Developing attributes and attribute-levels for a discrete choice experiment on micro health insurance in rural Malawi

BMC Health Services Research

Manuela De Allegri³  Paul J Robyn²  Grace Bongololo Mbera¹  Gerald Leppert⁴  Gilbert Abotisem Abiiro⁵ 

[1]Research for Equity and Community Health Trust (REACH Trust), Lilongwe, Malawi

[2]The World Bank, Washington, DC, USA

[3]Institute of Public Health, Medical Faculty, University of Heidelberg, Heidelberg, Germany

[4]Department for Cooperative Studies, University of Cologne, Cologne, Germany

[5]Department of Planning and Management, Faculty of Planning and Land Management, University for Development Studies, Wa, Ghana

关键词: Malawi;    Rural communities;    Micro health insurance;    Qualitative study;    Attribute and attribute-levels development;    Discrete choice experiment;   

Others  :  1130882
DOI  :  10.1186/1472-6963-14-235

 received in 2013-12-09, accepted in 2014-05-06,  发布年份 2014

【 摘 要 】

Background

Discrete choice experiments (DCEs) are attribute-driven experimental techniques used to elicit stakeholders’ preferences to support the design and implementation of policy interventions. The validity of a DCE, therefore, depends on the appropriate specification of the attributes and their levels. There have been recent calls for greater rigor in implementing and reporting on the processes of developing attributes and attribute-levels for discrete choice experiments (DCEs). This paper responds to such calls by carefully reporting a systematic process of developing micro health insurance attributes and attribute-levels for the design of a DCE in rural Malawi.

Methods

Conceptual attributes and attribute-levels were initially derived from a literature review which informed the design of qualitative data collection tools to identify context specific attributes and attribute-levels. Qualitative data was collected in August-September 2012 from 12 focus group discussions with community residents and 8 in-depth interviews with health workers. All participants were selected according to stratified purposive sampling. The material was tape-recorded, fully transcribed, and coded by three researchers to identify context-specific attributes and attribute-levels. Expert opinion was used to scale down the attributes and levels. A pilot study confirmed the appropriateness of the selected attributes and levels for a DCE.

Results

First, a consensus, emerging from an individual level analysis of the qualitative transcripts, identified 10 candidate attributes. Levels were assigned to all attributes based on data from transcripts and knowledge of the Malawian context, derived from literature. Second, through further discussions with experts, four attributes were discarded based on multiple criteria. The 6 remaining attributes were: premium level, unit of enrollment, management structure, health service benefit package, transportation coverage and copayment levels. A final step of revision and piloting confirmed that the retained attributes satisfied the credibility criteria of DCE attributes.

Conclusion

This detailed description makes our attribute development process transparent, and provides the reader with a basis to assess the rigor of this stage of constructing the DCE. This paper contributes empirical evidence to the limited methodological literature on attributes and levels development for DCE, thereby providing further empirical guidance on the matter, specifically within rural communities of low- and middle-income countries.

