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Drug overdose and child maltreatment across the United States’ rural-urban continuum
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Research Article
Drug overdose and child maltreatment across the United States’ rural-urban continuum ⁎
Rebecca Orsia, , Paula Yuma-Guerrerob, Kristen Sergic, Anita Alves Penad, Audrey M. Shillingtonb a
School of Social Work and School of Public Health, Colorado State University, Campus Delivery 1586, Fort Collins, CO, United States School of Social Work and School of Public Health, Colorado State University, Fort Collins, CO, United States c School of Public Health, Colorado State University, Fort Collins, CO, United States d Department of Economics, Colorado State University, Fort Collins, CO, United States b
A R T IC LE I N F O
ABS TRA CT
Keywords: Epidemiology Public health Substance abuse Opioid Rural communities
This national study of US counties (n = 2963) investigated whether county-level drug overdose mortality is associated with maltreatment report rates, and whether the relationship between overdose mortality and maltreatment reports is moderated by a county’s rural, non-metro or metro status. Data included county-level 2015 maltreatment reports from the National Child Abuse and Neglect Data System, modeled drug-overdose mortality from the Centers for Disease Control, United States Department of Agriculture Rural-Urban Continuum Codes, US Census demographic data and crime reports from the Federal Bureau of Investigation. All data were linked across counties. Zero-inflated negative binomial (ZINB) regression was used for countylevel analysis. As hypothesized, results from the ZINB model showed a significant and positive relationship between drug overdose mortality and child maltreatment report rates (χ = 101.26, p < .0001). This relationship was moderated by position on the rural-urban continuum (χ=8.76, p = .01). For metro counties, there was a 1.9% increase in maltreatment report rate for each additional increment of overdose deaths (IRR=1.019, CI=[1.010, 1.028]). For non-metro counties, the rate of increase was 1.8% higher than for metro counties (IRR=1.018, CI=[1.006, 1.030]); for rural counties, the rate of increase was 1.2% higher than for metro counties (IRR=1.012, CI=[0.999, 1.026]). Additional research is needed to determine why the relationship between drug overdose mortality and maltreatment reports is stronger in non-metro and rural communities. One potential driver requiring additional inquiry is that access to mental and physical health care and substance use treatment may be more limited outside of metropolitan counties.
1. Introduction In 2016, drug overdose accounted for 63,938 deaths in the United States (US), including 42,435 (66.3%) that involved an opioid (National Center for Health Statistics, 2018). The economic burden from prescription opioid misuse alone is estimated at over $78 billion per year (National Institute on Drug Abuse, 2018). Opioid use disorders have attained epidemic status in the US; in 2016 approximately 2.5 million people were diagnosed with an opioid use disorder (Lasser, 2017). Overdose deaths from any opioid
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Corresponding author. E-mail address: [email protected] (R. Orsi).
https://doi.org/10.1016/j.chiabu.2018.08.010 Received 29 May 2018; Received in revised form 14 August 2018; Accepted 17 August 2018 0145-2134/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Orsi, R., Child Abuse & Neglect (2018), https://doi.org/10.1016/j.chiabu.2018.08.010
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(prescription, synthetics and heroin, combined) have steadily climbed over the past 20 years, rising from 5 deaths per 100,000 population in 2000, to 12.3 in 2010, to 16.3 per 100,000 in 2016 (Mack, Jones, & Ballesteros, 2017). National Vital Statistics System data demonstrates unintentional drug overdose death has become a significant concern in America’s non-metropolitan areas (Mack et al., 2017). Non-metropolitan unintentional overdose death rates increased from 2.5 (per 100,000) in 1999 to 14.2 in 2015, a 468% increase, compared to metropolitan areas, which changed from 4.3 in 1999 to 13.8 in 2015, an increase of 221%. While staggering, this is less than half the increase seen in the non-metropolitan areas. For federal fiscal year 2015 approximately 4 million referrals for child abuse or neglect were made in the US (U.S. Department of Health & Human Services, Administration for Children & Families, Administration on Children Youth & Families, Children’s Bureau, 2017). Child maltreatment results in an estimated $124 billion in annual costs to society (due to fatalities, medical expenses, and behavioral and psychosocial problems over the life course) associated with new cases of maltreatment (Fang, Brown, Florence, & Mercy, 2012). Thus, both drug overdose deaths and child maltreatment continue to pose significant public health challenges in the United States. 1.1. Substance use disorders and child maltreatment Parental substance use disorder (SUD) has been linked to adverse health and well-being outcomes across the life course such as aggression, behavioral health conditions such as ADHD, teen pregnancy, unemployment and suicide attempts (Christoffersen & Soothill, 2003; Osborne & Berger, 2008). SUDs impact many aspects of the caregiving environment, and children of parents with an SUD are at-risk for poor developmental and social–emotional outcomes (Barnard & McKeganey, 2004). Parental SUDs within the family system are known to be important contributors to the risk of maltreatment and involvement of child protective services (Traube, 2012; Young, Boles, & Otero, 2007). Based on estimates from the National Survey on Drug Use and Health, more than 8 million children under age 18 lived in the past year with at least one parent who has a substance abuse problem (Lipari & VanHorn, 2017), accounting for over 12% of US children and making it a common situation in this country. 1.2. Ecological factors related to child maltreatment A growing literature examines aspects of child maltreatment at the ecological, neighborhood or community level (Ben-Arieh, 2015; Coulton, Crampton, Irwin, Spilsbury, & Korbin, 2007; Zielinski & Bradshaw, 2006) as opposed to understanding maltreatment solely in relation to individual or family characteristics. There exists evidence that a community’s economic situation is related to maltreatment rates including poverty (Drake & Pandey, 1996), income inequality (Eckenrode, Smith, McCarthy, & Dineen, 2014), changes to the minimum wage (Raissian & Bullinger, 2017) and even increases to regressive taxes (McLaughlin, 2018). Social and cultural factors related to social disorganization, such as alcohol access (Freisthler, 2004), racial diversity (Klein & Merritt, 2014) and a community’s level of conservativism (Breyer & MacPhee, 2015) have been shown to be related to maltreatment rates. There is some evidence for higher rates of maltreatment in rural areas (Sedlak et al., 2010) and fewer resources to respond to child maltreatment (Choo et al., 2010). The recent and rapid rise in drug overdose deaths in the US and the known relationship between parental substance use disorder and child maltreatment have motivated this national, ecological study exploring the intersection of maltreatment, overdose deaths and rurality. The aims are: (1) to examine whether county-level drug overdose mortality is associated with maltreatment report rates and (2) to examine whether the relationship between county overdose mortality and maltreatment is moderated by rurality of the county (i.e. metro, non-metro, rural). We hypothesize that drug overdose mortality rates will positively predict county maltreatment report rates and that this relationship will be moderated by county rurality (metro, non-metro or rural) with the effects being strongest in rural counties. 2. Methods This is a national ecological study of all available US counties (n = 2963) using primarily data from 2015. Counties were excluded if their maltreatment report count was so high as to be unreliable (two counties with rates in excess of 400 children per 1000) or if they were missing a measure required for the study (183 counties removed; spread geographically across 29 states). We estimated the relationship between drug overdose death rates (independent variable) and child maltreatment report rates (dependent variable). We controlled for community-level risk factors known to be associated with maltreatment (Palusci, Vandervort, & Lewis, 2016). Median family income and percent of owner-occupied units were included as measures of economic status; economic status at both the individual and community level has been shown to relate to maltreatment (Berger, 2005; Drake & Pandey, 1996). Previous community-level studies have controlled for race and ethnicity (Farrell et al., 2017); we did so by including percentage of the population that is non-Hispanic white in the model. We also included the percentage of the under age 18 population residing in a single parent household (Breyer & MacPhee, 2015; Weissman, Jogerst, & Dawson, 2003). Also, violent crime has been associated with some types of maltreatment at the community level (Coulton et al., 2007). Finally, we examined whether the relationship between county-level overdose mortality and maltreatment report rates is moderated by rurality, classified as metro, non-metro and rural. Drug overdose deaths have been documented to be higher in areas with more economic and family stress (Monnat, 2018a), so controlling for median income and single-parent status allowed us to analyze the unique contribution of overdose mortality to variation in maltreatment rates. 2
