• No results found

Influence of Multiple Social Risks on Children's Health

N/A
N/A
Protected

Academic year: 2020

Share "Influence of Multiple Social Risks on Children's Health"

Copied!
10
0
0

Loading.... (view fulltext now)

Full text

(1)

ARTICLE

Influence of Multiple Social Risks on

Children’s Health

Kandyce Larson, PhDa,b, Shirley A. Russ, MD, MPHa,c, James J. Crall, DDS, ScDa,d,e, Neal Halfon, MD, MPHa,b,f

aUCLA Center for Healthier Children, Families, and Communities, Los Angeles, California;bDepartment of Pediatrics, David Geffen School of Medicine, University of

California, Los Angeles, Los Angeles, California;cDepartment of Academic Primary Care Pediatrics, Cedars-Sinai Medical Center, Los Angeles, California;

dNational Oral Health Policy Center, Los Angeles, California;eUCLA School of Dentistry, Los Angeles, California;fDepartment of Health Services, School of Public Health;

and Department of Public Policy, School of Public Affairs, University of California, Los Angeles, Los Angeles, California

The authors have indicated they have no financial relationships relevant to this article to disclose.

ABSTRACT

OBJECTIVE.Social risk factors such as growing up in poverty, racial/ethnic minority status, and maternal depression have been associated with poorer health outcomes for children. This study examined the strength of association of 8 social risk factors, both individually and as part of a cumulative social risk index, on parent-reported child health status.

METHODS.We performed an analysis of cross-sectional data from the 2003 National Survey of Children’s Health, a telephone survey of 102 353 parents of children aged 0 to 17 years. In bivariate and multivariate logistic regression models, 8 social risk factors were tested as independent predictors of 4 parent-reported child health outcomes: global health status, dental health, socioemotional health, and over-weight. These risk factors were combined into a categorical “social risk index” ranging from low risk (0 risk factors) to very high risk (ⱖ6 risk factors), and risk gradients were examined using linear polynomial testing and multivariate logistic regression.

RESULTS.The percentage of children in poorer health increased with the number of social risk factors across all health outcomes. More than half of children hadⱖ2 risk factors, and 24% had ⱖ4. Low maternal mental health, black or Hispanic race/ ethnicity, ⬍200% of the federal poverty level, low household education, unsafe neighborhoods, and lack of health insurance increased the odds for less than very good child health in adjusted models.

CONCLUSIONS.Multiple social risk factors have a cumulative effect on parent-reported child health status across physical and socioemotional domains, demonstrating a very strong risk gradient effect. These findings emphasize the importance of addressing multiple levels of social risk to achieve improvements in child health.

S

OCIAL FACTORS AREincreasingly recognized as important determinants of children’s health.1–7Evidence

support-ing a role for social contributors comes from research that documents large disparities in the health status of children on the basis of stratifying factors such as social class and race/ethnicity.8–11Although these 2 factors have

been the primary focus of child health disparities research, evidence is accumulating that a wider range of social, economic, psychosocial, and community factors can also contribute to systematic differences in health.4Risk factors

including single-parent families,12–14 family conflict,15,16maternal mental health and depression,17–19 lack of health

insurance,20 and levels of community violence21–23 have been shown to predict the prevalence and severity of

different child health conditions with some association that is independent from family income, education, or race/ethnicity. This empirical work has led to the development of new conceptual models of children’s health that account for this broad range of child, family, and community influences on health and the cumulative toll that multiple co-occurring risk factors may take on health.4,24

The cumulative effects of multiple social risks on child health have been most closely studied in relation to socioemotional health and cognitive outcomes. The classic studies of Rutter in the Isle of Wight on family factors in

www.pediatrics.org/cgi/doi/10.1542/ peds.2007-0447

doi:10.1542/peds.2007-0447

Key Words

social environment, health outcomes, children, risk factors

Abbreviations

NSCH—National Survey of Children’s Health

FPL—federal poverty level OR— odds ratio CI— confidence interval Accepted for publication Jul 25, 2007

Address correspondence to Kandyce Larson, PhD, UCLA Center for Healthier Children, Families, and Communities, 1100 Glendon Ave, Suite 850, Los Angeles, CA 90024. E-mail: kandyce@ucla.edu

(2)

children with psychosocial problems,25 Sameroff in

Rochester,26,27and Furstenberg in Philadelphia28

exam-ining associations between multiple environmental risk variables and intelligence and socioemotional competen-cies suggest that the number of risk factors is more important than the nature of any 1 factor in determining outcomes.29 Associations between multiple social risks

and physical health outcomes have been less well stud-ied, so it is uncertain whether the same type of cumu-lative effect occurs. One recent analysis of National Health Interview Survey data showed that 3 social risks—poverty, low parent education, and not living in a 2-parent family—were associated with poorer parent-reported child health status, greater likelihood of the child’s having a chronic condition, and greater risk for activity restriction in a cumulative manner.30

In this study, we analyzed data from a nationally representative sample of 102 353 US children aged from birth to 17 years from the 2003 National Survey of Children’s Health (NSCH). This database is particularly suited to a study of social risks in relation to child health outcomes because it includes rich data on a broad variety of social risks operating at multiple levels, together with data on a range of physical and socioemotional out-comes.31 Risk factors were chosen for inclusion in the

study on the basis of the existence of previous re-search linking each risk individually with some aspect of child health. Risks were also chosen at the level of the child, family, and community following models from the Institute of Medicine4 and others that

de-scribe social environmental contributors to children’s health.5,24 The study examined the hypothesis that

multiple social risks will influence children’s health in a cumulative manner across 4 health outcomes: global child health, dental health, socioemotional health, and overweight.

