Housing environments and asthma outcomes within population-based samples of adults and children in NYC

Abstract

Exposure to indoor environmental risk factors is associated with patterns of asthma morbidity. In this study, we assessed the relationship between housing type (i.e., home ownership, public housing, rental assistance, rent-controlled housing and other rental housing) and asthma outcomes among New York City (NYC) adults and children (ages 1–13). We used the 2019 NYC Community Health Survey (CHS) and 2019 NYC KIDS survey to analyze associations between housing type and ever having been diagnosed with asthma (“ever asthma”) and experiencing a past-year asthma attack. We further examined whether associations were modified by smoking status (among adults), smoking within the home (among children), and overweight/obesity. Among adults, living in public housing, compared to home ownership, was associated with higher odds of ever asthma (odds ratio [OR] = 1.95; 95% confidence interval [CI] = 1.35, 2.84), and past-year asthma attack (OR = 2.24; 95% CI 1.21,4.18). Living in rental assistance housing was also significantly associated with ever asthma (OR = 1.75; 95% CI 1.16, 2.66). Associations between public or rental assistance housing and ever asthma were marginally non-significant among children. Associations between living in public or rental assistance housing and ever asthma were more pronounced among ever smokers than among never smokers. Housing environments remain important predictors of both pediatric and adult asthma morbidity. Associations between living in subsidized housing and asthma outcomes among adults are most apparent among ever smokers.

1. Introduction

In 2019, approximately 25 million adults and children had asthma in the United States (U.S.) (Centers for Disease Control and Prevention, 2019). Between 2008 and 2013, the annual economic cost of asthma was over $81.9 billion, including medical costs and loss of work and school days (Nurmagambetov et al., 2018). The prevalence of asthma is disproportionately higher among low-income individuals and racial/ethnic minorities (Centers for Disease Control and Prevention, 2019; Centers for Disease Control and Prevention, 2015). Prior research has identified environmental risk factors, including housing conditions and indoor environments, as potential determinants of these disparities (Gold and Wright, 2005; Bryant-Stephens, 2009; Canino et al., 2009; Bryant-Stephens et al., 2021). Salient poor housing conditions that can serve as risk factors for asthma (e.g. pest and rodent infestation, mold and fungus, poor ventilation, and secondhand smoke (SHS)) are more prevalent in lower income housing, including public and other subsidized housing (Findley et al., 2003; Northridge et al., 2010; Russo et al., 2015; Levy et al., 2004; Mehta et al., 2018; Rosenfeld et al., 2011).

In 2016, over 2 million people lived in public housing in the U.S. (Demographic Facts Residents Living in Public Housing, 2016) New York City (NYC) has the largest population of public housing residents with approximately 340,000 official residents, including 1 in every 16 New Yorkers (New York City Housing Authority. NYCHA, 2022). Several studies have examined the role of public housing in relation to asthma in the context of NYC (Northridge et al., 2010; Chew et al., 2006; Corburn et al., 2006). Northridge and colleagues (2010) reported that children living in private family homes had a decreased risk for current asthma compared to children in public housing (Northridge et al., 2010). An ecological study showed that neighborhoods with the highest asthma hospitalization rates had a higher proportion of public housing buildings (Corburn et al., 2006). However, there is scarce population-based research carefully examining the influence of various housing types – including public housing, rental assistance housing, and rent-controlled housing – on asthma in both adults and children. A study focused on an adult population in Boston found that living in public housing and receiving rental assistance were both associated with higher odds of current asthma, compared to owning a home (Mehta et al., 2018). Another nationally representative study examined children whose families received rental assistance compared to being on a wait-list for rental assistance to evaluate the effectiveness of federal assistance programs (Boudreaux et al., 2020), finding that receiving rental assistance was associated with lower rates of asthma ED visits among children with a recent asthma attack. Additional comparative studies among various housing types are needed to elucidate differential risk patterns for asthma outcomes based on living environments. In addition, although most prior studies have adjusted for individual-level socio-demographic covariates, the majority have not fully addressed potential confounding by neighborhood-level characteristics, which could lead to biased results.