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2.1. Measures County was the unit of study. Although smaller geographic units would be desirable (e.g. census tract, block group), neither child maltreatment report rates, nor drug overdose death estimates were available at a finer geographic level. There is precedent in the literature for national, ecological studies of maltreatment-related outcomes using county-level data (Farrell et al., 2017; MaguireJack, Lanier, Johnson-Motoyama, Welch, & Dineen, 2015). Our measures were linked using county Federal Information Processing Standard (FIPS) codes from five primary data sources. All data were publicly available at the county level, except for maltreatment report counts. Restricted-use report counts were from the National Child Abuse and Neglect Data System (NCANDS) child file for federal fiscal year 2015 (U.S. Department of Health & Human Services, Administration for Children & Families, Administration on Children Youth & Families, Childrens Bureau, 2015). To preserve anonymity, data were aggregated by county by the National Data Archive for Child Abuse and Neglect and provided to the authors as counts of unique children by county. Reports are both substantiated and unsubstantiated; we chose reports as the measure of maltreatment because substantiated reports are affected by a number of measurement challenges (Fluke, 2009; Gillingham, 2016). Report counts were divided by US Census Bureau American Community Survey (ACS) 2015 population estimates for those aged 0–17 for each county (U.S. Census Bureau, 2015a) and multiplied by 1000 to calculate rates. Report rates include both abuse and neglect. Drug overdose death rates were model-based, county-level estimates for drug-poisoning mortality, available from the Centers for Disease Control (CDC, Centers for Disease Control and Prevention, 2018). These estimates of overdose deaths measured serious SUDs by county, as many measures of SUD – for example, the National Survey on Drug Use and Health – are only available by state. Drug overdose death rates were age-adjusted, per 100,000 population for 2015. The CDC provides these data summarized into 16 ordinal ranges, from 0 to 2 per 100,000 through the highest ranges of 28.1–30 per 100,000 and 30+ per 100,000. Drug overdose death rate was selected as the primary independent variable because it is the best available estimate of serious substance use disorders available by county; these rates encompass deaths due to overdose of any drug. We accessed key covariate measures via the 2017 Robert Wood Johnson Foundation County Health Rankings & Roadmaps (CHRR). Median household income in the CHRR data originated from 2015 US Census Bureau Small Area Income and Poverty Estimates (U.S. Census Bureau, 2015c). Percentage of non-Hispanic white county population in the CHRR came from the Census Bureau's 2015 Population and Housing Estimates Program (U.S. Census Bureau, 2015b). CHRR percentage of children/youth under 18 living in single-parent households was estimated with ACS 5-year data from 2011 to 2015 (U.S. Census Bureau, 2015a). County violent crime rate was the number of reported violent crime offenses per 100,000 population and originated with the Federal Bureau of Investigation’s Uniform Crime Reporting Program for 2012–2014; the most recent estimates available for this study (Federal Bureau of Investigtion, 2014). Finally, percentage of owner-occupied housing was from the 2015 ACS 5-year estimates (2011–2015), and was the only covariate measure not obtained through the CHRR (U.S. Census Bureau, 2015a). For the purposes of this study, counties are classified as metro, non-metro or rural based on 2013 US Department of Agriculture’s Rural-Urban Continuum codes (RUCC; U.S. Department of Agriculture, 2013). We categorized these codes into the 3 groups. The metro group contains RUCCs of 1, 2 or 3; these are counties in metro areas of 1 million population or more, in metro areas of 250,000–1 million population and in metro areas of fewer than 250,000 (based on 2010 census estimates). The non-metro group contains RUCC codes 4 through 7; these are counties with an “urban” (i.e. densely-settled) population of 20,000 or more, either adjacent to or not adjacent to a metro area and those with an urban population of 2500–19,999, either adjacent to or not adjacent to a metro area. Finally, the rural group is comprised of RUCC codes 8 and 9; these counties have less than 2500 urban population, either adjacent or not adjacent to a metro area. To summarize: metro counties are central and have one or more large, urbanized areas; they include counties that are economically tied to a central, metro county via labor-force commuting. Non-metro counties may contain smaller urbanized areas. Rural counties are sparsely populated. Data sources are summarized in Table 1. 2.2. Analysis The dependent variable was maltreatment report rate per 1000 population under age 18. First, bivariate Pearson correlations were run for the variables included in the model. For multivariate modeling, a negative binomial regression model was appropriate for these data because the variance exceeded the mean (mean = 52.2 reported children per 1000 population under 18; variance = 958.3). In a national study, we necessarily included counties with very small populations and consequently, no child maltreatment reports for fiscal year 2015. About 3.8% of available counties nationally had no reports of child maltreatment for 2015. Therefore, we selected a zero-inflated negative binomial (ZINB) model for rate per 1000 population. This model assumes zero report counts result from either “sampling” – i.e. no children were reported in 2015 – or “structural” reasons – i.e. the county has a very small under-18 population, so it was unlikely to record any maltreatment reports. The primary independent variable – drug overdose death rate – was modeled as an ordinal variable, with a one unit increase representing a difference between two ranges of overdose death rates. For example, an increase from 0 to 2 deaths per 100,000 county population to 2.1–4 deaths per 100,000 population is equivalent to a one-unit increase in the variable. All applicable ranges for overdose death rate are shown in Table 2 below. Covariates included median income, children in single parent homes, non-Hispanic white population, owner occupied housing and the county violent crime rate. The single parent home covariate was modeled as a percentage of children in the county; the nonHispanic white covariate as a percentage of total county population. Owner-occupied housing was modeled as a percentage of housing units in the county. Median household income was an annual estimate, coded in 1000s of dollars. The violent crime rate was coded as offenses per 100,000 county population. 3
4 Covariate
Percentage of under age 18 county population who live in single-parent households Count of reported violent crime offenses per 100,000 population Percentage of county housing units which are owner-occupied Grouping of 9 values of rural-urban continuum codes into: metro, non-metro and rural
Covariate
Percentage of county population that is non-Hispanic white
US Census Bureau Population and Housing Estimates Program US Census Bureau American Community Survey 5-year Estimates Federal Bureau of Investigation Uniform Crime Reporting Program US Census Bureau American Community Survey 5-year Estimates Rural-Urban Continuum codes Moderator
Covariate
Covariate
Primary independent variable Covariate
Dependent variable
Counts of unique children reported annually for abuse or neglect Modeled, annual, age-adjusted overdose death rates due to drug-poisoning Median annual household income
National Child Abuse and Neglect Data System (NCANDS) Child File National Center for Health Statistics – Drug Poisoning Mortality by County US Census Bureau Small Area Income and Poverty Estimates
Variable type
Variable description
Dataset
Table 1 Summary of data sources.
US Department of Agriculture
National Data Archive for Child Abuse and Neglect Centers for Disease Control and Prevention Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings US Census Bureau
Source
2013
2011–2015
2012–2014
2011–2015
2015
2015
2015
Fiscal year 2015
Year
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Table 2 Maltreatment rates, demographic data, overdose death rates and county type; all available US counties (n = 2963). Combined
% counties Maltreatment rate per 1000 Median annual income (dollars) in 1000s % of non-Hispanic white county population 2015 % children in single-parent households % owner-occupied housing units Violent crime rate per 100,000 Drug overdose death rate per 100,000 0–2 2.1–4 4.1–6 6.1–8 8.1–10 10.1–12 12.1–14 14.1–16 16.1–18 18.1–20 20.1–22 22.1–24 24.1–26 26.1–28 28.1–30 > 30
Metro (38.3%) Mean (SD)
% counties
52.2 (31.0) 48.9 (12.4) 77.0 (19.4) 32.5 (10.0) 71.4 (8.0) 245.6 (190.3) 0.7 1.3 2.3 6.6 11.0 11.8 13.0 11.5 10.0 7.7 7.2 4.0 3.6 2.2 2.0 4.9
– – – – – – – – – – – – – – – –
Mean (SD)
48.8 55.2 73.8 32.6 69.6
Non-metro (43.0%)
Rural (18.7%)
% counties
% counties
(27.9) (14.0) (18.7) (9.0) (9.5)
57.6 45.0 77.4 33.8 71.2
288.5 (209.2) 0.2 0.2 1.3 6.6 10.8 12.7 13.5 12.7 11.4 7.4 8.2 4.1 3.3 2.3 1.9 3.6
– – – – – – – – – – – – – – – –
Mean (SD)
(30.7) (9.1) (20.0) (9.4) (6.3)
46.9 44.8 82.8 29.6 75.4
249.0 (170.0) 0.0 0.2 2.0 6.9 12.6 12.7 13.7 11.5 9.7 7.4 6.8 4.2 4.0 1.7 2.0 4.4
Mean (SD)
(35.3) (9.6) (18.2) (12.3) (6.7)
149.7 (157.2) 3.6 6.2 5.1 6.0 7.8 8.0 10.3 9.2 7.6 9.2 6.2 3.4 3.6 3.3 2.2 8.5
– – – – – – – – – – – – – – – –
Note: Maltreatment report rates by county type (metro, non-metro and rural) are here shown as descriptive statistics, without controlling for covariates.
