United States
WHAT’S KNOWN ON THIS SUBJECT: Income inequality is positively associated with several adverse child health and well-being outcomes. There is no existing research investigating the relationship between income inequality and child maltreatment rates.
WHAT THIS STUDY ADDS: This study is thefirst to demonstrate that increases in income inequality are associated with increases in child maltreatment rates at the county level.
abstract
OBJECTIVE: To examine the relation between county-level income inequality and rates of child maltreatment.
METHODS:Data on substantiated reports of child abuse and neglect from 2005 to 2009 were obtained from the National Child Abuse and Neglect Data System. County-level data on income inequality and children in poverty were obtained from the American Community Survey. Data for additional control variables were obtained from the American Community Survey and the Health Resources and Services Administration Area Resource File. The Gini coefficient was used as the measure of income inequality. Generalized additive models were estimated to explore linear and nonlinear relations among income inequality, poverty, and child maltreatment. In all models, state was included as a fixed effect to control for state-level differences in victim rates.
RESULTS:Considerable variation in income inequality and child mal-treatment rates was found across the 3142 US counties. Income inequal-ity, as well as child poverty rate, was positively and significantly correlated with child maltreatment rates at the county level. Controlling for child poverty, demographic and economic control variables, and state-level variation in maltreatment rates, there was a significant linear effect of inequality on child maltreatment rates (P , .0001). This effect was stronger for counties with moderate to high levels of child poverty.
CONCLUSIONS:Higher income inequality across US counties was signif-icantly associated with higher county-level rates of child maltreatment. Thefindings contribute to the growing literature linking greater income inequality to a range of poor health and well-being outcomes in infants and children.Pediatrics2014;133:454–461
AUTHORS:John Eckenrode, PhD,a,bElliott G. Smith, PhD,b
Margaret E. McCarthy, MA, JD,aand Michael Dineen, MAb
aDepartment of Human Development andbBronfenbrenner Center for Translational Research, Cornell University, Ithaca, New York
KEY WORDS
income inequality, child poverty, child maltreatment, child abuse
ABBREVIATIONS
ACS—American Community Survey GAM—generalized additive model
Gini—income inequality measured as a Gini coefficient LnVictim—natural log of child maltreatment victim rate NCANDS—National Child Abuse and Neglect Data System Poverty—percent of children living in poverty SES—socioeconomic status
Dr Eckenrode contributed significantly to the conception and design of the study, analysis and interpretation of the data, writing the manuscript, and critical revision of the manuscript for important intellectual content; Dr Smith gathered data from 2 of the sources, carried out all analyses, produced all tables andfigures, drafted portions of the initial manuscript, and reviewed the manuscript and suggested revisions; Ms McCarthy conducted the preliminary literature review, drafted portions of the initial manuscript, and reviewed the manuscript and suggested revisions; Mr Dineen gathered data from 2 of the sources, combined datafiles, and processed data for analysis; and all authors approved thefinal manuscript as submitted. Some data in this paper were presented as a poster at the biennial meeting of the Society for Research in Child Development; April 18–20, 2013, Seattle, WA. www.pediatrics.org/cgi/doi/10.1542/peds.2013-1707 doi:10.1542/peds.2013-1707
Accepted for publication Dec 9, 2013
Address correspondence to John Eckenrode, PhD,
Bronfenbrenner Center for Translational Research, Beebe Hall, Cornell University, Ithaca, NY 14853-4401. Email: jje1@cornell.edu PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online, 1098-4275). Copyright © 2014 by the American Academy of Pediatrics
FINANCIAL DISCLOSURE:All of the authors report grant support from Children’s Bureau, Administration for Children and Families, during the conduct of the study.
FUNDING:Supported through a cooperative agreement with the Children’s Bureau, Administration for Children and Families (90-CA-1785).
