| Literature DB >> 34492056 |
Isaiah Awintuen Agorinya1,2,3,4, Maxwell Dalaba4,5, Nathan Kumasenu Mensah5, Samuel Tamti Chatio6, Lan My Le1,2,7, Yadeta Dassie Bacha8, Jemima Sumboh5, Gabriela Flores9, Tessa Tan-Torres Edejer9, Amanda Ross1,3, Fabrizio Tediosi1,3, James Akazili2,5.
Abstract
Out of pocket health payment (OOPs) has been identified by the System of Health Accounts (SHA) as the largest source of health care financing in most low and middle-income countries. This means that most low and middle-income countries will rely on user fees and co-payments to generate revenue, rationalize the use of services, contain health systems costs or improve health system efficiency and service quality. However, the accurate measurement of OOPs has been challenged by several limitations which are attributed to both sampling and non-sampling errors when OOPs are estimated from household surveys, the primary source of information in LICs and LMICs. The incorrect measurement of OOP health payments can undermine the credibility of current health spending estimates, an otherwise important indicator for tracking UHC, hence there is the need to address these limitations and improve the measurement of OOPs. In an attempt to improve the measurement of OOPs in surveys, the INDEPTH-Network Household out-of-pocket expenditure project (iHOPE) developed new modules on household health utilization and expenditure by repurposing the existing Ghana Living Standards Survey instrument and validating these new tools with a 'gold standard' (provider data) with the aim of proposing alternative approaches capable of producing reliable data for estimating OOPs in the context of National Health Accounts and for the purpose of monitoring financial protection in health. This paper reports on the challenges and opportunities in using and linking household reported out-of-pocket health expenditures to their corresponding provider records for the purpose of validating household reported out-of-pocket health expenditure in the iHOPE project.Entities:
Mesh:
Year: 2021 PMID: 34492056 PMCID: PMC8423291 DOI: 10.1371/journal.pone.0256910
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Characteristics of the study area.
| Average km to nearest health facility 1 | 5km |
| Proportion of households with access to cell phones1 | 72% |
| Number of Health facilities at the HDSS site | 1-Hospital, 1-Health Research Centre, 3-private clinic, 7-health centres, 28-community-based health compounds, 3 Pharmacy shops, 7 high volume chemical shops and Over 50 small chemical/drug sellers, drug peddlers and provision shops |
| Types of Health insurance available at HDSS site | National |
| Health insurance coverage at the HDSS site1 | 50% |
| Proportion of individuals attending Public health facilities for In-patient cases1 | 93% |
| Proportion of individuals attending Private health facilities for out-patient cases1 | 6% |
| Disease classification type in the hospital setting (district hospital) | ICD-10 |
| Recording system in the hospital setting (district hospital) | Paper |
| Recording system In Pharmacy and chemical shops | Paper |
| In community health centre | Paper |
| In other outpatient care settings | Paper |
source: Computed from unpublished data from the Kassena-Nankana District Health and Management Team (DHMT).
* Data recorded is daily sales.
Fig 1Structure of data processing for qualitative analysis.
Demographic characteristics of household heads.
| Household characteristics | N | % |
|---|---|---|
|
| ||
| Male | 578 | 66.6 |
| Female | 290 | 33.4 |
|
| ||
| Not married | 292 | 33.6 |
| Married | 576 | 66.4 |
|
| ||
| No education | 463 | 53.3 |
| Primary | 166 | 19.1 |
| Junior High School | 137 | 15.8 |
| Senior High School | 45 | 5.2 |
| Tertiary | 57 | 6.6 |
|
| ||
| Christians | 452 | 52.1 |
| Islam | 85 | 9.8 |
| Traditional | 281 | 32.4 |
| No religion | 50 | 5.8 |
|
| ||
| 15–19 | 39 | 4.5 |
| 20–34 | 63 | 7.3 |
| 35–64 | 532 | 61.3 |
| 65 + | 234 | 27.9 |
|
| ||
| 1 person | 43 | 4.9 |
| 2–5 persons | 397 | 45.7 |
| 6 and above | 428 | 49.3 |
Distribution of type of providers visited by individuals.
| Type of provider | Total number of clients attending different provider | the proportion of clients attending different provider | proportion of clients with linked records to household |
|---|---|---|---|
|
| |||
| Hospital | 453 | 32.3 | 46.8 |
| Community Health Centre | 195 | 13.9 | 55.4 |
| CHPS | 196 | 14.0 | 90.3 |
|
| |||
| Chemical Shop | 194 | 13.8 | 71.1 |
| Clinic | 58 | 4.1 | 27.6 |
| Diagnostic laboratory | 29 | 2.1 | 82.8 |
| Hawker/Vendor/Mobile Van | 25 | 1.8 | 0.0 |
| General local shop | 81 | 5.8 | 33.3 |
| Other | 16 | 1.1 | 12.5 |
| Pharmacy | 155 | 11.1 | 73.6 |
| Total | 1402 | 59 | |
Proportion of clients at provider correctly linked with household information before and after intervention and by type of service provided.
| Before interventions | After interventions | |||||
|---|---|---|---|---|---|---|
| Spending category | Total number of cases | Number of cases matched | Proportion of cases matched (95% CI) | Total number of cases | Number of cases matched | Proportion of cases matched |
| Inpatient care | 339 | 159 | 47 (41.5–52.4) | 221 | 139 | 63 (56.2–69.3) |
| Out-patient | 551 | 351 | 63 (58.8–67.0) | 53 | 34 | 64 (49.8–76.9) |
| Medicines | 468 | 286 | 62 (57.4–66.4) | 579 | 482 | 83 (79.8–86.0) |
| Preventive care | 32 | 22 | 69 (44.9–83.9) | 60 | 44 | 73 (60.3–83.9) |
| Medical products | 7 | 1 | 14 (0.36–57.9) | 7 | 5 | 71 (29.0–96.3) |
| Total | 1397 | 820 | 59 (56.4–61.) | 921 | 705 | 77 (74.1–79.7) |