The sample includes children from the 1999 to 2001 and 2004 to 2012 National Health Interview Survey who reported an asthma attack. Percentage point differences come from the incremental differences implied by a multivariable logistic regression. The year before entry (−1) is the omitted reference category. Year 0 is the first year of entry. The error bars indicate 95% CIs.
eAppendix. Supplemental Material
eTable 1. Selected Characteristics by Sample and Sample Weight
eTable 2. Complete Descriptive Statistics by Group
eTable 3. Asthma Outcomes Among Those With No HUD History and Less than 150 FPL
eTable 4. Ever Homeless by HUD Status
eTable 5. Differences in Asthma Attack and ER Visit by Broader Denominator Definitions
eTable 6. Comparison of Coefficients from Linear Probability Models (LPM) and Incremental Marginal Effects from Logistic Regression
eTable 7. Percentage Point Differences in ER Use for Asthma Among Children Currently in HUD Housing Versus Those Waiting to Enter Among Those With a Diagnosis, by Program Type
eTable 8. Percentage Share of Ever Waitlisted Sample that Meets Quasi-waitlist Cut-offs
eTable 9. Demographic Characteristics of Quasi-Waitlist Sample by Waitlist Cutoffs
eTable 10. Sensitivity to Quasi-Waitlist Limits, Effect of HUD on ER Visit Given Asthma Attack
eTable 11. Logistic Regression Results (OR): Ever Asthma Diagnosis
eTable 12. Logistic Regression Results (OR): Asthma Attack Among Those Ever being Diagnosed
eTable 13. Logistic Regression Results (OR): ER Among Those With Attack in Last 12 Months
eFigure 1. Sample Flow Diagram
eFigure 2. Adjusted Percentage Point Differences in Past Year Asthma Related ER Visit for Those With a diagnosis, by Year Until HUD Entry
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Boudreaux M, Fenelon A, Slopen N, Newman SJ. Association of Childhood Asthma With Federal Rental Assistance. JAMA Pediatr. 2020;174(6):592–598. doi:10.1001/jamapediatrics.2019.6242
Is participation in a federal rental assistance program associated with asthma outcomes in children?
In this survey study of 2992 children compared those currently participating in a rental assistance program with those waiting to enter such a program, participation in a rental assistance program was associated with a reduction in emergency department use among children with an asthma attack in the past year, but no significant changes were found in asthma attack or asthma diagnoses.
Rental assistance may be associated with reduced use of emergency department services for the treatment of asthma.
Millions of low-income children in the United States reside in substandard or unaffordable housing. Relieving these burdens may be associated with changes in asthma outcomes.
To examine whether participation in the US Department of Housing and Urban Development’s (HUD) rental assistance programs is associated with childhood asthma outcomes and to examine whether associations varied by program type (public housing, multifamily housing, or housing choice vouchers).
Design, Setting, and Participants
This survey study used data from the nationally representative National Health Interview Survey linked to administrative housing assistance records from January 1, 1999, to December 31, 2014. A total of 2992 children aged 0 to 17 years who were currently receiving rental assistance or would enter a rental assistance program within 2 years of survey interview were included. Data analysis was performed from January 15, 2018, to August 31, 2019.
Participation in rental assistance provided by HUD.
Main Outcomes and Measures
Ever been diagnosed with asthma, 12-month history of asthma attack, and 12-month history of visiting an emergency department for the treatment of asthma among program participants vs those waiting to enter a program. Overall participation was examined, and participation in public or multifamily housing was compared with participation in housing choice vouchers.
This study included 2992 children who were currently participating in a HUD program or would enter a program within 2 years. Among children with an asthma attack in the past year, participation in a rental assistance program was associated with a reduced use of emergency departments for asthma of 18.2 percentage points (95% CI, −29.7 to −6.6 percentage points). Associations were only found after entrance into a program, suggesting that they were not confounded by time-varying factors. Statistically significant results were found for participation in public or multifamily housing (percentage point change, −36.6; 95% CI, −54.8 to −18.4) but not housing choice vouchers (percentage point change, −7.2; 95% CI, −24.6 to 10.3). No statistically significant evidence of changes in asthma attacks was found (percentage point change, −2.7; 95% CI, −12.3 to 7.0 percentage points). Results for asthma diagnosis were smaller and only significant at the 10% level (−4.3; 95% CI, −8.8 to 0.2 percentage points).
