Relationships Between Material Hardship, Resilience, and Health Care Use

We conducted a cross-sectional study using the 2016 National Survey of Children’s Health (NSCH)¹⁰  to measure differences in risk and resilience factors between children with and without special health care needs and to investigate whether resilience factors at the family and neighborhood level could promote optimal health care use.

The NSCH is a population-based survey of parents or primary caregivers about child health and use of health services as well as family and community characteristics. Details of the design and implementation of this survey are available through the Maternal Child Health Bureau: https://mchb.hrsa.gov/data/national-surveys/data-user.¹⁷  The database consists of ∼50 212 children aged 0 to 17.¹⁰  This is a publicly available national data set administered by the Census Bureau on behalf of the National Center for Health Statistics, Centers for Disease Control and Prevention. All data were collected in the NSCH.

Our study included CSHCN and N-CSHCN who have complete survey data for the main study variables. Given low rates of material hardship in participants who did not report low income (11.9% compared with 41.8% of low-income participants), we restricted our sample to children with a family income of <200% of the federal poverty level.¹⁸  The Albert Einstein College of Medicine Institutional Review Board found the study exempt from human subject review.

Special health care needs status was defined using the 5-item screener (CSHCN Screener) from the National Center for Health Statistics.¹⁰  This screener is used to address functional limitations or need for services due to a medical condition. The questions ask if the child (1) receives prescription medication; (2) receives more medical care, mental health care, or educational services than is usual for a child their age; (3) is limited in their ability to do the things most children of the same age can do; (4) needs or gets special therapy; or (5) has any kind of emotional, developmental, or behavioral problem for which he or she needs treatment.⁹  A second subset of questions inquires if this is related to any medical, behavioral, or mental health condition and if the problem is expected to last for >12 months. Affirmative responses to any of the questions along with the associated subquestions indicated a CSHCN.

Material hardship was measured by using a single question: “Since this child was born, how often has it been very hard to get by on your family’s income—hard to cover the basics like food or housing?” This single question, included in the survey as a measure of adverse childhood experience,¹⁹  was selected because it reflects the definition of material hardship and specifically mentions the most commonly studied material hardships: food and housing insecurity. Responses included never, rarely, somewhat often, or very often. We created a dichotomous variable defined as never or rarely versus somewhat often or very often.

Family resilience was measured by using 4 questions: “When your family faces problems, how often are you likely to do each of the following: (1) talk together about what to do, (2) work together to solve our problems, (3) know we have strengths to draw on, (4) stay hopeful even in difficult times.” Responses were measured via a 4-point Likert scale, with responses of none of the time, some of the time, most of the time, or all of the time. We created a 3-category variable on the basis of the NSCH codebook from the Data Resource Center for Child and Adolescent Health,¹⁰  and then for our analysis, we dichotomized the variable, defining high family resilience as a response of most or all of the time to all 4 questions.

Neighborhood cohesion was measured by using 3 questions: “To what extent do you agree with these statements about your neighborhood or community: (1) people in this neighborhood help each other out, (2) we watch out for each other’s children in this neighborhood, and (3) when we encounter difficulties, we know where to go for help in our community.” Responses were measured on a 4-point Likert scale, with responses of definitely agree, somewhat agree, somewhat disagree, or definitely disagree. We created a dichotomous variable on the basis of the NSCH codebook, defining high neighborhood cohesion as children whose parents reported “definitely agree” to at least 1 item and at least “somewhat agree” or “definitely agree” to the other 2 items.

Our dependent variables were 2 measures of health care use: ED visits and unmet health care needs. ED visits were measured by using the following question: “During the past 12 months, how many times did this child visit a hospital emergency room?” A dichotomous variable was created, defined as no visits or ≥1 visit. Unmet health care needs were measured by using the following question: “During the past 12 months, was there any time when this child needed health care but it was not received?” A dichotomous variable was created, defined as no unmet need or any unmet need.

Child and parent sociodemographic characteristics were collected in the NSCH. For children, these were age (years), sex, race and ethnicity (non-Hispanic white, non-Hispanic African American, Hispanic or Latino, or other), and insurance type (public only, private only, combination of public and private, uninsured, and other). For parents, these were age (years), education (high school, more than high school), any parent working (no, yes), and parent marital status (married, single).

