Background
Health financing in South Sudan
Objectives
Methods
Results
Socio-demographic characteristics of respondents
Variable | Descriptions | Frequency | Percentage (%) |
---|---|---|---|
Age of respondents | 18–27 | 98 | (26.72%) |
28–37 | 135 | (35.43%) | |
38–47 | 81 | (21.26%) | |
48–57 | 39 | (10.24%) | |
58 and above | 28 | (7.35%) | |
Sex of respondents | Male | 271 | (71.13%) |
Female | 110 | (28.87%) | |
Marital status | Monogamous | 248 | (65.09%) |
Polygamous | 64 | (16.80%) | |
Separated or divorced | 6 | (1.57%) | |
Widowed | 8 | (2.10%) | |
Single | 55 | (14.44%) | |
Household size | 1–4 | 139 | (36.48%) |
5–8 | 137 | (35.96%) | |
9 and above | 105 | (27.56%) | |
Religion | Christianity | 332 | (87.14%) |
Islam | 47 | (12.34%) | |
African traditional belief | 2 | (0.52%) | |
Health status (chronic illness) | Yes | 55 | (14.44%) |
No | 326 | (85.56%) | |
Education level | Tertiary level | 194 | (50.91%) |
Secondary | 136 | (35.70%) | |
Primary | 42 | (11.02%) | |
No formal education | 9 | (2.36%) | |
Private insurance | Yes | 62 | (16.27%) |
No | 319 | (83.73%) | |
Monthly salary (SSP) | 1–1000 | 191 | (50.13%) |
1001–2000 | 111 | (29.13%) | |
2001–3000 | 58 | (15.22%) | |
3001–4000 | 12 | (3.15%) | |
4000 and above | 9 | (2.36%) | |
Other sources of income | Yes | 106 | (27.82%) |
No | 275 | (72.18%) | |
Total monthly income (SSP) | 1–1000 | 151 | (39.63%) |
1.001–2000 | 118 | (30.97%) | |
2001–3000 | 51 | (13.39%) | |
3001–4000 | 28 | (7.35%) | |
4000 and above | 33 | (8.66%) |
Ownership of household assets and access to social amenities
Household assets | Yes | No |
---|---|---|
Electricity | 120 (32%) | 261 (69%) |
Running water (tap) | 39 (10%) | 342 (90%) |
Water closet toilet | 65 (17%) | 316 (83%) |
Solar power | 57 (15%) | 324 (85%) |
Television | 181 (48%) | 200 (53%) |
Vehicle | 95 (25%) | 286 (75%) |
Fridge | 81 (22%) | 300 (78%) |
Indoor bathroom | 93 (24%) | 288 (76%) |
Laundry machine | 12 (3%) | 369 (97%) |
Computer | 142 (37%) | 239 (62%) |
Micro wave oven | 6 (2%) | 375 (98%) |
Fixed telephone | 6 (2%) | 375 (98%) |
Mobile telephone | 341 (90%) | 40 (10%) |
Annual expenditure on selected basic needs
Distance from the nearest health facility
Preferred health facility
Reasons for the choice of health facility
Occupational injury status
Awareness about NHIF
Premium
Household assets | Yes | No |
---|---|---|
Electricity | 120 (32%) | 261 (69%) |
Running water (tap) | 39 (10%) | 342 (90%) |
Water closet toilet | 65 (17%) | 316 (83%) |
Solar power | 57 (15%) | 324 (85%) |
Television | 181 (48%) | 200 (53%) |
Vehicle | 95 (25%) | 286 (75%) |
Fridge | 81 (22%) | 300 (78%) |
Indoor bathroom | 93 (24%) | 288 (76%) |
Laundry machine | 12 (3%) | 369 (97%) |
Computer | 142 (37%) | 239 (62%) |
Micro wave oven | 6 (2%) | 375 (98%) |
Fixed telephone | 6 (2%) | 375 (98%) |
Mobile telephone | 341 (90%) | 40 (10%) |
“I have not seen the draft proposal but the idea of percentage is to enable people to pay according to capacity…” (KI, Staff of Drug and Food Control Authority Headquarters)
Respondents’ WTP for proposed health care services
Another reason was given uncertainty of disease occurrence. Paying for NHIF was seen as providing social security because a disease does not alert people when it would strike.“By being a member of health insurance, I will benefit by sharing the amount of money which may be needed from me for healthcare services from what I can afford” (KI, Staff of MOH headquarters)
It was observed that paying for the scheme would make people value health care services and thereby improve utilization.“Sickness does not inform you that I am coming. That is why it is good to contribute. Imagine getting sick and nowhere to borrow just like in the current economic crisis, what will happen? It is good to contribute even if one does not fall sick…” (KI, Health worker Alsabbah Children’s Hospital).
