Working Paper: NBER ID: w24347
Authors: Rebecca Mary Myerson; Darius Lakdawalla; Lisandro D. Colantonio; Monika Safford; David Meltzer
Abstract: Screening interventions can produce very different treatment outcomes, depending on the reasons why patients had been unscreened in the first place. Economists have paid scant attention to these complexities and their implications for evaluating screening programs. In this paper, we propose a simple economic framework to guide policy-makers and analysts in designing and evaluating the impact of screening on treatment uptake. We apply these insights to several salient empirical examples that illustrate the different kinds of effects screening programs might produce. Our empirical examples focus on contexts relevant to the top cause of death in the United States, heart disease. We find that currently undiagnosed patients differ from currently diagnosed patients in important ways, leading to lower predicted uptake of recommended treatment if these patients were diagnosed. Additionally, changes in the composition of diagnosed patients can produce misleading conclusions during policy analysis, such as spurious reductions in measured health system performance as screening expands.
Keywords: Health Screening; Treatment Uptake; Chronic Conditions; Health Policy
JEL Codes: D0; D8; I1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| increasing access to screening for chronic conditions (I14) | increase treatment (C22) |
| increasing access to screening for chronic conditions (I14) | lower treatment uptake due to higher ex ante costs (D61) |
| higher ex ante costs (D61) | lower treatment uptake (I12) |
| patients diagnosed through expanded screening (I12) | lower treatment uptake (I12) |
| patients who were least likely to have their biomarkers assessed (I11) | significantly less likely to use recommended treatments after diagnosis (I12) |
| changes in the composition of diagnosed patients (I12) | misleading conclusions about health system performance (I10) |
| higher diagnostic intensity (C32) | lower rates of treatment among diagnosed patients (I12) |
| expanded screening (I13) | less cost-effective due to low treatment uptake among newly diagnosed patients (I12) |