Finally, the rural-urban moderator variable was modeled as categorical. The moderation hypothesis was tested by including an interaction term in the ZINB model between the three-category rural-urban moderator and the overdose death rate. The comparison category for the moderation results was the metro county classification. A diagram of the model is shown in Fig. 1. The model was fit in SAS/STAT software using PROC GENMOD. 3. Results As shown in Table 2, the mean county maltreatment report rate was 52.2 per 1000 children. Thirty-eight percent of counties were considered metro, 43% non-metro and 19% rural. Less than 1% of counties experienced virtually no overdose deaths (between 0 and 2 per 100,000). The mode of the distribution (13% of counties) was 12–14 deaths per 100,000. Almost 5% of counties experienced more than 30 overdose deaths per 100,000 annually. Bivariate correlations are shown in Table 3 and demonstrate that maltreatment report rates were significantly and positively correlated with percentage of white population (r = 0.08, p < .0001), with percentage of children in single parent households (r = 0.23, p < .0001) and with rate of violent crimes (r = 0.17, p < .0001). Maltreatment report rates were significantly and
Fig. 1. Zero-inflated binomial model diagram. 5
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Table 3 Bivariate correlations for study variables (n = 2963). Correlations
Rural category p-value Drug mortality p-value % White p-value % Single parent p-value Median income p-value % Owner-occupied p-value Violent crime rate p-value
Rural category
Drug mortality
% White
% Single parent
Median income
% Owner occupied
Violent crime rate
Maltreat. report rate
1.000
−0.016 0.39 1.000
0.162 < .0001 0.106 < .0001 1.000
−0.076 < .0001 0.138 < .0001 −0.527 < .0001 1.000
−0.351 < .0001 −0.267 < .0001 0.106 < .0001 −0.497 < .0001 1.000
0.242 < .0001 0.020 0.28 0.477 < .0001 −0.369 < .0001 0.084 < .0001 1.000
−0.248 < .0001 0.118 < .0001 −0.475 < .0001 0.488 < .0001 −0.179 < .0001 −0.432 < .0001 1.000
0.015 0.42 0.339 < .0001 0.082 < .0001 0.232 < .0001 −0.402 < .0001 −0.028 0.13 0.172 < .0001
negatively correlated with median income (r=−0.40, p < .0001) and were not correlated with percentage of owner-occupied units. Drug overdose mortality was significantly and positively correlated with maltreatment report rates (r = 0.34, p < .0001). The threecategory rural classification for counties was not significantly correlated with maltreatment reports. As hypothesized, results from the ZINB model showed a significant and positive relationship between drug overdose mortality and child maltreatment report rates (χ = 101.26, p < .0001). Modeled results controlled for percentage of white population in the county, percentage of children in single family homes, median income, violent crime rate per 100,000 and percentage owner occupied units. As shown by incident rate ratios (IRRs) and associated confidence intervals (Hilbe, 2011), there was a 2% drop in maltreatment report rates for each additional $1000 in county median income (IRR = .982, CI=[0.980, 0.984]). There was a .7% increase in maltreatment report rate for each additional percentage of the population that was white (IRR = 1.007, CI=[1.006, 1.008]) and for each additional percentage of population under 18 in a single parent household (IRR = 1.007, CI=[1.005, 1.010]). Although these effect sizes appear small (rounding to IRR = 1.0), they are associated with small increases in each of the control variables. For example, a 5-point increase in the percentage of children in single-parent homes would be associated with a 3.5% larger maltreatment report rate for a county (1.0075 = 1.035). Thus, control variables were important to include, even with small effect sizes. The percentage of owner occupied units was not significantly related to report rates. Finally, although the violent crime rate positively predicted maltreatment (p < .0001), the effect of an additional violent crime per 100,000 was too small to detect, with the IRR rounding to 1.000. The ZINB model parameter for inflated zero counts was statistically significant (χ = 29.13, p < .0001), indicating the appropriateness of the ZINB model for these data. Furthermore, we compared Akaike’s Information Criterion for a standard negative binomial model (AIC=28026) and the ZINB model (AIC=26715); the AIC value is lower for the ZINB model, indicating better fit. Also as hypothesized, the significant relationship between drug overdose deaths and maltreatment report rates was moderated by position on the rural-urban continuum (χ = 8.76, p = .01). For average rates of overdose death and in comparison to metro counties, rates of maltreatment reports were lower for non-metro (IRR=0.854, CI=[0.776, 0.942]) and rural counties (IRR=0.805, CI= [0.719, 0.901]). However, increases in maltreatment report rates associated with an increase in drug overdose mortality were greater in non-metro and rural counties than in metro counties. For metro counties, there was a 1.9% increase in maltreatment report rate for each additional increment of overdose deaths (IRR = 1.019, CI=[1.010, 1.028]). That is, as the ordered category of overdose deaths increased from 0 to 2 per 100,000 to 2.1–4 per 100,000, the rate of maltreatment reports increased on average by 1.9%. For nonmetro counties, the rate of increase in maltreatment report rates for a unit increase in drug overdose deaths was 1.8% higher than for metro counties (IRR = 1.018, CI=[1.006, 1.030]); for rural counties, the rate of increase was 1.2% higher than for metro counties (IRR = 1.012, CI=[0.999, 1.026]). Multiplying together increases for the metro county comparison category with the additional increase for non-metro counties yields an average 3.7% increase in maltreatment reports per 1000 children for each incremental overdose increase. For rural counties, maltreatment report rate increased 3.1% for each incremental overdose increase. Modeled mean maltreatment report rates for each of the three county types and the varying rates of change with increasing overdose rates are shown in Fig. 2. The mean values of each covariate were as follows: median income=$49,000, percentage white = 77%, percentage children in single parent households = 33%, owner-occupied units = 71% and violent crime rate = 246/ 100,000. For metro counties, the mean rates of report per 1000 children ranged from a low of 46.6 (at the lowest rate of overdose deaths) to a high of 61.7. Mean rates varied from 39.9 to 68.8 for non-metro counties and from 37.6 to 59.8 for rural counties. Although among counties with low rates of overdose death, the rural counties displayed lower rates of maltreatment reports, among counties with high rates of mortality, rates of maltreatment reporting are similar, due to the higher rates of change for these county groups.
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Fig. 2. Modeled mean maltreatment reports and overdose deaths for all available US counties (n=2963), controlling for median income, percent non-Hispanic white population, percent children in single parent households, percent owner occupied housing and violent crime rate.
4. Discussion Children living in non-metro or rural counties with relatively lower drug overdose death rates experience lower rates of child maltreatment reports than their peers in metro counties, when controlling appropriately for ecological covariates of maltreatment. This is illustrated by the results on the left side of Fig. 2. However, the highest overdose death rates (over 30 per 100,000) are associated with maltreatment report rates in rural counties that are essentially equivalent to metro counties (59.8 vs 61.7 per 1000), and with even higher maltreatment report rates in non-metro areas (68.8 per 1000). This is illustrated by the results on the right side of Fig. 2. At the bivariate level, there is no correlation between metro/non-metro/rural status and maltreatment report rate (r = 0.02, p = .42; see Table 3). The complex interplay of drug overdose deaths, maltreatment and rural-urban status emerges only after modeling these effects and controlling for factors which are associated with county rates of reported maltreatment. There remains a need to explain why the relationship between drug overdose death rates and maltreatment report rates is substantially stronger in non-metro and rural communities. One possible explanation is that mental and physical health care in general, and substance use treatment specifically, are more difficult to access in non-metro and rural counties compared to metropolitan counties. 4.1. Limitations The results of this study should be considered in light of its limitations. The data for this study were secondary data at the countylevel, and the study design was cross-sectional. These data are limited both in terms of what is available and how it can be utilized. For example, optimally, we would use CDC modeled overdose death rates for adults only, as adults are the vast majority of maltreatment perpetrators. However, modeled rates are available only for the entire population. Also, the data vintages do not align with one year; some available measures (e.g. children in single-parent households) are based on multiple years. We used 2015 as the reference year for data sources and included it for multi-year measures. However, USDA rural-urban continuum codes were available only through 2013; data for violent crime were only available through 2014. Cross-sectional ecological studies, such as the current study, are inherently limited in meeting the criteria for establishing a causal relationship between independent and dependent variables, as the data cannot demonstrate that the independent variables precede the dependent variables in time. Secondly, there are community characteristics which correlate with maltreatment reports but are not available in national, county-level data, for example, differing rules on mandated maltreatment reporting or the number of mandated reporters by county. Issues around mandated reporting likely influence maltreatment report rates, in addition to reports being affected by the actual incidence of maltreatment. Financial resources for child protective services could be another relevant factor; these might influence the existence of a hotline for resident reporting of maltreatment and/or public health campaigns to educate residents about the importance of reporting suspected maltreatment. Third, some authors have used spatial methods for county-level analyses (Maguire-Jack et al., 2015), however, this was not accounted for in the present study. Given the choice of using statistical methods which could handle the inflated zero-report counts or the spatial aspect of the data, we chose to use the ZINB model, as described above. Finally, study of smaller geographic areas, such as census tracts or block groups, could be more precise for defining communities, rather than county (Freisthler, 2004; Krieger, Chen, Waterman, Rehkopf, & Subramanian, 2005). However, county level is the smallest unit of geography available in the NCANDS dataset (Maguire-Jack et al., 2015). Finer geographic breakouts could be achieved with a state-level study, using de-identified administrative data from a single state. Multiple years of maltreatment reports could be aggregated to protect child identities in small area analyses. 7
