The social and economic conditions that surround us can affect our health. This is not a new idea. If the notion was not broadly appreciated by the time it was formalized in the Lalonde report, the point was hammered down more thoroughly in the Marmot review. However, with little evidence quantifying the health impact of policies meant to address these conditions, this idea has large stayed as just that – an idea. Due to their complexity, we have been rarely able to answer questions like, ‘What exactly would happen to people’s health if we passed policy x, y, or z?’ or ‘How many less people would get sick?’
Part of the challenge is the lack of analyzable policy information. The other part is the inherent difficulty in using conventional epidemiologic methods to answer such questions. Taking on these challenges, the McGill-based MachEquity project (2010-) has been building databases and applying robust methods in an effort to build a slew of causal evidence of social policy effects on health in low and middle income countries. Below is a summary of their work and recent launch of data for public use.
How has MachEquity built ‘robust’ evidence?
First, there is the stringently-coded policy data. Focusing on national social policies, staff looked at full legislation documents in each country, amendments and appeals, or secondary sources if original documentation was not available. Two researchers then quantified the legislation into something meaningful (for example, the months paid maternity leave legislated), ultimately resulting in longitudinal datasets for with policy information from 1995-2013.
Second, the group is making use of quasi-experimental methods that attempt to mimic random assignment. The latter is the ‘gold standard’ to evaluate the impact of anything (policy, intervention, drug…) because it ensures that we eliminate all other potential explanations for any differences we see in the people’s outcomes (e.g. their health status post-experiment). Evidently, perfectly controlled randomized experiments are most often impossible when we are dealing with social determinants of health (can we randomize people to have more education?). Enter: quasi-experimental methods. There are entire books and courses on this (literally – look here), but the basic idea is that we can mimic randomization by eliminating other sources of variation, e.g. over time, in different types of people, or in different countries, by controlling for it in specific ways.
So what have they done so far?
Built policy databases. Published. Presented. A lot. See here. But not only that, researchers and staff on this project work in close collaboration with partners at the Department of Global Affairs, non-governmental organizations such as CARE, and ‘package‘ their work for policy-maker audiences . After all, the actual policy-making is in their hands, which are often far out of reach from academic research. Specific research topics have included the effect of removing tuition-fees, health service user-fees, maternity leave legislation, minimum age-of-marriage laws on outcomes like vaccination uptake, child mortality, adolescent birth rates and nutrition.
Where is the policy data and how can I use it?
Policy datasets on maternity leave, breastfeeding breaks at work, child marriage and minimum wage are now available for download here! For each determinant, longitudinal data on low and middle income countries’ policies is available. You can therefore make use of quantified social policy information and changes in legislation over time, and the infinite possible analyses that such data lends itself to.