The use of disaggregate data in evaluations of public health interventions: cross-sectional dependence can bias inference

Abstract

Abstract Higher availability of administrative data and better infrastructure for electronic surveys allow for large sample sizes in evaluations of national and other large scale policies. Although larger datasets have many advantages, the use of big disaggregate data (e.g., on individuals, households, stores, municipalities) can be challenging in terms of statistical inference. Measurements made at the same point in time may be jointly influenced by contemporaneous factors and produce more variation across time than suggested by the model. This excess variation, or co-movement over time, produce observations that are not truly independent (i.e., cross-sectional dependence). If this dependency is not accounted for, statistical uncertainty will be underestimated, and studies may indicate reform effects where there is none. In the context of interrupted time series (segmented regression), we illustrate the potential for bias in inference when using large disaggregate data, and we describe two simple solutions that are available in standard statistical software.

Keywords

• Cross-sectional dependence
• Contemporaneous error
• Public health
• Intervention
• Interrupted time series
• Segmented regression