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Cumulative family risks across income levels predict deterioration of children’s general health during childhood and adolescence

  • Yi-Ching Lin,

    Affiliation Department of Early Childhood and Family Education, College of Education, National Taipei University of Education, Taipei, Taiwan

  • Dong-Chul Seo

    seo@indiana.edu

    Affiliation Department of Applied Health Science, Indiana University School of Public Health, Bloomington, Indiana, United States of America

Cumulative family risks across income levels predict deterioration of children’s general health during childhood and adolescence

  • Yi-Ching Lin, 
  • Dong-Chul Seo
PLOS
x

Abstract

Family is considered an important agent in the health development of children. This process is significant but quite complex because the prevalence of potential risk factors in the family can hinder children’s health. This study examined if multiple family risks might have cumulative effect on children and youth’s health across various levels of household income. The data in this study were drawn from the 2011–2012 U.S. National Survey of Children’s Health (N = 79,601). A cumulative family risk (CFR) index was developed, which included such constructs as single-parenthood, unstable employment, large family, parenting stress, poor maternal education, poor maternal general health and poor maternal mental health. Multiple logistic regression analyses showed that CFR level was significantly related to children and youth’s poor health outcome (p < .001). When poverty levels were considered, however, the impact of CFRs on children and youth’s health was attenuated. The impact of CFRs was higher on children and youth from affluent families than on those from poor families. Overall there was a consistent pattern of trend in the point estimate as well as confidence limits as levels of affluence and numbers of family risk increased although some of the confidence intervals overlapped. Living in disadvantaged families might serve as a protective factor against CFRs possibly through repeated exposure to hardships and subsequent formation of resilience among some of the disadvantaged children.

Introduction

Family is considered an important agent in the health development of children[1]. It plays a large role in how children learn and grow throughout their childhood and adolescence [2, 3]. This process is significant but quite complex because the prevalence of potential risk factors in the family can hinder the health and development for children and youth [4, 5]. Risk can refer to any individual, social, or environmental factor that leads to undesirable or adverse development of children [6]. However, individual risk factors do not occur in a vacuum and often cluster together. Multiple family risks may concurrently and accumulatively affect children and youth, jeopardizing their development and health [4]. Moreover, cumulative family risk factors may invoke children and youth’s vulnerabilities [7, 8], because managing accumulated risks is extremely demanding for them and can induce distress and compromise their normative development [4]. Such distress can be overwhelming, adverse, and harmful to the children and youth’s general health [9, 10], mental health [11], behavior [12], school performance [13], psychosocial ability [14], and adjustment capability [4].

Cumulative risk approaches provide methods to examine how risk factors function, interact, and shape the overall health of children [15]. The advantage of using aggregated scores, in contrast to using a single risk factor, is the possibility of simultaneously measuring all accumulated risks within a particular family domain, which enables researchers to compare risk levels across groups. In addition, several studies have suggested that cumulative risk aggregations show stronger correlation with individual outcomes than do any single risk factor [15, 16]. These aggregated scores are presented in a cumulative risks index. The index comprises several dichotomized risk factors such as maternal illness, maternal anxiety, low maternal education, parenting stress, overcrowded housing, stressful life event, and job insecurity [9, 16, 17]. An increase in number of coexisting risk factors with long-standing adversities has been found to be injurious and destructive to the later developmental outcomes of children [15, 18].

The findings of the Rochester Longitudinal Study show that children in high-risk groups (i.e., 8 or more risk factors) were approximately 7 times more likely to have poor academic outcomes than were children in low-risk groups (i.e., 0–3 risk factors) [13]. Another study indicated that children with more than 6 risk factors were 17.31 times more likely to be of poorer health compared with those with no risk factors [19]. Although studies have demonstrated strong associations between cumulative risks and children’s health, little is known about the impact of poverty, as a social context, on children’s health outcomes [20]. Poverty is social status that contains a set of interrelated contexts, circumstances and experiences, such as divorced families, poor education, and being in a minority group. In turn, such characteristics affect the context surrounding the children within the home [21]. Through the perspective of ecological system model [22], it is assumed that a distal risk factor such as poverty may not directly impact child’s health but rather may influence it indirectly through other factors that are more proximal to the child [23].Therefore, the contextual influence should be examined through ecological system perspective to identify specific proximal and distal factors that place children with cumulative family risks on their health.