Conclusions and Relevance
Among children with a recent asthma attack, rental assistance was associated with less emergency department use. These results may have important implications for the well-being of low-income families and health care system costs.
Policy makers and health care practitioners are increasingly focused on leveraging health system resources in cross-sector collaborations that aim to mitigate socially determined health conditions.1-4 Such efforts highlight the need for credible evidence on which social services are associated with improved health and reduced health care costs. Rental assistance has garnered particular attention, especially for promoting child health.4,5 Unaffordable, inadequate, and unstable housing may adversely affect child health by increasing stress and environmental exposures and by creating barriers to health care, education, physical activity, and social networks.6 Nearly 3 million low-income families with children pay more than 50% of family income in rent or live in substandard housing.7
The US Department of Housing and Urban Development’s (HUD’s) rental assistance programs may be associated with improved child health.8 HUD assists approximately 4 million children through 3 programs: public housing (publicly owned developments in which all units are subsidized), multifamily housing (developments in which at least some units are subsidized), and housing choice vouchers (which enable renting in the private market). Benefits ensure that households pay approximately 30% of their income in rent, and all assisted units must pass annual quality inspections.9
The association of HUD’s rental assistance programs with child health have been relatively understudied because of insufficient data and the difficulty of overcoming unobserved selection.8 Several studies10,11 have examined the effects of changes in program type. For example, the Moving to Opportunity (MTO) trial, set in 5 US cities, randomized existing public housing residents to remain in public housing (the control group), to a traditional voucher, or to a restricted voucher that could be used only in a low-poverty neighborhood. Results for childhood health were mixed. For example, although many health outcomes were unaffected by treatment assignment, adolescent boys in the 2 voucher groups had worse mental health outcomes than did the control group.10,12 Adolescent girls had the opposite pattern. Children in the MTO trial assigned to the voucher conditions had worse asthma outcomes than did those in the control group.10,13
In this observational study, which accounted for time-invariant selection into assistance, we examined the association of rental assistance with the prevalence of childhood asthma and the use of emergency departments (EDs) for asthma. Asthma is a chronic condition that affects 1 in 12 children and disproportionately affects children from lower-income households.14 The condition can be managed by reducing environmental irritants and through care plans that require high levels of patient engagement.15
Measuring whether associations differ across program type is important given longstanding interest in promoting residence in low-poverty neighborhoods through mobility-based assistance (ie, vouchers) vs place-based programs (public and multifamily housing).16 Although the MTO trial suggested that public housing is associated with better asthma outcomes than voucher assistance, the mechanisms are not well understood and may differ by sex.13 Voucher holders face additional pressures not experienced by public housing residents. For example, searching for a private unit that accepts vouchers can be stressful and often results in participants settling for units that do not meet their needs.17,18 To our knowledge, no study that credibly accounts for unobserved selection has examined program differences in a national sample. We examined the association of rental assistance with the prevalence of childhood asthma and the use of emergency departments (EDs) for asthma.
A frequent argument in favor of expanding rental assistance is that it will improve health and reduce publicly financed health system costs. Indeed, asthma-related ED visits cost Medicaid/Children’s Health Insurance Program approximately $272 million per year or $433 for an average visit.19 The motivation for providing rental assistance clearly extends beyond the health system and housing initiatives driven by the health system risk overmedicalizing social problems.20 Nonetheless, developing evidence about how housing policy affects health and health system resources would inform cross-sector collaborations focused on the social determinants of health.
This survey study used data from the National Center for Health Statistics, which links longitudinal administrative records of HUD participation with cross-sectional data from the National Health Interview Survey (NHIS).21 The administrative records indicated the dates of program participation from January 1, 1999, to December 31, 2014. Data analysis was performed from January 15, 2018, to August 31, 2019. We used a deidentified secondary data set. Consent was managed by the National Center for Health Statistics. The University of Maryland Institutional Review Board reviewed and approved the study.
