Illness Mapping: a time and cost effective method to estimate healthcare data needed to establish community-based health insurance

BMC Medical Research Methodology

David M Dror²  Ruth Koren³  Meenakshi Gautham¹  Erika Binnendijk¹ 

[1] Institute of Health Policy and Management, Erasmus University Rotterdam, P.O. Box 1738, Rotterdam, 3000 DR, the Netherlands;Micro Insurance Academy, 52B Okhla Industrial Estate Phase III, New Delhi, 110020, India;Felsenstein Medical Research Centre, Tel Aviv University Sackler Faculty of Medicine, Ramat Aviv, Tel Aviv, Israel

关键词: Illness Mapping;    Incidence of hospitalization;    Illness prevalence;    Micro health insurance;    Community based health insurance (CBHI);    India;   

Others  :  1126689
DOI  :  10.1186/1471-2288-12-153

 received in 2012-04-03, accepted in 2012-09-19,  发布年份 2012

【 摘 要 】

Background

Most healthcare spending in developing countries is private out-of-pocket. One explanation for low penetration of health insurance is that poorer individuals doubt their ability to enforce insurance contracts. Community-based health insurance schemes (CBHI) are a solution, but launching CBHI requires obtaining accurate local data on morbidity, healthcare utilization and other details to inform package design and pricing. We developed the “Illness Mapping” method (IM) for data collection (faster and cheaper than household surveys).

Methods

IM is a modification of two non-interactive consensus group methods (Delphi and Nominal Group Technique) to operate as interactive methods. We elicited estimates from “Experts” in the target community on morbidity and healthcare utilization. Interaction between facilitator and experts became essential to bridge literacy constraints and to reach consensus.

The study was conducted in Gaya District, Bihar (India) during April-June 2010. The intervention included the IM and a household survey (HHS). IM included 18 women’s and 17 men’s groups. The HHS was conducted in 50 villages with1,000 randomly selected households (6,656 individuals).

Results

We found good agreement between the two methods on overall prevalence of illness (IM: 25.9% ±3.6; HHS: 31.4%) and on prevalence of acute (IM: 76.9%; HHS: 69.2%) and chronic illnesses (IM: 20.1%; HHS: 16.6%). We also found good agreement on incidence of deliveries (IM: 3.9% ±0.4; HHS: 3.9%), and on hospital deliveries (IM: 61.0%. ± 5.4; HHS: 51.4%). For hospitalizations, we obtained a lower estimate from the IM (1.1%) than from the HHS (2.6%). The IM required less time and less person-power than a household survey, which translate into reduced costs.

Conclusions

We have shown that our Illness Mapping method can be carried out at lower financial and human cost for sourcing essential local data, at acceptably accurate levels. In view of the good fit of results obtained, we assume that the method could work elsewhere as well.