METHODS

Sample

The NSCH was conducted in 2003–2004 by the National Center for Health Statistics as a module of the State and Local Area Integrated Telephone Survey. The NSCH was designed to address multiple aspects of child health and well-being, and a key strength of the survey was com-prehensive assessment of the social and family contexts where children grow and develop.31 The NSCH used a

stratified random-digit-dial sampling design to achieve a nationally representative sample of 102 353 parents of children 0 to 17 years of age. A detailed telephone in-terview was conducted with the parent or guardian who knew the most about the child’s health and health care experiences. Interviews of⬃30 minutes were conducted in English and Spanish. The overall weighted response rate was 55.3%.

The sample for this study varies across each of the 4 health outcome measures. Certain health measures were not relevant and/or not asked for infants or very young children. Global health status was assessed for all children who were younger than 18 years, dental health status for children aged 1 to 17, socioemotional status for

children aged 3 to 17, and overweight status for children aged 2 to 17. The final study sample is further restricted to include only individuals without missing data on the outcome under investigation and all study covariates. Cases that were excluded from the study because of missing data were examined for possible sources of bias. Excluded cases were more likely to be from low-income families, to be of black or Hispanic race/ethnicity, and to come from households in which the highest reported educational level was less than college.

For production of population-based estimates, data records for each interview were assigned a sampling weight. Additional details on the design and operation of NSCH are reported elsewhere.31This study was granted

exempt status by the UCLA institutional review board.

Measures

Child Health Outcomes

Global child health status was assessed by parent report and dichotomized as good/fair/poor versus excellent/ very good. Parental ratings of child health status are regarded as an acceptable proxy measure of the child’s actual health status and have been shown to be associ-ated with health services use.32,33A similar categorization

of parent-reported dental health status was used, with condition of teeth indicated as good/fair/poor versus excellent/very good. Previous research has shown that actual disease of teeth is significantly associated with parents’ perceptions of their child’s oral health, at least for children aged 2 to 5 years.34For children with special

health care needs, however, there is evidence that care-givers underestimate their child’s need for dental treat-ment.35

Parents were asked the following question about child socioemotional problems: “Overall, do you think that [child] has difficulties with 1 or more of the following areas: emotions, concentration, behavior, or being able to get along with other people?” The dichotomous socioemotional problem outcome variable is indicated by a response of yes.

Weight status of NSCH children was determined from calculations of BMI derived from parent-re-ported child height and weight. Children were iden-tified as overweight when their BMI was in the 95th percentile or greater compared with children of the same age and gender. Percentiles were determined using Centers for Disease Control and Prevention growth charts.31

Social Risk Factors

Eight dichotomous social risk variables were included in this study. These risk factors represent influences at the level of the child, family, and community. Consistent with the Institute of Medicine categorization of social-environmental influences on children’s health, we in-cluded both family demographic and family process vari-ables in the study as social risk indicators.4The cutoff

(3)

“No education beyond high school” indicated that the highest educational level of any household member was a high school diploma or less. An indicator for “family income⬍200% of the federal poverty level” (FPL) was used to identify children in poor or near-poor families. This categorization was chosen because previous re-search showed that the risk for poor child health ex-tended beyond the absolute poverty threshold.36–38Any

family structure type other than 2 parents was defined as “not a 2-parent household.” Two-parent families were defined as 2 biological parents, adoptive parents, or step-families with a mother and a father. Children’s race/ ethnicity was categorized as black or Hispanic versus non-Hispanic white, multiracial, or other. Uninsured children were those who lacked health insurance of any type at the time of interview.

An indicator of family conflict was derived from 2 questions that asked parents to rate how often they “argue heatedly or shout” or “end up hitting or throwing things” in response to serious disagreements with house-hold members. A response of sometimes/usually/always (versus rarely/never) to either question was used to indicate family conflict. Mother’s mental health was self-reported and dichotomized into good/fair/poor (low ma-ternal mental health) versus excellent/very good. Parent perception of an unsafe neighborhood was categorized as never/sometimes safe versus usually/always safe. Ad-ditional study variables that were used as statistical con-trols included child age in years, gender, and number of children in the household.

Statistical Analysis

All statistical analyses were performed using Stata 9.2 (Stata Corp, College Station, TX). Survey estimation pro-cedures were applied with the Taylor-series linearization method used to adjust the SEs for the complex survey design. We first present descriptive statistics for the study sample. Social risk factors were analyzed independently and also combined into a categorical “social risk index” ranging from low risk (0 risk factors) to very high risk (ⱖ6 risk factors).