Cumulative family risks (CFRs) capture the scope of ecological covariation that are exposed to risks by developing a measure that simultaneously accesses multiple sources of risk [16]. Eight risk factors that have been identified to predispose children to suboptimal health are under consideration of the current study: poverty [16, 24], single parenthood [25, 26], family stress [2729], unstable employment [30, 31], large families [32, 33], poor maternal health [34], emotional health[3537] and education [38, 39]. They can be managed as two segments: distal and proximal. From an ecological perspective [22], capturing effects of distal indicators is important as it adds contextual understanding to the relations between proximal indices of risk and child health quality outcomes. This is a theoretically compelling approach to risk research [16, 40] because ecological approaches provide structural framework especially when it is difficult to disentangle the effect of poverty per se and risk factors common in disadvantaged families [41, 42]. Furthermore, the framework of social determinants suggests that poor health of children and barriers to their development are largely caused by family poverty [43]. Poverty is generally conceptualized as the lack of financial resources to solve problems and make life choices, the prevalence of material difficulties and contextual barriers, and low accessibility to available resources [4346]. More importantly, poverty often leads to impaired parenting, family conflict, and poor health outcomes for children [47, 48], which underscores the potential impact of poverty on the association of CFRs and child health. There is also reasonable evidence of linear relations between singular risk factors and poverty. However, little is known about exposure to multiple risks across varying levels of poverty[49, 50].

In addition, contextual factors, both proximal and distal, profoundly affect the outcome of children’s health [16], but how the effects interact with the outcome has received limited attention. Children with high CFRs, which are a proximal factor, may have long suffered from a series of adversities and social disadvantages. In other words, the association between children’s health risk and CFR could be moderated by poverty level. Hence, the question is, “Could the situation deteriorate with additional negative distal impact?” The current study attempts to answer this question by examining the impact of CFRs across poverty levels on child health [51].

Method

Participants

The study data were retrieved from the 2011–2012 U.S. National Survey of Children’s Health, which was sponsored by the Maternal and Child Health Bureau of the U.S. Health Resources and Services Administration. The survey was conducted as a random-digital-dial survey and the results have been weighted to represent the population of non-institutionalized children aged 0–17 at both nationwide- and state- levels [52]. The respondent was a parent or guardian in the household who was knowledgeable about the child’s health [53].

The data includes information regarding the states of children’s physical, emotional health as well as their wellbeing, such as family functions, parental health, medical homes, insurance information, and safe neighborhoods. In this study, the inclusion criterion for the survey respondents was providing complete responses for all the family risk indicators, poverty levels, and covariate variables. The inclusion criterion yielded a valid sample size of 79,601 cases. The de-identified NSCH data are publicly available from is available from http://www.cdc.gov/nchs/slaits/nsch.htm and the approval from the institutional review board was exempted. [54]

Measures

CFR index.

The following indicators constituted the CFR Index and were dichotomized as described herein:

(1) Single-parenthood that was defined as a single parent who may be separated, divorced, or widowed was coded “1” while families with two parents were coded “0”; (2) unstable employment defined as families with no member employed for at least 50 of the past 52 weeks was coded “1” while others were coded “0”; (3) large family that was defined as a family with four or more children was coded “1” while others were coded “0”; (4) parenting stress was coded “1” if parents answered “coping somewhat well”, “not very well” or “not very well at all” to the question, “In general, how well do you feel you are coping with the day to day demands of parenthood/ raising children?” whereas having parents who answered “very well” were coded “0”; (5) poor maternal education level was defined as mothers whose highest education level was a high school degree or lower and coded “1” whereas others were coded “0.” The data of (6) poor maternal general health and (7) poor maternal emotional health were retrieved through two questions, “Would you say that, in general, your health (and emotional health, respectively) is excellent, very good, good, fair or poor?” The responses were dichotomized by combining the self-reported responses. Excellent and very good were combined as having “good health” (coded 0) and good, fair, and poor were combined as having “poor health” (coded 1).

The cumulative risks exposure (0–7) was calculated by summing the 7 single risk indicators, following which the score was categorized into 5 levels: children exposed to 0, 1, 2, 3, 4 or more risk indicators. Groups with 4 or more risk indicators were combined for analyses because the small sample size of respondents with 5 and 6 risks.

Poverty levels.

Poverty levels were defined in accordance with the Federal Poverty Guidelines of the Department of Health and Human Services. This variable consisted of 4 levels: 1) below 100% of the Federal Poverty Level (FPL), 2) 100%–199% of the FPL, 3) 200%–399% of the FPL, and 4) at or above 400% of the FPL.

Children’s general health.

The health outcome for children in this study was parent-reported child health status. In the survey, parents were asked by the question: “In general, how well do you describe your child’s health? Would you say his/her health is excellent, very good, good, fair, or poor?” This variable was dichotomized by combining the responses of excellent and very good (as one category labeled as good health), and those of good, fair, and poor (as the other category labeled as poor health). Studies have shown that adults tend to overestimate health condition, internalizing problems, or physical activity for themselves or for their children [5557]. Thus, we collapsed responses of “good” with responses of “fair” and “poor.”