【 授权许可】

   
2012 Binnendijk et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]World Development IndicatorsRetrieved 09/13, 2012 from [http://data.worldbank.org/data-catalog/world-development-indicators webcite]
• [2]Karan AK, Selvaraj S: Why publicly-financed health insurance schemes are ineffective in providing financial risk protection. Econ Polit Wkly 2012, 47(11):61.
• [3]Ma S, Sood N: A comparison of the health systems in China and India. Santa Monica, USA: Rand Corporation, Center for Asia Pacific Policy; 2008.
• [4]Binnendijk E, Koren R, Dror DM: Hardship financing of healthcare among rural poor in Orissa. India. BMC health services research 2012, 12(1):23. BioMed Central Full Text
• [5]Peters DH, Yazbeck AS, Sharma RP, Ramana GNV, Pritchett LH, Wagstaff A: Better health systems for India’s poor: Findings, analysis and options. Washington (DC): World Bank; 2002.
• [6]Binnendijk E, Koren R, Dror DM: Can the rural poor in India afford to treat non-communicable diseases. Tropical Medicine & International Health 2012. [Epub ahead of print]
• [7]Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ: Global and regional burden of disease and risk factors, 2001: Systematic analysis of population health data. Lancet 2006, 367(9524):1747-1757.
• [8]World Health Organization: Global status report on noncommunicable diseases 2011. Geneva, Switzerland: World Health Organization; 2010.
• [9]Ahuja R: Health insurance for the poor in India. New Delhi, India: Working paper No. 123, Indian Council for research on International Economic Relation (ICRIER); 2004.
• [10]Ahuja R: Health insurance for the poor in India: An analytical study. New Delhi, India: Working Paper No. 161, Indian Council for Research on International Economic Relations (ICRIER); 2005.
• [11]Bhat R, Jain N: Factoring affecting the demand for health insurance in a micro insurance scheme. Ahmedabad: IIMA Working Papers WP2006-07-02, Indian Institute of Management; 2006.
• [12]National Commission on Macroeconomics and Health: Financing and delivery of health care services in India. New Delhi: National Commission on Macroeconomics and Health Background Papers, Ministry of Health and Family Welfare, Government of India; 2005.
• [13]Dror DM, Radermacher R, Khadilkar SB, Schout P, Hay FX, Singh A, Koren R: Microinsurance: Innovations in low-cost health insurance. Health Aff (Millwood) 2009, 28(6):1788-1798.
• [14]Doyle C, Panda P, Van de Poel E, Radermacher R, Dror DM: Reconciling research and implementation in micro health insurance experiments in India: Study protocol for a randomized controlled trial. Trials 2011, 12(1):224. BioMed Central Full Text
• [15]Dong H, Mugisha F, Gbangou A, Kouyate B, Sauerborn R: The feasibility of community-based health insurance in Burkina Faso. Health Policy 2004, 69(1):45-53.
• [16]AC Nielsen ORG-MARG Pvt Ltd: Needs and demands for healthcare and health insurance amng landless agriculture laborers in Burdwan district, West Bengal. Kolkota, India: Final Report submitted to GTZ Technical Assistance Team, Health Sector Support, Kolkata, AC Nielsen ORG-MARG Pvt Ltd; 2001.
• [17]Dror DM: Why “one-size-fits-all” health insurance products are unsuitable for low-income persons in the informal economy in India. Asian Economic Review 2007, 49(1):47-56.
• [18]Dror DM, van Putten-Rademaker O, Koren R: Cost of illness: Evidence from a study in five resource-poor locations in India. Indian J Med Res 2008, 127(4):347-361.
• [19]Dror DM, van Putten-Rademaker O, Koren R: Incidence of illness among resource-poor households: Evidence from five locations in India. Indian J Med Res 2009, 130(2):146-154.
• [20]National Sample Survey Organization: Morbidity, health care and the condition of the aged. 2006, Report No. 507 (60th round January-June: National Sample Survey Organization, Ministry of Statistics and Programme Implementation. New Delhi: Government of India; 2004.
• [21]National Sample Survey Organization: Morbidity and treatment of ailments. 1998, Report No. 441 (52th round July 1995-June: National Sample Survey Organization, Ministry of Statistics and Programme Implementation. New Delhi: Government of India; 1996.
• [22]Jones J, Hunter D: Consensus methods for medical and health services research. BMJ 1995, 311(7001):376-380.
• [23]Aurey JP, Fonteneau R: Local consensus and estimates of medical risk. In Social Reinsurance, A new approach to sustainable community health financing. Edited by Dror DM, Preker AS. Geneva: The World Bank and the International Labour Organisation; 2002:187-222.
• [24]Woudenberg F: An evaluation of Delphi. Technol Forecast Soc Chang 1991, 40(2):131-150.
• [25]Sample JA, Nominal Group Technique: Sample JA, Nominal group technique: An alternative to brainstorming. J Ext 1984., 22(2)
• [26]Van de Ven AH, Delbecq AL: The effectiveness of nominal, Delphi, and interacting group decision making processes. Acad Manag J 1974, 17(4):605-621.
• [27]Van de Ven AH, Delbecq AL: Nominal versus interacting group processes for committee decision-making effectiveness. Acad Manag J 1971, 14(2):203-212.
• [28]Kitzinger J: Qualitative research: Introducing focus groups. BMJ 1995, 311(7000):299-302.
• [29]Bouchard TJ: Personality, problem-solving procedure, and performance in small groups. J Appl Psychol 1969, 53(1 (supplement 1):1-29.
• [30]Bouchard TJ: Training, motivation, and personality as determinants of the effectiveness of brainstorming groups and individuals. J Appl Psychol 1972, 56(4):324-331.
• [31]Hart S, Boroush M, Enk G, Hornick W: Managing complexity through consensus mapping: Technology for the structuring of group decisions. Acad Manag Rev 1985, 10(3):587-600.
• [32]Lowry PB: Research on process structure for distributed, asynchronous collaborative writing groups. Dallas: Proceedings of the 8th Annual Americas Conference on Information Systems; 2002:2172-2179.
• [33]Som RK: Practical sampling techniques. 2nd edition. New York: Marcel Dekker; 1996.
• [34]Danis M, Binnendijk E, Vellakkal S, Ost A, Koren R, Dror DM: Eliciting health insurance benefit choices of low income groups. Econ Polit Wkly 2007, 42(32):3331-3339.
• [35]National Sample Survey Organization: Household Consumer Expenditure in India, 2006–07. New Delhi: Report No. 527, National Sample Survey Organization, Ministry of Statistics and Programme Implementation, Government of India; 2008.
• [36]National Sample Survey Organization: Level and pattern of consumer expenditure, 2009–2010. New Delhi: Report No. 538, National Sample Survey Organization, Ministry of Statistics and Programme Implementation, Government of India; 2011.
• [37]Dror DM, Koren R, Ost A, Binnendijk E, Vellakkal S, Danis M: Health insurance benefit packages prioritized by low-income clients in India: three criteria to estimate effectiveness of choice. Soc Sci Med 2007, 64(4):884-896.
• [38]Basu S, Schroeder RG: Incorporating judgments in sales forecasts: Application of the Delphi method at American Hoist & Derrick. Interfaces 1977, 7(3):18-27.
• [39]Hilbert M, Miles I, Othmer J: Foresight tools for participative policy-making in inter-governmental processes in developing countries: Lessons learned from the eLAC Policy Priorities Delphi. Technol Forecast Soc Chang 2009, 76(7):880-896.
• [40]Surowiecki J: The wisdom of crowds. USA: Double Day (Random House Inc); 2004.
• [41]Sudman S, Bradburn NM: Effects of time and memory factors on response in surveys. J Am Stat Assoc 1973, 68(344):805-815.
• [42]Harlow SD, Linet MS: Agreement between questionnaire data and medical records: The evidence for accuracy of recall. Am J Epidemiol 1989, 129(2):233-248.