Bivariate relationships between the categorical social risk index and the child health outcomes were examined using linear polynomial testing. Bivariate and multivar-iate logistic regression analyses were used to examine the associations between the individual social risks and the child health outcomes. Adjusted odds ratios (ORs) from the multivariate models incorporate controls for all of the social risk factors, plus child age, gender, and number of children in the household. Although consid-erable associations were noted between the social risk factors (eg, race/ethnicity and household education, family income and maternal mental health), collinearity diagnostics were within an acceptable range. In the final logistic regression models, child health outcomes were examined in relation to the categorical social risk index (0 as the reference category), controlling for child age, gender, and number of children in the household.

RESULTS

Sample Characteristics

Descriptive statistics for the study analysis sample are shown in Table 1. A substantial proportion of children experienced social risk or vulnerability factors. Approx-imately 40% of children were in low-income families

⬍200% FPL, and 32% lived in a household where no resident had an education beyond high school. Fifty-five percent of children had ⱖ2 social risk factors, whereas 24% hadⱖ4.

Bivariate Analyses

Cumulative social risk gradients are depicted in Fig 1. The percentage of children in worse health increased with the number of social risk factors across all health outcomes (linear polynomialP⬍.05). The percentage of parents who reported less than very good child health status was 5% for those with 0 risk factors compared with 46% of those withⱖ6 risk factors. The gradient for suboptimal condition of teeth went from 14% (0 risk factors) to 64% (ⱖ6 risk factors). Substantial gradients are also shown for child socioemotional problems and overweight status.

Bivariate associations between each risk indicator and the child health outcomes are reported in Table 2. Every social risk factor increased the odds for suboptimal health and suboptimal condition of teeth (P⬍.05). For these health outcomes, unadjusted ORs ⬎2.0 were found for all risk indicators except family structure type

TABLE 1 Sample Characteristics (N86 249)

Characteristic n(%)a

Health outcomesb

Good/fair/poor health 10 262 (15)

Good/fair/poor teeth 20 874 (30)

Socioemotional problem 12 172 (18)

Overweight 15 652 (24)

Social risk factorsc

No education more than high school 20 336 (32)

Family income⬍200% FPL 27 271 (40)

Not 2-parent household 18 752 (24)

Black/Hispanic 18 202 (30)

Uninsured 6109 (8)

Family conflict 30 772 (39)

Low maternal mental health 21 102 (28)

Unsafe neighborhood 10 379 (16)

No. of risk factors

0 22 572 (21)

1 23 362 (24)

2 15 951 (18)

3 10 357 (13)

4 7039 (11)

5 4111 (7)

ⱖ6 2857 (6)

aThe sample sizes (n) in each category are unweighted; the frequencies (%) are weighted. bThe samples sizes (n) of the health outcome variables match thenfor the multivariate

regres-sion model for each specific variable (see Table 2).

cThe sample size for the social risk indicators comes from the “good/fair/poor health” variable

(4)

and family conflict. Nearly all of the risk indicators were associated with socioemotional problems and over-weight.

Multivariate Analyses

Most social risk factors remained significant independent predictors of children’s health outcomes in multivariate logistic regression models, although with some attenua-tion in the magnitude of the associaattenua-tions (Table 2). Low maternal mental health (OR: 3.15; 95% confidence in-terval [CI]: 2.90 –3.42), black or Hispanic race/ethnicity (OR: 2.05; 95% CI: 1.88 –2.23), family income⬍200% FPL (OR: 1.87; 95% CI: 1.70 –2.04), low household ed-ucation (OR: 1.48; 95% CI: 1.36 –1.62), unsafe neigh-borhoods, and lack of health insurance increased the odds for less than very good child health in adjusted models. Low maternal mental health (OR: 2.29; 95% CI: 2.14 –2.44), family income⬍200% FPL (OR: 1.68; 95% CI: 1.56 –1.80), black or Hispanic race/ethnicity (OR: 1.62; 95% CI: 1.51–1.73), no education beyond high school (OR: 1.47; 95% CI: 1.37–1.57), lack of health insurance (OR: 1.47; 95% CI: 1.32–1.64), unsafe neigh-borhoods, and family conflict increased the odds for less than very good condition of teeth.

For child socioemotional problems, low maternal mental health (OR: 2.07; 95% CI: 1.91–2.24), not resid-ing in a 2-parent household (OR: 1.53; 95% CI: 1.41– 1.67), and family conflict (OR: 1.47; 95% CI: 1.37–1.58) were strong independent correlates, along with resi-dence in an unsafe neighborhood and family income

⬍200% FPL. All of the risk factors except insurance status increased the odds for overweight, with a

partic-ularly strong effect for black or Hispanic race/ethnicity (OR: 1.71; 95% CI: 1.57–1.85) and family income and education.

These results suggest a cumulative influence of mul-tiple risk factors on children’s health. In logistic regres-sion models that control for child age, gender, and num-ber of children in the household, there was a 17-fold increase in the odds for less than very good health and an almost 11-fold increase in the odds for less than very good teeth for children with ⱖ6 versus no social risks. The respective figure for child socioemotional well-being and overweight status was⬃4.