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4.2. Access to treatment for substance use disorders (SUDs) While effective approaches to SUDs exist, even for highly addictive opioids, there continues to be a large treatment gap for SUDs in the US (Chuang, Wells, Bellettiere, & Cross, 2013; Pullen & Oser, 2014). According to the 2015 National Survey on Drug Use and Health, an estimated 21.7 million Americans (8.1%) needed treatment for a problem related to drugs or alcohol, but only about 2.3 million people (10.8% of those in need) received SUD treatment (Lipari, Park-Lee, & Van Horn, 2016). Barriers to care for SUDs exist across social-ecological levels and are disproportionately present in rural America. At the individual level, barriers include stigma associated with substance use treatment and mental health care, and mistrust of institutions providing treatment, particularly among persons who have experienced institutionalization and/or who have co-occurring mental health disorders (Priester et al., 2016). Cost is also a common barrier to treatment, particularly among those without insurance (Fisher, Reynolds, D’Anna, Hosmer, & Hardan-Khalil, 2017; Pullen & Oser, 2014). Personal characteristics such as the inability to follow through with appointments or becoming incarcerated before treatment are also cited as barriers by substance-using individuals (Fisher et al., 2017). Women often face additional barriers as they attempt to access mental health and SUD treatment, such as locating and paying for childcare (Marsh, Smith, & Bruni, 2011). Women on welfare may not seek treatment, out of fear of losing benefits (Johnson et al., 2015). Pregnancy and/or the responsibility of caring for children also affect women’s desire to seek substance and mental health services (Marsh et al., 2011; Ryan, Choi, Hong, Hernandez, & Larrison, 2008). Community and structural barriers to treatment also impede individuals with SUDs from accessing needed care. The same nonmetropolitan, rural and resource-poor communities that are experiencing rapidly rising drug overdose rates also face greater challenges in accessing treatment than more urban and financially sound communities (Fisher et al., 2017; Pullen & Oser, 2014). Access to care is hampered by geographic distance and lack of transportation (Fisher et al., 2017; Marsh et al., 2011; Pullen & Oser, 2014). The current SUD workforce in the US is undersized and inadequately resourced, and the available providers often lack the skills and experience to offer the effective evidence-based, integrated care necessary for successful SUD treatment (Chuang et al., 2013; Creedon & Lê Cook, 2016; Fisher et al., 2017; Lasser, 2017; Pullen & Oser, 2014). Racial, ethnic, and geographic diversity of the workforce is lacking, and there is a dearth of behavioral health professionals serving people in rural and impoverished areas (Chuang et al., 2013; Fisher et al., 2017; Pullen & Oser, 2014). By addressing barriers to treatment, increasing availability of effective evidence-based SUD treatment, and providing harm reduction interventions, rates of SUD could decrease in the US, with a possible associated decrease in child maltreatment reports (Fisher et al., 2017; Morton, Simmel, & Peterson, 2014). In a study of 163 census tracts in New Jersey, the presence of SUD treatment services in a community was associated with a lower child maltreatment report rate in neighborhoods (Morton et al., 2014). In an Illinois demonstration study of over 800 families, completion of substance use treatment had a positive influence on family reunification (Choi, Huang, & Ryan, 2012). In addition to access to care, there are likely multiple contributing factors to the differing relationships between overdose deaths and child maltreatment reports on the rural-urban continuum. In the current study, the highest rates of reported maltreatment associated with high overdose mortality did not occur in the most rural communities, as we would anticipate if access to care were the only mediating factor. Rather, the rates of reported maltreatment were highest for high overdose mortality in the middle, nonmetro category. More rural counties typically have lower levels of education and lower median household incomes (see Table 2), both conditions are associated with higher rates of maltreatment. Literature suggests that SUD may also be related to lower educational attainment (Chatterji, 2006) and lowered workforce participation (MacDonald & Pudney, 2000). Differences by type of community in the relationship between overdose deaths and maltreatment reports may indeed lie in the interaction of economic and educational factors with substance use and maltreatment. Finally, the extensive review by Coulton et al. (2007) identified concerns with measurement of maltreatment that may be of particular relevance to more rural areas; variation in reports of child maltreatment may have more to with a community’s ability and willingness to define, recognize and report maltreatment than with actual parental behaviors. This may also impact relationships with substance use. 5. Conclusion In a comparison across metro, non-metro, and rural counties, the relationship between drug overdose death rates and child maltreatment reporting rates was strongest in the middle category, non-metro. The most rural counties also have a substantially stronger relationship between the rates of drug overdose death and maltreatment compared to urban counties. The results of this study support future research exploring why this is the case. Social work and public health researchers should consider study of the protective effects against maltreatment of parents’ access to primary health care – for detection and treatment of problematic substance use – and their access to SUD treatment and behavioral healthcare, as such access is likely protective against child maltreatment. Studies of healthcare supply and its relationship with drug overdose rates at the county level have not yet detected differences in mortality rates; this may be due to inability to account for supply of and access to mental health and substance use treatment providers (Monnat, 2018b). Thus, the possible mediating effect of healthcare on the relationship between drug overdose mortality and child maltreatment remains unclear. Behavioral health and SUD treatment services remain an important component of overall services coordinated through a child protective services (CPS) agency. In addition, broader availability of services within a community may contribute to primary prevention of maltreatment, preventing the necessity of CPS involvement. There remains a need for professionals in both public health and social work to better understand and interrupt the relationship between SUDs and child maltreatment; access to healthcare may be an important component of these relationships. Studies at smaller areas of analysis, studies incorporating spatial analysis, and 8
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studies using additional methods, such as qualitative methods and geographic information systems (GIS), are warranted to elucidate the protective aspects of healthcare, as well as to generate ideas to maximize community-level prevention strategies, especially in non-metropolitan and rural communities. Funding source This work was supported by the Colorado School of Public Health. Declaration of competing interests The authors state that they have no potential conflicts of interest with respect to the research, authorship, and/or publication of this manuscript. Acknowledgements The authors acknowledge Mr. Michael Dineen at the National Data Archive for Child Abuse and Neglect for summarizing NCANDS data by county and Mr. Luke McConnell for managing and linking multiple data sources for the study. The authors acknowledge Ms. Tricia Howley for her assistance formatting figures. References Barnard, M., & McKeganey, N. (2004). 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Choi, S., Huang, H., & Ryan, J. P. (2012). Substance abuse treatment completion in child welfare: Does substance abuse treatment completion matter in the decision to reunify families? Children and Youth Services Review, 34(9), 1639–1645. https://doi.org/10.1016/j.childyouth.2012.04.022. Choo, E. K., Spiro, D. M., Lowe, R. A., Newgard, C. D., Hall, M. K., & McConnell, K. J. (2010). Rural-urban disparities in child abuse management resources in the emergency department. The Journal of Rural Health: Official Journal of the American Rural Health Association and the National Rural Health Care Association, 26(4), 361–365. https://doi.org/10.1111/j.1748-0361.2010.00307.x. Christoffersen, M., & Soothill, K. (2003). The long-term consequences of parental alcohol abuse: A cohort study of children in Denmark. Journal of Substance Abuse Treatment, 25, 107–116. Chuang, E., Wells, R., Bellettiere, J., & Cross, T. (2013). 