We then followed the two groups for a few weeks to evaluate the effect of the mandate by comparing the difference between paired counties. We ended up with two groups of counties that were comparable in demographics and pandemic severity, but one group had mask mandates during our study period while the other did not. Therefore, for every county we included in our study, we used a matching strategy to find a control county that had similar population density, acceptance towards mask wearing, and number of cases, but no mask mandate. Moreover, a mask mandate may have different effects depending on the characteristics of the community in which it is enacted (e.g., population density, acceptance of mask wearing). Any change in case incidence before and after a mask mandate is put in place could actually be due to (confounded by) those other factors. Since mask mandates are most likely to be enacted when cases are surging, the timing may align with changes in other factors, such as the public starting to reduce social activities and increase personal hygiene practices. In our newly published Health Affairs study, we used a matching method to study the impact of mask mandates on COVID-19 case incidence at the county level between March and October 2020. What is “weighting”? It is to replicate study units based on their characteristics. What is “matching”? Matching is used to group study units with similar characteristics that may be associated with the outcome by pairing, sub-classification or sub-setting the study units. Two general types of statistical methods to use are “matching” and “weighting.” The key here is to make or find similar and comparable study units under different policies. Therefore, we often try to emulate a RCT to study the effect of health policies. However, to evaluate the effect of a health policy, particularly a policy to mitigate a pandemic, it is not practical to randomly assign the population to different policies. In health-focused research, randomized controlled trials (RCT) have long been considered the gold standard for causal inference because the randomization, which randomly assigns study units to intervention categories, is believed to break any links between intervention and confounders so that the estimated effect is not impacted by other factors. To learn whether the switch can turn off the light, we can apply causal inference. In the power outage example, flicking the switch is the exposure, to turn off the light is the outcome and the unobserved factors are called confounders. In statistics, to answer this type of question we often need to use special study designs or analysis methods, called causal inference, given that regular association analysis could lead to biased results that are confounded by other factors. It is like a power outage happened in the neighborhood at the same time we flicked the switch it’s hard to conclude whether the switch can control the light even though the light was off after we flicked it. The change could also be due to other unobserved factors that occurred around the same time that the policy was enacted. However, evaluating the impact of health policies is not as simple as flicking the switch, as just because we see changes following the start of a new policy, it does not necessarily mean that policy caused the change. For example, did a particular policy reduce transmission of the virus or decrease COVID-related deaths? If we imagine a policy as a switch and the pandemic as a light, it is similar to asking whether the switch can turn off/down the light. The question we often want to ask is whether the policy can mitigate the impact of the pandemic. For example, what was the source of this pandemic? Will there be new breakthrough variants? And, more importantly, did we use the right strategies to mitigate this pandemic, and have we gained sufficient knowledge to do better next time?Įvaluating the effectiveness of health policies during a pandemic is critical for informing future efforts. More than two years into the COVID-19 pandemic when people everywhere are working hard to summon the energy for another chapter, many questions remain unanswered.
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