In our study, we utilized two population-based datasets representing adult and child populations in NYC. We investigated associations between housing type and lifetime asthma/asthma attacks with specific categories for homeowners, public housing residents, renters receiving rental assistance, renters in rent-controlled/rent-stabilized units, and other renters, after adjusting for potential individual- and neighborhood-level confounders. We also examined whether associations between housing type and lifetime asthma were modified by other established risk factors for poor asthma outcomes, including smoking status (among adults), frequency of smoking within the home (among children), and body mass index (BMI) (Strine et al., 2007; Eisner et al., 2005; Figueroa-Munoz et al., 2001).

2. Methods

2.1. Data and variables

We used 2019 NYC Community Health Survey (CHS) and 2019 NYC KIDS data, which are population-based representative samples of adults (aged 18 and older) and children (aged 1 to 13), respectively, in the five boroughs of New York City (Manhattan, Brooklyn, Queens, Bronx, and Staten Island). The NYC CHS is a cross-sectional telephone survey of approximately 8500 adults with an annual stratified randomized sample to establish citywide estimates. Sampling for NYC CHS is done from land lines and cell phones including random digit dialing landline and cell numbers as well as ‘out of area’ cell numbers of NYC residents with non-NYC numbers. Among those who completed the survey, 26.6% and 73.5% of participants were recruited from land lines and cell phones, respectively. The survey used computer-assisted telephone interviewing, and the cooperation rates were 76.9% for landline and 80.6% for cell phone, defined by American Association for Public Opinion Research (AAPOR) cooperation rate #3 (the number of those who participated in the survey divided by the number of individuals in the sample who were contacted and identified as eligible). The post-stratification weights were created by age, gender, race, number of adults and children in household, marital status and educational attainment, as well as the distribution of the adult population comprising three telephone usage categories (landline only, landline and cell, cell only) using data from the NYC Housing and Vacancy Survey. The 2019 NYC KIDS combined three subsamples: 2019 NYC CHS respondents who had children aged 1–13 (n = 738), mothers who gave birth to children in NYC between April 1, 2013 and October 1, 2018 ascertained from birth certificates (n = 5453), and families of NYC public school students born between January 1, 2005 and April 1, 2015 (n = 2098) ascertained through the Automated Student Health Record. Approximately 8289 households with one or more children aged 1 to 13 years were interviewed. The respondents were parents, guardians, or other family members who were sufficiently knowledgeable about a randomly selected child’s health, doctor visits, and general activities, as well as family and neighborhood characteristics. The 2019 NYC CHS and 2019 NYC KIDS data were each weighted to population estimates as per the 2018 American Community Survey (ACS). Study methods for the NYC CHS and KIDS data used were approved by the NYC Department of Health and Mental Hygiene’s IRB.

All individuals without missing data for any of the variables were included in the analytic sample (NYC CHS: N = 8271; NYC KIDS: N = 7896 for the “ever asthma” analysis). Variables used in this study had relatively low levels of missingness in the CHS and KIDS data files. Descriptive statistics for the complete datasets, including missing values, are provided in Appendix Table 10. The highest prevalence of missing data occurred with regard to housing type (2.9% and 3.5% missing in CHS and KIDS, respectively).

The two outcome measures were ever having been diagnosed with asthma (“ever asthma”) and experiencing an asthma attack in the past year, which were assessed with the following CHS/KIDS questions: (1) “Have you ever been told by a doctor, nurse or other health professional that you had asthma?”/“Has a doctor or health professional ever told you or another caregiver that [child] has asthma?” and (2) “In the last 12 months, have you had an episode of asthma or an asthma attack?”/“During the past 12 months, has [child] had an episode of asthma or an asthma attack?” Questions about past-year asthma attacks were asked among individuals who had ever been diagnosed with asthma. Responses were coded as “Yes”, “No”, “Don’t know”, and “Refused”. The outcomes were analyzed as binary variables, with “Don’t know” and “Refused” recoded to missing.

The primary exposure variable was the housing type of respondents. Participants were first asked whether their current home was owned or rented, and then among the non-homeowners, type of rental housing was asked, with the following options: (1) public housing, (2) rental assistance, (3) rent-controlled/rent-stabilized, or (4) none of these. From the responses, we coded the types of housing as (1) owned home, (2) public housing, (3) rental assistance, (4) rent-controlled, and (5) other rented housing as a categorical variable.

