Housing affordability and health among homeowners and renters

BACKGROUND

Although lack of affordable housing is common in the U.S., few studies have examined the association between housing affordability and health.

PURPOSE

Using quasi-experimental methods, the aim of this study was to examine whether housing affordability is linked to a number of important health outcomes, controlling for perceptions of neighborhood quality, and determining whether this association differs by housing tenure (renting versus owning).

METHODS

Data from the 2008 Southeastern Pennsylvania Household Health Survey, a telephone-based survey of 10,004 residents of Philadelphia and its four surrounding counties, were analyzed. The association between housing affordability and health outcomes was assessed using propensity score methods to compare individuals who reported living in unaffordable housing situations to similar individuals living in affordable ones.

RESULTS

Overall, 48.4% reported difficulty paying housing costs. People living in unaffordable housing had increased odds of poor self-rated health (AOR=1.75, 95% CI=1.33, 2.29); hypertension (AOR=1.34, 95% CI=1.07, 1.69); arthritis (AOR=1.92, 95% CI=1.56, 2.35); cost-related healthcare nonadherence (AOR=2.94, 95% CI=2.04, 4.25); and cost-related prescription nonadherence (AOR=2.68, 95% CI=1.95, 3.70). There were no significant associations between housing affordability and heart disease, diabetes, asthma, psychiatric conditions, being uninsured, emergency department visits in the past year, obesity, and being a current smoker. Renting rather than owning a home heightened the association between unaffordable housing and self-rated health (AOR=2.55, 95% CI=1.93, 3.37 for renters and not significant among homeowners) and cost-related healthcare nonadherence (AOR=4.74, 95% CI=3.05, 7.35 for renters and AOR=1.99, 95% CI=1.15, 3.46 for homeowners).

CONCLUSIONS

The financial strain of unaffordable housing is associated with trade-offs that may harm health. Programs that target housing affordability for both renters and homeowners may be an important means for improving health.

ANCOVA versus change from baseline had more power in randomized studies and more bias in nonrandomized studies

BACKGROUND AND OBJECTIVE

For inferring a treatment effect from the difference between a treated and untreated group on a quantitative outcome measured before and after treatment, current methods are analysis of covariance (ANCOVA) of the outcome with the baseline as covariate, and analysis of variance (ANOVA) of change from baseline. This article compares both methods on power and bias, for randomized and nonrandomized studies.

METHODS

The methods are compared by writing both as a regression model and as a repeated measures model, and are applied to a nonrandomized study of preventing depression.

RESULTS

In randomized studies both methods are unbiased, but ANCOVA has more power. If treatment assignment is based on the baseline, only ANCOVA is unbiased. In nonrandomized studies with preexisting groups differing at baseline, the two methods cannot both be unbiased, and may contradict each other. In the study of depression, ANCOVA suggests absence, but ANOVA of change suggests presence, of a treatment effect. The methods differ because ANCOVA assumes absence of a baseline difference.

CONCLUSION

In randomized studies and studies with treatment assignment depending on the baseline, ANCOVA must be used. In nonrandomized studies of preexisting groups, ANOVA of change seems less biased than ANCOVA, but two control groups and two baseline measurements are recommended.

Assessing non-consent bias with parallel randomized and nonrandomized clinical trials

In some randomized clinical trials, a large proportion of patients eligible for randomization may withhold consent to be randomized. When the subjects in the randomized trial differ from the eligible population with respect to characteristics that are associated with the magnitude of the treatment effect, there may be non-consent bias, i.e., the treatment effect for those in the randomized trial may not reflect the treatment effect for the eligible population. In response to this problem, some investigators have conducted, in addition to the randomized trial, a separate nonrandomized but otherwise identical trial consisting of those patients who are eligible for randomization, but instead choose their own treatment. Observed baseline covariate data can be used to adjust for differences between the randomized population and the eligible population when estimating the treatment effect for the eligible population. After adjusting, different outcomes for the randomized versus nonrandomized treated groups and/or the randomized versus nonrandomized control groups reflect the presence of hidden non-consent bias resulting from differences between the trial population and the eligible population with respect to unobserved covariates. A sensitivity analysis can display how hidden non-consent bias can account for an imbalance in the treatment groups with respect to an unobserved covariate. A parallel randomized and nonrandomized trial which compares adenoidectomy versus medical treatment for children with recurrent otitis media [Paradise et al. Efficacy of adenoidectomy for recurrent otitis media in children previously treated with tympanostomy-tube placement. J Am Med Assoc 1990; 263: 2066-2073] is used as an illustration.