A social gradient in child and adult health by socioeconomic status (SES) exists across a range of health con-ditions and mortality causes.1,2Poverty may impact health and development owing to poor housing and sanitation, lack of access to healthy food, health care, and other social services, expo-sure to toxic environments and haz-ardous working conditions, behavioral risk factors, and exposure to psycho-social stressors.2,3
A growing literature suggests an effect of income inequality, in addition to poverty, on health and well-being. In-come inequality refers to the degree to which income in a given geographic area is distributed equally or unequally. A positive correlation between state-level (in the United States) or nation-level income inequality and aggregate rates of morbidity and mortality, and social problems like crime, is com-monly documented, even when con-trolling for individual SES.4–6
Income inequality is also associated with several adverse child health and well-being outcomes. Earlier research using the United Nations Children’s Fund index of child well-being from 21 Organization for Economic Co-operation and Development countries indicated that income inequality was signifi -cantly related to infant mortality, low birth weight, injuries, low educational achievement, teenage births, obesity, and poor peer relations.7 In a recent study of US state data from 2000 to 2004, income inequality was positively correlated with rates of preterm births, low birth weight, very low birth weight, and infant mortality, after controlling for median household income.8
Child abuse and neglect is a significant public health problem in the United States. The Fourth National Incidence Study estimated that in 2005 to 2006, nearly 3 million children younger than age 18 years were physically abused, sexually abused, physically neglected,
or emotionally abused in 1 year, rep-resenting 4% of children.9SES is 1 of the strongest and most consistent pre-dictors of child maltreatment.10,11 In the Fourth National Incidence Study, children in low SES households were 3 times more likely to be abused and∼7 times as likely to be neglected as chil-dren in higher SES households.9
Several studies have reported associ-ations between economic factors and maltreatment rates at the neighbor-hood and community levels.12–15 Re-cently, a retrospective study of children admitted to 38 hospitals between 2000 and 2009 showed that rates of admis-sions for physical abuse and high-risk traumatic brain injury were signifi -cantly related to increases in local mortgage foreclosure and delinquency rates in the associated metropolitan areas.16
Although there is considerable evi-dence that income inequality has a negative impact on health and well-being for both adults and children, and that poverty is associated with rates of child maltreatment, no study has examined the association of income inequality with child maltreatment. The goal of this study was to examine this association using county-level data on income inequality and corresponding child maltreatment rates in the United States over a 5-year period.
METHODS
Data Sources and Measures
Data on income inequality and children living in poverty were obtained from the US Census Bureau’s American Com-munity Survey (ACS).17 County-level data were extracted for all 3142 US counties and statistically equivalent entities from the 5-year estimates from 2005 to 2009. Only 5-year estimates are available from ACS for all counties. The Gini coefficient was used as a measure of income inequality. A value of 0 indicates
perfect equality of income for all mem-bers of a population, while a value of 1 indicates perfect inequality, with all in-come in the population concentrated in a single individual.18The percent of chil-dren living at or below the federal pov-erty line was used as a measure of child poverty.
Child maltreatment data were obtained from the US Children’s Bureau’s Na-tional Child Abuse and Neglect Data System (NCANDS).19 NCANDS data are collected from state child welfare sys-tems on an annual basis. For each year from 2005 to 2009, counts of child maltreatment victims were derived from the investigated Child Protective Service reports disposed in that year. Victims were defined as children for whom a maltreatment allegation was substantiated, indicated, or designated as“alternative response–victim.” Al-ternative response is a dual-track sys-tem in some states to handle lower risk cases that are reported to child pro-tective services. Alternative response– victim is a category used in only 3 states, and represented 0.5% of cases receiving a child protective response in 2011.20The US Census counts of county-level child population provided in the NCANDS data sets were used to con-struct annual rates of victims per 1000 children in the population. The annual mean victim rates over the 5-year pe-riod were averaged and merged with the 5-year Gini estimates and child poverty rates from the ACS data set. Owing to the skewed nature of the victim rate distribution, a natural log transformation was applied before conducting statistical analyses. For counties with no victims, a small con-stant was added to 0 so that the value could be log transformed. Data from Puerto Rico and the District of Colum-bia were excluded from analyses.
In 7 states (Alaska, Louisiana, Nebraska, Rhode Island, South Dakota, Vermont, and Virginia), numerous counties reported
had no victims of maltreatment. To ad-just for this reporting artifact, counties were combined into regions that mir-rored these administrative practices in those states. As a result, data from 259 counties were absorbed into regions. An additional 6 counties across 4 states were dropped from the analysis owing to missing NCANDS data. Thefinal analytic sample consisted of 2877 counties, re-coded regions, and statistically equiva-lent entities. Statistically equivaequiva-lent entities (state subdivisions) exist for Alaska, Louisiana, Maryland, Missouri, Nevada, and Virginia.
