Health Results-Based Financing Impact Evaluation 2011, Health Facility Baseline Survey
Zambia, 2011
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ZMB_2011_HRBFIE-HFBL_v01_M
Health Results-Based Financing Impact Evaluation 2011, Health Facility Baseline Survey
| Name | Country code |
|---|---|
| Zambia | ZMB |
Baseline household data was collected over the period November to December 2011. Endline data was collected between November 2014 and January 2015, using the same survey tools and in the same study areas and was undertaken in 18 IE districts (all of the study districts in six of the matched district triplets yielding information from 6 RBF districts, 6 C1 districts, and 6 C2 districts). For the health facility survey, baseline data was collected between November and December 2011, and endline data was collected between November 2014 and January 2015.
Note: The household, health facility endline and community data and are available online as separate entries under Impact Evaluation Surveys Collection in the Central Data Catalog.
Zambia was awarded a grant in 2008 by the World Bank through the Health Results Innovation Trust Fund (HRITF) to implement a RBF pilot project with an accompanying Impact Evaluation (IE) led by the World Bank. Motivated by inadequate progress to achieving MDGs 4 and 5 targets, the primary objective of the project was to catalyze the country’s efforts to reduce under-five and maternal mortality in 11 districts in nine (9) of Zambia’s 10 provinces (except Lusaka) countrywide.
The Zambia health RBF (HRBF) pilot project was implemented by the Government through the Zambian health system (contracted-in) and is one of the few examples of a Lower Middle Income Country (LMIC) with this type of model. After a pre-pilot phase, which lasted approximately 2 years in the Eastern Province district of Katete, the RBF model was expanded to ten (10) additional districts in April 2012. By the end of the project, 203 health centres were covered across the country. This represented a total catchment population of about 1.5 million people of which the direct beneficiaries were 338,248 children aged between 0-59 months, and 372,073 women of childbearing age.
The accompanying IE comprised both quantitative and qualitative approaches. Quantitative data for the IE at household and facility level was collected at baseline, implementation stage, and endline from 10 RBF intervention districts; 10 Control 1 (C1) districts; and 10 Control 2 (C2) districts. The method of selecting districts for the IE was based on district-matched randomization. Inputs were assigned to the three district groups as follows: (a) The RBF Intervention group to receive Emergency Obstetric and Neonatal Care (EmONC) equipment and RBF performance-based grants; (b) The C1 group (“enhanced financing” arm) to receive EmONC equipment exactly as in the RBF and the equivalent in money of the average RBF performance-related grants as input financing; and (c) The C2 (“pure control” arm) group to receive nothing.
The IE investigated the impact of the RBF over a broad range of targeted and non-targeted indicators related to maternal and child health services.
Health facilities
Version
v01. Edited, anonymized datasets for public distribution.
Scope
The survey covered the following themes:
-
Health Facility Survey
Two rounds of facility surveys were undertaken for the same set of rural health centres and health posts, i.e. baseline (October-November,2011) and endline (November 2014-January 2015).The facility survey consisted of a facility checklist; a health worker instrument; exit interview of sick children and antenatal women. The training for the data collection of the enumerators included a classroom didactic part and a field visit. The instruments were pre-tested and modified appropriately before the actual survey: -
Facility survey checklist
Health facilities were selected by a simple random sampling technique. In the baseline, 176 health facilities were surveyed whereas 210 were surveyed in the endline. The facility checklist was used to collect information on infrastructure, administration, availability of basic drugs and equipment, governance and autonomy. -
Health Worker Interviews
Up to two health workers were selected for the interview at each health facility. The criterion for selection was provision of maternal and child health care on the day of the interview. Sample sizes were 326 and 355 for the baseline and endline surveys, respectively. The instrument included questions on remuneration, knowledge, job satisfaction and motivation. -
Exit Interviews
A patient exit interview assessed patient satisfaction and quality of care received for patients exiting ante-natal care and child health consultations. For child consultation, the child’s parent or caretaker was interviewed. Up to six clients were selected per service through a systematic random sampling strategy (based on caseload for the same day of previous week for the facility). Sample sizes were as follows: Child health (baseline 1,059 and endline 1,266), and antenatal care (baseline 893 and endline 1254).
Coverage
The survey was undertaken in 30 districts (all of the study districts in 10 matched district triplets across 8 provinces: Central, Copperbelt, Eastern, Luapula, Northern, North-Western, Southern, Western).
Producers and sponsors
| Name | Affiliation |
|---|---|
| Jed Friedman | World Bank |
| Name | Affiliation |
|---|---|
| Jumana Qamruddin | World Bank |
| Collins Chansa | World Bank |
| Ashis Kumar Das | World Bank |
Sampling
Within each province (except for Northern and Southern Provinces, where six districts were sampled), three districts at or near a derived provincial median index score were selected and then randomly assigned to each of the three arms. Thus, there are a total of 30 districts distributed equally among the three study arms with 10 districts in each.
For statistical purposes, each district in Zambia is subdivided into Census Supervisory Areas (CSAs), which in turn nests Standard Enumeration Areas (SEAs). Thus, for data collection purposes, the SEA is the smallest geographical unit above the household and is the primary sampling unit (PSU). The SEAs were sampled from the catchment areas of selected health facilities. The sampling frame of SEAs in each treatment arm was arrived at by digitally overlaying SEA maps (obtained from the CSO) with health facility catchment area maps. After grouping the PSUs by stratum (treatment vs control), the sample was then selected in two stages: i) selection of PSUs in the first stage using probability proportional to size, and ii) selection of 10 eligible households, or secondary sampling units (SSUs), in the second stage using systematic random sampling. Prior to household selection, a full PSU listing of eligible households (households with a pregnancy related outcome, i.e. live birth, stillbirth, abortion and miscarriage within the two years prior to the survey) was undertaken by the survey team in each cluster. At baseline, 3,064 households in the relevant districts were surveyed at baseline, and 3,500 households at follow up. Health facilities were selected by a simple random sampling technique. In the baseline, 176 health facilities were surveyed whereas 210 were surveyed in the endline.
Data collection
| Start | End | Cycle |
|---|---|---|
| 2011-10-01 | 2011-11-01 | Baseline |
- Other [oth]
Data Access
| Name | Affiliation |
|---|---|
| Jed Friedman | World Bank |
Use of the dataset must be acknowledged using a citation which would include:
- the Identification of the Primary Investigator
- the title of the survey (including country, acronym and year of implementation)
- the survey reference number
- the source and date of download
Example:
Jed Friedman (World Bank). Zambia - Health Results-Based Financing Impact Evaluation 2011, Health Facility Baseline Survey (HRBFIE-HFBL 2011). Ref: ZMB_2011_HRBFIE-HFBL_v01_M. Downloaded from [uri] on [date].
Disclaimer and copyrights
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Contacts
| Name | Affiliation | |
|---|---|---|
| Jed Friedman | World Bank | jfriedman@worldbank.org |
Metadata production
DDI_ZMB_2011_HRBFIE-HFBL_v01_M_WB
| Name | Affiliation | Role |
|---|---|---|
| Development Data Group | World Bank | Documentation of the study |
2023-06-27
Metadata version
Version 01 (2023-06-27)