DISCUSSION

A large proportion of the study sample exhibited social risks, with more than half having ⱖ2 risks and 24% having ⱖ4, underscoring the challenges faced by chil-dren growing up in America. For individual social risks, modest associations with poorer health outcomes were obtained across all domains, and these associations largely persisted in multivariate models. When risks were combined into a social risk index, large cumulative effects were observed, with a 17-fold increase in odds for poorer child health status for children with ⱖ6 risks compared with those with no risks and a near 11-fold increase in odds for poorer oral health. These findings support the hypothesis that the cumulative impact of multiple risks is much greater than the effect of any 1 risk acting alone. These findings are broadly consistent with those of Sameroff and others25–28on the importance

of total number of risks for child cognitive and socio-emotional outcomes and extends the concept of

cumu-Good/Fair/Poor Health

5 6 10 17 26 35 46 0 10 20 30 40 50 60 70

0 1 2 3 4 5 6+

No. of Social Risk Factors

Percent of children

Good/Fair/Poor Teeth 14 20 28 36 46 54 64 0 10 20 30 40 50 60 70

0 1 2 3 4 5 6+

No. of Social Risk Factors

Percent o f ch il dren

Social/Emotional Problem

9 13 19 21

25 26 30

0 10 20 30 40 50 60 70

0 1 2 3 4 5 6+

No. of Social Risk Factors

Percent of children

Overweight

18 19 25

28 32 35 36 0 10 20 30 40 50 60 70

0 1 2 3 4 5 6+

No. of Social Risk Factors

Percen t o f c h ildren FIGURE 1

(5)

lative risk to physical health outcomes including global health status, dental health, and overweight.

To date, few studies have addressed associations be-tween multiple social risks and child physical health outcomes. Bauman et al30reported that 3 social risks—

poverty, low parental education, and single-parent household—were associated with poorer parent-re-ported child health in a cumulative manner. Stevens20

reported that greater numbers of risk factors (race/ ethnicity, maternal education and family poverty, child health insurance coverage, and maternal mental health) were associated with poorer health status and being at higher risk for developmental delays with a risk gradient effect in a sample of children who were younger than 3 years. Our findings are broadly consistent with these studies and also show that a wider range of factors operating at the child, family, and community levels contribute to child health risk. These results highlight the importance of incorporating multiple levels of risk into child health development models.

Considering the overall patterns of risk, family in-come, race/ethnicity, and maternal mental health had strong independent associations with physical well-be-ing, considerably more than health insurance status. Family structure, family conflict, and neighborhood safety were more important for child socioemotional health but not inconsequential for physical well-being. Income disparities in children’s health were reduced by statistically controlling for related social risks, but a

sub-stantial direct association of this form of material disad-vantage remained for all health outcomes. For socio-emotional health, both minority race/ethnicity and uninsured status actually seemed to be associated with fewer reported problems, potentially reflecting under-recognition, as described in other studies of black and Hispanic youth,39,40rather than a true protective effect.

Differential parent recognition of children’s mental health problems may help to explain why social gradi-ents in socioemotional health were less pronounced than those for global child health status and condition of teeth.

Poor maternal mental health showed consistent asso-ciations with each child health outcome included in this study. Previous studies demonstrated a relationship be-tween maternal depression and poorer child socioemo-tional health;18,41however, relationships with child

phys-ical health outcomes have been less comprehensively studied. The considerable association between maternal mental health and condition of child’s teeth found in this study is particularly intriguing. Although low income and racial/ethnic minority status are known to be associated with poorer child oral health,42,43far less is known about

the relationship between oral health and other social risks. These findings suggest a need for increasing attention to maternal mental health as part of a larger strategy to com-bat social risks and their negative impact.

This study does not address the mechanism whereby multiple social risks might influence child health, but 1

TABLE 2 Effect Parameters for Logistic Regression Models Predicting Children’s Health Outcomes

Parameter Good/Fair/Poor Health (N⫽86 249)

Good/Fair/Poor Teeth (N⫽80 554)

SocioEmotional Problem (N⫽71 476)

Overweight (N⫽70 091)

OR 95% CI OR 95% CI OR 95% CI OR 95% CI

Bivariate, unadjusted OR

No education more than high school 3.09 2.87–3.33 2.57 2.42–2.73 1.39 1.29–1.50 1.57 1.46–1.68 Family income⬍200% FPL 3.85 3.58–4.15 2.93 2.77–3.11 1.74 1.62–1.87 1.74 1.63–1.86 Not 2-parent household 1.98 1.83–2.15 1.70 1.59–1.81 1.96 1.82–2.12 1.41 1.31–1.51

Black/Hispanic 3.64 3.37–3.92 2.62 2.46–2.79 1.17 1.08–1.26 1.96 1.83–2.11

Uninsured 2.29 2.04–2.58 2.30 2.08–2.54 0.93 0.81–1.06 1.17 1.04–1.33

Family conflict 1.57 1.45–1.69 1.56 1.48–1.65 1.80 1.68–1.93 1.05 0.98–1.12

Low maternal mental health 4.69 4.34–5.06 3.25 3.05–3.45 2.54 2.36–2.73 1.36 1.27–1.45 Unsafe neighborhood 2.72 2.48–2.98 2.18 2.02–2.36 1.77 1.61–1.94 1.57 1.43–1.72 Multivariate, adjusted ORa

No education more than high school 1.48 1.36–1.62 1.47 1.37–1.57 0.99 0.91–1.08 1.27 1.18–1.38 Family income⬍200% FPL 1.87 1.70–2.04 1.68 1.56–1.80 1.37 1.25–1.49 1.30 1.20–1.41 Not 2-parent household 0.98 0.89–1.08 0.95 0.88–1.02 1.53 1.41–1.67 1.13 1.03–1.23

