1. The use of a wide range of methodological approaches/and inconsistent data sets is resulting in contradictory and non-comparable studies. This situation is to some extent inherent to the scientific process in this context. However, studies should attempt to meet a minimum standard of good practice in data use and methods, including: the need to account for relevant non-environmental predictors; to justify the data quality and relevance of the selected response variable; to be clear about the epidemic phase being tested (including time lags); to spatially and temporally align epidemiological and CME data; and to distinguish between analyses that are suited to describe observed relationships and those that have been confirmed to provide skillful prediction and forecast.
The release of a huge volume of studies (many made available pre-review) that do not meet such criteria, presents a challenge for non-experts attempting to distill actionable information from the literature.
2. Even well-designed and strongly controlled studies are currently limited by the fact that the COVID-19 data record is short and of inconsistent quality to enable sound intercomparisons and associations to be identified. This, combined with significant local heterogeneity in confounding variables, demographics, and non-pharmaceutical interventions, and CME conditions themselves has made it difficult to identify CME sensitivities of the disease. It is observed CME factors may also interact with each other to modify disease risk, as well as with other co-prevalent diseases.
3. The lack of firm mechanistic understandings of how CME factors may influence SARS-CoV-2 or COVID-19 disease is a significant constraint. While empirical studies cannot wait for a complete mechanistic explanation for observed CME influence, the strength of empirical studies will improve as complementary work on mechanisms becomes available.