Participants provided individual-level sociodemographic information on age, sex, race/ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, non-Hispanic other), household income (<100% of the federal poverty level (FPL), 100- < 200% of the FPL, 200- < 400% of the FPL, 400- < 600% of the FPL, 600% + of the FPL), and educational attainment (< high school, high school or equivalent, some college, college graduate), which was measured at an individual-level in CHS and at a household-level in KIDS. Neighborhood ZIP code-level poverty status (percentage of households with incomes below the federal poverty level) was also included in the analysis and coded as low (<10%), medium (10 to <20%), high (20 to <30%), and very high (30%+). Additionally, we adjusted for overweight/obesity (binary), based on current self-reported weight and height for CHS participants and child lifetime overweight or obesity, as diagnosed by health professionals for KIDS participants. Finally, we adjusted for smoking status (ever versus never) among CHS participants, and frequency of smoking in the home (any versus none) among KIDS participants. We considered overweight/obesity and exposure to tobacco smoke as potential confounders, given differential rates of exposure to these factors across housing environments (Bowen et al., 2018), as well as their contributions to patterns of asthma morbidity (Beuther et al., 2006; Stapleton et al., 2011). Following examples in prior literature (Mehta et al., 2018), overweight/obesity, smoking status, and smoking in the home were also examined as potential effect modifiers. Theoretical mechanisms include the potential for the effects of exposure to sub-standard housing conditions on asthma outcomes to be magnified by obesity status, as there are effects of leptin and obesity on airway inflammation (Beuther et al., 2006). There is also established evidence of potential synergistic associations between smoking status and household-based exposures with regard to adult-onset asthma (Jaakkola et al., 2020).

2.2. Statistical analysis

We calculated weighted descriptive statistics for the 2019 NYC CHS and KIDS analytic samples. We then fit weighted bivariate and multi-variable logistic regression models to estimate associations between housing type and odds of ever asthma and asthma attack in the past year, with 95% confidence intervals. Minimally adjusted models included age, sex, and race/ethnicity. Fully adjusted models included individual (CHS)/household (KIDS) educational attainment, household income, neighborhood-level poverty, smoking status (CHS), smoking within the home (KIDS), and current (CHS)/lifetime (KIDS) overweight/obesity. We assessed the effect modification by examining the significance of interactions between housing type and smoking status, smoking within the home, and overweight/obesity. We tested both multiplicative and additive interactions, as interactions are dependent on scale, and reporting both scales is encouraged (VanderWeele and Knol, 2014). Where there was evidence of a significant interaction, we plotted the predicted probabilities of the outcome, based on average marginal effects (AMEs) from the fully adjusted logistic regression models, holding all covariates at their observed values (Dow et al., 2019).

We conducted several sensitivity analyses. For both samples, we estimated models including a categorical variable representing the five boroughs of NYC to account for potential geographic variations in ambient air quality and other environmental factors. Within the CHS sample, we also restricted the analysis to individuals who had lived in their current home for at least four years (a housing tenure variable was not available in the KIDS dataset). Finally, we restricted both samples to household income <200% of the FPL.

All analyses were conducted using Stata version 16.0 and incorporated weights and survey design parameters. Analyses used secondary, de-identified data.

3. Results

Weighted descriptive statistics for both analytic samples are included in Table 1. Among NYC adults in 2019, 37.3% reported owning their home, while 7.0% lived in public housing, 5.5% received rental assistance, 11.7% lived in rent-controlled housing, and 38.5% lived in other rented housing. The prevalence of ever having asthma was 14.4%, and 4.6% of adults had experienced a past-year asthma attack. Among NYC children in 2019, 29.8% reported living in an owned home, while 8.0% lived in public housing, 7.6% received rental assistance, 11.4% lived in rent-controlled housing, and 43.2% lived in other rented housing. The prevalence of ever having asthma was 12.2%, and 4.9% of children in NYC had experienced a past-year asthma attack. Appendix Tables 1 and 2 include distributions of all variables used in regression models across housing types in each sample.

