Ecological analyses are not hypothesis testing but hypothesis generating studies. As illustrated in Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70), they assume that a strong correlation between the trend in an indicator of an exposure (vaccine coverage in this example) and the trend in incidence of a disease (trends calculated over time or across geographical regions) is consistent with a causal relationship. Such comparisons at the population level may only generate hypotheses as they do not allow controlling for time-related confounding variables, such as age and seasonal factors. Moreover, they do not establish that the vaccine effect occurred in the vaccinated individuals.
Case-population studies are a form of ecological studies where cases are compared to an aggregated comparator consisting of population data. The case-population study design: an analysis of its application in pharmacovigilance (Drug Saf 2011;34(10):861-8) explains its design and its application in pharmacovigilance for signal generation and drug surveillance. The design is also explained in Chapter 2: Study designs in drug utilization research of the textbook Drug Utilization Research - Methods and Applications (M Elseviers, B Wettermark, AB Almarsdóttir, et al. Editors. Wiley Blackwell, 2016). An example is a multinational case-population study aiming to estimate population rates of a suspected adverse event using national sales data (see Transplantation for Acute Liver Failure in Patients Exposed to NSAIDs or Paracetamol (Acetaminophen, Drug Saf 2013;36(2):135–44). Based on the same study, Choice of the denominator in case population studies: event rates for registration for liver transplantation after exposure to NSAIDs in the SALT study in France (Pharmacoepidemiol Drug Saf 2013;22(2):160-7) compared sales data and healthcare insurance data as denominators to estimate population exposure and found large differences in the event rates. Choosing the wrong denominator in case population studies might generate erroneous results. The choice of the right denominator depends not only on a valid datasource but will also depend on the hazard function of the adverse event.
A pragmatic attitude towards case-population studies is recommended: in situations where nation-wide or region-wide EHR are available and allow assessing the outcomes and confounders with sufficient validity, a case-population approach is neither necessary nor desirable, as one can perform a population-based cohort or case-control study with adequate control for confounding. In situations where outcomes are difficult to ascertain in EHRs or where such databases do not exist, the case-population design might give an approximation of the absolute and relative risk when both events and exposures are rare. This is limited by the ecological nature of the reference data that restricts the ability to control for confounding.