Additional county level data were obtained from the ACS data source and from the 2011–2012 Health Resources and Services Administration Area Re-source File, covering the 2005 to 2009 5-year period.21 Potential confounders for income inequality and child poverty were chosen that had significant bi-variate correlations with the natural log of victim rate. Our analyses con-trolled for the following potential confounders: percent of the total pop-ulation who are African American; percent of the total population who are Latino; percent of adults age 25+ years who have 4 or more years of college; percent of households with Supple-mental Security Income or public as-sistance income; and the ratio of infant deaths to 1000 live births.
Statistical Analysis
The statistical analysis was performed by using SAS 9.3 software (SAS Institute, Cary, NC). Examination of the relations among the Gini coefficient (Gini), child poverty rate (Poverty), and the natural log of victim rate (LnVictim) was con-ducted by using the generalized additive model (GAM) procedure in SAS. GAMs are a powerful andflexible nonparam-etric regression modeling technique that allows parametric variables and
Using this technique can uncover non-linearities in the relationships among variables while controlling for other variables.22,23 In the present analysis, smoothers using a local regression method known as Loess were used, and their smoothing parameters were se-lected automatically by using the gen-eralized cross validation method.24The results of the GAM were used to inform the parametric specification for a gen-eral linear model predicting LnVictim from Gini and Poverty to test for the presence of an interaction between Gini and Poverty. State was included as afixed effect in all analyses to control for state-level differences in victim rates.
RESULTS
Table 1 provides descriptive statistics for the study variables and the bi-variate correlations among them. The correlations are all positive and highly significant. Wide variability in victim rate exists among states. Average state rates range from 0.2% to 3.1%, and the
tim rate is provided in the table, but LnVictim was used for all analyses.
A GAM was constructed to examine the relation between Gini and LnVictim, controlling for State, Poverty, and the control variables described above. As reported in Table 2, the linear param-eters for Gini and Poverty were both significantly different from 0, as were those for each of the control variables. Significant nonlinear components were also found for both variables. In Table 2, the linear regression parame-ter for Gini has been multiplied by 100 so that it appears on a similar scale to the other variables.
The estimated partial relationship be-tween Gini and LnVictim is shown in Fig 1. The linear component can be seen in the overall upward slope of the line; the nonlinear component results from a more strongly positive associ-ation between inequality and victim rate before the mean value of Gini (0.431) than after that value.
To test for the presence of an interaction between Gini and Poverty, a fully
TABLE 1 Descriptive Statistics and Bivariate Correlations for US County Victim Rate, Gini Coefficient, and Child Poverty
Variable Victim Rate, % Natural Log of Victim Rate
Gini Coefficient Child Poverty, % Victim rate, % Mean (SD): 1.21 (0.90)
Max: 11.0 Median: 1.05 Min: 0.01 Natural log of
victim rate
r= 0.83 Mean (SD):
20.11 (0.88) Max: 2.40 Median: 0.05 Min:24.50 Gini
coefficient
r= 0.17 r= 0.19 Mean (SD): 0.431 (0.036) Max: 0.621
Median: 0.429 Min: 0.272 Child
poverty, %
r= 0.25 r= 0.27 r= 0.49 Mean (SD): 21.0 (9.5) Max: 68.0
Median: 19.9 Min: 0.0
parametric approximation of the GAM was constructed. Linear and quadratic terms for Gini and Poverty and the in-teraction of Poverty with both Gini terms were included in addition to the control variables and State classifi ca-tion variable. The Gini*Poverty in-teraction was significant (P= .013). The Gini*Gini*Poverty interaction was not (P= .396). Figure 2 illustrates that the inequality effect was stronger at mod-erate and higher levels of Poverty, but was less positive at lower levels.