Table 1.

Weighted descriptive statistics for analytic samples. NYC CHS and NYC KIDS 2019.

NYC CHS		NYC KIDS
Total unweighted N	8271	Total unweighted N	7896
Weighted N	6,075,000	Weighted N	1,208,000
Housing (%)		Housing (%)
Owned home	37.3%	Owned home	29.8%
Public housing	7.0%	Public housing	8.0%
Rental assistance	5.5%	Rental assistance	7.6%
Rent-controlled housing	11.7%	Rent-controlled housing	11.4%
Other rented housing	38.5%	Other rented housing	43.2%
Age (%)		Age (%)
18–24	13.0%	1–4	33.6%
25–44	40.0%	5–13	66.4%
45–64	31.8%
65+	15.3%
Sex at birth (%)		Sex (%)
Male	46.8%	Male	50.9%
Female	53.2%	Female	49.1%
Race/ethnicity (%)		Race/ethnicity (%)
Non-Hispanic white	36.3%	Non-Hispanic white	26.4%
Non-Hispanic black	22.6%	Non-Hispanic black	21.3%
Hispanic	26.0%	Hispanic	34.9%
Non-Hispanic Asian	12.9%	Non-Hispanic Asian	11.7%
Non-Hispanic other	2.2%	Non-Hispanic other	5.7%
Education (individual) (%)		Education (household) (%) 1
College graduate	36.7%	College graduate	45.8%
Less than high school	16.7%	Less than high school	7.2%
High school or equivalent	23.8%	High school or equivalent	20.6%
Some college	22.9%	Some college	26.4%
Household income (%)		Household income (%)
>600% FPL	22.5%	>600% FPL	14.8%
<100% FPL	22.6%	<100% FPL	32.8%
100 - <200% FPL	19.9%	100 - <200% FPL	27.1%
200 - <400% FPL	15.3%	200 - <400% FPL	13.7%
400 - <600% FPL	19.7%	400 - <600% FPL	11.7%
Neighborhood poverty (%) 2		Neighborhood poverty (%) 2
Low	20.8%	Low	17.2%
Medium	42.8%	Medium	39.3%
High	23.6%	High	26.6%
Very high	12.9%	Very high	16.9%
Ever asthma (%)		Ever asthma (%)
Ever had asthma	14.4%	Ever had asthma	12.2%
Never had asthma	85.6%	Never had asthma	87.8%
Past-year asthma attack (%)		Past-year asthma attack (%)
Past-year asthma attack	4.6%	Past-year asthma attack	4.9%
No past-year asthma attack	95.4%	No past-year asthma attack	95.1%
Overweight/obesity (current) (%) 3		Overweight/obesity (lifetime) (%) 4
Overweight or obese	58.6%	Ever overweight or obese	9.8%
Not overweight or obese	41.4%	Never overweight or obese	90.2%
Smoking status (%)		Frequency of smoking in home (%) 5
Ever smoker	31.1%	Any of the time	5.8%
Never smoker	68.9%	None of the time	94.2%

¹⁾Refers to highest level of educational attainment for anyone in the household

²⁾Based on percent of zip code residents below 100% FPL, per ACS 2014–2018 (Low: <10%; Medium: 10 to <20%; High: 20 to <30%; Very high: 30%+)

³⁾Derived variable based on current self-reported weight and height (Over-weight/obesity: 25 ≤ BMI <100)

⁴⁾Based on response to the question “Has a doctor, nurse, or other health professional ever told you or another caregiver that (CHILD) was overweight or obese?”

⁵⁾Based on response to the question “How often does anyone smoke cigarettes, cigars, or other tobacco products inside your home or apartment?” Respondents who selected “All of the time”, “Most of the time”, or “Occasionally”, were considered to be exposed to smoking within the home “Any of the time”.

Bivariate, minimally adjusted, and fully adjusted model results are included in Table 2. In the fully adjusted models, NYC adults living in public housing had an elevated odds of ever asthma (fully adjusted odds ratio (OR) = 1.95; 95% confidence interval (CI) = 1.35,2.84), as did adults receiving rental assistance (fully adjusted OR = 1.75; 95% CI = 1.16,2.66), compared to home owners as the reference category. Likewise, there was a significantly higher odds of past-year asthma attack among adults in public housing (fully adjusted OR = 2.24; 95% CI = 1.21,4.18), compared to home ownership.