Black/Hispanic 2.05 1.88–2.23 1.62 1.51–1.73 0.76 0.69–0.83 1.71 1.57–1.85

Uninsured 1.29 1.14–1.47 1.47 1.32–1.64 0.74 0.64–0.86 1.03 0.90–1.17

Family conflict 1.07 0.99–1.16 1.19 1.11–1.26 1.47 1.37–1.58 1.10 1.03–1.18

Low maternal mental health 3.15 2.90–3.42 2.29 2.14–2.44 2.07 1.91–2.24 1.22 1.13–1.32 Unsafe neighborhood 1.39 1.25–1.54 1.28 1.18–1.40 1.40 1.26–1.56 1.13 1.02–1.25 No. of risk factors, adjusted ORa

0 — — — — — — — —

1 1.42 1.24–1.64 1.52 1.38–1.67 1.42 1.27–1.59 1.25 1.14–1.37

2 2.34 2.03–2.69 2.35 2.14–2.58 2.24 1.99–2.52 1.76 1.60–1.94

3 4.23 3.66–4.89 3.50 3.16–3.87 2.63 2.32–2.98 2.22 1.99–2.49

4 7.44 6.43–8.60 5.33 4.78–5.94 3.35 2.92–3.84 2.75 2.44–3.11

5 11.05 9.43–12.94 7.25 6.37–8.26 3.46 2.95–4.05 3.22 2.77–3.75

ⱖ6 17.31 14.66–20.44 10.59 9.14–12.27 4.29 3.61–5.08 3.80 3.19–4.54

(6)

candidate explanation emerges from empirical studies that demonstrate how cumulative risks can induce high levels of toxic stress, leading to greater susceptibility to poorer health outcomes.44–47 Each risk factor might also

influence health in unique ways (eg, restricted diet and nutrition for children with limited family income, dis-rupted health routines for families with psychosocial difficulties, inadequate preventive care for children who lack health insurance) with diverse health insults exert-ing a cumulative negative toll on the health status of children with multiple risks. Although the social risk factors included in this study were associated with one another, it is noteworthy that most retained an indepen-dent association with health in mutually adjusted mod-els. This suggests that each factor represents a unique source of health vulnerability, even considering that certain risks, such as poor maternal mental health, may mediate some of the influence of the other risks.

What are the clinical implications of these findings? First, they suggest that clinical strategies that either ignore the presence of social risks or address only indi-vidual risks are unlikely to be successful. Current ap-proaches to well-child care in the United States lack both a systematic approach to the identification of numbers of risks and effective strategies to target children with mul-tiple risks for additional preventive or treatment ser-vices. There is a growing realization that our existing system of well-child care is not matching services to child and family needs and needs to be reconceptual-ized.48All too often, important psychosocial risks remain

unrecognized and unaddressed.49 In addition to lack of

tools to identify systematically both patterns and degrees of risk, primary care providers lack the resources, rela-tionships, and case management capacity to connect families to appropriate services.50

Well-child care that is sufficiently flexible to provide more intense services for families with greatest biologi-cal, psychologibiologi-cal, and social risks might prove more effective than the existing “one size fits all” approach.51,52

Kemper53,54 and Fleegler et al49 suggested that by

rou-tinely using a standardized psychosocial risk assessment, child health providers could improve identification of risks, like those that were compiled for this study. A brief social risk assessment could be performed at or even before birth, and children with the greatest number of risks could be offered enhanced or supplemented well-child care. This might include longer or more frequent visits, as well as encouraging access to comprehensive community-based interventions designed to reduce the impact of common psychosocial risks, such as home visiting programs for new mothers.55Children with

mul-tiple risks might also benefit from evidence-based sup-plements to traditional well-child care, such as the Healthy Steps program for developmental monitoring,56

and coordinated referral programs, such as the Help Me Grow program in Connecticut.57

The sizable cumulative toll that multiple social risks may take on child health points toward a need for com-prehensive childhood programs and service systems that address a variety of family needs with diverse forms of assistance, such as parenting support, education,

finan-cial planning, health care, and mental health and do-mestic violence services. As advocates for children at the community level, pediatricians can establish for-mal linkages with other child service providers aimed at improving the entire system of community supports and services;58however, services alone are not enough.

The vulnerabilities resulting from low social class, mi-nority status (a probable proxy for various forms of discrimination), single parenthood, being uninsured, poor maternal mental health, family conflict, and com-munity violence must be addressed through coordinated national policy action aimed at reducing developmental risk. That social gradients in children’s health are vari-able in magnitude across different countries with differ-ing child and family policy schemes suggests that they are amenable to change.3,59Pediatricians would be well

positioned to advocate for social policy change aimed at reducing social risks and their negative impact through the provision of adequate family income, adequate time for parenting, and generous supportive services.60

The strengths of this study include the very large sample size and the nationally representative nature of the study sample. The NSCH provided detailed information on the social and environmental contexts in which children live, allowing us to construct a social risk index composed of risks that operate at multiple ecological levels and to test it against a range of child health outcomes.

