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Estimating causal effects of community health financing via principal stratification. (English) Zbl 07778001

Summary: When a treatment cannot be enforced, but only encouraged, noncompliance naturally arises. In applied economics, the common empirical strategy for dealing with noncompliance is to rely on Instrumental Variables methods. When the effects are heterogeneous, these methods allow, under a set of assumptions, to identify the causal effect for Compliers, i.e., the subset of units whose treatment is affected by the encouragement. One of the identification assumptions is the Exclusion Restriction (ER), which essentially rules out the possibility of a causal effect for Never Takers, i.e., those whose treatment is not affected by the encouragement. In this paper, we show the consequences of violations of this assumption in the impact evaluation of an intervention implemented in Uganda, where targeted households were encouraged to join a community health financing (CHF) scheme through activities of sensitization. We conduct the analyses using Bayesian model-based principal stratification, first assuming and then relaxing the ER for Never Takers. This allows showing the positive impact of the intervention on the health costs of both Compliers and Never Takers. While the causal effects for the former could be due to the encouragement but also to the actual participation in the scheme, those for the latter are unequivocally attributable to the encouragement. This indicates that sensitization alone is extremely effective in reducing vulnerability against health costs. This finding is of paramount importance for policy-making, as it is much easier and more cost-effective to implement awareness-raising campaigns than CHF schemes.

MSC:

62-XX Statistics

Software:

Stan; NUTS

References:

