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Harel, Y., Overpeck, M.D., Jones, D.H., Scheidt, P.C., Bijur, P.E., Trumble, A.C. and Anderson, J. (1994) The Effects of Recall on Estimating Annual Nonfatal Injury Rates for Children and Adolescents. American Journal of Public Health, 84, 599-605.
http://dx.doi.org/10.2105/AJPH.84.4.599

has been cited by the following article:

• TITLE: A Model to Estimate the Impact of Thresholds and Caps on Coverage Levels in Community-Based Health Insurance Schemes in Low-Income Countries

  AUTHORS: Erika Binnendijk, Ruth Koren, David M. Dror

  KEYWORDS: Distributions; Healthcare Costs; Community-Based Health Insurance; Micro Health Insurance; Thresholds; Caps

  JOURNAL NAME: Health, Vol.6 No.9, March 28, 2014

  ABSTRACT: Background: Community-based health insurance (CBHI) schemes are increasingly implemented in low-income settings. These schemes limit the coverage they offer both by the types of care considered, and by applying thresholds and/or caps to costs reimbursed. The consequences of these thresholds and/or caps on insurance coverage have hitherto been usually ignored, for lack of data on the distributions of healthcare costs or understanding of their impact on effective coverage levels. This article describes a theoretical model to obtain the distributions even without data collection in the field, and demonstrates the quantitative impact of thresholds and/or caps on claim reimbursements. Methods: This model applies to applications on healthcare expenditures in low-income settings, following research methods examined in the Western world. We looked at hospitalizations and tests; we compared the simulated distributions to empirical data obtained through 11 household surveys conducted between 2008 and 2010 in rural locations (9 in India and 2 in Nepal). Results: We found that the shape of the distributions was very similar in all locations for both benefits, and could be represented by a model based on a lognormal distribution. The agreement between theoretical and empirical results was satisfactory (mostly within 10% difference). Conclusions: The model makes it possible to simulate the expected performance of the CBHI (represented by the percentage of costs or bills covered). The aim is to match costs with local levels of willingness-to-pay for health insurance. This model makes it possible to determine at the stage of package-design the optimal levels of thresholds and/or caps for each benefit-type included.