4.8% above the expected rate [ 59 ].

Behaviour changes because of the CoL crisis are likely to confound the issues that are affecting physical and mental health. Changes in food buying patterns, changes in the frequency and temperature to which homes are heated, and reduced physical activity levels can contribute to poor physical and mental health and impact wellbeing. Other impacts include the inability to afford travels costs to attend screening or hospital services or the reduction in community services such as community pharmacies [ 13 ]. This CoL crisis has had a negative impact on voluntary services and community groups. Charity and food donations have decreased, whilst the need for charity and food banks have exponentially increased. The Trussell Trust report that from March 2022-March 2023, they provided almost 3 million emergency food parcels, higher than during the pandemic and more than double the number in the same period 5 years prior [ 60 ], a finding further highlighted by Broadbent and colleagues [ 54 ].

What can be done to mitigate the impacts?

Our evidence suggests that financial help should be provided to those most at risk of living in a cold home because of the CoL crisis, such as lone parents, multigenerational families, and those living alone via targeted support for energy bills with a focus on those at risk from the CoL crisis [ 13 ]. In addition to these groups, Broadbent [ 54 ]and Richardson [ 55 ] also suggest focus on women, unemployed people and those who are living with a disability, as being most vulnerable to the cost of living crisis. Financial support could also be provided in terms of reduction of council tax, provision of universal free school meals [ 61 ], and bespoke advice on how to save energy when using appliances at home [ 13 ]. To mitigate against the effects of the CoL crisis on physical health, particularly from respiratory illness, evidence was suggestive of investing in-home improvements for those living in cold homes, strength training to reduce the risk of falls, and that flu vaccination uptake should be maximised to reduce the spread of influenza alongside good hand washing guidance [ 13 ]. Campaigns aimed at suicide prevention and linking people with community support may also benefit those with mental health illnesses and those who live alone. Our evidence also found that linking system partners across the health and social care arena and including community-based partners such as the voluntary, charity and faith sectors may collectively have better outreach and impact.

What are the strengths and limitations of this review?

The key strength of this review is the immediate availability of a succinct report which considers the volume of evidence, both published and grey, on the current cost of living crisis. The authors also expanded the original search to include mitigating actions, behaviours, and strategies, so that colleagues may benefit from evidence-based solutions that may work at a population level. We have attempted to synthesis the most recent evidence, to build a picture of why the current economic crisis is unique, and what that means for the population. As with all original reviews, there are several limitations to this work. Firstly, the review period covers 2020–2023, rather than considering the impacts of historic CoL crises on population health. Whilst more evidence on population health outcomes may be needed, the provenance of the current CoL crisis remains unique and warranted specific focus and attention, hence our selection of the time window for review. Secondly, we were not able to ascertain peer-reviewed literature on the impacts on system partners including Fire & Rescue colleagues as these data remain unpublished, anecdotal, and discussed in privileged meetings. However, as more evidence gets published through this crisis, we may consider updating our review in due course to reflect the published evidence. Another limitation is the lack of consideration for any positive impacts of a cost of living crisis, for example a reduced consumption of alcohol due to reduced affordability, reduced usage of public transport due to costs, and a reduction in air quality in major cities. Lastly, due to the time, resources and need for this review, it was conducted in a 3-month timeframe to be of immediate use, value, and impact for current system partners but may have introduced bias to our findings such as a publication bias due to the shorter timeframe [ 62 ]. However, to mitigate this, we have provided a detailed description of methods used including search strategies and discussed the implications of the chosen method in terms of bias. Should more time and resources be made available, future iterations of this review should consider the wider impacts of the CoL crisis at a personalised and individual level, and family and community level but also include different types of study designs rather than the focus on reviews alone in this rapid synthesis which may not fully capture data on the cost of living impacts.

At a population level, the current CoL affects everyone, but particularly exacerbates poorer physical and mental health outcomes in those already vulnerable in our society. Our review found that while the most vulnerable are people living alone, single parents and those living in multigenerational households, more can be done at a community and societal level to support and improve health outcomes. This review brings together the evidence to enable decision makers to act at the right time whilst targeting their resources at the right groups.

Availability of data and materials

All data used or analysed during this study are included in this published article and its supplementary information files.

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This work was undertaken and supported by the South West Critical Thinking Unit (NHS England) with collaboration and partner contributions from across the health and care sector including colleagues from NHS England, the Office for Health Improvement and Disparities, and the UK Health Security Agency. NK, GF, and JM were involved in the initial concept, design, and development of the protocol. CDB and JH devised the search strategy and RC helped with theme development. GF, JM, and MM completed the abstract review, and GF, JM, MM, OB, and AA conducted the full text screening. JM and GF wrote the main manuscript text. NK, GF, JM, AA, OB, MM, JH, CDB and RC contributed to the manuscript development and multiple draft versions before finalising. No further funding or commissions to declare.

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Additional file 1:.

Appendix 1. Search Strategy. Appendix 2. Interventions to mitigating the impacts of the CoL crisis.

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Meadows, J., Montano, M., Alfar, A.J.K. et al. The impact of the cost-of-living crisis on population health in the UK: rapid evidence review. BMC Public Health 24 , 561 (2024). https://doi.org/10.1186/s12889-024-17940-0

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