【 授权许可】

   
2014 Abiiro et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

【 参考文献 】

• [1]De Bekker-Grob EW, Ryan M, Gerard K: Discrete choice experiments in health economics: a review of the literature. Health Econ 2012, 21:145-172.
• [2]Kjær T: A review of the Discrete Choice Experiment-with Emphasis on its Application in Health Care. Denmark: Syddansk Universitet; 2005.
• [3]Mengoni A, Seghieri C, Nuti S: The Application of Discrete Choice Experiments in Health Economics: A Systematic Review of the Literature. Working Paper n. 01/2013. Scuola Superiore Sant’Anna di Pisa. Istituto di Management; 2013. http://www.idm.sssup.it/wp/201301.pdf webcite]. Accessed on 28-07-2013
• [4]Bridges JFP, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, Johnson FR, Mauskopf J: Conjoint analysis applications in health–a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health 2011, 14:403-413.
• [5]Lagarde M, Blaauw D: A review of the application and contribution of discrete choice experiments to inform human resources policy interventions. Hum Resour Health 2009, 7:62. BioMed Central Full Text
• [6]Van Helvoort-Postulart D, van der Weijden T, Dellaert BG, De Kok M, Von Meyenfeldt MF, Dirksen CD: Investigating the complementary value of discrete choice experiments for the evaluation of barriers and facilitators in implementation research: a questionnaire survey. Implement Sci 2009, 4:10. BioMed Central Full Text
• [7]Farley K, Thompson C, Hanbury A, Chambers D: Exploring the feasibility of Conjoint Analysis as a tool for prioritizing innovations for implementation. Implement Sci 2013, 8:56. BioMed Central Full Text
• [8]Louviere JJ, Hensher DA, Swait JD: Stated Choice Methods: Analysis and Applications. Cambridge: University Press; 2010.
• [9]Lancsar E, Louviere J: Conducting discrete choice experiments to inform healthcare decision making. Pharmacoecon 2008, 26:661-677.
• [10]Mangham LJ, Hanson K, McPake B: How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country. Health Policy Plan 2009, 24:151-158.
• [11]WHO: How to Conduct a Discrete Choice Experiment for Health Workforce Recruitment and Retention in Remote and Rural Areas: A User Guide with Case Studies. Geneva: World Health Oragnisation; 2012.
• [12]Louviere JJ, Carson R, Pihlens D: Design of discrete choice experiments: a discussion of issues that matter in future applied research. J Choice Model 2011, 4:1-8.
• [13]Johnson RF, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier DA, Bresnahan BW, Kanninen B, Bridges JFP: Constructing Experimental Designs for Discrete-Choice Experiments: Report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health 2013, 16:3-13.
• [14]Blaauw D, Erasmus E, Pagaiya N, Tangcharoensathein V, Mullei K, Mudhune S, Lagarde M: Policy interventions that attract nurses to rural areas: a multicountry discrete choice experiment. Bull World Health Organ 2010, 88:350-356.
• [15]Chowdhury ME, Johnson JC, Gyakobo M, Agyei-Baffour P, Asabir K, Kotha SR, Dzodzomenyo M: Rural practice preferences among medical students in Ghana: a discrete choice experiment. Bull World Health Organ 2010, 88:333-341.
• [16]Mangham LJ, Hanson K: Employment preferences of public sector nurses in Malawi: results from a discrete choice experiment. Tropical Med Int Health 2008, 13:1433-1441.
• [17]Rockers PC, Jaskiewicz W, Wurts L, Kruk ME, Mgomella GS, Ntalazi F, Tulenko K: Preferences for working in rural clinics among trainee health professionals in Uganda: a discrete choice experiment. BMC Health Serv Res 2012, 12:212. BioMed Central Full Text
• [18]Hanson K, McPake B, Nakamba P, Archard L: Preferences for hospital quality in Zambia: results from a discrete choice experiment. Health Econ 2005, 14:687-701.
• [19]Baltussen R, Stolk E, Chisholm D, Aikins M: Towards a multi-criteria approach for priority setting: an application to Ghana. Health Econ 2006, 15:689-696.