Statistical analyses

This research had two objectives: to determine 1) whether CFR and children’s health were associated for the current population, and 2) whether the association changes with poverty levels. Statistical significance was calculated through bivariate associations between the variables, which were examined using cross tabulations and X2 tests. In addition, multivariable logistic regression was performed to examine the associations among CFRs, poverty levels, and children’s general health with 95% confident interval after controlling for age, sex, and ethnicity. The interaction between CFRs and poverty level was first tested to validate the effect of the former on children’s health across poverty levels. Family income of 400%+ of the FPL and zero family risks were used as reference categories. In each of the income levels, a significant gradient was used to indicate the extent to which the cumulative family risk deteriorated children and youth health.

Results

The sample consisted of 79,601 children and youth aged 0–17 years. The average age was 8.75 years (SD: 5.25); 51.2% of the sample were boys and 67% were non-Hispanic White. Approximately one third of the participants (31.7%) were from low-income families below the poverty line (200% FPL). The prevalence of poor health for each family risk indicator and the accumulated family risk level is listed in Table 1. Approximately 40% of the families reported to not be coping with family stress well, and approximately 30% of the mothers with low levels of education or poor health (emotional or overall health). In addition, about 7% of the participants come from large families having 4 more children living in a household. More than 60% lived with no or 1 family risk, whereas 7.4% lived with 4 or more family risks. Pearson Chi-square tests were used to compare the demographic categories and family risk indicators in the children and youth’s health groups. As shown in Tables Tables 1 and 2, all results were statistically significant for every category at the p < .001 level.

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Table 1. Descriptive statistics for sample background and risk indicators, NSCH 2011–2012 (N = 79,601).

https://doi.org/10.1371/journal.pone.0177531.t001

Bivariate logistic regression was performed to examine the association between individual family risk indicators and participants’ general health. The results revealed that each family risk indicator was significantly associated with children and youth’s general health outcome. Participants living in large families (OR = 1.34, 95% CI = 1.24–1.45) and non-intact families (OR = 1.69, 95% CI = 1.60–1.78) were more likely to be of poor health than their counterparts. Furthermore, parenting stress increased the risk of poor health (OR = 1.58, 95% CI = 1.51–1.66). Participants of parents without stable employment were more likely (OR = 2.22, 95% CI = 2.09–2.35) to have poorer health than were those of parents with stable employment. Regarding maternal risk indicators, children and youth of mothers with low levels of education, emotional, and general health were, respectively, more likely to have poorer health, as shown in Table 2.

The 7 indicators were summed to generate a CFR index by categorizing the number of family risks into 5 levels: 0, 1, 2, 3, and 4 or more risks. Subsequently, logistic regression (controlled for sex, age, and ethnicity) was conducted to investigate the relationship between CFRs and children and youth’s poor health. Table 3 shows that each CFR level was significantly related to their poor health outcome. A gradient clarified the deteriorating impact on children and youth’s health as the number of family risks accumulated. The increases in odds with each additional family risk were significant (all ps < .001)

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Table 3. Cumulative family risk, poverty levels and children's poor health.

https://doi.org/10.1371/journal.pone.0177531.t003

When poverty levels were considered, however, the impact of CFRs on children and youth’s health attenuated (Table 3). The tendency of having poor health increased from 1.83 (95% CI: 1.59–2.10) (1 risk) to 3.65 (95% CI: 3.15–4.24) (2 risks), 6.60 (95% CI: 5.63–7.74) (3 risks), and 8.96 (95% CI: 6.70–11.99) (4 or more risks) (p < .0001). It appears that poverty modified the association between CFRs and child health. We therefore tested the interaction between CFRs and poverty levels and it was significant at the .05 level. When the interaction effect was probed through simple effects analysis, it was found that while participants from poor families and with higher family risks were associated with poor health, the difference in the outcome narrowed as income level increased. This indicates that children and youth from affluent families might be more vulnerable to the influence of cumulative family risk than their counterparts. According to these findings (Table 3), the impact of CFRs across poverty levels was explored further.

Table 4 presents the odds of children having poor health at each CFRs level across the identified poverty levels. For children and youth living in households below poverty line (<200% FPL), the odds of poor health increased from 1.44 (95% CI: 1.23–1.70) (1 risk), to 7.17 (95% CI: 6.17–8.33) (4 or more risks). However, the odds of poor health of those from households at or above 400% FPL increased from 1.86 (95% CI: 1.62–2.14) (1 risk) to 9.49 (95% CI: 7.09–12.69) (4 or more risks). Table 4 also shows the gradient of the odds that are prominent at each income level. More importantly, as the household income increased, the discrepancy between the odds of being in poor health for cases with no CFRs and for high number of CFRs became larger. Thus, the findings suggest that children and youth from affluent families could be more vulnerable to CFRs than are those from disadvantaged families.