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A spatial analysis of social disorganization, alcohol access, and rates of child maltreatment in neighborhoods. Children and Youth Services Review, 26(9), 803–819. https://doi.org/10.1016/j.childyouth.2004.02.022. Gillingham, P. (2016). Predictive risk modelling to prevent child maltreatment and other adverse outcomes for service users: Inside the “Black Box” of machine learning. British Journal of Social Work, 46(4), 1044–1058. https://doi.org/10.1093/bjsw/bcv031. Hilbe, J. M. (2011). Negative binomial regression (2nd ed.). New York, NY: Cambridge University Press. Johnson, J., Schonbrun, Y., Peabody, M., Shefner, R., Fernandes, K., Rosen, R., et al. (2015). Provider experiences with prison care and aftercare for women with cooccurring mental health and substance use disorders: Treatment, resource, and systems integration challenges. The Journal of Behavioral Health Services & Research, 42(4), 417–436. https://doi.org/10.1007/s11414-014-9397-8. Klein, S., & Merritt, D. H. (2014). Neighborhood racial & ethnic diversity as a predictor of child welfare system involvement. Children and Youth Services Review, 41, 95–105. Krieger, N., Chen, J. T., Waterman, P. D., Rehkopf, D. H., & Subramanian, S. V. (2005). Painting a truer picture of US socioeconomic and racial/ethnic health inequalities: The public health disparities geocoding project. American Journal of Public Health, 95(2), 312–323. Lasser, K. E. (2017). Prescription opioid use among US adults: Our brave new world. Annals of Internal Medicine, 167(5), 351–352. https://doi.org/10.7326/M17-1559. Lipari, R. N., & VanHorn, S. (2017). from Children living with parents who have a substance use disorder. https://www.samhsa.gov/data/sites/default/files/report_3223/ ShortReport-3223.html. Lipari, R. N., Park-Lee, E., & Van Horn, S. (2016). from America’s need for and receipt of substance use treatment in 2015. https://www.samhsa.gov/data/sites/default/ files/report_2716/ShortReport-2716.html. 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Factors associated with county-level differences in US drug-related mortality rates. American Journal of Preventive Medicine, 54(5), 611–619. https://doi.org/10.1016/j.amepre.2018.01.040. Morton, C. M., Simmel, C., & Peterson, N. A. (2014). Neighborhood alcohol outlet density and rates of child abuse and neglect: Moderating effects of access to substance abuse services. Child Abuse & Neglect, 38(5), 952–961. https://doi.org/10.1016/j.chiabu.2014.01.002. National Center for Health Statistics (2018). Retrieved April 16, 2018, from Provisional drug overdose death counts. https://www.cdc.gov/nchs/nvss/vsrr/drugoverdose-data.htm. National Institute on Drug Abuse (2018). Retrieved April 16, 2018, from Opioid overdose crisis. https://www.drugabuse.gov/drugs-abuse/opioids/opioid-overdosecrisis. Osborne, C., & Berger, L. M. (2008). Parental substance abuse and child well-being: A consideration of parents’ gender and coresidence. 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Children and Youth Services Review, 72, 60–70. https://doi.org/10.1016/j.childyouth.2016.09.033. Ryan, J. P., Choi, S., Hong, J. S., Hernandez, P., & Larrison, C. R. (2008). Recovery coaches and substance exposed births: An experiment in child welfare. Child Abuse & Neglect: The International Journal, 32(11), 1072–1079. https://doi.org/10.1016/j.chiabu.2007.12.011. Sedlak, A. J., Mettenburg, J., Basena, M., Peta, I., McPherson, K., & Greene, A. (2010). Fourth national incidence study of child abuse and neglect (NIS-4). Washington, DC: US Department of Health and Human Services Retrieved on July, 9, 2010. Traube, D. E. (2012). The missing link to child safety, permanency, and well-being: Addressing substance misuse in child welfare. Social Work Research, 36(2), 83–87. https://doi.org/10.1093/swr/svs043. U.S. Census Bureau (2015a). from American community survey. https://www.census.gov/programs-surveys/acs/. 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Research Article
Drug overdose and child maltreatment across the United States’ rural-urban continuum ⁎
Rebecca Orsia, , Paula Yuma-Guerrerob, Kristen Sergic, Anita Alves Penad, Audrey M. Shillingtonb a
School of Social Work and School of Public Health, Colorado State University, Campus Delivery 1586, Fort Collins, CO, United States School of Social Work and School of Public Health, Colorado State University, Fort Collins, CO, United States c School of Public Health, Colorado State University, Fort Collins, CO, United States d Department of Economics, Colorado State University, Fort Collins, CO, United States b
A R T IC LE I N F O
ABS TRA CT
Keywords: Epidemiology Public health Substance abuse Opioid Rural communities
This national study of US counties (n = 2963) investigated whether county-level drug overdose mortality is associated with maltreatment report rates, and whether the relationship between overdose mortality and maltreatment reports is moderated by a county’s rural, non-metro or metro status. Data included county-level 2015 maltreatment reports from the National Child Abuse and Neglect Data System, modeled drug-overdose mortality from the Centers for Disease Control, United States Department of Agriculture Rural-Urban Continuum Codes, US Census demographic data and crime reports from the Federal Bureau of Investigation. All data were linked across counties. Zero-inflated negative binomial (ZINB) regression was used for countylevel analysis. As hypothesized, results from the ZINB model showed a significant and positive relationship between drug overdose mortality and child maltreatment report rates (χ = 101.26, p < .0001). This relationship was moderated by position on the rural-urban continuum (χ=8.76, p = .01). For metro counties, there was a 1.9% increase in maltreatment report rate for each additional increment of overdose deaths (IRR=1.019, CI=[1.010, 1.028]). For non-metro counties, the rate of increase was 1.8% higher than for metro counties (IRR=1.018, CI=[1.006, 1.030]); for rural counties, the rate of increase was 1.2% higher than for metro counties (IRR=1.012, CI=[0.999, 1.026]). Additional research is needed to determine why the relationship between drug overdose mortality and maltreatment reports is stronger in non-metro and rural communities. One potential driver requiring additional inquiry is that access to mental and physical health care and substance use treatment may be more limited outside of metropolitan counties.
1. Introduction In 2016, drug overdose accounted for 63,938 deaths in the United States (US), including 42,435 (66.3%) that involved an opioid (National Center for Health Statistics, 2018). The economic burden from prescription opioid misuse alone is estimated at over $78 billion per year (National Institute on Drug Abuse, 2018). Opioid use disorders have attained epidemic status in the US; in 2016 approximately 2.5 million people were diagnosed with an opioid use disorder (Lasser, 2017). Overdose deaths from any opioid
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Corresponding author. E-mail address: [email protected] (R. Orsi).
https://doi.org/10.1016/j.chiabu.2018.08.010 Received 29 May 2018; Received in revised form 14 August 2018; Accepted 17 August 2018 0145-2134/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Orsi, R., Child Abuse & Neglect (2018), https://doi.org/10.1016/j.chiabu.2018.08.010
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(prescription, synthetics and heroin, combined) have steadily climbed over the past 20 years, rising from 5 deaths per 100,000 population in 2000, to 12.3 in 2010, to 16.3 per 100,000 in 2016 (Mack, Jones, & Ballesteros, 2017). National Vital Statistics System data demonstrates unintentional drug overdose death has become a significant concern in America’s non-metropolitan areas (Mack et al., 2017). Non-metropolitan unintentional overdose death rates increased from 2.5 (per 100,000) in 1999 to 14.2 in 2015, a 468% increase, compared to metropolitan areas, which changed from 4.3 in 1999 to 13.8 in 2015, an increase of 221%. While staggering, this is less than half the increase seen in the non-metropolitan areas. For federal fiscal year 2015 approximately 4 million referrals for child abuse or neglect were made in the US (U.S. Department of Health & Human Services, Administration for Children & Families, Administration on Children Youth & Families, Children’s Bureau, 2017). Child maltreatment results in an estimated $124 billion in annual costs to society (due to fatalities, medical expenses, and behavioral and psychosocial problems over the life course) associated with new cases of maltreatment (Fang, Brown, Florence, & Mercy, 2012). Thus, both drug overdose deaths and child maltreatment continue to pose significant public health challenges in the United States. 1.1. Substance use disorders and child maltreatment Parental substance use disorder (SUD) has been linked to adverse health and well-being outcomes across the life course such as aggression, behavioral health conditions such as ADHD, teen pregnancy, unemployment and suicide attempts (Christoffersen & Soothill, 2003; Osborne & Berger, 2008). SUDs impact many aspects of the caregiving environment, and children of parents with an SUD are at-risk for poor developmental and social–emotional outcomes (Barnard & McKeganey, 2004). Parental SUDs within the family system are known to be important contributors to the risk of maltreatment and involvement of child protective services (Traube, 2012; Young, Boles, & Otero, 2007). Based on estimates from the National Survey on Drug Use and Health, more than 8 million children under age 18 lived in the past year with at least one parent who has a substance abuse problem (Lipari & VanHorn, 2017), accounting for over 12% of US children and making it a common situation in this country. 1.2. Ecological factors related to child maltreatment A growing literature examines aspects of child maltreatment at the ecological, neighborhood or community level (Ben-Arieh, 2015; Coulton, Crampton, Irwin, Spilsbury, & Korbin, 2007; Zielinski & Bradshaw, 2006) as opposed to understanding maltreatment solely in relation to individual or family characteristics. There exists evidence that a community’s economic situation is related to maltreatment rates including poverty (Drake & Pandey, 1996), income inequality (Eckenrode, Smith, McCarthy, & Dineen, 2014), changes to the minimum wage (Raissian & Bullinger, 2017) and even increases to regressive taxes (McLaughlin, 2018). Social and cultural factors related to social disorganization, such as alcohol access (Freisthler, 2004), racial diversity (Klein & Merritt, 2014) and a community’s level of conservativism (Breyer & MacPhee, 2015) have been shown to be related to maltreatment rates. There is some evidence for higher rates of maltreatment in rural areas (Sedlak et al., 2010) and fewer resources to respond to child maltreatment (Choo et al., 2010). The recent and rapid rise in drug overdose deaths in the US and the known relationship between parental substance use disorder and child maltreatment have motivated this national, ecological study exploring the intersection of maltreatment, overdose deaths and rurality. The aims are: (1) to examine whether county-level drug overdose mortality is associated with maltreatment report rates and (2) to examine whether the relationship between county overdose mortality and maltreatment is moderated by rurality of the county (i.e. metro, non-metro, rural). We hypothesize that drug overdose mortality rates will positively predict county maltreatment report rates and that this relationship will be moderated by county rurality (metro, non-metro or rural) with the effects being strongest in rural counties. 