Optimizing Medical Guidelines: Insights Using a Generalized Method of Moments Approach

Noncompliance with public health guidelines is a widely discussed public health issue, but it can also reflect rational, utility-maximizing behavior. Using 41.1 million birth records from 2011-2021, we explore three possible explanations: asymmetric information, divergent objectives between experts and the public, and limitations in the guidelines themselves. We model the expert committee's objective function as minimizing an aggregate health risk score conditional on prepregnancy BMI and estimate its underlying risk weights by developing and using a Generalized Method of Moments (GMM) estimator. We then compare these with the risk preferences revealed by birthing people. Our findings show that experts implicitly assign equal weight to the risks of low and high birth weight, whereas birthing people place less weight on the risk of high birth weight. We then demonstrate that the current guidelines are suboptimal-even under the criteria they were designed to meet. We propose a new, individualized guideline structure that adjusts continuously with prepregnancy BMI. These improved guidelines not only reduce aggregate health risks but also better reflect birthing people's preferences-offering a path to significantly higher compliance and more effective public health policy. The paper illustrates the broader applicability of the GMM framework for recovering latent preferences in structural models of health behavior. JEL Classification: I12, I15, J18.

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Journal
Statistics in Medicine
Published
2026-04-01
DOI
https://doi.org/10.1002/sim.70486
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing Medical Guidelines: Insights Using a Generalized Method of Moments Approach

Vidhura Tennekoon
Statistics in Medicine
Advanced Causal Inference Techniques
article

Optimizing Medical Guidelines: Insights Using a Generalized Method of Moments Approach

Vidhura Tennekoon
article en

Abstract

Noncompliance with public health guidelines is a widely discussed public health issue, but it can also reflect rational, utility-maximizing behavior. Using 41.1 million birth records from 2011-2021, we explore three possible explanations: asymmetric information, divergent objectives between experts and the public, and limitations in the guidelines themselves. We model the expert committee's objective function as minimizing an aggregate health risk score conditional on prepregnancy BMI and estimate its underlying risk weights by developing and using a Generalized Method of Moments (GMM) estimator. We then compare these with the risk preferences revealed by birthing people. Our findings show that experts implicitly assign equal weight to the risks of low and high birth weight, whereas birthing people place less weight on the risk of high birth weight. We then demonstrate that the current guidelines are suboptimal-even under the criteria they were designed to meet. We propose a new, individualized guideline structure that adjusts continuously with prepregnancy BMI. These improved guidelines not only reduce aggregate health risks but also better reflect birthing people's preferences-offering a path to significantly higher compliance and more effective public health policy. The paper illustrates the broader applicability of the GMM framework for recovering latent preferences in structural models of health behavior. JEL Classification: I12, I15, J18.

Statistics in MedicineVol. 45(8-9)
Indiana University – Purdue University Indianapolis (US)
Indiana University
Openalex Percentile: Top 23%
Advanced Causal Inference Techniques
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