[1] Adebayo, EF; Uthman, OA; Wiysonge, CS; Stern, EA; Lamont, KT; Ataguba, JE, A systematic review of factors that affect uptake of community-based health insurance in low-income and middle-income countries, BMC Health Serv Res, 15, 1, 1-13 (2015) · doi:10.1186/s12913-015-1179-3
[2] Angrist, JD; Pischke, J-S, Mostly harmless econometrics (2008), Princeton University Press · Zbl 1159.62090 · doi:10.2307/j.ctvcm4j72
[3] Angrist, JD; Imbens, GW; Rubin, DB, Identification of causal effects using instrumental variables, J Am Stat Assoc, 91, 434, 444-455 (1996) · Zbl 0897.62130 · doi:10.1080/01621459.1996.10476902
[4] Barnard, J.; Frangakis, CE; Hill, JL; Rubin, DB, Principal stratification approach to broken randomized experiments: a case study of school choice vouchers in New York city, J Am Stat Assoc, 98, 462, 299-323 (2003) · Zbl 1047.62120 · doi:10.1198/016214503000071
[5] Biggeri, M.; Nannini, M.; Putoto, G., Assessing the feasibility of community health insurance in Uganda: a mixed-methods exploratory analysis, Soc Sci Med, 200, 145-155 (2018) · doi:10.1016/j.socscimed.2018.01.027
[6] Chemin, M., Informal groups and health insurance take-up evidence from a field experiment, World Dev, 101, 54-72 (2018) · doi:10.1016/j.worlddev.2017.08.001
[7] Ferrari, S.; Cribari-Neto, F., Beta regression for modelling rates and proportions, J Appl Stat, 31, 7, 799-815 (2004) · Zbl 1121.62367 · doi:10.1080/0266476042000214501
[8] Forastiere, L.; Lattarulo, P.; Mariani, M.; Mealli, F.; Razzolini, L., Exploring encouragement, treatment, and spillover effects using principal stratification, with application to a field experiment on teens’ museum attendance, J Bus Econ Stat, 39, 1, 244-258 (2021) · Zbl 07925204 · doi:10.1080/07350015.2019.1647843
[9] Frangakis, CE; Rubin, DB, Principal stratification in causal inference, Biometrics, 58, 1, 21-29 (2002) · Zbl 1209.62288 · doi:10.1111/j.0006-341X.2002.00021.x
[10] Gelman, A.; Meng, X-L; Stern, H., Posterior predictive assessment of model fitness via realized discrepancies, Stat Sin, 6, 733-760 (1996) · Zbl 0859.62028
[11] Gustafson, P., Bayesian inference for partially identified models, Int J Biostat, 6, 2, 17 (2010) · doi:10.2202/1557-4679.1206
[12] Hirano, K.; Imbens, GW; Rubin, DB; Zhou, X-H, Assessing the effect of an influenza vaccine in an encouragement design, Biostatistics, 1, 1, 69-88 (2000) · Zbl 0972.62104 · doi:10.1093/biostatistics/1.1.69
[13] Hoffman, MD; Gelman, A., The no-u-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo, J Mach Learn Res, 15, 1, 1593-1623 (2014) · Zbl 1319.60150
[14] Imbens, GW; Angrist, JD, Identification and estimation of local average treatment effects, Econometrica, 62, 2, 467-475 (1994) · Zbl 0800.90648 · doi:10.2307/2951620
[15] Imbens, GW; Rubin, DB, Bayesian inference for causal effects in randomized experiments with noncompliance, Ann Stat, 25, 305-327 (1997) · Zbl 0877.62005 · doi:10.1214/aos/1034276631
[16] Jütting, JP, Do community-based health insurance schemes improve poor people’s access to health care? Evidence from rural Senegal, World Dev, 32, 2, 273-288 (2004) · doi:10.1016/j.worlddev.2003.10.001
[17] Kieschnick, R.; McCullough, BD, Regression analysis of variates observed on (0, 1): percentages, proportions and fractions, Stat Model, 3, 3, 193-213 (2003) · Zbl 1070.62056 · doi:10.1191/1471082X03st053oa
[18] Kruk, ME; Goldmann, E.; Galea, S., Borrowing and selling to pay for health care in low-and middle-income countries, Health Affairs, 28, 4, 1056-1066 (2009) · doi:10.1377/hlthaff.28.4.1056
[19] Leive, A.; Xu, K., Coping with out-of-pocket health payments: empirical evidence from 15 African countries, Bull World Health Organ, 86, 849-856C (2008) · doi:10.2471/BLT.07.049403
[20] Mattei, A.; Li, F.; Mealli, F., Exploiting multiple outcomes in Bayesian principal stratification analysis with application to the evaluation of a job training program, Ann Appl Stat, 7, 4, 2336-2360 (2013) · Zbl 1283.62054 · doi:10.1214/13-AOAS674
[21] Mealli, F.; Mattei, A., A refreshing account of principal stratification, Int J Biostat, 8, 1, 8 (2012) · doi:10.1515/1557-4679.1380
[22] Mealli, F.; Pacini, B., Using secondary outcomes to sharpen inference in randomized experiments with noncompliance, J Am Stat Assoc, 108, 503, 1120-1131 (2013) · Zbl 06224991 · doi:10.1080/01621459.2013.802238
[23] Mealli, F.; Rubin, DB, Assumptions when analyzing randomized experiments with noncompliance and missing outcomes, Health Serv Outcomes Res Methodol, 3, 3, 225-232 (2002) · doi:10.1023/A:1025802028890
[24] Mealli, F.; Pacini, B.; Stanghellini, E., Identification of principal causal effects using additional outcomes in concentration graphs, J Educ Behav Stat, 41, 5, 463-480 (2016) · doi:10.3102/1076998616646199
[25] Meng, X-L, Posterior predictive \(p\)-values, Ann Stat, 22, 3, 1142-1160 (1994) · Zbl 0820.62027 · doi:10.1214/aos/1176325622
[26] Mladovsky, P.; Soors, W.; Ndiaye, P.; Ndiaye, A.; Criel, B., Can social capital help explain enrolment (or lack thereof) in community-based health insurance? Results of an exploratory mixed methods study from Senegal, Soc Sci Med, 101, 18-27 (2014) · doi:10.1016/j.socscimed.2013.11.016
[27] Nannini, M.; Biggeri, M.; Putoto, G., Financial protection and coping strategies in rural Uganda: an impact evaluation of community-based zero-interest healthcare loans, Health Policy Plan, 36, 7, 1090 (2021) · doi:10.1093/heapol/czab073
[28] Ospina, R.; Ferrari, SL, Inflated beta distributions, Stat Pap, 51, 1, 111-126 (2010) · Zbl 1247.62043 · doi:10.1007/s00362-008-0125-4
[29] Ospina, R.; Ferrari, SL, A general class of zero-or-one inflated beta regression models, Comput Stat Data Anal, 56, 6, 1609-1623 (2012) · Zbl 1243.62099 · doi:10.1016/j.csda.2011.10.005
[30] Rubin, DB, Randomization analysis of experimental data: the fisher randomization test comment, J Am Stat Assoc, 75, 371, 591-593 (1980)
[31] Rubin, DB, Bayesianly justifiable and relevant frequency calculations for the applied statistician, Ann Stat, 12, 1151-1172 (1984) · Zbl 0555.62010 · doi:10.1214/aos/1176346785
[32] Sommerfeld, J.; Sanon, M.; Kouyate, BA; Sauerborn, R., Informal risk-sharing arrangements (IRSAS) in rural Burkina Faso: lessons for the development of community-based insurance (CBI), Int J Health Plan Manag, 17, 2, 147-163 (2002) · doi:10.1002/hpm.661
[33] Stan Development Team (2014) Stan: a c++ library for probability and sampling. http://mc-stan.org
[34] van de Schoot, R.; Depaoli, S.; King, R.; Kramer, B.; Märtens, K.; Tadesse, MG; Vannucci, M.; Gelman, A.; Veen, D.; Willemsen, J., Bayesian statistics and modelling, Nat Rev Methods Primers, 1, 1, 1-26 (2021) · doi:10.1038/s43586-020-00001-2
[35] Wagstaff, A.; Bilger, M.; Sajaia, Z.; Lokshin, M., Health equity and financial protection: streamlined analysis with ADePT software (2011), World Bank Publications · doi:10.1596/978-0-8213-8459-6
[36] Wagstaff, A.; Flores, G.; Hsu, J.; Smitz, M-F; Chepynoga, K.; Buisman, LR; van Wilgenburg, K.; Eozenou, P., Progress on catastrophic health spending in 133 countries: a retrospective observational study, Lancet Glob Health, 6, 2, e169-e179 (2018) · doi:10.1016/S2214-109X(17)30429-1
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