• [20]Kruk ME, Paczkowski MM, Tegegn A, Tessema F, Hadley C, Asefa M, Galea S: Women’s preferences for obstetric care in rural Ethiopia: a population-based discrete choice experiment in a region with low rates of facility delivery. J Epidemiol Community Health 2010, 64:984-988.
• [21]Van Rijsbergen B, D’Exelle B: Delivery Care in Tanzania: A Comparative Analysis of Use and Preferences. World Dev 2013, 43:276-287.
• [22]Kruk ME, Rockers PC, Tornorlah VS, Macauley R: Population preferences for health care in liberia: insights for rebuilding a health system. Health Serv Res 2011, 46:2057-2078.
• [23]Akaah IP, Becherer RC: Integrating a consumer orientation into the planning of HMO programs: an application of conjoint segmentation. J Health Care Mark 1982, 3:9-18.
• [24]Becker K, Zweifel P: Age and choice in health insurance: evidence from a discrete choice experiment. Patient 2008, 1:27-40.
• [25]Van den Berg B, Van Dommelen P, Stam P, Laske-Aldershof T, Buchmueller T, Schut FT: Preferences and choices for care and health insurance. Soc Sci Med 2008, 66:2448-2459.
• [26]Chakraborty G, Ettenson R, Gaeth G: How consumers choose health insurance. J Health Care Mark 1994, 14:21-33.
• [27]Gates R, McDaniel C, Braunsberger K: Modeling Consumer Health Plan Choice Behavior To Improve Customer Value and Health Plan Market Share. J Bus Res 2000, 48:247-257.
• [28]Hershey JC, Kunreuther H, Schwartz JS, Williams SV: Health insurance under competition: would people choose what is expected? Inquiry 1984, 21:349-360.
• [29]Vroomen JM, Zweifel P: Preferences for health insurance and health status: does it matter whether you are Dutch or German? Eur J Health Econ 2011, 12:87-95.
• [30]Wellman GS, Vidican C: Pilot study of a hierarchical Bayes method for utility estimation in a choice-based conjoint analysis of prescription benefit plans including medication therapy management services. Res Social Adm Pharm 2008, 4:218-230.
• [31]Matul M, McCord MJ, Phily C, Harms J: The landscape of micro health insurance in sub-Saharan Africa. In Handbook of Micro Health Insurance in Africa. Edited by Rösner H-J, Leppert G, Degens P, Ouedraogo L-M. Berlin: Lit Verlag; 2012:59-87.
• [32]Basaza R, Pariyo G, Criel B: What are the emerging features of community health insurance schemes in east Africa? Risk Manage Healthc Policy 2009, 2:47-53.
• [33]Criel B, Atim C, Basaza R, Blaise P, Waelkens MP: Community health insurance (CHI) in sub-Saharan Africa: researching the context. Tropical Med Int Health 2004, 9:1041-1043.
• [34]Coast J, Al-Janabi H, Sutton EJ, Horrocks SA, Vosper AJ, Swancutt DR, Flynn TN: Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ 2012, 21:730-741.
• [35]Hiligsmann M, Van Durme C, Geusens P, Dellaert J, Dirksen CD, van der Weijden T, Boonen A: Nominal group technique to select attributes for discrete choice experiments: an example for drug treatment choice in osteoporosis. Patient Preference Adherence 2013, 7:133-139.
• [36]Coast J, Horrocks S: Developing attributes and levels for discrete choice experiments using qualitative methods. J Health Serv Res Policy 2007, 12:25-30.
• [37]World Bank: Malawi Country Data Profile. Country report Malawi. The World Bank; 2014. [http://www.worldbank.org/en/country/malawi webcite]. Accessed on 9/05/2014
• [38]National Statistical Office: Population and Housing Census: Peliminary Report. Malawi: National Statistical Office; 2008:2008.
• [39]Phiri I, Masanjala W: Willingness to pay for micro health insurance in Malawi. In Handbook of Micro Health Insurance in Africa. Edited by Rösner H-J, Leppert G, Degens P, Ouedraogo L-M. Berlin: Lit Verlag; 2012:285-308.
• [40]Zere E, Walker O, Kirigia JM, Zawaira F, Magombo F, Kataika E: Health financing in Malawi: evidence from national health accounts. BMC Int Health Human Rights 2010, 10:27. BioMed Central Full Text
