Under the NUHDSS the households are visited in two informal settlements in Nairobi every four months to collect information on health and other related issues so that we can understand the health and well-being of members of these communities. Specifically, we would like to know about the birth history of the female aged between 12 to 49 years.
Two informal settlements (slums) in Nairobi county, Kenya (specifically, Korogocho and Viwandani slums).
Unit of Analysis
All DSS female residents aged 12-49 years.
The survey covered all DSS female residents aged 12-49 years.
Producers and sponsors
Authoring entity/Primary investigators
African Population and Health Research Center
African Population and Health Research Center
Data Collection, Processing, and Documentation
Rockefeller Foundation (USA)
Wellcome Trust (UK)
William and Flora Hewlett Foundation (USA)
Comic Relief (UK)
Swedish International Development Cooperation
The Bill and Melinda Gates Foundation (USA)
Residents of Korogocho and Viwandani Slums
Community leaders - chiefs and village elders
Support to field teams
All females aged 12-49 years who are the household members (usual residents) in the geographic coverage area.
Dates of Data Collection (YYYY/MM/DD)
Round 9 to Round 19
Mode of data collection
Interviewing teams in the two sites of study comprised of:
- Korogocho: 1 field supervisor, 2 editting team leaders, 1 data quality control team leader, 1 deaths' monitoring team leader, 2 data quality control officers, 12 interviewers
- Viwandani: 1 field supervisor, 2 editting team leaders, 1 data quality control team leader, 1 deaths' monitoring team leader, 3 data quality control officers, 17 interviewers
The roles of the various members of the interviewing teams were:
- Interviewer: Conducting face-to-face paper-based interviews(Round 0- Round 38) and using Netbooks (Round 39 onwards) in assigned zone within the study site
- Data Quality Control Officer: Performing random spot-checks on 10% of the questionnaires and reporting inconsistencies to the Data Quality Control Team Leader for harmonization
within the study community
- Data Quality Control Team Leader: Harmonizing inconsistencies within questionnaires and performing a random spot-check on 10% of the 10% questionnaires that have already undergone spot-checking
- Editting Team Leader: Editting 100% of questionnaires from randomly selected field workers and documenting issues emerging during data collection
- Field supervisor: Responsible for overseeing general operations, resolving issues that cannot be harmonized by data quality control and ensuring that field work progressed on schedule. They also conducted sit-in interviews along with Data Quality Control Team Leader
The Field Co-ordinator, Research Officer and/or Project Managers visited the field and field teams regularly to monitor and review progress and support field operations.
Type of Research Instrument
1. BIRTH HISTORY FORM (FOR ALL FEMALES AGED 12-49)
Data editing took place at a number of stages throughout the processing, including:
1. Quality control through back-checks on 10 percent of completed questionnaires and editing of all completed questionnaires by supervisors and project management staff.
2. A quality control officer performed internal consistency checks for all questionnaires and edited all paper questionnaires coming from the field before their submission for data entry with return of incorrectly filled questionnaires to the field for error-resolution.
3. During data entry, any questionnaires that were found to be inconsistent were returned to the field for resolution.
4. Data cleaning and editting was carried out using STATA Version 13 software.
Detailed documentation of the editing of data can be found in the "Standard Procedures Manual" document provided as an external resource.
Some corrections are made automatically by the program (80%) and the rest by visual control of the questionnaire (20%).
Where changes are made by the program, a cold deck imputation is preferred; where incorrect values are imputed using existing data from another dataset. If cold deck is found to be insufficient, hot deck imputation is used. In this case, a missing value is imputed from a randomly selected similar record in the same dataset.
Data were entered as follows:
Typed based on paper questionnaires at APHRC's headquarters on desktop computers. Double data entry was carried out on 10% of the questionnaires.
Data was captured using in-house software developed with a Visual Basic. Net front-end and a Microsoft Structured Query Language (SQL) Server back-end.
African Population & Health Research Center
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9. Acknowledgement: Any work/reports from this data must acknowledge APHRC as the source of these data. For example, the suggested acknowledgement for NUHDSS data is:
"This research uses livelihoods data collected under the longitudinal Nairobi Urban Health and Demographic Surveillance System (NUHDSS) since 2006. The NUHDSS is carried out by the African Population and Health Research Center in two slums settlements (Korogocho and Viwandani) in Nairobi City."
Additionally all funders, the study communities that provided the data, and staff who collected and analyzed or processed the data should be acknowledged.
10. Deposit of Reports/Papers: The user should submit electronic and paper copies of all publications generated using APHRC data to the Policy Engagement and Communications Department, with copies to the Director of Research.
11. Change of contact details: The user will promptly inform the Director of Research of any change in your personal details as contained on this data request form.
African Population and Health Research Center, Nairobi Urban Health and Demographic Surveillance System - Birth History 2005-2009. December 2017. APHRC, Nairobi - Kenya. doi:10.20369/aphrc-044:2003.1.0
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