The NHIS is a nationally representative survey that collects information on the sociodemographic, health, and health care characteristics of the US civilian noninstitutional population.22 The in-person survey generally achieves an approximately 70% response rate. We limited our analysis to the sample child files, which include detailed information about 1 randomly selected child (aged 0-17 years) per household. We used NHIS variables that are disseminated by the Minnesota Population Center.23
HUD administrative records were linked to NHIS respondents who consented to linking and provided US Social Security numbers and other identifying characteristics required for linkage. Social Security numbers were collected only for sample child cases in 1999 to 2001 and 2004 to 2012. Within those years, only a portion of respondents consented to linking or provided complete Social Security numbers.21 We created sample weights to ensure that children eligible to be linked were representative of the US population (eAppendix in the Supplement). Our final analytic sample consisted of 2992 children from 1999 to 2001 and 2004 to 2012 that were participating in a HUD program at interview or would enter a program within 2 years (eFigure 1 in the Supplement).
A challenge in measuring the association of rental assistance with child asthma outcomes is that program participation is voluntary, and unobserved participation factors could also be associated with child health. To address that challenge, we compared outcomes in children currently participating in rental assistance programs (the treatment group) with those in a similar group of children who were not participating when outcomes were measured but would enter such programs within 2 years (the comparison group). The 2-year restriction is aligned with the mean wait-list length and was considered to be suitable for the remitting and relapsing nature of asthma. However, we examined sensitivity to longer waiting periods. Our design accounted for unobserved time-invariant factors associated with a family’s decision to participate and reflects the association of the treatment (rental assistance) for those who selected to receive the treatment.
Rental assistance is not an entitlement. As a result, participants must first wait for a program to accept applications and then spend a mean of 2 years on a wait-list between when they apply and when they receive assistance.24 However, there is substantial variability in wait-list lengths across local public housing authorities. We did not directly observe participation on a formal wait-list. Instead, the wait-list group included those on a formal list and those who had not yet applied. As such, we refer to the group yet to enter housing as the quasi–wait-list group. Previous studies25-27 have used this method to examine the association of rental assistance with adult health, adult health care access, and child mental health.
We examined whether differences varied by program type (housing choice vouchers vs multifamily or public housing). Differences across subgroups were determined by interactions of HUD status with program indicators (eTable 1 to eTable 4 in the Supplement). Multifamily and public housing differ substantively, but we combined them to enhance statistical power and because they share the similar feature of being a nonportable place-based benefit, unlike housing choice vouchers.
We examined 3 dichotomous outcomes reported by a knowledgeable household adult: (1) ever been diagnosed with asthma, (2) asthma attack in the 12 months preceding interview among those ever diagnosed, and (3) asthma-related ED visit in the past 12 months. The ED outcome included urgent care centers, and we examined ED rates for the population with a diagnosis and the population experiencing an asthma attack in the past year. Before 2010, the ED question was asked only of children who had an asthma attack, and in subsequent years, it was asked of all children with a diagnosis. We edited the asthma attack variable for consistency so that all those reporting ED use were coded as having an asthma attack in every year. The edit affected 1.2% of all patients coded as having an asthma attack. In accordance with previous work,28 we considered an ED visit to be an adverse outcome. eTable 5 in the Supplement reports results for asthma attack and ED use in the general population. Although results were similar, we preferred the conditional outcomes because they focus on children at risk for the events of interest.29
We used logistic regression to implement the quasi–wait-list comparisons and report the mean incremental differences (percentage point differences) estimated by the models with 95% CIs.30 For each outcome, we examined 4 models. The first included only the HUD status variable. The second included controls for demographic characteristics: age, race/ethnicity, sex, census region, and survey year. The third added individual socioeconomic- and community-level factors that could potentially mediate the association between rental assistance and the outcomes: family poverty level, parental work status, parental marriage status, parental educational attainment, county-by-year air pollution (measured using ozone and particulate matter <2.5 μm), county-by-year rates of violent and property crime, and a series of census tract-by-year indexes that measured the racial/ethnic, economic, and housing characteristics of neighborhoods (eAppendix in the Supplement). The final model added individual health care access variables: health insurance coverage, usual source of care, and financial and nonfinancial barriers to care (eg, difficulty accessing care because of transportation). All models accounted for the complex sample design. Missing data were excluded using case-wise deletion except for poverty level, which was measured using imputed family income provided by the National Center for Health Statistics. Two-sided significance testing using a threshold of P ≤ .05 was used. Analyses were conducted at a federal statistical research data center using Stata software, version 14 (StataCorp).