On the other hand, those who were not willing to pay for NHIF cited inadequate information on the scheme, limited or unreliable income, corruption and mistrust and prior bad experience with the former NHIF of the Republic of the Sudan, where services did not meet the contributors’ expectations.“When people contribute to service, they attach value to such services and access becomes easier” (KI, staff of Drug and Control Authority HQs).
The results of the respondents’ willingness to pay and ownership of assets are presented in Table 4. The assets considered for this study were: solar lighting system, a television set, a vehicle, a fridge, indoor bathroom, laundry machine, personal computer and microwave. This is a select list from the South Sudan Household Health Survey 2010. To get the number of assets a given respondent had, we summed the household assets from 0 to 8, with possession of assets =1 and no assets =2, thus a total of all assets = 8. Table 4 (a) shows that close to two thirds (62%, 79/259) with no assets are willing to pay and 100% of those with assets (7–8 category) are willing to pay. Table 4(b) shows that the close to two thirds of the majority (62.2%, 79/127) of those who don’t possess assets are willing to pay compared to the 37.8% of those who are not willing to pay. Over seven tenth (70.9%, 180/254) of those with assets are willing to pay compared to the 29.1% who possess assets but are not willing to pay. Table 4(c) shows that there was no significant relationship between willingness to pay and the possession of assets (P = 0.088 > 0.05). In addition, a unit change in the possession of assets affected the WTP by 0.39 points. Those who possess assets are 1.48 times more likely to pay for insurance than those who don’t possess assets. With the Wald statistic 2.903 and 1df, we fail to reject the null hypothesis and conclude that there is no significant relationship between possession of assets and WTP.“I am not aware of the benefits” (KI, staff of Juba County HQs)
a) Willing to pay against the number of assets | |||||||||||
Number of assets | Total | ||||||||||
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |||
Willing to pay | 79 | 67 | 30 | 28 | 25 | 23 | 4 | 2 | 1 | 259 | |
62% | 76% | 61% | 72% | 64% | 77% | 67% | 100% | 100% | 68% | ||
Not willing to pay | 48 | 21 | 19 | 11 | 14 | 7 | 2 | 0 | 0 | 122 | |
38% | 24% | 39% | 28% | 36% | 23% | 33% | 0% | 0% | 32% | ||
Total | 127 | 88 | 49 | 39 | 39 | 30 | 6 | 2 | 1 | 381 | |
b) Willing to pay against the number of assets | |||||||||||
Possession of assets | Total | ||||||||||
Doesn’t possess assets | Possess assets | ||||||||||
Willing to pay | Count | 79 | 180 | 259 | |||||||
% within | 62.2% | 70.9% | 68.0% | ||||||||
Not Willing to pay | Count | 48 | 74 | 122 | |||||||
% within | 37.8% | 29.1% | 32.0% | ||||||||
Total | Count | 127 | 254 | 381 | |||||||
% within | 100.0% | 100.0% | 100.0% | ||||||||
c) Binary logistic regression | |||||||||||
Variables in the equation | |||||||||||
B | S.E. | Wald | df | Sig. | Exp (B) | ||||||
Step 1a
| PA (1) | .391 | .229 | 2.903 | 1 | .088 | 1.478 | ||||
Constant | −.889 | .138 | 41.435 | 1 | .000 | .411 |
Discussion
Factors influencing WTP for NHIF in Juba City
Awareness of public servants in juba about NHIF
Premium
Study limitations
Conclusions
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The Government of South Sudan could run a public campaign and provide more information on the national health insurance scheme to all stakeholders (public servants, policy makers, private sector workers and the general community) so that they own (buy-in) the program right from the start. Some public officers not willing to pay cited lack of adequate information. A task force could be established by the Ministry of Gender, Child and Social Welfare with technical support from MOH to accomplish such an undertaking. This could ensure a robust and successful start.
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The involvement of all stakeholders is crucial in fast tracking the process; it is therefore incumbent upon the national Ministry of Gender, Child and Social Welfare supported by the national Ministry of Health to engage all those who will be affected by the scheme as early as possible by carrying out adequate feasibility studies including stakeholder analysis.