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Table 4. Comparison of the prevalence and the odds of poor health by cumulative family across poverty levels.

https://doi.org/10.1371/journal.pone.0177531.t004

Overall there was a consistent pattern of trend in the point estimate as well as confidence limits as levels of affluence and numbers of family risk increased although some of the confidence intervals overlapped. However, there was no statistical difference in the odds ratios between CFR3 and CFR4+ for the >400% FTL group, which means that for the participants in the most affluent families, the odds difference between CFR3 and CFR4+ did not reach statistical significance. The statistical differences of the set of odds ratios was tested using Z values and the result confirmed the statistical insignificance as well.

All analyses were conducted using the Statistical Package for Social Sciences statistical software version 20.0 (IBM Corp., Armonk, NY).

Discussion

The findings of this study indicated that each investigated family risk, especially risks related to maternal condition, was significantly associated with the suboptimal health of children and youth. The accumulation of family risks corresponded to the rise in the poor health of them. These findings are consistent with previous studies [4, 9, 19, 58]. Our analyses showed that the odds of deterioration increased, as demonstrated by a steep gradient. This implies that the accumulation of family risk factors drastically increased the threat for children and youth’s health. However, when poverty levels were considered, the impact of CFRs on their health was attenuated. This was due to the interaction between poverty levels and CFRs. The impact of CFRs was higher on children and youth from affluent families than on those from poor families. One possible speculation is that children and youth from affluent families may be exposed to less hardship and thus may not be able to cope with CFRs better than the ones from poor families. Also, there is potential for parents in different socioeconomic circumstances to rate their children’s health differently. For instance, parents who have greater economic resources or higher educational background in difficult circumstances may be more likely to identify issues affecting their children’s health and rate their children’s health as less than very good, compared to parents with few economic resources or lower education in the same circumstances.

As mentioned earlier, research studies have claimed that CFRs aggravate child health and well-being and that poverty is a predominant threat that exacerbates the negative impact on child health [5963]. On the contrary, this study finds that living in disadvantaged families might serve as a protective factor against CFRs possibly through repeated exposure to hardships and subsequent formation of resilience among some of the disadvantaged children.

The American Psychological Association [64] defines resilience as “the process of adapting well in the face of adversity, trauma, tragedy, threats, or even significant sources of stress (paragraph 4)”. In the family unit, resilience refers to the abilities of an individual or a family to achieve specific objectives despite the challenges and risks that can disrupt their health and wellness [44, 46, 6569]. The term has an intrinsic sense of optimism and plasticity, such as the abilities to recover from overwhelming pressure and be flexible to adapt to ongoing challenges.

Resilience at the levels of family and community was deemed essential to being resourceful in order to enable positive health outcomes [70]. It is also a process through which families adapt and function after traumatic incidents or negative experiences such as poverty [71]. Adversity in circumstances stimulates children’s sensitivity that shapes their brain development and plasticity [68]. Children from low-income households may experience more stress and conflict because of their poor living conditions and limited life choices and resources [65]. However, they may still be able to overcome these challenges through their resilience, which is a capacity to adapt to situations that threaten their functions, viability, or development [72] in order to survive [73, 74].

Therefore, competence-based programs that target capability and strength development for prevention or intervention deserve equal attention as do deficit-based studies [7, 67, 75, 76]. This perspective has profound implications for health support services, preventions, and interventions that maximize optimal responses to challenges and adversities faced by families, especially those at a disadvantage, under cumulative risks.

This study has limitations. First, the data were cross-sectional and thus no causal inferences could be derived. Future studies may be able to verify directional causality between the variables if longitudinal data can be accessed and analyzed. Second, child health was self-rated by parents, which could have confounded the results because parents tend to overestimate their child’s health [77, 78]. We tried to attune the potential bias by taking into account such parent’s tendency of overestimation of their child’s health on the cutoff of dichotomization of the responses. Third, the equal treatment of all risk factors (i.e., each is assigned a value of 1) could have biased the results because in reality some may have a bigger effect on health outcomes than others, let alone possible interaction effects amongst them.

Despite these shortcomings, this study demonstrated the impact of CFRs on children and youth’s health by integrating ecological perspectives and analyzing the influences of poverty. Several studies have discussed cumulative risks in a familial setting but very few, if any, have assessed its impact on child health based on stratified income levels. This study therefore contributes to the literature by recognizing that poverty could make children resilient in the face of adversity and motivate them to pursue health. Our results may differ from studies that claim that poverty would always be detrimental to children’s health, but challenging this understanding provides a novel phenomenon to address and problems to redefine.

