Back

Anticipating and assessing adverse and other unintended consequences of public health interventions: the (CONSEQUENT) framework

Stratil, J. M.; Biallas, R. L.; Movsisyan, A.; Oliver, K.; Rehfuess, E. A.

2023-02-07 public and global health
10.1101/2023.02.03.23285408 medRxiv
Show abstract

1Despite the best intentions public health interventions (PHIs) can have adverse and other unintended consequences (AUCs). AUCs are rarely systematically examined when developing, evaluating or implementing PHIs. We used a structured, multi-pronged and evidence-based approach to develop a framework to support researchers and decision-makers in conceptualising and categorising AUCs of PHIs. We employed the best-fit framework synthesis approach. We designed the a-priori framework using elements of the WHO-INTEGRATE framework and the Behaviour Change Wheel. Next, we conducted a qualitative systematic review of theoretical and conceptual publications on the AUCs of PHIs in the databases Medline and Embase as well as through grey literature searches. Based on these findings, we iteratively revised and advanced the a-priori framework based on thematic analysis of the identified research. To validate and further refine the framework, we coded four systematic reviews on AUCs of distinct PHIs against it. The CONSEQUENT framework includes two components: the first focuses on AUCs and serves to categorise them; the second component highlights the mechanisms through which AUCs may arise. The first component comprises eight domains of consequences - health, health system, human and fundamental rights, acceptability and adherence, equality and equity, social and institutional, economic and resource, and ecological. The CONSEQUENT framework is intended to facilitate conceptualisation and categorising of AUCs of PHIs during their development, evaluation and implementation to support evidence-informed decision-making.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.