Table 2.

Odds ratios for ever asthma and past-year asthma attack associated with housing type, NYC CHS and NYC KIDS 2019.

	Bivariate, OR (95% CI)	Minimally adjusted, OR (95% CI)	Fully adjusted, OR (95% CI)
Ever asthma, CHS (unweighted N = 8271)
Home ownership (ref.)	1	1	1
Public housing	1.97 (1.41,2.77)	1.75 (1.23,2.48)	1.95 (1.35,2.84)
Rental assistance housing	1.72 (1.19,2.49)	1.57 (1.08,2.29)	1.75 (1.16,2.66)
Rent-controlled housing	1.04 (0.75,1.45)	1.03 (0.74,1.43)	1.08 (0.77,1.51)
Other rented housing	1.04 (0.82,1.32)	1.03 (0.80,1.33)	1.09 (0.83,1.42)
Ever asthma, KIDS (unweighted N = 7896)
Home ownership (ref.)	1	1	1
Public housing	3.04 (2.13,4.34)	1.58 (1.07,2.32)	1.42 (0.93,2.17)
Rental assistance housing	3.10 (2.12,4.54)	1.77 (1.20,2.61)	1.52 (0.97,2.36)
Rent-controlled housing	1.90 (1.32,2.73)	1.29 (0.87,1.89)	1.16 (0.77,1.76)
Other rented housing	1.34 (1.01,1.79)	1.03 (0.76,1.41)	0.97 (0.70,1.35)
Past-year asthma attack, CHS (unweighted N = 8271)
Home ownership (ref.)	1	1	1
Public housing	2.68 (1.58,4.56)	2.23 (1.28,3.87)	2.24 (1.21,4.18)
Rental assistance housing	2.35 (1.28,4.29)	1.98 (1.07,3.69)	1.94 (0.99,3.80)
Rent-controlled housing	1.65 (0.93,2.94)	1.64 (0.93,2.89)	1.62 (0.91,2.89)
Other rented housing	1.33 (0.87,2.03)	1.33 (0.86,2.08)	1.37 (0.86,2.20)
Past-year asthma attack, KIDS (unweighted N = 7893)
Home ownership (ref.)	1	1	1
Public housing	2.06 (1.27,3.35)	1.11 (0.67,1.84)	1.14 (0.65,2.02)
Rental assistance housing	1.57 (0.87,2.84)	0.92 (0.51,1.67)	0.92 (0.46,1.82)
Rent-controlled housing	1.16 (0.68,1.99)	0.77 (0.44,1.33)	0.77 (0.43,1.41)
Other rented housing	0.92 (0.61,1.40)	0.68 (0.44,1.04)	0.70 (0.44,1.11)

Note: CI = confidence interval; OR = odds ratio; ORs for ever asthma and past-year asthma attack were estimated in logistic regression models with homeowner as the reference category. Minimally adjusted models controlled for age, sex, and race/ethnicity, and fully adjusted models controlled for these variables along with highest educational attainment, poverty level, smoking within the home (KIDS), smoking status (CHS), lifetime overweight/obesity (KIDS), and current overweight/obesity (CHS). Bold indicates statistical significance at a 0.05 level.

Among NYC children, living in public housing was significantly associated with ever having asthma in the minimally adjusted model (OR = 1.58; 95% CI = 1.07,2.32), compared to home ownership. The point estimate was attenuated in the fully adjusted model (OR = 1.42; 95% CI = 0.93,2.17). Likewise, the association between receiving rental assistance and ever having asthma was statistically significant in the minimally adjusted model (OR = 1.77; 95% CI = 1.20,2.61), but was attenuated in the fully adjusted model (OR = 1.52; 95% CI = 0.97,2.36). There were no statistically significant associations between any housing type and past-year asthma attack in minimally or fully adjusted models among NYC children.