A potential threat to the validity of our study was omitted variables that varied over time and were associated with the timing of HUD entry. To investigate that issue, we estimated similar models to those described above except that we replaced the 0 or 1 HUD status variable with variables that measured years relative to program entry (ranging from 3 years earlier to ≥3 years after entry). These models used a longer wait-list period to better measure trends leading to program entry. If outcome trends were observed before entry, it would suggest that the comparison of interest was being affected by a time-varying process other than entry itself. However, if outcomes changed only after entry, it would support the validity of our design. We also examined whether the comparisons of interest were sensitive to alternative wait-list lengths.
A total of 2992 children who were currently participating in a HUD program or would enter a program within 2 years participated in the study. Table 1 gives selected characteristics of children in the quasi–wait-list and current participation groups. Members of the quasi–wait-list group were younger (mean [SD] age, 6.8 [3.1] vs 8.2 [3.2] years) and less likely to reside in the Midwest (168 [weighted 17.1%] vs 470 [weighted 23.2%]). We did not find evidence that the 2 groups differed by sex, race/ethnicity, or parental educational attainment. eTable 2 in the Supplement provides comparisons across a larger set of variables and comparisons with low-income children never participating in a HUD program.
In the quasi–wait-list group, 24.1% of children had been diagnosed with asthma, 45.6% of those with an asthma diagnosis had an asthma attack in the past 12 months, and 60.7% of those with an asthma attack visited the ED for asthma (Table 2). These rates were higher than rates in the general low-income population (eTable 3 in the Supplement). Unadjusted logistic regression did not reveal a statistically significant difference in asthma diagnosis (0.9 percentage points; 95% CI, −5.1 to 3.2 percentage points) or past-year asthma attack (−2.0 percentage points; 95% CI, −11.7 to 7.7 percentage points) for current HUD participants compared with those waiting to enter HUD programs. Emergency department use, conditional on having an asthma attack, was 15.3 percentage points (95% CI, −29.0 to −1.6 percentage points) lower among current participants compared with the quasi–wait-list group. This finding represents a 25% relative reduction.
Table 2 gives results from models that include control variables. After demographic controls were included, the size of the difference for ED use for those having an asthma attack was −18.9 percentage points (95% CI, −31.5 to −6.2 percentage points). This estimate was not substantively affected by the inclusion of the individual socioeconomic, community, or health care access variables. The difference for asthma attack was approximately the same magnitude in every model and never statistically significant. Differences in asthma diagnosis were larger when including demographic controls (−4.1 percentage points; 95% CI, −8.5 to 0.2 percentage points). The point estimates and 95% CIs in the asthma diagnosis model were substantively unchanged in the fully adjusted model. eTable 6 in the Supplement compares logistic regression results found in Table 2 with results from a linear probability model.
The percentage point difference between current participants in housing choice voucher programs and their quasi–wait-list counterparts was −7.2 (95% CI, −24.6 to 10.3 percentage points). The difference for those in multifamily or public housing was −36.6 percentage points (95% CI, −54.8 to −18.4 percentage points; P = .02 for interaction). eTables 7 in the Supplement give program-specific results for those with a diagnosis of asthma.
The Figure displays results from a fully adjusted logistic regression model that replaced the 0 or 1 HUD status indicator with time relative to HUD entry. The percentage point differences plotted in the Figure were in comparison with 1 year before program entry. The outcome was ED visit, and we estimated differences for those with an asthma attack (eFigure 2 in the Supplement gives results for those with an asthma diagnosis). The Figure shows no discernable trend in ED use in the years before entry. However, after HUD entry, ED use was consistently lower among current HUD participants by 10 to 18 percentage points, and the association was larger after at least 2 years of participation. Only the point estimate for 3 or more years of participation was statistically significant (−18.0 percentage points; 95% CI, −34.5 to −1.0 percentage points). The shape of the coefficients suggests that differences in ED use were not associated with preexisting trends leading to HUD entry. The eAppendix in the Supplement shows that using a longer wait-list period to define the comparison group led to moderately different results but not enough to alter conclusions (eTables 8-10 in the Supplement).
