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The impact of subsidized private health insurance and health facility upgrades on healthcare utilization and spending in rural Nigeria

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Abstract

This paper analyzes the quantitative impact of an intervention that provides subsidized low-cost private health insurance together with health facility upgrades in Nigeria. The evaluation, which measures impact on healthcare utilization and spending, is based on a quasi-experimental design and utilizes three population-based household surveys over a 4-year period. After 4 years, the intervention increased healthcare use by 25.2 percentage points in the treatment area overall and by 17.7 percentage points among the insured. Utilization of modern healthcare facilities increased after 4 years by 20.4 percentage points in the treatment area and by 18.4 percentage points among the insured due to the intervention. After 2 years of program implementation, the intervention reduced healthcare spending by 51% compared with baseline, while after 4 years, spending resumed to pre-intervention levels.

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Notes

  1. See Gustafsson-Wright and Schellekens (2013) for an analysis of the public–private partnership aspect of this intervention.

  2. See http://www.pharmaccess.org/.

  3. The insurance does not cover high technology investigations (for example magnetic resonance imaging), major surgeries and complex eye surgeries, family planning commodities, treatment for substance abuse/addiction, cancer care requiring chemotherapy and radiation therapy, provision of spectacles, contact lenses and hearing aids, dental care, management of acute cardiovascular events other than admission to a hospital intensive care treatment and dialyses.

  4. Treatment of HIV/AIDS and tuberculosis is covered by government health programs.

  5. http://www.oanda.com/currency/historical-rates/. The 300 Naira co-premium represents approximately 5% of the monthly per capita consumption of treatment group individuals in 2009. For treatment group individuals in the poorest (richest) consumption quintile the co-premium represents 13 (2)% of monthly per capita consumption in 2009.

  6. Although many of the migrants were tracked and interviewed, not all migrants were found. Nonetheless, migrants were excluded from this analysis even when they were tracked down and found because they were deemed fundamentally different than the general sample since their household and community characteristics are different once they have moved. When we consider only individuals where the age and gender is consistent over the 3 years of the survey, and who have non-missing consumption in all years, the sample is reduced additionally.

  7. Attrition is not equal between the treatment and control group. However, we do robustness checks where we reweigh our sample so that the treatment and control groups are balanced at baseline on observed characteristics and find that our results hold. This gives us assurance that unequal attrition does not strongly affect our results.

  8. Modern includes hospital, clinic, (primary) health center, or private doctor/nurse/midwife/paramedic.

  9. Non-modern includes a traditional healer, pharmacist, patent medicine vendor, alternative medicine provider, or religious person.

  10. Corrected for inflation using www.tradingeconomics.com/National Bureau of Statistics Nigeria, with 1 October 2011 as reference point. Calculation based on 2009 inflation of 13.9%, 2010 inflation of 11.8%, and 2011 inflation of 10.3%.

  11. The consumption split is made at the median of the per capita aggregate consumption at baseline, which is equivalent to $1.54 per day. Note that therefore the so-called “richest half” of the sample does by no means consist of rich individuals only.

  12. As of May, 2013.

  13. The insurance status of an individual is only captured the time of the follow-up interview. It is possible that an individual enrolled the 1st year but didn’t reenroll the second and it is also possible that an individual just very recently enrolled. It is therefore possible that the findings underestimate the impact of the program.

  14. Following an anonymous reviewer’s suggestion, we have explored whether the EA is an adequate clustering level for standard error calculation, and have concluded that it is sufficient. For more details see section “Robustness of the results”. 

  15. Note that these terms are common when studying true experiments, such as RCTs, while the Kwara project is a quasi-experiment. For a more detailed description of the methodology, see Khandker et al. (2010) and Ravallion (2001).

  16. See Dehejia and Wahba (2002) for a discussion on PSM methods.

  17. For 2013, the matching is likely to be less precise than for 2011 because individuals in the treatment area had already been exposed to the first years of the project intervention, which may have changed their propensity to enroll in health insurance. We thank an anonymous reviewer for pointing this out. We are implicitly making the additional assumption that the control group individuals with a certain set of baseline observed characteristics would be influenced in the same way by the KSHI over time as were the treatment group individuals with those baseline characteristics. We do include the 2013 ATET results in the tables, but given this caveat, we give the results less weight in the discussion.

  18. As an alternative to PSM, we have also considered matching on Mahalanobis distance at a suggestion of an anonymous reviewer. However, this method did not yield a good balance between the insured individuals and the matched controls.