DISCUSSION
This study found that higher rates of income inequality across US counties are significantly associated with higher county-level rates of child maltreat-ment. Our findings contribute to the growing literature linking greater in-come inequality to a range of poor health and well-being outcomes in infants and children.7,8
Child maltreatment is a significant and common adversity in the lives
of children in the United States and worldwide. Child maltreatment is a toxic stressor25in the lives of children that may result in childhood mortality and morbidities and have lifelong effects on leading causes of death in adults.26This is in addition to long-term effects on mental health, substance use, risky sexual behavior, and criminal behavior.27 Child maltreatment is also associated with increased rates of unemployment, poverty, and Medicaid use in adult-hood,28 suggesting that economic
TABLE 2 Results of the Generalized Additive Model Regression
Variablea Linear Component Nonlinear Component
Parameter (95% confidence interval) P Loess Smoothing Parameter Degrees of Freedom x2 P
Gini coefficient (3100) 0.022 (0.014 to 0.029) ,.0001 0.779 1.35 12.55 .0007 Child poverty, % 0.010 (0.006 to 0.013) ,.0001 0.545 2.23 43.52 ,.0001 4+ years college, % 20.015 (20.018 to20.012) ,.0001
African American, % 20.009 (20.012 to20.007) ,.0001 Latino, % 20.006 (20.008 to20.003) ,.0001 Infant mortality (per 1000) 0.013 (0.007 to 0.020) ,.0001 Public assistance, % 0.021 (0.002 to 0.040) .0283
aState was also included in the model as a 50 level classification variable.
FIGURE 1
GAM of the relation between Gini and LnVictim. The line represents partial predictions of the natural log of the maltreatment victim rate from the linear and nonlinear components for income inequality expressed as a Gini coefficient3100. The shaded area is the 95% confidence interval. Values were adjusted for poverty, state, percent of the population who are African American, percent of the population who are Latino, percent of adults who have 41years of college, percent of households with public assistance income, and rates of infant mortality.
disadvantages associated with child maltreatment may persist across gen-erations.
Our study adds significant newfindings to the literature on the association of socioeconomic factors with the risk for child maltreatment. Some studies have shown that socioeconomic character-istics, such as family-level poverty,9 increase the risk for maltreatment. Other studies show that socioeconomic factors measured at a more aggregate ecological level are also associated with maltreatment.16,29 To our knowl-edge, our study is the first to specifi -cally examine income inequality as an important risk factor associated with child maltreatment.
Our study has several strengths. It includes a national census of mal-treatment victims rather than using local or regional data or estimates based on samples. The use of county-level data provided a large sample and thus more precise estimates, in
comparison with state data. With state as a fixed effect in our models, we controlled for state variations in child welfare policies and systems that could impact child maltreatment reporting and substantiation rates. Maltreatment data were also averaged across a 5-year period to provide more stable esti-mates similar to other studies that have examined the effect of income in-equality on child and adult health out-comes.30,31 Finally, we used GAMs to capture nonlinearities in the relationship between inequality and maltreatment.
Our results showed significant non-linear and non-linear components in the relation of inequality to child mal-treatment. The slope was steeper below the mean Gini value than for values above the mean. Previous studies of income inequality have generally used correlational or linear regression analyses, and thus have not reported nonlinear relationships. A study using 1990 data from all US counties, however,
showed that mortality rates for all causes, heart disease, and cancer in-creased substantially when counties with the least inequality (#25th per-centile) were compared with counties with higher levels of inequality, but the increase was much less when com-paring counties in the middle range of inequality (eg, 50th–75th percentile) with the counties with the highest lev-els of inequality (.75th percentile).31 This suggests a nonlinear inequality effect similar to what we report, but these authors did not discuss or pro-vide a formal test for such an effect. Future research should explore and rep-licate nonlinear effects more fully, and for child health outcomes specifically.
also moderated the effects of in-equality. The linear effect of inequality was positive at all levels of county-level child poverty, but was more positive for counties with moderate to high rates of child poverty. This serves to qualify the overall nonlinear effect of income in-equality on child maltreatment, with the attenuation of the inequality effect much more evident in counties with low child poverty levels. In counties with high child poverty, increases in income inequality resulted in higher victim rates throughout the range of values of the Gini index.