The study has several limitations. First, the data were collected by telephone survey of parents or other care-givers. Most of the indicators obtained, therefore, reflect parent perceptions of their child’s health and of family and community characteristics. Some parents, such as mothers with poorer mental health, might overestimate health problems in their children.61Maternal

psycholog-ical distress has been associated with more pessimistic assessments on the problem-focused Child Behavior Checklist; however, related problems such as maternal substance abuse have been demonstrated to result in more optimistic assessments.62 We consider it unlikely

that the observed associations between poorer maternal mental health and reported child health outcomes can be entirely explained by parental bias but acknowledge that additional research is required to confirm these findings. Similarly, some parents may give socially de-sirable rather than objective responses to survey ques-tions. Finally, inferences about causality are limited by the cross-sectional nature of the study: it can be argued, for example, that poorer maternal mental health or greater family conflict may result from the effects of having a child with poorer health status.

CONCLUSIONS

Social risk factors threaten children’s health in a cumu-lative manner across physical health, oral health, and mental health domains. Attention to social risk factors is an important part of child health optimization. Early identification of children with multiple social risks would allow targeting of enhanced well-child preventive care services, tailored to the child’s family and commu-nity context.63 The high proportion of the childhood

(7)

ongoing advocacy efforts to improve the circumstances in which America’s youngest citizens are growing up. These findings suggest that there are multiple avenues through which children’s health status might be im-proved. Programs and policies that address multiple do-mains of social risk offer the best hope for achieving improvements in child health.

ACKNOWLEDGMENTS

This research was supported in part by funding from the Maternal and Child Health Bureau of the Health Re-sources and Services Administration Interdisciplinary Maternal and Child Health Training Program (2 T76M600014:11; Dr Halfon) and National Oral Health Policy Center (5 U44MC03661; Dr Crall).

We thank Louba Benassaya and Nicole Garro, who assisted with data analysis and manuscript preparation.

REFERENCES

1. Evans RG, Stoddart GL. Producing health, consuming health care.Soc Sci Med.1990;31(12):1347–1363

2. Heymann J, Hertzman C, Barer ML, Evans RG, eds.Healthier

Societies: From Analysis to Action. Oxford, England: Oxford

Uni-versity Press; 2006

3. Keating DP, Hertzman C.Developmental Health and the Wealth of

Nations: Social, Biological, and Educational Dynamics. New York,

NY: Guilford Press; 1999

4. National Research Council and Institute of Medicine, Commit-tee on Evaluation of Children’s Health, Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education.Children’s Health, the Nation’s Wealth: Assessing

and Improving Child Health. Washington, DC: The National

Academies Press; 2004

5. Szilagyi PG, Schor EL. The health of children.Health Serv Res. 1998;33(4 Pt 2):1001–1039

6. Wadsworth M, Butterworth S. Early life. In: Wilkinson RG, Marmot MG, eds. Social Determinants of Health. Oxford, England: Oxford University Press; 2006:31–53

7. Wise PH. The transformation of child health in the United States.Health Aff (Millwood).2004;23(5):9 –25

8. Chen E, Martin AD, Matthews KA. Understanding health disparities: the role of race and socioeconomic status in chil-dren’s health.Am J Public Health.2006;96(4):702–708 9. Flores G, Olson L, Tomany-Korman SC. Racial and ethnic

disparities in early childhood health and health care.Pediatrics. 2005;115(2). Available at: www.pediatrics.org/cgi/content/ full/115/2/e183

10. Newacheck P, Jameson WJ, Halfon N. Health status and income: the impact of poverty on child health.J Sch Health. 1994;64(6):229 –233

11. Starfield B, Robertson J, Riley AW. Social class gradients and health in childhood.Ambul Pediatr.2002;2(4):238 –246 12. Bramlett MD, Blumberg SJ. Family structure and children’s

physical and mental health.Health Aff (Millwood).2007;26(2): 549 –558

13. Crall JJ, Edelstein B, Tinanoff N. Relationship of microbiolog-ical, social, and environmental variables to caries status in young children.Pediatr Dent.1990;12(4):233–236

14. Weitoft GR, Hjern A, Haglund B, Rosen M. Mortality, severe morbidity, and injury in children living with single parents in Sweden: a population-based study. Lancet. 2003;361(9354): 289 –295

15. McFarlane JM, Groff JY, O’Brien JA, Watson K. Behaviors of children who are exposed and not exposed to intimate partner

violence: an analysis of 330 black, white, and Hispanic chil-dren.Pediatrics.2003;112(3). Available at: www.pediatrics.org/ cgi/content/full/112/3/e202

16. Montgomery SM, Bartley MJ, Wilkinson RG. Family conflict and slow growth.Arch Dis Child.1997;77(4):326 –330 17. Fendrich M, Warner V, Weissman MM. Family risk factors,

parental depression, and psychopathology in offspring. Dev

Psychol.1990;26(1):40 –50

18. Lesesne CA, Visser SN, White CP. Attention-deficit/ hyperactivity disorder in school-aged children: association with maternal mental health and use of health care resources.