  19. We have also tested for imbalance over each observed characteristics. The results were consistent with the results of the joint balance test.

  20. The matched individuals are then similar on the many observed characteristics related to health insurance take up, and – because we exclude non-insured treatment individuals as potential matches – not necessarily different on unobserved ones. For 2011, this gives us confidence that the assumption of ignorability, which is needed for the validity of PSM, is likely to hold. For 2013, see the caveat in Footnote 17.

  21. We also sought to estimate the impact of insurance on poverty, but find no impact.

  22. By contrast, Finkelstein et al. (2012) find a positive effect on self-reported health status.

  23. To test for this, we included a dummy variable in the DD regression for whether any health care was sought in the past 12 month, interacted with the year of the survey. Indeed health care utilization in the past explains most of the effect (see Table 14 in “Appendix 3”). However, since health care utilization is endogenous in this analysis, these are just correlations and we do not claim causality.

  24. Similar results are reported in King et al. (2009), Bernal et al. (2015), Wagstaff and Yu (2007), and Cheng et al. (2015).

  25. Nonlinear panel data models with individual fixed effects tend to give biased parameter estimates, because they give rise to an incidental parameters problem in the maximum likelihood estimation (Neyman and Scott 1948). Thus we do not control for individual fixed effects in the hurdle model.

  26. Note that it is not clear whether the wild cluster bootstrap-t correction from Cameron et al. Cameron et al. (2008) performs well for three clusters, since the lowest number of clusters they explore is five.

  27. See discussion on financing healthcare in van der Gaag and Stimac (2012).

  28. Though implementation of these plans has been delayed due to the economic crisis in Nigeria.

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Author information

Authors and Affiliations

  1. Brookings Institution, Washington, DC, USA

    Emily Gustafsson-Wright & Jacques van der Gaag

  2. Department of Primary Health Care, Center for Health Service Economics and Organisation, University of Oxford, Oxford, UK

    Gosia Popławska

  3. Vrije University (VU), Amsterdam, The Netherlands

    Zlata Tanović

  4. Amsterdam Institute for International Development, Amsterdam, The Netherlands

    Zlata Tanović

  5. Department of Economics and Business, University of Amsterdam, Amsterdam, The Netherlands

    Jacques van der Gaag

Authors
  1. Emily Gustafsson-Wright
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  2. Gosia Popławska
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  3. Zlata Tanović
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  4. Jacques van der Gaag
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Corresponding author

Correspondence to Emily Gustafsson-Wright.

Additional information

This research was made possible by funding from the Dutch Ministry of Foreign Affairs. This paper is also funded under the Grant “Policy Design and Evaluation Research in Developing Countries” Initial Training Network (PODER), which is funded under the Marie Curie Actions of the EU’s Seventh Framework Programme (Contract Number: 608109). The authors would like to thank the research teams in the Netherlands at AIID and AIGHD and in Nigeria at UITH for their tireless efforts and in particular Wendy Janssens, Berber Kramer and Bas van der Klaauw for their comments, Marijn van der List for her project management and Anne Duynhouwer and Marc Fabel for their research assistance. In addition, we are thankful to comments received at presentations given on previous versions of the paper at Abt Associates, Brookings Institution, DC Health Systems Board, Center for Global Development, the European Conference on Health Economics, and the University of Amsterdam. Finally, we would like to thank the anonymous reviewers for their excellent comments and suggestions to improve to this paper.

Appendices

Appendix 1: Research design and sampling

A rural area of Kwara State was chosen as the treatment area for the program based on need and availability of potential program clinics. Ajasse Ipo and surroundings was selected as the control area after a scan conducted by University of Ilorin Teaching Hospital (UITH). The district was chosen because it was deemed most similar to the program area in terms of the language, socio-economic characteristics, and distribution of health facilities, urban/rural composition, and population size.

Once treatment and control areas were chosen, sample stratification ensured a representative sampling of subgroups of the population. One notable level of sample stratification was based on the presence of a (potential) program health facility in a community since the impact of the program is likely to depend on a household’s distance to the health facilities. Specifically, the sample included households living within a radius of 5 km from a (potential) program health facility, and households living outside these communities, with a maximum distance of 15 kilometers to one of the health facilities. Households outside this 15 km radius were not included in the sample, as they were judged to live too far from the facility to be effected by any quality upgrades. For each of these four strata, a complete listing of all enumeration areas (EAs) from the 2005 National Population Census was obtained, and from every list, 100 EAs were randomly selected. Accordingly, UITH conducted a pre-survey to list all households in these areas. From these listings, on average 15 households were sampled, giving a total sample of 1500 households. The precise number of households within an EA depended on its population size, such that all households within a stratum had the same probability of being selected. Within every EA, those households that were not sampled served as replacement households.

