Quantitative Service Delivery Survey in Health 2000
Uganda, 2000
Get MicrodataIdentification
| Name | Country code |
|---|---|
| Uganda | UGA |
QSDS are often combined with Public Expenditure Tracking Surveys (PETS) in order to obtain a more complete picture of the efficiency and equity of a public allocation system, activities at the provider level, as well as various agents involved in the process of service delivery.
While most of PETS and QSDS have been conducted in the health and education sectors, a few have also covered other sectors, such as justice, Early Childhood Programs, water, agriculture, and rural roads.
In the past decade, about 40 PETS and QSDS have been implemented in about 30 countries. While a large majority of these surveys have been conducted in Africa, which currently accounts for 66 percent of the total number of studies, PETS/QSDS have been implemented in all six regions of the World Bank (East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, South Asia and Sub-Saharan Africa).
The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.
This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.
Objectives of the study included:
1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level;
2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided;
4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit;
5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance;
6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.
Version
Documented here are final, cleaned datasets prepared by the World Bank based on the raw datasets provided by the study researchers.
The description of the difference between raw and edited datasets is taken from "Data Cleaning Guide for PETS/QSDS Surveys" (p.10):
"Each country set includes two data files. The first file, the "raw" data file, presents the data as collected and entered by the survey teams. While field teams do conduct very high-level coherence tests with regards to responses collected, the data contained therein has generally not been thoroughly checked for internal coherence across questions, variable outliers and other such involved data cleaning procedures.
The second file, the "final" data file, has been reviewed in order to ensure consistency both within and across single observations. While the sanctity of data is paramount, such that no changes are made if it cannot be asserted that the edited value is closer to the "true" value than the previous entry, data edits are introduced into the final data set. The list of edits applied are listed in the available Stata 10 © do-file associated with each data set. Furthermore, each do-file includes other tests that were applied to the data set. In addition, basic statistical analysis is applied to variables in order to identify potential statistical outliers. Outlier values that cannot be explained are replaced by missing values in the "final" data set; these changes are reported both in the do-file and in the Data Quality Report.
Finally, independently of the values presented in the questionnaires, missing values are replaced across all "final" data sets to ensure consistency across countries. Following industry best practices, negative 3-digit integers are used in order to ensure there is no confusion between missing values and valid data points. "
"Data Cleaning Guide for PETS/QSDS Surveys" is available in external resources.
Scope
| Topic | Vocabulary |
|---|---|
| Health | World Bank |
| Health Systems & Financing | World Bank |
Coverage
The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.
The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.
Producers and sponsors
| Name |
|---|
| World Bank |
| Makerere Institute for Social Research, Uganda |
| Ministry of Health, Uganda |
| Ministry of Finance, Planning and Economic Development, Uganda |
Sampling
The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.
The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.
Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.
The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.
Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.
The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.
For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.
In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.
Survey instrument
The following survey instruments are available:
- District Health Team Questionnaire;
- District Facility Data Sheets;
- Uganda Health Facility Survey Questionnaire;
- Facility Data Sheets;
- Facility Patient Exit Poll Questionnaire.
The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.
At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.
The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.
At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.
Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.
Data collection
| Start | End |
|---|---|
| 2000-10 | 2000-12 |
The survey team consisted of a team leader, five supervisors, and five research assistants. Together they formed five separate teams for the fieldwork. One team was assigned to each region; the fifth team acted as a support group to the central region team, which had the largest number of health facilities to cover. The team leader supervised the teams during the survey period. Each team spent at least two days in its district interviewing the district health official and extracting data from the district records. On average, each team also spent another one and a half days interviewing the in-charge at each facility and reviewing facility records. The total number of days spent by each team in the field depended on the number of facilities in the region.
Before taking the survey to the field, the entire research team was trained for over three weeks by the Ugandan team leader and World Bank staff. The training acquainted enumerators with the instruments and techniques to be used in data collection. Following the training, the instruments were pretested in Mukono and Mpigi districts.
Data processing
Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.
STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.
Access policy
Public use file
The use of this dataset must be acknowledged using a citation which would include:
- the identification of the Primary Investigator (including country name)
- the full title of the survey and its acronym (when available), and the year(s) of implementation
- the survey reference number
- the source and date of download (for datasets disseminated online)
Example:
World Bank, Makerere Institute for Social Research, Ministry of Health and Ministry of Finance, Planning and Economic Development, Uganda. Quantitative Service Delivery Survey in Health (QSDS) 2000, Ref. UGA_2000_QSDS_v01_M. Dataset downloaded from http://microdata.worldbank.org 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 | |
|---|---|---|
| Hooman Dabidian | World Bank | hdabidian@worldbank.org |
| Cindy Audiguier | World Bank | caudiguier@worldbank.org |
Metadata production
| Name | Affiliation | Role |
|---|---|---|
| Antonina Redko | DECDG, World Bank |
DDI documentation |