Conclusion

This study elucidates that family risks, single or cumulative, aggravate children and youth’s health. The ones who exposed to larger numbers of family risks should be prioritized for intervention. Interestingly, however, our findings corroborate that adversity may actually encourage plasticity in children from disadvantaged families. The poor who suffered from relatively higher economic hardship and family challenges were the most vulnerable, but they exhibited a comparably better ability to respond to and recover from the difficulties than their counterparts [79]. With the resilience framework, children who grow up with in poverty may exhibit positive outcomes [80]. Resilience provokes creative strategies to help people overcome poor life conditions [46]. For instance, they may reach out social support and community ties that help buffer the negative effects of economic distress. They also more likely to be involved in organized programs that assist with their needs and help them feel secure [81], which promotes resilience among children and youth. Hence, their capabilities and resilience can serve as a protective factor that attenuates the impact of cumulative family risks on their health. Nonetheless, up to date, not much is known about the epidemiology of childhood experiencing cumulative family risks among U.S. children through population-based studies [82]. Although resilience was not directly measured and analyzed in this study, the findings of this study suggest the potential role of resilience. Further research is warranted to investigate the role of resilience in the relation between cumulative family risk and child health especially in disadvantaged families. This finding also points to the importance of prevention efforts than interventions. Cultivating resilience and capabilities for children in adverse circumstances may be more efficient to promote child health in disadvantaged families than implementing interventions. In this manner, disadvantaged children may have better chances to pursue health equity and survive the inherited family risks.

Author Contributions

  1. Conceptualization: YL.
  2. Data curation: YL.
  3. Formal analysis: YL.
  4. Investigation: YL.
  5. Methodology: DCS.
  6. Project administration: DCS.
  7. Supervision: DCS.
  8. Validation: DCS.
  9. Visualization: YL.
  10. Writing – original draft: YL.
  11. Writing – review & editing: DCS.