We used descriptive statistics to calculate means and proportions of characteristics of our study population. We used χ² tests for categorical variables and Mann-Whitney tests for continuous variables, because these were not normally distributed, to compare CSHCN and N-CSHCN with respect to each resilience factor, material hardship, and health care use outcome as well as study covariates. We also tested bivariate relationships between our independent and dependent variables. We used logistic regression models to measure associations between independent and dependent variables and special health care needs status. We then used logistic regression models to measure associations between material hardship as our independent variable and each health care use outcome as the dependent variable, with separate models for CSHCN and N-CSHCN. We developed similar models for each resilience factor as the independent variable. Finally, we created a set of models including each resilience factor and material hardship. For these models, we tested for effect modification using interaction terms and stratified models by the resilience factor for those with interaction term P < .05. If there was no evidence of effect modification, we examined for evidence of mediation, which we suspected when there was a statistically significant association between a resilience factor and the outcome, between material hardship and the outcome, and between the resilience factor and material hardship. All logistic regression models were adjusted for child age, sex, insurance type, and ethnicity and parent age, education, employment status, and marital status. We tested for mediation using generalized structural equation models with linearized coefficients,²⁰  adjusting for covariates that were significantly associated with outcomes in logistic regression models. Figure 1 summarizes the analysis plan. Established survey weights from the National Center for Health Statistics were used for all analyses. Significance was assessed at a 2-tailed α of .05. Statistical analyses were performed using Stata (v 14.2, College Station, TX).²¹ 

Our study sample consisted of 11 543 children, representing an estimated 28 465 581 children in the United States with household income <200% of the federal poverty level. Of these, 2998 (26%) screened positive for special health care needs. Table 1 shows sample characteristics. Compared with those without special health care needs, CSHCN were more likely to be male, have public health insurance, and to have parents report that they are not employed or that there is no parent working outside the home.

After adjustment for covariates, CSHCN reported higher odds of material hardship (odds ratio [OR]: 1.61; 95% confidence interval [CI]: 1.35–1.94) and lower odds of both family resilience and neighborhood cohesion (OR: 0.76; 95% CI: 0.63–0.92 and OR: 0.73; 95% CI: 0.61–0.87, respectively) compared with N-CSHCN. CSHCN were also more likely to report an ED visit (OR: 2.24; 95% CI: 1.83–2.71) or an unmet health care need (OR: 4.25; 95% CI: 3.05–5.94) compared with N-CSHCN (Table 2).

Direct associations between resilience factors and use for N-CSHCN and CSHCN examined separately are found in Table 3. The only direct associations were found for CSHCN and unmet health care needs. CSHCN who reported high family resilience and neighborhood cohesion had lower odds of unmet health care needs (OR: 0.58; 95% CI: 0.36–0.94 and OR: 0.53; 95% CI: 0.32–0.88, respectively).

Material hardship was associated with higher odds of an ED visit for both N-CSHCN (OR: 1.54; 95% CI: 123–1.93) and CSHCN (OR: 1.37; 95% CI: 1.02–1.87) (Table 3). We found evidence of effect modification of the association between material hardship and ED visits by neighborhood cohesion among CSHCN only (interaction term: P = .02). We therefore stratified this model and found that material hardship was associated with higher odds of ED visits only among those with low neighborhood cohesion (OR: 1.75; 95% CI: 1.17–2.6) and was nonsignificant for those with high neighborhood cohesion (OR: 0.94; 95% CI: 0.62–1.42) after adjustment.

Material hardship was associated with higher odds of unmet health care needs for both N-CSHCN (OR: 3.14; 95% CI: 1.86–5.32) and CSHCN (OR: 2.71; 95% CI: 1.52–4.85) (Table 3). We found no evidence of effect modification of this association by resilience factors. Path analysis was used to investigate whether lower material hardship mediated the association between resilience factors and lower unmet health care needs. Figure 2A demonstrates the associations between family resilience, material hardship, and unmet health care needs. Four standard criteria for mediation were met: (1) higher family resilience is associated with lower unmet health care needs, (2) higher family resilience is also associated with lower material hardship, (3) material hardship is associated with higher unmet health care needs, and (4) the association between family resilience and unmet health care needs is attenuated after adjustment for material hardship (indirect effect coefficient: −0.01; P = .01). Figure 2B demonstrates similar associations for neighborhood cohesion (indirect effect coefficient: −0.01; P = .008).