Results from regression models exploring effect modification by overweight/obesity, smoking status (among adults), and smoking within the home (among children) are provided in Appendix Tables 3–6. Interactions for the ever asthma outcome were non-significant with regard to overweight/obesity among NYC adults (multiplicative p = 0.390, additive p = 0.178), overweight/obesity among NYC children (multiplicative p = 0.499, additive p = 0.616), and smoking within the home among NYC children (multiplicative p = 0.413, additive p = 0.400). The interaction between housing type and smoking status with regard to ever asthma among NYC adults was significant on the additive scale (multiplicative p = 0.062, additive p = 0.046). Fig. 1 includes predicted probabilities of ever having asthma by housing type for adults who ever smoked versus those who had never smoked. The increase in ever asthma associated with living in public or rental assistance housing was more pronounced among ever smokers, compared to never smokers. We did not find evidence of effect modification in any instance with regard to past-year asthma outcomes. Plots of AMEs for each interaction model are included in Appendix Figs. 1–7.

Fig. 1.

Predicted probabilities of ever asthma across housing types, with effect modification by smoking status. Average marginal effects from logistic regression model. CHS 2019 (unweighted N = 8271).^a

a) Average marginal effects estimated from results included in Appendix Table 4 (ever asthma analysis).

When we adjusted for borough of residence, estimates were similar in sign and significance to the main analysis (Appendix Table 7). Point estimates were similar in magnitude when the samples were restricted to households with incomes <200% of the FPL, although we had less power to detect significant associations. Consequently, the association between receiving rental assistance and ever having asthma among NYC adults was no longer statistically significant in the fully adjusted model (Appendix Table 8). Finally, we restricted the NYC adult sample to respondents who had reported living in their home for at least four years prior to the survey. In fully adjusted models, associations between living in public housing and ever having asthma or experiencing a past-year asthma attack remained significant, while associations between receiving rental assistance and ever having asthma or experiencing a past-year asthma attack were attenuated (Appendix Table 9).

4. Discussion

We explored associations between housing type (public housing, rental assistance housing, rent-controlled housing, other rented housing, and home ownership) and asthma outcomes among representative samples of NYC adults (ages 18+) and children (ages 1–13). In both samples, we found that living in public housing or receiving rental assistance was associated with an elevated odds of ever having asthma in minimally adjusted models, compared to living in an owned home. These associations remained significant in fully adjusted models for adults but were attenuated and marginally non-significant in fully adjusted models for children. Within the adult sample, living in public housing was also significantly associated with a higher odds of experiencing a past-year asthma attack, compared to home ownership.

These findings are in line with prior studies that have found that living in public or other types of subsidized housing may be associated with higher levels of asthma morbidity. A recent study of adults in Boston found that individuals living in public housing or receiving rental assistance experienced over 1.5 times the odds of ever having asthma, compared to homeowners, in adjusted models (Mehta et al., 2018). An analysis of survey data from a representative sample of NYC school-children similarly found a higher prevalence of asthma among children living in public housing, compared to private housing, although the study did not explore other types of subsidized housing (including rental assistance housing) (Northridge et al., 2010). Differential patterns of asthma morbidity by housing type may reflect differences in exposure to asthma risk factors – including dampness, mold, pests, and SHS (Hughes et al., 2017; Sahakian et al., 2008; Gent et al., 2012; Tiotiu et al., 2020; Watcharoot et al., 2015)– as prior research suggests that these risk factors may be more prevalent in public or multi-family housing environments (Diaz Lozano Patino and Siegel, 2018; Adamkiewicz et al., 2013; Homa et al., 2015). Differences in residential density or age of the housing stock may also contribute to asthma outcomes (Wang et al., 2014; Wang et al., 2017). For example, a recent cluster analysis found that building age and density were key determinants of childhood asthma patterns within NYC (Khan et al., 2021). If these factors are also associated with living in public housing or receiving rental assistance, this may help explain the differential patterns of asthma outcomes observed in this study.