References

  1. 1. Drotar D. Measuring health-related quality of life in children and adolescents: implications for research and practice: Psychology Press; 2014.
  2. 2. Lin C-Y, Tsai M-C. Effects of Family Context on Adolescents’ Psychological Problems: Moderated by Pubertal Timing, and Mediated by Self-Esteem and Interpersonal Relationships. Applied Research in Quality of Life. 2016:1–17.
  3. 3. Tsai M-C, Hsieh Y-P, Strong C, Lin C-Y. Effects of pubertal timing on alcohol and tobacco use in the early adulthood: a longitudinal cohort study in Taiwan. Research in developmental disabilities. 2015;36:376–83.
  4. 4. Buehler C, Gerard JM. Cumulative family risk predicts increases in adjustment difficulties across early adolescence. Journal of Youth and Adolescence. 2013;42(6):905–20. pmid:22915131
  5. 5. Strong C, Tsai M-C, Lin C-Y, Cheng C-P. Childhood Adversity, Timing of Puberty and Adolescent Depressive Symptoms: A Longitudinal Study in Taiwan. Child Psychiatry & Human Development. 2016;47(3):347–57.
  6. 6. Kraemer HC, Lowe KK, Kupfer DJ. To your health. New York, NY Oxford University Press 2005.
  7. 7. Spear LP. Heightened stress responsivity and emotional reactivity during pubertal maturation: Implications for psychopathology. Development and Psychopathology. 2009;21(01):87–97.
  8. 8. Véronneau MH, Dishion TJ. Middle school friendships and academic achievement in early adolescence: A longitudinal analysis. The Journal of Early Adolescence. 2010:1–26.
  9. 9. Evans GW. A multimethodological analysis of cumulative risk and allostatic load among rural children. Developmental Psychology. 2003;39(5):924. pmid:12952404
  10. 10. Jones DJ, Forehand R, Brody G, Armistead L. Psychosocial adjustment of African American children in single-mother families: A test of three risk models. Journal of Marriage and Family. 2002;64(1):105–15.
  11. 11. Copeland WE, Shanahan L, Costello EJ, Angold A. Childhood and adolescent psychiatric disorders as predictors of young adult disorders. JAMA Psychiatry. 2009;66(7):764–72.
  12. 12. VanderLaan DP, Gothreau LM, Bartlett NH, Vasey PL. Separation anxiety in feminine boys: Pathological or prosocial? Journal of Gay & Lesbian Mental Health. 2010;15(1):30–45.
  13. 13. Sameroff AJ, Bartko WT, Baldwin A, Baldwin C, Seifer R. Family and social influences on the development of child competence. In: Lewis M, Feiring C, editors. Families, Risk, and Competence. Mahwah, NJ: Erlbaum; 1998. p. 161–83.
  14. 14. Jessor R. New perspectives on adolescent risk behavior. Cambridge: Cambridge University Press; 1998.
  15. 15. Appleyard K, Egeland B, van Dulmen MHM, Sroufe LA. When more is not better: The role of cumulative risk in child behavior outcomes. Journal of Child Psychology and Psychiatry. 2005;46(3):235–45. pmid:15755300
  16. 16. Wells NM, Evans GW, Beavis A, Ong AD. Early childhood poverty, cumulative risk exposure, and body mass index trajectories through young adulthood. American Journal of Public Health. 2010;100(12):2507–12. pmid:20966374
  17. 17. Sameroff A, Seifer R, McDonough SC. Contextual contributors to the assessment of infant mental health. Handbook of Infant, Toddler, and Preschool Mental Health Assessment2004. p. 61–76.
  18. 18. Sameroff A. A dialectic integration of development for the study of psychopathology: Springer US; 2014. 25–43 p.
  19. 19. Larson K, Russ SA, Crall JJ, Halfon N. Influence of multiple social risks on children's health. Pediatrics. 2008;121(2):337–44. pmid:18245425
  20. 20. Eamon MK. The effects of poverty on children's socioemotional development: An ecological systems analysis. Social Work. 2001;46(3):256–66. pmid:11495370
  21. 21. Huston AC, Bentley AC. Human development in societal context. Annual review of psychology. 2010;61:411–37. pmid:19572786
  22. 22. Bronfenbrenner U, Morris PA. The bioecological model of human development. Handbook of Child Psychology. 2006.
  23. 23. Jones DJ, Forehand R, Brody G, Armistead L. Psychosocial Adjustment of African American Children in Single‐Mother Families: A Test of Three Risk Models. Journal of Marriage and Family. 2002;64(1):105–15.
  24. 24. McLoyd VC. Socioeconomic disadvantage and child development. American psychologist. 1998;53(2):185. pmid:9491747
  25. 25. Krueger PM, Jutte DP, Franzini L, Elo I, Hayward MD. Family structure and multiple domains of child well-being in the United States: a cross-sectional study. Population health metrics. 2015;13:6. Epub 2015/03/03. PubMed Central PMCID: PMCPmc4343278. pmid:25729332
  26. 26. Scharte M, Bolte G. Increased health risks of children with single mothers: the impact of socio-economic and environmental factors. European journal of public health. 2013;23(3):469–75. Epub 2012/06/12. pmid:22683774
  27. 27. Heath CL, Curtis DF, Fan W, McPherson R. The Association Between Parenting Stress, Parenting Self-Efficacy, and the Clinical Significance of Child ADHD Symptom Change Following Behavior Therapy. Child Psychiatry & Human Development. 2015;46(1):118–29.
