Service Delivery Indicators Health Survey 2014 - Harmonized Public Use Data
The SDI provides a set of metrics to benchmark the performance of schools and health facilities in Africa. The Indicators can be used to track progress within and across countries over time, and aim to enhance active monitoring of service delivery to increase public accountability and good governance. Ultimately, the goal of this effort is to help policymakers, citizens, service providers, donors, and other stakeholders enhance the quality of services and improve development outcomes. The perspective adopted by the Indicators is that of citizens accessing a service. The Indicators assemble objective and quantitative information from a survey of frontline service delivery units, using modules from the Public Expenditure Tracking Survey (PETS), Quantitative Service Delivery Survey (QSDS), and Staff Absence Survey (SAS). The SDI initiative is a partnership of the World Bank, the African Economic Research Consortium (AERC), and the African Development Bank. More information on the SDI survey instruments and data, and more generally on the SDI initiative can be found at: www.worldbank.org/sdi, or by contacting SDI@worldbank.org <mailto:SDI@worldbank.org>.
Kind of data
Sample survey data [ssd]
- v01: Harmonized, anonymous dataset for public distribution.
Representative of Tanzania, Dar es Salaam, other urban, and rural.
Unit of analysis
There are two different units of analysis: facilities and individuals. Individuals are assessed both for absence and for knowledge
Producers and sponsors
World Bank Group
Research on Poverty Alleviation
Data collection and processing
Department for International Development, United Kingdom
The sampling frame that the Tanzania health SDI used was the 2012 list of health facilities obtained from the services of the MoHSW before the start of the field work. The original sample frame contained 7,472 health facilities with geographic identifier variables such as region, the division, the ward and even the street. This sample frame was merged with the list of wards from the most recent 2012 census to obtain the size of the population a specific facility is serving which will be later used as a weight for selecting facilities. The sample frame was then purged of 899 facilities which were not functional because they were either closed or under construction. A further 91 facilities were deleted from the frame because they were not eligible for the SDI i.e. regional hospitals, dental clinics, specialized clinics, etc. Two more facilities were suppressed because they served prison's population. This process left us with a final sample frame of 6,480 health facilities. With 995 (15 percent) of health facilities with missing information on ownership (i.e. public/private), the sample frame had an important challenge to offer. Because there was no way to determine the ownership status of those health facilities before going to the field, the facilities were left in the frame but categorized as unknown for ownership. During the data collection the head of facility was asked about their facility's ownership status and the data collected. This new information will be used for post-stratification adjustment. Although the SDI is usually representative at the national and urban and rural areas, in Tanzania it was requested that the survey be also representative of the traditional strata in household surveys which are (1) Dar es Salaam, (2) other urban areas, and (3) rural areas.
Sampling was done using a two-stage sampling method. First, in each stratum districts are drawn with probability proportional to size (PPS). Then the allocated number of health facilities are drawn using PPS again within the set of selected districts in the stratum. Once at a selected health facility, the enumerator selected health workers from the staff roster filled out by the head of facility. The facilities were chosen using PPS, where size is the population served by the facility as provided by the 2012 census database. As for the selection of the cluster, the use of PPS implies that each individual within a stratum has an equal probability for her facility to be selected. Finally within each health facility, up to 10 health workers are selected. There are 2 different procedures for measuring absenteeism or assessing knowledge. For absence, 10 health workers are selected in the staff roster using a random numbers table and the whereabouts of those health workers is ascertained in a return surprise visit. For the assessment, however, only health workers who actually see patients i.e. provide a diagnostic and treatment are eligible. These procedures imply that facilities across strata as well as health workers across strata and within facility (for assessment) do not all have the same probability of selection. It is therefore warranted to compute weights for reporting the survey results.
Deviations from sample design
To be representative of the population of interest, sample estimates from the 2014 Tanzania SDI have to be properly weighted using a sampling weight, or expansion factor. Note that different weights will need to be applied depending on the relevant level for the variable which can be the facility or the staff. The basic weight for each entity is equal to the inverse of its probability of selection which is computed by multiplying the probabilities of selection at each sampling stage. All the weights have been computed and included in the dataset.
Dates of collection
Mode of data collection
Four modules were used, with multiple sections under the following themes:
• Module 1: Facility information
• Module 2: Staff roster
• Module 3: Patient case simulations (to measure provider knowledge)
• Module 4: Facility expenditure, resources and governance (not included in public release files)
Data sets are harmonized to a common questionnaire and subsequently anonymized. For more information on both procedures, please refer to the technical documents: Note to Users (readme_sdi_health.pdf/lisezmoi.pdf), Tanzania Health - Harmonization Information (tanzania_health_20150824.xlsx) as well as the SDC report provided as external resources.
Data has been anonymized, but users commit to not seeking to re-identify statistical units in the data set.
The harmonized, anonymized datasets are available as public use files.
Researchers who feel that they need non-anonymized data should contact email@example.com with a statement of research objectives and a rationale for why they require such data. That will start the Research Use File discussion.
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
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.
L'utilisateur des données reconnait que le producteur des données, le distributeur agréé, ainsi que les bailleurs de fonds ayant contribué au financement de la production de ces données, ne sont nullement responsables de l'utilisation qui sera faite de ces données, ni des interprétations et conclusions dérivées de leur analyse et utilisation.
Service Delivery Indicators
World Bank Group
Development Economics Data Group
The World Bank
Documentation of the DDI
Version 01 (February 2016)
Version 02 (January 2017)
The following changes were made in this version:
- Survey title changed from Service Delivery Indicators Health Survey 2013-2014 to Service Delivery Indicators Health Survey 2013-2014 - Harmonized Public Use Data
- Series Information added
- Scope edited