2. Methods This is a national ecological study of all available US counties (n = 2963) using primarily data from 2015. Counties were excluded if their maltreatment report count was so high as to be unreliable (two counties with rates in excess of 400 children per 1000) or if they were missing a measure required for the study (183 counties removed; spread geographically across 29 states). We estimated the relationship between drug overdose death rates (independent variable) and child maltreatment report rates (dependent variable). We controlled for community-level risk factors known to be associated with maltreatment (Palusci, Vandervort, & Lewis, 2016). Median family income and percent of owner-occupied units were included as measures of economic status; economic status at both the individual and community level has been shown to relate to maltreatment (Berger, 2005; Drake & Pandey, 1996). Previous community-level studies have controlled for race and ethnicity (Farrell et al., 2017); we did so by including percentage of the population that is non-Hispanic white in the model. We also included the percentage of the under age 18 population residing in a single parent household (Breyer & MacPhee, 2015; Weissman, Jogerst, & Dawson, 2003). Also, violent crime has been associated with some types of maltreatment at the community level (Coulton et al., 2007). Finally, we examined whether the relationship between county-level overdose mortality and maltreatment report rates is moderated by rurality, classified as metro, non-metro and rural. Drug overdose deaths have been documented to be higher in areas with more economic and family stress (Monnat, 2018a), so controlling for median income and single-parent status allowed us to analyze the unique contribution of overdose mortality to variation in maltreatment rates. 2
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2.1. Measures County was the unit of study. Although smaller geographic units would be desirable (e.g. census tract, block group), neither child maltreatment report rates, nor drug overdose death estimates were available at a finer geographic level. There is precedent in the literature for national, ecological studies of maltreatment-related outcomes using county-level data (Farrell et al., 2017; MaguireJack, Lanier, Johnson-Motoyama, Welch, & Dineen, 2015). Our measures were linked using county Federal Information Processing Standard (FIPS) codes from five primary data sources. All data were publicly available at the county level, except for maltreatment report counts. Restricted-use report counts were from the National Child Abuse and Neglect Data System (NCANDS) child file for federal fiscal year 2015 (U.S. Department of Health & Human Services, Administration for Children & Families, Administration on Children Youth & Families, Childrens Bureau, 2015). To preserve anonymity, data were aggregated by county by the National Data Archive for Child Abuse and Neglect and provided to the authors as counts of unique children by county. Reports are both substantiated and unsubstantiated; we chose reports as the measure of maltreatment because substantiated reports are affected by a number of measurement challenges (Fluke, 2009; Gillingham, 2016). Report counts were divided by US Census Bureau American Community Survey (ACS) 2015 population estimates for those aged 0–17 for each county (U.S. Census Bureau, 2015a) and multiplied by 1000 to calculate rates. Report rates include both abuse and neglect. Drug overdose death rates were model-based, county-level estimates for drug-poisoning mortality, available from the Centers for Disease Control (CDC, Centers for Disease Control and Prevention, 2018). These estimates of overdose deaths measured serious SUDs by county, as many measures of SUD – for example, the National Survey on Drug Use and Health – are only available by state. Drug overdose death rates were age-adjusted, per 100,000 population for 2015. The CDC provides these data summarized into 16 ordinal ranges, from 0 to 2 per 100,000 through the highest ranges of 28.1–30 per 100,000 and 30+ per 100,000. Drug overdose death rate was selected as the primary independent variable because it is the best available estimate of serious substance use disorders available by county; these rates encompass deaths due to overdose of any drug. We accessed key covariate measures via the 2017 Robert Wood Johnson Foundation County Health Rankings & Roadmaps (CHRR). Median household income in the CHRR data originated from 2015 US Census Bureau Small Area Income and Poverty Estimates (U.S. Census Bureau, 2015c). Percentage of non-Hispanic white county population in the CHRR came from the Census Bureau's 2015 Population and Housing Estimates Program (U.S. Census Bureau, 2015b). CHRR percentage of children/youth under 18 living in single-parent households was estimated with ACS 5-year data from 2011 to 2015 (U.S. Census Bureau, 2015a). County violent crime rate was the number of reported violent crime offenses per 100,000 population and originated with the Federal Bureau of Investigation’s Uniform Crime Reporting Program for 2012–2014; the most recent estimates available for this study (Federal Bureau of Investigtion, 2014). Finally, percentage of owner-occupied housing was from the 2015 ACS 5-year estimates (2011–2015), and was the only covariate measure not obtained through the CHRR (U.S. Census Bureau, 2015a). For the purposes of this study, counties are classified as metro, non-metro or rural based on 2013 US Department of Agriculture’s Rural-Urban Continuum codes (RUCC; U.S. Department of Agriculture, 2013). We categorized these codes into the 3 groups. The metro group contains RUCCs of 1, 2 or 3; these are counties in metro areas of 1 million population or more, in metro areas of 250,000–1 million population and in metro areas of fewer than 250,000 (based on 2010 census estimates). The non-metro group contains RUCC codes 4 through 7; these are counties with an “urban” (i.e. densely-settled) population of 20,000 or more, either adjacent to or not adjacent to a metro area and those with an urban population of 2500–19,999, either adjacent to or not adjacent to a metro area. Finally, the rural group is comprised of RUCC codes 8 and 9; these counties have less than 2500 urban population, either adjacent or not adjacent to a metro area. To summarize: metro counties are central and have one or more large, urbanized areas; they include counties that are economically tied to a central, metro county via labor-force commuting. Non-metro counties may contain smaller urbanized areas. Rural counties are sparsely populated. Data sources are summarized in Table 1. 2.2. Analysis The dependent variable was maltreatment report rate per 1000 population under age 18. First, bivariate Pearson correlations were run for the variables included in the model. For multivariate modeling, a negative binomial regression model was appropriate for these data because the variance exceeded the mean (mean = 52.2 reported children per 1000 population under 18; variance = 958.3). In a national study, we necessarily included counties with very small populations and consequently, no child maltreatment reports for fiscal year 2015. About 3.8% of available counties nationally had no reports of child maltreatment for 2015. Therefore, we selected a zero-inflated negative binomial (ZINB) model for rate per 1000 population. This model assumes zero report counts result from either “sampling” – i.e. no children were reported in 2015 – or “structural” reasons – i.e. the county has a very small under-18 population, so it was unlikely to record any maltreatment reports. The primary independent variable – drug overdose death rate – was modeled as an ordinal variable, with a one unit increase representing a difference between two ranges of overdose death rates. For example, an increase from 0 to 2 deaths per 100,000 county population to 2.1–4 deaths per 100,000 population is equivalent to a one-unit increase in the variable. All applicable ranges for overdose death rate are shown in Table 2 below. Covariates included median income, children in single parent homes, non-Hispanic white population, owner occupied housing and the county violent crime rate. The single parent home covariate was modeled as a percentage of children in the county; the nonHispanic white covariate as a percentage of total county population. Owner-occupied housing was modeled as a percentage of housing units in the county. Median household income was an annual estimate, coded in 1000s of dollars. The violent crime rate was coded as offenses per 100,000 county population. 3
4 Covariate
Percentage of under age 18 county population who live in single-parent households Count of reported violent crime offenses per 100,000 population Percentage of county housing units which are owner-occupied Grouping of 9 values of rural-urban continuum codes into: metro, non-metro and rural
Covariate
Percentage of county population that is non-Hispanic white
US Census Bureau Population and Housing Estimates Program US Census Bureau American Community Survey 5-year Estimates Federal Bureau of Investigation Uniform Crime Reporting Program US Census Bureau American Community Survey 5-year Estimates Rural-Urban Continuum codes Moderator
Covariate
Covariate
Primary independent variable Covariate
Dependent variable
Counts of unique children reported annually for abuse or neglect Modeled, annual, age-adjusted overdose death rates due to drug-poisoning Median annual household income
National Child Abuse and Neglect Data System (NCANDS) Child File National Center for Health Statistics – Drug Poisoning Mortality by County US Census Bureau Small Area Income and Poverty Estimates
Variable type
Variable description
Dataset
Table 1 Summary of data sources.
