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      Understanding community health worker employment preferences in Malang district, Indonesia, using a discrete choice experiment

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          Abstract

          Background

          Community health workers (CHWs) play a critical role in supporting health systems, and in improving accessibility to primary healthcare. In many settings CHW programmes do not have formalised employment models and face issues of high attrition and poor performance. This study aims to determine the employment preferences of CHWs in Malang district, Indonesia, to inform policy interventions.

          Methods

          A discrete choice experiment was conducted with 471 CHWs across 28 villages. Attributes relevant to CHW employment were identified through a multistage process including literature review, focus group discussions and expert consultation. Respondents’ choices were analysed with a mixed multinomial logit model and latent class analyses.

          Results

          Five attributes were identified: (1) supervision; (2) training; (3) monthly financial benefit; (4) recognition; and (5) employment structure. The most important influence on choice of job was a low monthly financial benefit (US$~2) (β=0.53, 95% CI=0.43 to 0.63), followed by recognition in the form of a performance feedback report (β=0.13, 95% CI=0.07 to 0.20). A large monthly financial benefit (US$~20) was most unappealing to respondents (β=−0.13, 95% CI=−0.23 to −0.03). Latent class analysis identified two groups of CHWs who differed in their willingness to accept either job presented and preferences over specific attributes. Preferences diverged based on respondent characteristics including experience, hours’ worked per week and income.

          Conclusion

          CHWs in Malang district, Indonesia, favour a small monthly financial benefit which likely reflects the unique cultural values underpinning the programme and a desire for remuneration that is commensurate with the limited number of hours worked. CHWs also desire enhanced methods of performance feedback and greater structure around training and their rights and responsibilities. Fulfilling these conditions may become increasingly important should CHWs work longer hours.

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          Most cited references42

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          Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.

          The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies. Copyright © 2011 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.
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            Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force.

            Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.
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              Sample Size Requirements for Discrete-Choice Experiments in Healthcare: a Practical Guide

              Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care. Electronic supplementary material The online version of this article (doi:10.1007/s40271-015-0118-z) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                BMJ Glob Health
                BMJ Glob Health
                bmjgh
                bmjgh
                BMJ Global Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-7908
                2022
                11 August 2022
                : 7
                : 8
                : e008936
                Affiliations
                [1 ]departmentHealth Systems Science , George Institute for Global Health , Sydney, New South Wales, Australia
                [2 ]departmentPublic Administration , Brawijaya University , Malang, Indonesia
                [3 ]departmentFaculty of Medicine , Brawijaya University , Malang, Indonesia
                [4 ]departmentFaculty of Health and Education , Manchester Metropolitan University , Manchester, UK
                [5 ]departmentGlobal Development Institute , The University of Manchester , Manchester, UK
                [6 ]departmentDivision of Cardiovascular Sciences , The University of Manchester , Manchester, UK
                [7 ]departmentBetter Care India , The George Institute for Global Health India , Hyderabad, India
                [8 ]departmentPrasanna School of Public Health , Manipal Academy of Higher Education , Manipal, India
                Author notes
                [Correspondence to ] Thomas Gadsden; tgadsden@ 123456georgeinstitute.org.au
                Author information
                http://orcid.org/0000-0002-9371-420X
                http://orcid.org/0000-0003-4197-4592
                http://orcid.org/0000-0002-5550-5175
                http://orcid.org/0000-0002-7188-7740
                http://orcid.org/0000-0002-8127-9351
                Article
                bmjgh-2022-008936
                10.1136/bmjgh-2022-008936
                9379506
                35953209
                271b164f-5d44-4927-9ca4-80197e1c378a
                © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 28 February 2022
                : 28 July 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: APP1149987
                Categories
                Original Research
                1506
                Custom metadata
                unlocked

                community-based survey,health policy,health economics

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