Has mortality due to other causes increased during the Covid-19 pandemic? Early evidence from England and Wales

Sotiris Vandoros

Looking at trends in a variable alone before and after a "treatment" can be misleading as there may be other factors driving any change. For that purpose, a control group can help filter out any other effects. Such a control group will have to remain unaffected by the treatment. The covid-19 pandemic is a major global crisis, involving over 120,000 deaths so far and a lockdown in many countries, so identifying a control group for the same period seems impossible. Instead, I follow an approach similar to that by Metcalfe et al5 who used trends in the same variable in the year before as a control group. Similarly, I used deaths in the first 14 weeks in 2019 as a control group for non-covid-19 deaths in the 14 first weeks in 2020. In addition, in one of the graphs I also used the average number of deaths in the previous five years, which is, however, subject to changes in medical technology and other factors that may affect mortality, particularly any type of mortality that may demonstrate seasonal effects, as well as changes in the population. Population data for 2020 by demographic group are yet to be released.14 In addition to a graphical analysis comparing trends in deaths excluding covid-19 deaths in every week so far in 2020 to the control group, I also used a differences-in-differences (D-I-D) econometric approach. The dependent variable is the number of deaths in each of the 14 first weeks of 2020 and 2019, excluding any deaths that mentioned covid-19 in the death certificate. In other words, deaths of people who suffered from covid-19 are not included in the analysis, in order to study any spillover effects on other deaths. A differences-in-differences approach includes a treatment group dummy variable, which takes the value of 1 for the group that is affected by the intervention, and zero otherwise. In this case, observations in 2020 take the value of 1, and observations in 2019 take the value of zero. Another dummy that is included is an "after" variable, which takes the value of 1 in the period after an intervention (for both groups, 2019 and 2020), and zero otherwise. We consider the treatment period to start in week 10, as that is the week when the first covid-19 death was reported, thus indicating an escalating situation and capturing any spillover effects of the virus. One might argue that the treatment period should start later, when the number of deaths started increasing steeply, but a question that remains is where we should draw the line, and this would possibly relate with the cause of any spillover effects on the number of deaths, which is currently unknownso identifying where the treatment period should start becomes particularly challenging. The interaction of these two dummy variables (treatment*after) is the main variable of interest. I also used region fixed effects, depending on the model, as well as a variable for sex and/or dummies for each age group. Robust standard errors were used in all regressions. A differences-in-differences approach requires that the common trend assumption is met. This may not always be the case, . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.