US Department of Agriculture
National Data Archive for Child Abuse and Neglect Centers for Disease Control and Prevention Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings Robert Wood Johnson County Health Rankings US Census Bureau
Source
2013
2011–2015
2012–2014
2011–2015
2015
2015
2015
Fiscal year 2015
Year
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Table 2 Maltreatment rates, demographic data, overdose death rates and county type; all available US counties (n = 2963). Combined
% counties Maltreatment rate per 1000 Median annual income (dollars) in 1000s % of non-Hispanic white county population 2015 % children in single-parent households % owner-occupied housing units Violent crime rate per 100,000 Drug overdose death rate per 100,000 0–2 2.1–4 4.1–6 6.1–8 8.1–10 10.1–12 12.1–14 14.1–16 16.1–18 18.1–20 20.1–22 22.1–24 24.1–26 26.1–28 28.1–30 > 30
Metro (38.3%) Mean (SD)
% counties
52.2 (31.0) 48.9 (12.4) 77.0 (19.4) 32.5 (10.0) 71.4 (8.0) 245.6 (190.3) 0.7 1.3 2.3 6.6 11.0 11.8 13.0 11.5 10.0 7.7 7.2 4.0 3.6 2.2 2.0 4.9
– – – – – – – – – – – – – – – –
Mean (SD)
48.8 55.2 73.8 32.6 69.6
Non-metro (43.0%)
Rural (18.7%)
% counties
% counties
(27.9) (14.0) (18.7) (9.0) (9.5)
57.6 45.0 77.4 33.8 71.2
288.5 (209.2) 0.2 0.2 1.3 6.6 10.8 12.7 13.5 12.7 11.4 7.4 8.2 4.1 3.3 2.3 1.9 3.6
– – – – – – – – – – – – – – – –
Mean (SD)
(30.7) (9.1) (20.0) (9.4) (6.3)
46.9 44.8 82.8 29.6 75.4
249.0 (170.0) 0.0 0.2 2.0 6.9 12.6 12.7 13.7 11.5 9.7 7.4 6.8 4.2 4.0 1.7 2.0 4.4
Mean (SD)
(35.3) (9.6) (18.2) (12.3) (6.7)
149.7 (157.2) 3.6 6.2 5.1 6.0 7.8 8.0 10.3 9.2 7.6 9.2 6.2 3.4 3.6 3.3 2.2 8.5
– – – – – – – – – – – – – – – –
Note: Maltreatment report rates by county type (metro, non-metro and rural) are here shown as descriptive statistics, without controlling for covariates.
Finally, the rural-urban moderator variable was modeled as categorical. The moderation hypothesis was tested by including an interaction term in the ZINB model between the three-category rural-urban moderator and the overdose death rate. The comparison category for the moderation results was the metro county classification. A diagram of the model is shown in Fig. 1. The model was fit in SAS/STAT software using PROC GENMOD. 3. Results As shown in Table 2, the mean county maltreatment report rate was 52.2 per 1000 children. Thirty-eight percent of counties were considered metro, 43% non-metro and 19% rural. Less than 1% of counties experienced virtually no overdose deaths (between 0 and 2 per 100,000). The mode of the distribution (13% of counties) was 12–14 deaths per 100,000. Almost 5% of counties experienced more than 30 overdose deaths per 100,000 annually. Bivariate correlations are shown in Table 3 and demonstrate that maltreatment report rates were significantly and positively correlated with percentage of white population (r = 0.08, p < .0001), with percentage of children in single parent households (r = 0.23, p < .0001) and with rate of violent crimes (r = 0.17, p < .0001). Maltreatment report rates were significantly and
Fig. 1. Zero-inflated binomial model diagram. 5
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Table 3 Bivariate correlations for study variables (n = 2963). Correlations
Rural category p-value Drug mortality p-value % White p-value % Single parent p-value Median income p-value % Owner-occupied p-value Violent crime rate p-value
Rural category
Drug mortality
% White
% Single parent
Median income
% Owner occupied
Violent crime rate
Maltreat. report rate
1.000
−0.016 0.39 1.000
0.162 < .0001 0.106 < .0001 1.000
−0.076 < .0001 0.138 < .0001 −0.527 < .0001 1.000
−0.351 < .0001 −0.267 < .0001 0.106 < .0001 −0.497 < .0001 1.000
0.242 < .0001 0.020 0.28 0.477 < .0001 −0.369 < .0001 0.084 < .0001 1.000
−0.248 < .0001 0.118 < .0001 −0.475 < .0001 0.488 < .0001 −0.179 < .0001 −0.432 < .0001 1.000
0.015 0.42 0.339 < .0001 0.082 < .0001 0.232 < .0001 −0.402 < .0001 −0.028 0.13 0.172 < .0001
negatively correlated with median income (r=−0.40, p < .0001) and were not correlated with percentage of owner-occupied units. Drug overdose mortality was significantly and positively correlated with maltreatment report rates (r = 0.34, p < .0001). The threecategory rural classification for counties was not significantly correlated with maltreatment reports. As hypothesized, results from the ZINB model showed a significant and positive relationship between drug overdose mortality and child maltreatment report rates (χ = 101.26, p < .0001). Modeled results controlled for percentage of white population in the county, percentage of children in single family homes, median income, violent crime rate per 100,000 and percentage owner occupied units. As shown by incident rate ratios (IRRs) and associated confidence intervals (Hilbe, 2011), there was a 2% drop in maltreatment report rates for each additional $1000 in county median income (IRR = .982, CI=[0.980, 0.984]). There was a .7% increase in maltreatment report rate for each additional percentage of the population that was white (IRR = 1.007, CI=[1.006, 1.008]) and for each additional percentage of population under 18 in a single parent household (IRR = 1.007, CI=[1.005, 1.010]). Although these effect sizes appear small (rounding to IRR = 1.0), they are associated with small increases in each of the control variables. For example, a 5-point increase in the percentage of children in single-parent homes would be associated with a 3.5% larger maltreatment report rate for a county (1.0075 = 1.035). Thus, control variables were important to include, even with small effect sizes. The percentage of owner occupied units was not significantly related to report rates. Finally, although the violent crime rate positively predicted maltreatment (p < .0001), the effect of an additional violent crime per 100,000 was too small to detect, with the IRR rounding to 1.000. The ZINB model parameter for inflated zero counts was statistically significant (χ = 29.13, p < .0001), indicating the appropriateness of the ZINB model for these data. Furthermore, we compared Akaike’s Information Criterion for a standard negative binomial model (AIC=28026) and the ZINB model (AIC=26715); the AIC value is lower for the ZINB model, indicating better fit. Also as hypothesized, the significant relationship between drug overdose deaths and maltreatment report rates was moderated by position on the rural-urban continuum (χ = 8.76, p = .01). For average rates of overdose death and in comparison to metro counties, rates of maltreatment reports were lower for non-metro (IRR=0.854, CI=[0.776, 0.942]) and rural counties (IRR=0.805, CI= [0.719, 0.901]). However, increases in maltreatment report rates associated with an increase in drug overdose mortality were greater in non-metro and rural counties than in metro counties. For metro counties, there was a 1.9% increase in maltreatment report rate for each additional increment of overdose deaths (IRR = 1.019, CI=[1.010, 1.028]). That is, as the ordered category of overdose deaths increased from 0 to 2 per 100,000 to 2.1–4 per 100,000, the rate of maltreatment reports increased on average by 1.9%. For nonmetro counties, the rate of increase in maltreatment report rates for a unit increase in drug overdose deaths was 1.8% higher than for metro counties (IRR = 1.018, CI=[1.006, 1.030]); for rural counties, the rate of increase was 1.2% higher than for metro counties (IRR = 1.012, CI=[0.999, 1.026]). Multiplying together increases for the metro county comparison category with the additional increase for non-metro counties yields an average 3.7% increase in maltreatment reports per 1000 children for each incremental overdose increase. For rural counties, maltreatment report rate increased 3.1% for each incremental overdose increase. Modeled mean maltreatment report rates for each of the three county types and the varying rates of change with increasing overdose rates are shown in Fig. 2. The mean values of each covariate were as follows: median income=$49,000, percentage white = 77%, percentage children in single parent households = 33%, owner-occupied units = 71% and violent crime rate = 246/ 100,000. For metro counties, the mean rates of report per 1000 children ranged from a low of 46.6 (at the lowest rate of overdose deaths) to a high of 61.7. Mean rates varied from 39.9 to 68.8 for non-metro counties and from 37.6 to 59.8 for rural counties. Although among counties with low rates of overdose death, the rural counties displayed lower rates of maltreatment reports, among counties with high rates of mortality, rates of maltreatment reporting are similar, due to the higher rates of change for these county groups.
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Fig. 2. Modeled mean maltreatment reports and overdose deaths for all available US counties (n=2963), controlling for median income, percent non-Hispanic white population, percent children in single parent households, percent owner occupied housing and violent crime rate.
