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International Journal of Health Economics and Management
Erschienen in: International Journal of Health Economics and Management 3/2018

08.12.2017 | Research Article

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

Erschienen in: International Journal of Health Economics and Management | Ausgabe 3/2018

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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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Fußnoten
1
See Gustafsson-Wright and Schellekens (2013) for an analysis of the public–private partnership aspect of this intervention.
 
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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Metadaten
Titel
The impact of subsidized private health insurance and health facility upgrades on healthcare utilization and spending in rural Nigeria
Publikationsdatum
08.12.2017
Erschienen in
International Journal of Health Economics and Management / Ausgabe 3/2018
Print ISSN: 2199-9023
Elektronische ISSN: 2199-9031
DOI
https://doi.org/10.1007/s10754-017-9231-y