The interaction of income inequality and poverty in our results is consistent with research showing that inequality has a larger impact on health outcomes in poorer versus wealthier counties. In 1 study of all US counties, income in-equality interacted with county-level income for several causes of death for adults, with the inequality effect greater in poorer counties, although this pattern did not hold for infant mortality.33 Contrary to our findings, however, a state-level study reported stronger inequality effects on infant mortality, preterm births, and birth weights in states with lower versus higher child poverty rates, although a formal test of an interaction between inequality and poverty levels was not presented.8 Other studies conclude that income in-equality exerts negative effects on health outcomes regardless of the level of income or poverty. In 1 study of Na-tional Health Interview Survey adult respondents, an overall dose-like re-sponse relationship between income inequality on mortality risk was found for white men at all income levels above poverty.34 Another study of US states found a significant relation between income inequality and rates of infant mortality that did not vary by county-level median income.33Comparing such studies is difficult, because different measures of poverty at different levels
of analysis (state versus county) were used, and outcomes varied across adults and children. Given the mixed evidence in the literature, a replication of the moderating effect of income we report here is warranted.
We chose county as the level of ag-gregation for examining the impact of inequality, rather than a larger (eg, state) or smaller (eg, zip codes or census tracts) unit of analysis. Al-though income inequality is by defi ni-tion an ecological factor, studies may use a different geographic lens de-pending on the research question and outcome of interest. County is a mean-ingful unit of organization for health and human services that could affect the impact of income inequality on individuals and families. Multiple pro-grams including Temporary Aid to Needy Families, Supplemental Nutrition Assistance Program, Medicaid, and child welfare are often administered at the county level.20,35,36
At the same time, we controlled for state variations in maltreatment rates, recognizing that states vary in their regulations, administrative reporting systems, or funding for services that impact counties in that state. Although many studies of income inequality and health outcomes in the United States have used states as the unit of analysis, others30,31,37,38 have reported county-level effects of inequality on health outcomes. Some reviews6,37 conclude that inequality effects are generally larger at larger units of analysis (eg, nations, states) and become smaller when smaller units such as neighbor-hoods are used,39 where income is a more powerful predictor. Further research on the impact of inequality measured at different levels of geo-graphic aggregation is needed.
Our findings should be considered in light of certain limitations. First, our measure of maltreatment was drawn from administrative data collected by
states and reflects substantiated cases of abuse or neglect. It is well accepted that reports to child protective services systems are undercounts of the true incidence of child maltreatment.9,40We cannot be certain that the findings reported here would be the same for unreported cases of maltreatment. A recent study, however, has shown an effect of macroeconomic trends on hospital admissions for physical abuse,16 consistent with ourfindings. More re-search is needed to confirm our results by using alternative measures of mal-treatment and by examining specific subtypes of maltreatment (eg, neglect, physical abuse, sexual abuse, and emotional maltreatment). Second, we did not have access to individual-level income data to use as a control vari-able. A review of mostly US research from 1997 through 2003,6 however, showed that studies that used the strongest multilevel designs to adjust for individual income still showed sub-stantial evidence for worse health outcomes among adults in states with higher levels of inequality, although not all studies show this effect.41,42
CONCLUSIONS
More equal societies, states, and com-munities have fewer health and social problems than less equal ones.37Our study extends the list of unfavorable child outcomes associated with income inequality to include child abuse and neglect. Ourfindings show that the ef-fect of income inequality remained significant after adjustments were made for county-level variations in child poverty and for state variations in child maltreatment rates. The impact of income inequality was also greatest in counties with the highest child pov-erty rates. Future research should replicate this finding at other geo-graphic units of analysis in the United States and in other countries.
adult health outcomes has provoked a discussion of the role of the pediatric profession in promoting early child-hood policies and services that would reduce exposures of children to toxic stressors or mitigate their impact.25,43 Such policy and practice discussions have included a separate report on the role of the pediatrician in child maltreatment prevention.44 Included in this report are recommendations
programs), community-based pro-grams (eg, home visitation and parent training programs), as well as guid-ance for the pediatrician for ap-proaches to prevention within health supervision visits. Ourfindings, how-ever, suggest that a comprehensive approach will ultimately require ad-vocacy and action at the societal and community levels aimed at reducing income inequality.
Deborah Sellers for their comments on the paper. The NCANDS data were provided by the National Data Archive on Child Abuse and Neglect at Cornell University (NDACAN). The collector of the original data, the funding agency, NDACAN, Cornell University, and the agents or employees of these institu-tions bear no responsibility for the analyses or interpretations presented here.
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