Pediatrics.2003;111(5 Part 2):1232–1237

19. Weil CM, Wade SL, Bauman LJ, Lynn H, Mitchell H, Lavigne J. The relationship between psychosocial factors and asthma morbidity in inner-city children with asthma.Pediatrics.1999; 104(6):1274 –1280

20. Stevens G. Gradients in the health status and developmental risks of young children: the combined influences of multiple social risk factors. Matern Child Health J. 2006;10(2): 187–199

21. Bailey BN, Delaney-Black V, Hannigan JH, Ager J, Sokol RJ, Covington CY. Somatic complaints in children and community violence exposure.J Dev Behav Pediatr.2005;26(5):341–348 22. Graham-Bermann SA, Seng J. Violence exposure and

trau-matic stress symptoms as additional predictors of health prob-lems in high-risk children.J Pediatr.2005;146(3):349 –354 23. Wright RJ, Mitchell H, Visness CM, et al. Community violence

and asthma morbidity: the Inner-City Asthma Study. Am J

Public Health.2004;94(4):625– 632

24. Halfon N, Hochstein M. Life course health development: an integrated framework for developing health, policy, and re-search.Milbank Q.2002;80(3):433– 479

25. Rutter M, Pickles A, Murray R, Eaves L. Testing hypotheses on specific environmental causal effects on behavior.Psychol Bull. 2001;127(3):291–324

26. Sameroff AJ, Seifer R, Barocas R, Zax M, Greenspan S. Intel-ligence quotient scores of 4-year-old children: social-environmental risk factors.Pediatrics.1987;79(3):343–350 27. Sameroff AJ. Environmental risk factors in infancy.Pediatrics.

1998;102(5 suppl E):1287–1292

28. Furstenberg FF, Cook TD, Eccles J, Elder GH, Sameroff AJ.

Managing to Make It: Urban Families and Adolescent Success.

Chicago, IL: University of Chicago Press; 1999

29. Shonkoff J, Meisels S.Handbook of Early Childhood Intervention. 2nd ed. Cambridge, England: Cambridge University Press; 2000

30. Bauman LJ, Silver EJ, Stein RE. Cumulative social disadvan-tage and child health.Pediatrics.2006;117(4):1321–1328 31. Blumberg SJ, Olson L, Frankel MR, Osborn L, Srinath KP,

Giambo P. Design and operation of the National Survey of Children’s Health, 2003. National Center for Health Statistics.

Vital Health Stat 1.2005;(43):1–124

32. Minkovitz CS, O’Campo PJ, Chen YH, Grason HA. Associations between maternal and child health status and patterns of med-ical care use.Ambul Pediatr.2002;2(2):85–92

33. Monette S, Seguin L, Gauvin L, Nikiema B. Validation of a measure of maternal perception of the child’s health status.

Child Care Health Dev.2007;33(4):472– 481

34. Talekar BS, Rozier RG, Slade GD, Ennett ST. Parental percep-tions of their preschool-aged children’s oral health.J Am Dent

Assoc.2005;136(3):364 –372

35. Hennequin M, Faulks D, Roux D. Accuracy of estimation of dental treatment need in special care patients.J Dent. 2000; 28(2):131–136

36. Chen E, Martin AD, Matthews KA. Socioeconomic status and health: do gradients differ within childhood and adolescence?

(8)

37. Chen E, Matthews KA, Boyce WT. Socioeconomic differences in children’s health: how and why do these relationships change with age?Psychol Bull.2002;128(2):295–329

38. Inkelas M, Halfon N, Uyeda K, et al. The Health of Young Children in California: Findings From the 2001 California Health

Interview Survey. Los Angeles and Sacramento, CA: UCLA

Center for Health Policy Research and First 5 California; July 2003

39. Samaan RA. The influences of race, ethnicity, and poverty on the mental health of children.J Health Care Poor Underserved. 2000;11(1):100 –110

40. Roberts RE, Alegria M, Roberts CR, Chen IG. Concordance of reports of mental health functioning by adolescents and their caregivers: a comparison of European, African and Latino Americans.J Nerv Ment Dis.2005;193(8):528 –534

41. Lewinsohn PM, Olino TM, Klein DN. Psychosocial impairment in offspring of depressed parents. Psychol Med. 2005;35(10): 1493–1503

42. Tinanoff N, Kanellis MJ, Vargas CM. Current understanding of the epidemiology mechanisms, and prevention of dental caries in preschool children.Pediatr Dent.2002;24(6):543–551 43. Vargas CM, Crall JJ, Schneider DA. Sociodemographic

distri-bution of pediatric dental caries: NHANES III, 1988 –1994.J Am

Dent Assoc.1998;129(9):1229 –1238

44. Evans GW. A multimethodological analysis of cumulative risk and allostatic load among rural children. Dev Psychol. 2003; 39(5):924 –933

45. Evans GW, English K. The environment of poverty: multiple stressor exposure, psychophysiological stress, and socioemo-tional adjustment.Child Dev.2002;73(4):1238 –1248

46. Evans GW, Kim P, Ting AH, Tesher HB, Shannis D. Cumulative risk, maternal responsiveness, and allostatic load among young adolescents.Dev Psychol.2007;43(2):341–351

47. Johnston-Brooks CH, Lewis MA, Evans GW, Whalen CK. Chronic stress and illness in children: the role of allostatic load.