• [41]Abiiro GA, Mbera GB, De Allegri M: Gaps in Universal Health Coverage in Malawi: a qualitative study in Rural Communities. BMC Health Serv Res 2014, 14:234. BioMed Central Full Text
• [42]Ministry of Health (Malawi): Malawi Health Sector Strategic Plan 2011–2016 - Moving towards equity and quality. Lilongwe: Ministry of Health; 2011.
• [43]Ryan M, Scott DA, Donaldson C: Valuing health care using willingness to pay: a comparison of the payment card and dichotomous choice methods. J Health Econ 2004, 23:237-258.
• [44]Hensher DA, Rose JM, Greene WH: Applied Choice Analysis: A Primer. Cambridge: University Press; 2005.
• [45]Creswell JW, Clark PVL: Designing and Conducting Mixed Methods Research. 2nd edition. Thousand Oaks: SAGE Publications; 2010.
• [46]Kutzin J: A descriptive framework for country-level analysis of health care financing arrangements. Health Policy 2001, 56:171-204.
• [47]Berki SE, Ashcraft MLF: HMO enrollment: who joins what and why: a review of the literature. Milbank Memorial Fund Quarter Health Soc 1980, 58:588-632.
• [48]Government of Ghana: National Health Insurance Act. 650. Accra: Government of Ghana; 2003.
• [49]Federal Government of Nigeria: National Health Insurance Scheme Decree No 35 of 1999. Abuja: Federal Govergment of Nigeria; 1999.
• [50]Department of Health: National Health Insurance in South Africa, Policy paper. South Africa: Department of Health; 2011.
• [51]Ministry of Health: Rwanda National Health Insurance Policy. Kigali: Ministry of Health; 2010.
• [52]Ministry of Health: National Social Health Insurance Bill. Nairobi: Ministry of Health; 2004.
• [53]Government of Tanzania: The Community Health Fund ACT, 2001. Dar es salaam: Government of Tanzania; 2001.
• [54]Criel B, Waelkens MP: Declining subscriptions to the Maliando Mutual Health Organisation in Guinea-Conakry (West Africa): what is going wrong? Soc Sci Med 2003, 57:1205-1219.
• [55]De Allegri M, Kouyaté B, Becher H, Gbangou A, Pokhrel S, Sanon M, Sauerborn R: Understanding enrolment in community health insurance in sub-Saharan Africa: a population-based case–control study in rural Burkina Faso. Bull World Health Organ 2006, 84:852-858.
• [56]De Allegri M, Sauerborn R, Kouyate B, Flessa S: Community health insurance in sub-Saharan Africa: what operational difficulties hamper its successful development? Tropical Med Int Health 2009, 14:586-596.
• [57]Twikirize JM, O’Brien C: Why Ugandan rural households are opting to pay community health insurance rather than use the free healthcare services. Int J Soc Welf 2012, 21:66-78.
• [58]Mulupi S, Kirigia D, Chuma J: Community perceptions of health insurance and their preferred design features: implications for the design of universal health coverage reforms in Kenya. BMC Health Serv Res 2013, 13:474. BioMed Central Full Text
• [59]De Allegri M, Sanon M, Bridges J, Sauerborn R: Understanding consumers’ preferences and decision to enrol in community-based health insurance in rural West Africa. Health Policy 2006, 76:58-71.
• [60]Chankova S, Sulzbach S, Diop F: Impact of mutual health organizations: evidence from West Africa. Health Policy Plan 2008, 23:264-276.
• [61]Royalty AB, Hagens J: The effect of premiums on the decision to participate in health insurance and other fringe benefits offered by the employer: evidence from a real-world experiment. J Health Econ 2005, 24:95-112.
• [62]Jehu-Appiah C, Aryeetey G, Agyepong I, Spaan E, Baltussen R: Household perceptions and their implications for enrolment in the National Health Insurance Scheme in Ghana. Health Policy Plan 2012, 27:222-233.
• [63]Onwujekwe O, Onoka C, Uguru N, Nnenna T, Uzochukwu B, Eze S, Kirigia J, Petu A: Preferences for benefit packages for community-based health insurance: an exploratory study in Nigeria. BMC Health Serv Res 2010, 10:162. BioMed Central Full Text
• [64]Patton MQ: Qualitative Research & Evaluation Methods. 3rd edition. Thousand Oaks: SAGE Publications; 2002.