  28. 28. Wood BL, Miller BD, Lehman HK. Review of family relational stress and pediatric asthma: the value of biopsychosocial systemic models. Family process. 2015;54(2):376–89. Epub 2015/02/17. pmid:25683472
  29. 29. Thompson RA. Stress and child development. The Future of Children. 2014;24(1):41–59. pmid:25518702
  30. 30. Bacikova-Sleskova M, Benka J, Orosova O. Parental employment status and adolescents' health: the role of financial situation, parent-adolescent relationship and adolescents' resilience. Psychology & health. 2015;30(4):400–22. Epub 2014/10/18.
  31. 31. Frasquilho D, de Matos MG, Marques A, Neville FG, Gaspar T, Caldas-de-Almeida JM. Unemployment, Parental Distress and Youth Emotional Well-Being: The Moderation Roles of Parent-Youth Relationship and Financial Deprivation. Child psychiatry and human development. 2015. Epub 2015/12/10.
  32. 32. Atkinson L, Beitchman J, Gonzalez A, Young A, Wilson B, Escobar M, et al. Cumulative risk, cumulative outcome: A 20-year longitudinal study. PloS one. 2015;10(6):e0127650. pmid:26030616
  33. 33. Lundberg U. On the psychobiology of stress and health. Time pressure and stress in human judgment and decision making: Springer; 1993. p. 41–53.
  34. 34. Kaiser KL, Barry Hultquist T, Chen LW. Maternal Health-Seeking on Behalf of Low-Income Children. Public health nursing (Boston, Mass). 2016;33(1):21–31. Epub 2015/09/15.
  35. 35. Kingston D, Tough S. Prenatal and postnatal maternal mental health and school-age child development: a systematic review. Maternal and child health journal. 2014;18(7):1728–41. Epub 2013/12/20. pmid:24352625
  36. 36. O'Donnell M, Maclean MJ, Sims S, Morgan VA, Leonard H, Stanley FJ. Maternal mental health and risk of child protection involvement: mental health diagnoses associated with increased risk. Journal of epidemiology and community health. 2015;69(12):1175–83. Epub 2015/09/16. pmid:26372788
  37. 37. Rahman A, Surkan PJ, Cayetano CE, Rwagatare P, Dickson KE. Grand challenges: integrating maternal mental health into maternal and child health programmes. PLoS Med. 2013;10(5):e1001442. pmid:23667345
  38. 38. Davey TM, Cameron CM, Ng SK, McClure RJ. The Relationship Between Maternal Education and Child Health Outcomes in Urban Australian Children in the First 12 Months of Life. Maternal and child health journal. 2015;19(11):2501–11. Epub 2015/07/01. pmid:26122254
  39. 39. Güneş PM. The role of maternal education in child health: Evidence from a compulsory schooling law. Economics of Education Review. 2015;47:1–16.
  40. 40. Trentacosta CJ, Hyde LW, Shaw DS, Dishion TJ, Gardner F, Wilson M. The relations among cumulative risk, parenting, and behavior problems during early childhood. Journal of Child Psychology and Psychiatry. 2008;49(11):1211–9. pmid:18665880
  41. 41. Aber JL, Bennett NG, Conley DC, Li J. The effects of poverty on child health and development. Annual review of public health. 1997;18(1):463–83.
  42. 42. McClain DB, Wheeler LA, Wong JJ, Mauricio AM, Gonzales NA. The role of parents and peers in the psychological and academic adaptation of youth in urban communities. Adolescent Development and School Achievement in Urban Communities: Resilience in the Neighborhood. 2012;48:227–42.
  43. 43. Lin YC, Wu JCL, Chiou ST, Chiang TL. Healthy living practices in families and child health in Taiwan. International Journal of Public Health. 2015;60(6):691–8. pmid:26140858
  44. 44. Juby C, Rycraft J. Family preservation strategies for families in poverty. Families in Society: The Journal of Contemporary Social Services. 2004;85(4):581–7.
  45. 45. Mullin WJ, Arce M. Resilience of families living in poverty. Journal of Family Social Work. 2008;11(4):424–40.
  46. 46. Orthner DK, Jones‐Sanpei H, Williamson S. The Resilience and Strengths of Low‐Income Families. Family Relations. 2004;53(2):159–67.
  47. 47. Conger RD, Conger KJ. Resilience in Midwestern families: Selected findings from the first decade of a prospective, longitudinal study. Journal of Marriage and Family. 2002;64(2):361–73.
  48. 48. Organization WH. Global report for research on infectious diseases of poverty 2012. Geneva, Switzerland: WHO. 2012.
  49. 49. Poverty and chaos. Paper presented at the First Bronfenbrenner Conference, Chaos and Children's Development: Levels of Analysis and Mechanisms [Internet]. Ithaca, NY.; 2007
  50. 50. Evans GW, Kim P. Childhood poverty, chronic stress, self‐regulation, and coping. Child Development Perspectives. 2013;7(1):43–8.
  51. 51. Knol MJ, VanderWeele TJ. Recommendations for presenting analyses of effect modification and interaction. International journal of epidemiology. 2012;41(2):514–20. pmid:22253321
  52. 52. NSCH. National Survey of Children's Health. Overview of surveys.: Data Resource Center for Child & Adolescent Health; 2016 [cited 2016 2016.07.17]. Available from: http://childhealthdata.org/about/overview/metadata.
  53. 53. Zill N, Bramlett MD. Health and well-being of children adopted from foster care. Children and Youth Services Review. 2014;40:29–40.