Psychosom Med.1998;60(5):597– 603

48. Schor EL. Rethinking well-child care.Pediatrics.2004;114(1): 210 –216

49. Fleegler EW, Lieu TA, Wise PH, Muret-Wagstaff S. Families’ health-related social problems and missed referral opportuni-ties.Pediatrics.2007;119(6). Available at: www.pediatrics.org/ cgi/content/full/119/6/e1332

50. Fine A, Mayer R. Beyond Referral: Pediatric Care Linkages to

Improve Developmental Health. New York, NY: The

Common-wealth Fund; December 2006

51. Barreto P, Perez V, Halfon N. Underserved children: pre-venting chronic illness and promoting health. In: King TE, Wheeler M, Fernandez A, et al, eds.Medical Management of Vulnerable and Underserved Patients: Principles, Practice and

Popu-lations. New York, NY: McGraw-Hill Medical; 2007:169 –177

52. Bergman D, Plsek P, Saunders M.A High-Performing System for

Well-Child Care: A Vision for the Future. New York, NY: The

Commonwealth Fund; 2006

53. Kemper KJ. Self-administered questionnaire for structured psychosocial screening in pediatrics. Pediatrics. 1992;89(3): 433– 436

54. Kemper KJ, Kelleher KJ. Rationale for family psychosocial screening.Ambul Child Health.1996;1(4):311–324

55. Olds DL, Kitzman H. Can home visitation improve the health of women and children at environmental risk?Pediatrics.1990; 86(1):108 –116

56. Minkovitz CS, Hughart N, Strobino D, et al. A practice-based intervention to enhance quality of care in the first 3 years of life: the Healthy Steps for Young Children Program.JAMA. 2003;290(23):3081–3091

57. Dworkin PH. Historical overview: from ChildServ to Help Me Grow.J Dev Behav Pediatr.2006;27(1 suppl):S5–S7

58. Rushton FE Jr. The pediatrician’s role in community pediatrics.

Pediatrics.2005;115(4):1092–1094

59. Siddiqi A, Kawachi I, Berkman L, Subramanian SV, Hertzman C. Variation of socioeconomic gradients in children’s develop-mental health across advanced Capitalist societies: analysis of 22 OECD nations.Int J Health Serv.2007;37(1):63– 87 60. Kamerman SB. Early childhood intervention policies: an

inter-national perspective. In: Shonkoff JP, Meisels SJ, eds.Handbook

of Early Childhood Intervention. Cambridge, United Kingdom:

Cambridge University Press; 2000:613– 629

61. Angel R, Worobey JL. Single motherhood and children’s health.J Health Soc Behav.1988;29(1):38 –52

62. Hennigan K, O’Keefe M, Noether C, Rinehart D, Russell L. Through a mother’s eyes: sources of bias when mothers with co-occurring disorders assess their children.J Behav Health Serv Res.2006;33(1):87–104

(9)

DOI: 10.1542/peds.2007-0447

2008;121;337

Pediatrics

Kandyce Larson, Shirley A. Russ, James J. Crall and Neal Halfon

Influence of Multiple Social Risks on Children's Health

Services

Updated Information &

http://pediatrics.aappublications.org/content/121/2/337

including high resolution figures, can be found at:

References

http://pediatrics.aappublications.org/content/121/2/337#BIBL

This article cites 48 articles, 13 of which you can access for free at:

Subspecialty Collections

http://www.aappublications.org/cgi/collection/medical_home_sub

Medical Home

_sub

http://www.aappublications.org/cgi/collection/community_pediatrics

Community Pediatrics

following collection(s):

This article, along with others on similar topics, appears in the

Permissions & Licensing

http://www.aappublications.org/site/misc/Permissions.xhtml

in its entirety can be found online at:

Information about reproducing this article in parts (figures, tables) or

Reprints

http://www.aappublications.org/site/misc/reprints.xhtml

(10)

DOI: 10.1542/peds.2007-0447

2008;121;337

Pediatrics

Kandyce Larson, Shirley A. Russ, James J. Crall and Neal Halfon

Influence of Multiple Social Risks on Children's Health

http://pediatrics.aappublications.org/content/121/2/337

located on the World Wide Web at:

The online version of this article, along with updated information and services, is

by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.

Figure

TABLE 1Sample Characteristics (N � 86 249)
FIGURE 1
TABLE 2Effect Parameters for Logistic Regression Models Predicting Children’s Health Outcomes

References

Related documents

Accordingly, the 2015 Report says that “Members of the Security Council, and in particular its permanent members, as well as other member states with

knowledge transfer case study using analysis and design methods to transfer innovative technologies and business concepts to Austrians SMEs with mock-ups.. Knowledge

Design of Op-amp Inverter is Simulated using MATLAB Software and the results of Simulation of Voltage regulator ,Multivibrator, Inverting and Non-Inverting Amplifier ,Summing

VISSIM software was used to simulate the adjustment effect of import and export of the massive in saturated traffic conditions with the specific parameters such

Fig. Similar to Fig. 5 but instead of the model vorticity forcing, the observational forcing is applied. At a) is the response to the observed tropical RWS forcing for positive

happens when your time in service ends?  If you do not choose to stay in and reenlist, you attend some classes dubbed Seps and TAPS which stands for Separations and

This week effect of seed size on seedlings height can be explained, to some extent, by favorable and uniform growing conditions in the nursery, as previously have been

In the current research, we examined whether people would be more willing to donate to charity when they perceived charitable expenses as relatively more exceptional rather