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A decision-making model to optimize the impact of community-based health programs.

ABSTRACT: Hospitals and clinics are increasingly interested in building partnerships with community-based organizations to address the social determinants of health. Choosing among community-based health programs can be complex given that programs may have different effectiveness levels and implementation costs. This study develops a decision-making model that can be used to evaluate multiple key factors that would be relevant in resource allocation decisions related to a set of community-based health programs. The decision-making model compares community-based health programs by considering funding limitations, program duration, and participant retention until program completion. Specifically, the model allows decision makers to select the optimal mix of community-based health programs based on the profiles of the population given the above constraints. The model can be used to improve resource allocation in communities, ultimately contributing to the long-term goal of strengthening cross-sector partnerships and the integration of services to improve health outcomes.

SUBMITTER: Perez E

PROVIDER: S-EPMC8207482 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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A decision-making model to optimize the impact of community-based health programs.

Pérez Eduardo E Li Yan Y Pagán José A JA

Preventive medicine 20210514

Hospitals and clinics are increasingly interested in building partnerships with community-based organizations to address the social determinants of health. Choosing among community-based health programs can be complex given that programs may have different effectiveness levels and implementation costs. This study develops a decision-making model that can be used to evaluate multiple key factors that would be relevant in resource allocation decisions related to a set of community-based health pro ...[more]

PMID: 33992658

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Project description:IntroductionThe results from different Cochrane studies justify considerable professional equipoise concerning different treatment options for tinnitus. In case of professional equipoise, Shared Decision Making (SDM) is an indispensable tool for guiding patients to the intervention that best fits their needs. To improve SDM we developed a method to assess the accuracy and utility of decisions made by tinnitus patients when freely choosing between different treatment options during their patient journey. The different treatment options were audiological care and psychosocial counseling.MethodsWe developed a statistical model by combining Signal Detection Theory (SDT) with the Health Belief Model (HBM). HBM states that perceived severity of an illness is strongly related to sick-role behavior. As proxies for perceived severity, we selected hearing loss and Tinnitus Handicap Inventory (THI) score at baseline. Next, we used these proxies as predictors in linear regression models based on SDT to determine the likelihood ratio of true positive decisions (choosing a treatment option and experiencing an improvement of more than 7 points in THI-score) and false positive decisions (choosing a treatment option and experiencing an improvement of less than 7 points in THI-score) for audiological care and psychosocial counseling, respectively. Data was gathered in a prospective cohort of 145 adults referred for tinnitus care to an outpatient audiology clinic in the Netherlands. The participants were asked to decide freely on uptake of audiological care (provision of hearing aids with or without a sound generator) and uptake of psychosocial counseling. Logistic regression with Bayesian inference was used to determine the cumulative distribution functions and the probability density functions of true positive decisions and false positive decisions as function of hearing loss and baseline THI-score for both treatment options, respectively. With the cumulative distribution functions, we determined the accuracy of the decisions. With the probability density functions we calculated the likelihood ratios of true positive decisions versus false positive decisions as function of hearing loss and baseline THI-score. These likelihood ratio functions allow assessment of the utility of the decisions by relating a decision criterion to perceived benefits and costs.ResultsBaseline THI-score drives decisions about psychosocial counseling and hearing loss drives decisions about audiological care. Decisions about psychosocial counseling are more accurate than decisions about audiological care. Both decisions have a low accuracy (0.255 for audiological care and  - 0.429 for psychosocial counseling), however. For decisions about audiological care the unbiased decision criterion is 37 dB(HL), meaning that a lenient decision criterion (likelihood ratio < 1) is adopted by patients with a hearing loss below 37 dB and a strict criterion (likelihood ratio > 1) by patients with a hearing loss exceeding 37 dB. For psychosocial counseling uptake the decision criterion is always strict, regardless of baseline THI-score. The distributions of the populations, that do and do not experience a clinically important change in THI-score, have unequal variances for psychosocial counseling, while they have almost equal variances for audiological care.DiscussionCombining SDT and HBM can help assess accuracy and utility of patient decisions and thus may provide valuable information that can help to improve SDM by combining patient related outcome measures, decision drivers, and perceived benefits and costs of a treatment.

Dataset Information

A decision-making model to optimize the impact of community-based health programs.

Publications

A decision-making model to optimize the impact of community-based health programs.

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