  54. 54. CDC CfDCaP, National Center for Health Statistics, State and Local Area Integrated telephone Survey. 2011–2012 National Survey of Children’s Health Frequently Asked Questions 2013. Available from: http://www.cdc.gov/nchs/slaits/nsch.htm.
  55. 55. Corder K, Crespo NC, van Sluijs EM, Lopez NV, Elder JP. Parent awareness of young children's physical activity. Preventive medicine. 2012;55(3):201–5. pmid:22766008
  56. 56. Su C-T, Wang J-D, Lin C-Y. Child-rated versus parent-rated quality of life of community-based obese children across gender and grade. Health and quality of life outcomes. 2013;11(1):1.
  57. 57. Hesketh KR, McMinn AM, Griffin SJ, Harvey NC, Godfrey KM, Inskip HM, et al. Maternal awareness of young children’s physical activity: levels and cross-sectional correlates of overestimation. BMC public health. 2013;13(1):1.
  58. 58. Bauman LJ, Silver EJ, Stein REK. Cumulative social disadvantage and child health. Pediatrics. 2006;117(4):1321–8. pmid:16585330
  59. 59. Bloom B, Cohen RA, Freeman G. Summary health statistics for us Children: national health interview survey, 2011. Vital and health statistics Series 10, Data from the National Health Survey. 2012;(254):1–88. pmid:25116332
  60. 60. Bradley RH, Corwyn RF. Socioeconomic status and child development. Annual Review of Psychology. 2002;53(1):371–99.
  61. 61. Larson K, Halfon N. Family income gradients in the health and health care access of US children. Maternal and child health journal. 2010;14(3):332–42. pmid:19499315
  62. 62. Marmot M. Social determinants of health inequalities. The Lancet. 2005;365(9464):1099–104.
  63. 63. Turney K, Lee H, Mehta N. The social determinants of child health. Social Science & Medicine (1982). 2013;95:1–5.
  64. 64. Association AP. The Road to Resilience Washington, DC: 2010.
  65. 65. Walsh F. Strengthening family resilience. 2nd ed. Guilford, editor. New York: Guilford 2006.
  66. 66. Felner RD, DeVries ML. Poverty in childhood and adolescence: A transactional–ecological approach to understanding and enhancing resilience in contexts of disadvantage and developmental risk. Handbook of Resilience in Children: Springer; 2013. p. 105–26.
  67. 67. Southwick SM, Bonanno GA, Masten AS, Panter-Brick C, Yehuda R. Resilience definitions, theory, and challenges: Interdisciplinary perspectives. European Journal of Psychotraumatology. 2014;5.
  68. 68. Sapienza JK, Masten AS. Understanding and promoting resilience in children and youth. Current Opinion in Psychiatry. 2011;24(4):267–73. pmid:21546838
  69. 69. Zolkoski SM, Bullock LM. Resilience in children and youth: A review. Children and Youth Services Review. 2012;34(12):2295–303.
  70. 70. Panter‐Brick C, Leckman JF. Editorial commentary: resilience in child development–interconnected pathways to wellbeing. Journal of Child Psychology and Psychiatry. 2013;54(4):333–6. pmid:23517424
  71. 71. Patterson JM. Integrating family resilience and family stress theory. Journal of Marriage and Family. 2002;64(2):349–60.
  72. 72. Masten AS. Global perspectives on resilience in children and youth. Child Development. 2014;85(1):6–20. pmid:24341286
  73. 73. Edin K, Lein L. Making Ends Meet: How Single Mothers Survive Welfare and Low-Wage Work: How Single Mothers Survive Welfare and Low-Wage Work: Russell Sage Foundation; 1997.
  74. 74. Crosnoe R, Mistry RS, Elder GH. Economic disadvantage, family dynamics, and adolescent enrollment in higher education. Journal of Marriage and Family. 2002;64(3):690–702.
  75. 75. Coleman M, Ganong L. Resilience and families. Family Relations. 2002;51(2):101–2.
  76. 76. Bingham B, McFadden K, Zhang X, Bhatnagar S, Beck S, Valentino R. Early adolescence as a critical window during which social stress distinctly alters behavior and brain norepinephrine activity. Neuropsychopharmacology. 2011;36(4):896–909. pmid:21178981
  77. 77. Lee C-T, Tsai M-C, Lin C-Y, Strong C. Longitudinal Effects of Self-Report Pubertal Timing and Menarcheal Age on Adolescent Psychological and Behavioral Outcomes in Female Youths from Northern Taiwan. Pediatrics & Neonatology. 2016.
  78. 78. Tsai M-C, Strong C, Lin C-Y. Effects of pubertal timing on deviant behaviors in Taiwan: a longitudinal analysis of 7th-to 12th-grade adolescents. Journal of adolescence. 2015;42:87–97. pmid:25956430
  79. 79. Akter S, Mallick B. The poverty–vulnerability–resilience nexus: Evidence from Bangladesh. Ecological Economics. 2013;96:114–24.
  80. 80. Fergus S, Zimmerman MA. Adolescent resilience: A framework for understanding healthy development in the face of risk. Annu Rev Public Health. 2005;26:399–419. pmid:15760295
  81. 81. Furstenberg FF. Banking on families: How families generate and distribute social capital. Journal of Marriage and Family. 2005;67(4):809–21.
  82. 82. Bethell CD, Newacheck P, Hawes E, Halfon N. Adverse childhood experiences: assessing the impact on health and school engagement and the mitigating role of resilience. Health Affairs. 2014;33(12):2106–15. pmid:25489028