In this study of low-income parents of children with and without special health care needs, we found that parents of CSHCN were more likely to report material hardship, less likely to report resilience factors, and more likely to report an ED visit or unmet health care need. However, although material hardship was associated with adverse health care use patterns for all children, resilience factors were related to better outcomes only for CSHCN. Among CSHCN, the association between material hardship and ED visits was moderated by neighborhood cohesion: this association was found only for CHSCN with low neighborhood cohesion. In addition, associations between both resilience factors and unmet health care needs were partially mediated by lower material hardship for CSHCN. Part of the association between resilience factors and lower unmet health care needs was through reduced material hardship.

Even in the setting of multiple risks, we identified family and neighborhood resilience factors associated with better health care use outcomes for CSHCN. The mechanisms of these factors are likely varied. Supports may be material (such as child care), emotional (reducing stress), or informational (such as resource navigation),²²  and resilience factors likely provide support in multiple ways. Our study broadens previous research that is condition specific, showing that family and community factors were associated with better child health outcomes in studies investigating specific conditions, including attention-deficit/hyperactivity disorder,²³  autism,²⁴  diabetes,²⁵  and mental illness.²⁶  By demonstrating differences between CSHCN and N-CSHCN, this study adds to our understanding of possible mechanisms of these protective factors. In the setting of special health care needs, families may engage in recruiting supports through family interactions or seeking neighborhood resources, and so these factors are more active. Because there is greater adversity, there may be greater opportunity for resilience factors to have effects.

We found that neighborhood cohesion modified the association between material hardship and ED visits, whereas for unmet health care needs, reduced material hardship mediated the effects of family resilience and neighborhood cohesion. One notable difference between the 2 outcomes is that ED visits may be a more-concrete reflection of the child’s medical condition, whereas unmet health care needs may be addressed by increasing access to resources. There may be factors associated with high neighborhood cohesion that are associated with better child health, or families living in more-cohesive neighborhoods may have access to health care resources that mitigate the need for ED visits. Resilience factors may provide access to more material resources, directly reducing material hardships. Alternatively, there may be unmeasured shared characteristics of families with high resilience factors that are also associated with lower material hardship and reduced unmet health care needs.

This study has important implications for better understanding the mechanisms of risk and resilience factors for children with and without special health care needs with respect to health care use, a potentially important marker of health status. Understanding these implications can help inform interventions that capitalize on family and neighborhood strengths. Interventions that actively bolster family strengths while working concomitantly on addressing challenges have shown promise in improving child outcomes. Examples include the Incredible Years program and The Family Check-Up, which address the roles of risk and protective factors, and have shown better behavioral health outcomes for children in low-income families.^27,28  These interventions are parenting focused and likely improve family resilience, so they potentially could be applied to broader special health care needs, although tailored interventions may be needed for different conditions. A comprehensive literature review revealed that community development programs, such as rental assistance, neighborhood watch programs, and access to child care, can improve child health.²⁹  However, few interventions directed at CSHCN intervene at the neighborhood level, which could optimize scalability and reach.

This study had several strengths. This was a nationally representative data set with a large sample size. We identified factors at the family and neighborhood level that may promote more optimal health care use outcomes. It is possible that this was through directly reducing material hardship or through other pathways. More research is needed to better understand the mechanisms of these associations. However, our study had several limitations. We restricted our sample to participants reporting low income, so findings may not be generalizable to other groups. In future studies, researchers could consider comparing low-income children to those who are not low income. We also did not distinguish between different types of special health care needs, and so it is possible that there are differential effects on these subgroups that were not identified. This study relied on self-reported data. Measures should be comparable to other studies using the NSCH but were not validated instruments. This was a cross-sectional study, and so we cannot infer direction of relationships. Finally, the constructs examined are complex, and there are likely other important factors involved that have not been measured.

In summary, in this study of low-income CSHCN and N-CSHCN, we found that resilience factors, specifically high family resilience and neighborhood cohesion, were related to better health care use outcomes in the setting of material hardship for CSHCN only. These findings indicate an important opportunity for further research examining how these resilience factors and other strengths of families of CSHCN can buffer the unique stressors they face and also point to an opportunity for clinicians to work with families to identify strengths and leverage these to promote better outcomes for vulnerable children.

CI

confidence interval

CSHCN

children with special health care needs

ED

emergency department

N-CSHCN

children without special health care needs

NSCH

National Survey of Children’s Health

OR

odds ratio