In this study, we investigated whether participation in a federal rental assistance program was associated with childhood asthma outcomes. We found that among children receiving rental assistance who had an asthma attack, there was a 15.3– to 18.2–percentage point reduction in ED use for asthma compared with children waiting to enter a program. This finding translates into a 25% to 30% relative reduction.
Differences in ED use were larger for participants in multifamily or public housing but smaller and not significant among those with housing choice vouchers. This finding aligns with evidence from the MTO trial, which found that providing vouchers to public housing residents was associated with worse asthma outcomes.10,13 Similarly, a study31 of a Chicago housing lottery found no association with ED use when low-income families in private housing were provided a voucher. The reasons why place-based housing might be beneficial in ways that vouchers are not are not well understood.13 The presence of richer social networks is one avenue worth investigation.32,33 Voucher recipients may also face additional challenges associated with finding a private market unit that meets their needs.16,17
Our estimates were not sensitive to a large set of controls. Reductions in ED use were not explained by neighborhood characteristics or better access to primary care. Our study could not determine whether lower ED use was associated with differential asthma attack severity, unobserved differences in access to an ED or alternative sites of care, or better management of symptoms outside the ED. Given that adherence to asthma treatment can be disrupted by social stressors and enhanced by regular household routines,34,35 it is possible that the stability that rental assistance provides could enable families to better manage asthma symptoms when they occur.
We did not find evidence that rental assistance was associated with reduced asthma attacks, which could suggest that rental assistance may not be associated with improved underlying health. Such results are consistent with experimental evidence from the Family Options Study36 and evidence from the Panel Study of Income Dynamics,37 which suggests that rental assistance has no association with parent-reported child health. However, the CIs in the asthma attack models were wide, and we cannot rule out clinically meaningful increases or decreases in the incidence of asthma attack, and we did not observe asthma attack severity.
We also observed differences in ever being diagnosed with asthma, which were significant at the 10% level in the fully adjusted model. That difference was unexpected given that current HUD assistance program recipients were previously exposed to a wait period and thus might be expected to have the same or an increased risk of a past diagnosis. It is possible that in some cases the ED visit was the child’s first asthma-related medical encounter, in which case it would follow that the group that was less likely to visit the ED would also have a lower prevalence of ever being diagnosed. Another explanation is differential response error. Emergency department visits are salient events that could improve recall of past asthma diagnoses. The higher rate of ED use in the quasi–wait-list group might have been associated with improved reporting compared with current participants.
This study had a number of limitations. We cannot definitively exclude the possibility that the changes we observed were associated with factors other than entry into a HUD program. However, the design accounted for time-invariant selection by comparing current HUD program participants with those who would enter such programs in the future. We also did not observe trends leading to HUD entry, suggesting that our results are not associated with time-varying factors, such as changes in a family’s economic status or regression to the mean. Our study period extended back to 1999 and might not be generalizable to current HUD programs. For example, the size and composition of the HUD program population change with changes in housing markets, the broader economy, and program rules and regulations. Furthermore, the relative benefit of rental assistance is a function of the affordability of housing.
Our findings may have important implications for policy makers and health care practitioners who are considering how to leverage health care system resources in cross-sector collaborations that address the social determinants of health. Our estimates suggest that rental assistance may be associated with health care system savings. However, the magnitude remains uncertain given that we did not observe the number of avoided ED visits or typical treatment intensity.
Accepted for Publication: October 16, 2019.
Corresponding Author: Michel Boudreaux, PhD, Department of Health Policy and Management, University of Maryland School of Public Health, 4200 Valley Dr, College Park, MD 20742 (email@example.com).
Published Online: March 9, 2020. doi:10.1001/jamapediatrics.2019.6242
Author Contributions: Dr Boudreaux had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: All authors.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Boudreaux.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Boudreaux, Fenelon, Slopen.
Obtained funding: Boudreaux, Fenelon, Slopen.
Administrative, technical, or material support: Boudreaux, Fenelon.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported by a grant from The Eunice Kennedy Shriver National Institute of Child Health and Human Development.
Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
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