A primary goal of this analysis was to compare associations between housing type and asthma outcomes among both adults and children. While point estimates for ever having asthma were elevated among both adults and children in rental assistance or public housing, this relationship was modestly attenuated among children when potential confounding from economic variables, as well as measures of SHS exposure and overweight/obesity, were included. These findings suggest that studies that do not incorporate information on economic characteristics, SHS exposure, and obesity – particularly when focused on asthma outcomes among children – may be residually confounded. In addition, we found a strong association between housing type and past-year asthma attack among adults, but not among children. Differential associations between housing type and asthma outcomes among children and adults may reflect sampling variation, differential sources of residual confounding, or the underlying heterogeneity of asthma etiology and presentation (Dharmage et al., 2019; Trivedi and Denton, 2019). For example, adults with asthma include individuals with persistent asthma from childhood as well as individuals with adult-onset disease, and prior studies suggest that adults with new-onset asthma may experience more severe respiratory symptoms (Trivedi and Denton, 2019). Future studies probing differential susceptibility to household-based exposures with regard to asthma outcomes among children and adults are warranted, particularly in longitudinal samples.

Following examples in prior literature (Mehta et al., 2018), we also examined the potential for effect modification of the relationship between housing type and asthma outcomes by overweight/obesity, smoking status (among adults), and smoking within the home (among children). We found evidence of statistically significant effect modification by smoking status on the additive scale. Specifically, the association between living in public or rental assistance housing and experiencing an elevated probability of ever having asthma was particularly pronounced among ever smokers. This finding is similar to Mehta et al.’s (2018) analysis of adults in Boston, which found that associations between living in public or rental assistance housing and experiencing a higher odds of current asthma were only significant among ever smokers (Mehta et al., 2018). Further research elucidating patterns of asthma morbidity at the intersection of housing environments and smoking status is warranted to determine whether such associations are the result of biologic interaction between asthma risk factors or other differences between smokers and non-smokers.

Strengths of this study include leveraging the large population of children and adults in NYC who lived in public housing or rental assistance housing to examine asthma outcomes across different types of housing environments and at different stages of the lifecourse. The large sample also enabled an effect modification analysis by overweight/obesity, smoking status, and smoking within the home.

Limitations include the use of cross-sectional data, which did not allow us to examine longitudinal trajectories of asthma outcomes or to establish the temporal sequence between housing exposures and asthma diagnoses. Therefore, associations should not be viewed as causal. Within the adult sample, in particular, it is likely that some asthma diagnoses preceded the current housing environment. For example, when we restricted the sample to adults with at least four years of housing tenure, we found that associations between receiving rental assistance and asthma outcomes were attenuated, which suggests that associations in the main analysis may be partially explained by past living environments. It is also possible that our results were impacted by residual confounding. For example, we were not able to incorporate measures of the built environment (e.g., number of units in the building, building age, or ventilation systems) (Nguyen et al., 2016; Wilson et al., 2011; Kraev et al., 2009), outdoor air pollution (Guarnieri and Balmes, 2014; Brewer et al., 2017) or individual psychosocial factors, including stress (Wright et al., 1998), all of which may be associated with housing type and may be linked to asthma outcomes. Some housing type categories, such as rent-controlled housing, may represent a particularly heterogeneous group of renters and building types; this heterogeneity was not explored in this study. The measurement of some variables (e.g., obesity) differed across child and adult questionnaires. This limits the ability to directly compare results across the two populations. This analysis was conducted among a population of NYC residents, and findings may not be generalizable to other contexts. In addition, the housing type of some respondents may be misclassified, due to errors in self-reporting.

Using two population-based samples, we found that living in public housing or receiving rental assistance was associated with asthma morbidity among NYC adults, while living in public or rental assistance housing was suggestively associated with ever having asthma among NYC children. We further found that associations between public or rental assistance housing and ever having asthma were most pronounced among adults who had ever smoked, compared to those who had never smoked. The results from this study suggest that housing environments remain an important predictor of asthma morbidity. Indeed, reviews have suggested that multi-component housing interventions may improve some asthma-related health outcomes, however, more research is needed to examine the impacts of specific interventions, including those aimed at smoking and SHS exposure (Crocker et al., 2011; Leas et al., 2018). Ongoing research to further elucidate the role of housing environments and housing-based interventions in addressing patterns of asthma morbidity is critical, particularly given persistent disparities in asthma outcomes among both children and adults.