Week 7 Post

Week 7 Post

by Lekha Tummalapalli -

· Identify a policy that is not usually intended to be a health policy but that you think may have important health implications.

A policy with critical health implications is California Senate Bill 50, a bill that would lift restrictions to build housing near train stations and bus stops. The state of California ranks 49th, only behind Utah, in the number of housing units per capita. This artificial restriction of housing development increases living costs, commute times, and homelessness. If SB-50 passes, it would increase housing supply and drive down rent prices. Excessive commute times are associated with worse mental and physical health. Homelessness is strongly associated with worse health outcomes. Increasing the number of housing units in urban areas (near transit) may decrease the use of cars and increase walking, thus having a positive impact on exercise and physical health. Furthermore, if economic productivity increases with SB-50, these benefits could be reclaimed by the state through taxation and funneled towards other social programs. 

· Describe why an evaluation of that policy is informative (e.g., determining effects of the policy, or primarily a test of hypothesized mediators).

Evaluating SB-50 would quantify its impact on private and public income and provide justification for the policy. Opponents of SB-50 are current property owners, whose property values would suffer, and local neighborhood planning commissions. Demonstrating the benefits of SB-50 on the health of their neighborhoods and communities may dissuade some political opposition.

· Specify the outcomes and populations you think most affected or least affected by the policy.

Population: Low-income and medium-income individuals and families.  Outcomes: Commute times, cortisol levels, self-reported health, mental health scores, depression, blood pressure, weight, incident heart disease. 

Outcomes: Homeless individuals. Outcomes: Housing insecurity, hospitalization, readmission, mortality. 

· Propose a study design to evaluate the policy.

We could implement the policy at different times in different cities in California, and compare city-level health statistics of the above outcomes using difference-in-differences. We could then select a sample of low-income individuals in cities affected by the policy compared with cities not affected, and assess individuals' housing instability, self-reported health, blood pressure, weight, and laboratory measurements. 

· Describe biggest challenge to implementing and drawing inferences about the impact of the policy on health.

First, it may be difficult to convince policy makers to implement the policy at different times in different cities.  Second, there is a time lag between when the policy takes effect and when housing units will actually be build, and individuals will realize expected benefits from the housing. Third, it will be difficult to ascertain which individuals specifically were affected by the policy because it is difficult to imagine the counterfactual. Lastly, the comparison group will have to be carefully selected - comparing individuals across cities is difficult because the cities are so different and there may be different secular trends within different cities. 

Re: Week 7 Post

by Chi Chu -
This reminded me of a related thought from epi methods class I wanted to share --

In terms of evaluating and interpreting results/drawing conclusions of an evaluation of this policy, trying to frame it or understand it as an effect of housing, it reminds me of the question of "consistency", which is one of the criteria for identifying causal effects. It has to do with having different "versions" of treatment, in other words, the details of *how* a policy houses people matters, and is something you'd have to think about when trying to understand/explain the effect of housing (or argue for policies to improve housing in different settings). I can also imagine how policies might interact with local factors and would not be expected to work similarly in diverse settings, because the intervention isn't just "housing", it is the whole policy.

This difficulty also isn't necessarily unique to these kind of research questions/treatments/outcomes -- for example, if we're trying to see if high cholesterol causes heart disease, it matters *how* a study manipulated cholesterol (statin? exercise? diet? not clear how to disentangle the "high cholesterol causes heart disease", "lowering cholesterol prevents heart disease", or by extension, "lowering cholesterol is an acceptable surrogate outcome").