4. Discussion Children living in non-metro or rural counties with relatively lower drug overdose death rates experience lower rates of child maltreatment reports than their peers in metro counties, when controlling appropriately for ecological covariates of maltreatment. This is illustrated by the results on the left side of Fig. 2. However, the highest overdose death rates (over 30 per 100,000) are associated with maltreatment report rates in rural counties that are essentially equivalent to metro counties (59.8 vs 61.7 per 1000), and with even higher maltreatment report rates in non-metro areas (68.8 per 1000). This is illustrated by the results on the right side of Fig. 2. At the bivariate level, there is no correlation between metro/non-metro/rural status and maltreatment report rate (r = 0.02, p = .42; see Table 3). The complex interplay of drug overdose deaths, maltreatment and rural-urban status emerges only after modeling these effects and controlling for factors which are associated with county rates of reported maltreatment. There remains a need to explain why the relationship between drug overdose death rates and maltreatment report rates is substantially stronger in non-metro and rural communities. One possible explanation is that mental and physical health care in general, and substance use treatment specifically, are more difficult to access in non-metro and rural counties compared to metropolitan counties. 4.1. Limitations The results of this study should be considered in light of its limitations. The data for this study were secondary data at the countylevel, and the study design was cross-sectional. These data are limited both in terms of what is available and how it can be utilized. For example, optimally, we would use CDC modeled overdose death rates for adults only, as adults are the vast majority of maltreatment perpetrators. However, modeled rates are available only for the entire population. Also, the data vintages do not align with one year; some available measures (e.g. children in single-parent households) are based on multiple years. We used 2015 as the reference year for data sources and included it for multi-year measures. However, USDA rural-urban continuum codes were available only through 2013; data for violent crime were only available through 2014. Cross-sectional ecological studies, such as the current study, are inherently limited in meeting the criteria for establishing a causal relationship between independent and dependent variables, as the data cannot demonstrate that the independent variables precede the dependent variables in time. Secondly, there are community characteristics which correlate with maltreatment reports but are not available in national, county-level data, for example, differing rules on mandated maltreatment reporting or the number of mandated reporters by county. Issues around mandated reporting likely influence maltreatment report rates, in addition to reports being affected by the actual incidence of maltreatment. Financial resources for child protective services could be another relevant factor; these might influence the existence of a hotline for resident reporting of maltreatment and/or public health campaigns to educate residents about the importance of reporting suspected maltreatment. Third, some authors have used spatial methods for county-level analyses (Maguire-Jack et al., 2015), however, this was not accounted for in the present study. Given the choice of using statistical methods which could handle the inflated zero-report counts or the spatial aspect of the data, we chose to use the ZINB model, as described above. Finally, study of smaller geographic areas, such as census tracts or block groups, could be more precise for defining communities, rather than county (Freisthler, 2004; Krieger, Chen, Waterman, Rehkopf, & Subramanian, 2005). However, county level is the smallest unit of geography available in the NCANDS dataset (Maguire-Jack et al., 2015). Finer geographic breakouts could be achieved with a state-level study, using de-identified administrative data from a single state. Multiple years of maltreatment reports could be aggregated to protect child identities in small area analyses. 7
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4.2. Access to treatment for substance use disorders (SUDs) While effective approaches to SUDs exist, even for highly addictive opioids, there continues to be a large treatment gap for SUDs in the US (Chuang, Wells, Bellettiere, & Cross, 2013; Pullen & Oser, 2014). According to the 2015 National Survey on Drug Use and Health, an estimated 21.7 million Americans (8.1%) needed treatment for a problem related to drugs or alcohol, but only about 2.3 million people (10.8% of those in need) received SUD treatment (Lipari, Park-Lee, & Van Horn, 2016). Barriers to care for SUDs exist across social-ecological levels and are disproportionately present in rural America. At the individual level, barriers include stigma associated with substance use treatment and mental health care, and mistrust of institutions providing treatment, particularly among persons who have experienced institutionalization and/or who have co-occurring mental health disorders (Priester et al., 2016). Cost is also a common barrier to treatment, particularly among those without insurance (Fisher, Reynolds, D’Anna, Hosmer, & Hardan-Khalil, 2017; Pullen & Oser, 2014). Personal characteristics such as the inability to follow through with appointments or becoming incarcerated before treatment are also cited as barriers by substance-using individuals (Fisher et al., 2017). Women often face additional barriers as they attempt to access mental health and SUD treatment, such as locating and paying for childcare (Marsh, Smith, & Bruni, 2011). Women on welfare may not seek treatment, out of fear of losing benefits (Johnson et al., 2015). Pregnancy and/or the responsibility of caring for children also affect women’s desire to seek substance and mental health services (Marsh et al., 2011; Ryan, Choi, Hong, Hernandez, & Larrison, 2008). Community and structural barriers to treatment also impede individuals with SUDs from accessing needed care. The same nonmetropolitan, rural and resource-poor communities that are experiencing rapidly rising drug overdose rates also face greater challenges in accessing treatment than more urban and financially sound communities (Fisher et al., 2017; Pullen & Oser, 2014). Access to care is hampered by geographic distance and lack of transportation (Fisher et al., 2017; Marsh et al., 2011; Pullen & Oser, 2014). The current SUD workforce in the US is undersized and inadequately resourced, and the available providers often lack the skills and experience to offer the effective evidence-based, integrated care necessary for successful SUD treatment (Chuang et al., 2013; Creedon & Lê Cook, 2016; Fisher et al., 2017; Lasser, 2017; Pullen & Oser, 2014). Racial, ethnic, and geographic diversity of the workforce is lacking, and there is a dearth of behavioral health professionals serving people in rural and impoverished areas (Chuang et al., 2013; Fisher et al., 2017; Pullen & Oser, 2014). By addressing barriers to treatment, increasing availability of effective evidence-based SUD treatment, and providing harm reduction interventions, rates of SUD could decrease in the US, with a possible associated decrease in child maltreatment reports (Fisher et al., 2017; Morton, Simmel, & Peterson, 2014). In a study of 163 census tracts in New Jersey, the presence of SUD treatment services in a community was associated with a lower child maltreatment report rate in neighborhoods (Morton et al., 2014). In an Illinois demonstration study of over 800 families, completion of substance use treatment had a positive influence on family reunification (Choi, Huang, & Ryan, 2012). In addition to access to care, there are likely multiple contributing factors to the differing relationships between overdose deaths and child maltreatment reports on the rural-urban continuum. In the current study, the highest rates of reported maltreatment associated with high overdose mortality did not occur in the most rural communities, as we would anticipate if access to care were the only mediating factor. Rather, the rates of reported maltreatment were highest for high overdose mortality in the middle, nonmetro category. More rural counties typically have lower levels of education and lower median household incomes (see Table 2), both conditions are associated with higher rates of maltreatment. Literature suggests that SUD may also be related to lower educational attainment (Chatterji, 2006) and lowered workforce participation (MacDonald & Pudney, 2000). Differences by type of community in the relationship between overdose deaths and maltreatment reports may indeed lie in the interaction of economic and educational factors with substance use and maltreatment. Finally, the extensive review by Coulton et al. (2007) identified concerns with measurement of maltreatment that may be of particular relevance to more rural areas; variation in reports of child maltreatment may have more to with a community’s ability and willingness to define, recognize and report maltreatment than with actual parental behaviors. This may also impact relationships with substance use. 5. Conclusion In a comparison across metro, non-metro, and rural counties, the relationship between drug overdose death rates and child maltreatment reporting rates was strongest in the middle category, non-metro. The most rural counties also have a substantially stronger relationship between the rates of drug overdose death and maltreatment compared to urban counties. The results of this study support future research exploring why this is the case. Social work and public health researchers should consider study of the protective effects against maltreatment of parents’ access to primary health care – for detection and treatment of problematic substance use – and their access to SUD treatment and behavioral healthcare, as such access is likely protective against child maltreatment. Studies of healthcare supply and its relationship with drug overdose rates at the county level have not yet detected differences in mortality rates; this may be due to inability to account for supply of and access to mental health and substance use treatment providers (Monnat, 2018b). Thus, the possible mediating effect of healthcare on the relationship between drug overdose mortality and child maltreatment remains unclear. Behavioral health and SUD treatment services remain an important component of overall services coordinated through a child protective services (CPS) agency. In addition, broader availability of services within a community may contribute to primary prevention of maltreatment, preventing the necessity of CPS involvement. There remains a need for professionals in both public health and social work to better understand and interrupt the relationship between SUDs and child maltreatment; access to healthcare may be an important component of these relationships. Studies at smaller areas of analysis, studies incorporating spatial analysis, and 8
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studies using additional methods, such as qualitative methods and geographic information systems (GIS), are warranted to elucidate the protective aspects of healthcare, as well as to generate ideas to maximize community-level prevention strategies, especially in non-metropolitan and rural communities. Funding source This work was supported by the Colorado School of Public Health. Declaration of competing interests The authors state that they have no potential conflicts of interest with respect to the research, authorship, and/or publication of this manuscript. Acknowledgements The authors acknowledge Mr. Michael Dineen at the National Data Archive for Child Abuse and Neglect for summarizing NCANDS data by county and Mr. Luke McConnell for managing and linking multiple data sources for the study. The authors acknowledge Ms. Tricia Howley for her assistance formatting figures. References Barnard, M., & McKeganey, N. (2004). 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