| ID | Title | level | total | percent |
|---|---|---|---|---|
| HF.1 | Government schemes and compulsory contributory health care financing schemes | 1 | 39523.4 | 25.3 |
| HF.1.1 | Government schemes | 2 | 37974.8 | 24.3 |
| HF.1.1.1 | Federal government schemes | 3 | 37943.1 | 24.2 |
| HF.1.1.2 | State/regional/local government schemes | 3 | 18.8 | 0.0 |
| HF.1.1.nec | Unspecified government schemes (n.e.c.) | 3 | 12.8 | 0.0 |
| HF.1.2 | Compulsory Contributory health insurance schemes | 2 | 1548.1 | 1.0 |
| HF.1.2.1 | Social health insurance schemes | 3 | 1548.1 | 1.0 |
| HF.2 | Voluntary health care payment schemes | 1 | 26637.3 | 17.0 |
| HF.2.1 | Voluntary health insurance schemes | 2 | 4398.5 | 2.8 |
| HF.2.1.nec | Unspecified voluntary health insurance schemes (n.e.c.) | 3 | 4398.5 | 2.8 |
| HF.2.2 | NPISH financing schemes (excluding HF.2.2.2) | 2 | 19484.1 | 12.4 |
| HF.2.2.1 | Resident foreign agencies schemes | 3 | 9223.9 | 5.9 |
| HF.2.3 | Enterprise financing schemes | 2 | 2755.0 | 1.8 |
| HF.2.3.1 | Enterprises (except health care providers) financing schemes | 3 | 2755.0 | 1.8 |
| HF.3 | Household out-of-pocket payment | 1 | 90340.3 | 57.7 |
| HF.3.1 | Out-of-pocket excluding cost-sharing | 2 | 90340.3 | 57.7 |
| HF.nec | Unspecified financing schemes (n.e.c.) | 1 | 0.1 | 0.0 |
Decoding OOP - NHA Data-Exercise
This is a preliminary and cursory data exercise of OOP based on the data provided in the NHA report (the data was scraped from the report and used) and still a work in progress.
OOP among other Health Care Financing Schemes (FS) (2017/18)
Following table provides information on Health Care Financing Scheme (FS) for the FY 2017-18. OOP stands at staggering 57.7% of the CHE among all financing schemes. (following table is just a replication of table in the NHA report; the format of the table is flawed!)
Proportion of OOP among provinces
The Direct household OOP payment is the largest scheme, accounting for 57.7% of CHE in the FY 2017/18. Let us examine the distribution of the OOP payment across all provinces.
Figure 2 depicts proportion (of total OOP across) of OOP in each of seven provinces. Bagmati Province has the highest proportion/composition of OOP which stands at 25.74%. A quarter of the OOP spending happens in Bagmati Province. We could presume that the concentration of health care facilities (including privately operated) in bigger cities such as Kathmandu Valley and Chitwan is a reason behind such inflated proportion of OOP at Bagmati region. Likewise, the proportion distribution at two neighboring provinces of Lumbini and Karnali presents interesting relationship. The Lumbini Province stands second with total of 16.8% of total OOP while the proportion of OOP of Karnali Province is lowest at 6.75%. We could presume that there exists net outflow of individual from Karnali to Lumbini province for medical services which could presumbly increased the total OOP proporation of Lumbini province.
Figure 3 represents the proportion of population and proportion of OOP for each Provinces.
Bagmati Province which as the highest population proportion in the country also has the highest OOP proportion among all provinces. There is significant difference between the OOP proportion and the population proportion of the Bagmati Province (~5%). We could presume the concentration of health facility in Bagmati Province along with a certain reputation (the capital !) provides a reasonable explanation for a concentration of citizens from all over the country.
Madesh Province seems be an interesting case for further study as there is highest difference between the proportion of population and OOP (~8%).
how are we to explain this significant disproportion ? (this could be a area of further study of PE of public health in Madhesh)
Proportion of OOP in each Provinces
Lumbini province has the highest OOP proportion among its financing schemes. It can be argued that Lumbini Province needs a dedicated plan (at provincial level) to reduce OOP (and maybe be towards more national level of OOP). However, there could be multiple reasons such as for such higher OOP; such as concentration of health facilities at cities such as Nepalgunj, bhairawa and Butwal which serves are major centers of health services for people in Western Nepal. What is more insteresting is the data on Madhesh (again!), figure 4 suggests that the OOP is at 67.59% which is not as low in comparistion with other provinces. However, if we are to look at it considering the information obtained from figure 3 (low overall contribution of OOP but higher population proportion) it can be presumed that total FS of Madesh Province is lower than other province (in relation to the size of population they cater to.)