Box A1: Sampling details

 

Afon and Aboto Oja: program area

Ajasse Ipo : control group

(Potential) program clinic in community

30 EAs, 450 households

20 EAs, 300 households

No (potential) program clinic in community

30 EAs, 450 households

20 EAs, 300 households

Total

60 EAs, 900 households

40 EAs, 600 households

Box A1 gives an overview of the sampling details. The reason that more households were sampled from the treatment area than from the control area was to have enough observations on insured individuals at the time of the follow-up surveys, for the ATET estimates.

Appendix 2: Control variables

When estimating the ITT effect, we use the following set of variables to control for the time-varying characteristics (note that time invariant characteristics are excluded, because these are absorbed in the individual fixed effect of the DD regression):

  1. 1.

    Age squared of an individual (not age itself as it is collinear with the time trend variables, when controlling for fixed effects),

  2. 2.

    If an individual is a household head,

  3. 3.

    Marital status of an individual,

  4. 4.

    Employment status of an individual,

  5. 5.

    If household was located close to a potential program health facility (less than 5 km),

  6. 6.

    Distance from a household to the nearest non-program health facility (any health facility),

  7. 7.

    Household size,

  8. 8.

    Gender of the household head,

  9. 9.

    Per capita consumption (excluding health expenditures) level and level squared,

  10. 10.

    Wealth indicator of an individual (normalized),

  11. 11.

    If an individual has access to a good quality toilet,

  12. 12.

    If an individual has access to good quality water,

  13. 13.

    If an individual smokes,

  14. 14.

    If an individual drinks alcohol,

  15. 15.

    Additional variable included to attempt to control for time-variant differences between the treatment and control group: Storage, crop or livestock disease in the community, in a given year.

In order to match the insured individuals in the treatment group with similar individuals in the control group we use a rich set of characteristics at baseline:

  1. 1.

    Gender of an individual,

  2. 2.

    If and individual is a household head,

  3. 3.

    Age and age squared of an individual,

  4. 4.

    Marital status of an individual,

  5. 5.

    Employment status of an individual,

  6. 6.

    Level of education of the household head (if no education or if only primary education),

  7. 7.

    If household was located close to a potential program health facility (less than 5 km),

  8. 8.

    Distance from a household to the nearest non-program health facility (any health facility),

  9. 9.

    Logarithm of per capita consumption (excluding health expenditures),

  10. 10.

    If an individual has access to a good quality toilet,

  11. 11.

    If an individual has access to good quality water,

  12. 12.

    General healthcare utilization,

  13. 13.

    Modern/formal healthcare utilization,

  14. 14.

    Non-formal healthcare utilization,

  15. 15.

    Healthcare spending,

  16. 16.

    Indicator if individual had any positive healthcare spending,

  17. 17.

    Four self-reported health measures:

    1. (i)

      whether the individual can perform daily activities without difficulties,

    2. (ii)

      whether individual’s health improved compared to 1 year ago,

    3. (iii)

      whether the person has a chronic illness,

    4. (iv)

      whether the person had an illness or injury in the past 12 months.

Appendix 3: Tables

See Tables 7, 8, 9, 10, 11, 12, 13 and 14.

Table 7 Baseline characteristics of the insured at follow-up compared to the uninsured, in the treatment group.
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Table 8 Full estimation results of ITT regressions for health care utilization and financial protection.
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Table 9 Estimation results of ITT regressions for health care utilization and financial protection using full set of community shocks as controls.
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Table 10 Summary of the ITT estimation results for health care utilization and financial protection (DD) by subgroup.
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Table 11 Summary of the estimation results of ATET (PSM) for health care utilization and financial protection by subgroup.
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Table 12 Summary of the ITT estimation results for self-reported health status (DD).
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Table 13 Summary of the ATET estimation results for self-reported health status (PSM).
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Table 14 ITT estimation results for self-reported health status when adding health-care utilization as control variable (DD).
Full size table

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Gustafsson-Wright, E., Popławska, G., Tanović, Z. et al. The impact of subsidized private health insurance and health facility upgrades on healthcare utilization and spending in rural Nigeria. Int J Health Econ Manag. 18, 221–276 (2018). https://doi.org/10.1007/s10754-017-9231-y

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