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Examining the Complex Dynamics Influencing Persistent Acute Malnutrition in Turkana and Samburu Counties – A Longitudinal Mixed Methods Study to Support Community Driven Activity Design (USAID Nawiri Wave IV), NAWIRI WAVE IV

KENYA, 2022
Health and Well-Being (HaW)
Dr. Estelle M. Sidze, Dr. Faith Thuita
Last modified August 28, 2025 Page views 466908 Documentation in PDF Metadata DDI/XML JSON

Identification

IDNO
DDI-KEN-APHRC-NAWIRI-2023-W4-v10
Title
Examining the Complex Dynamics Influencing Persistent Acute Malnutrition in Turkana and Samburu Counties – A Longitudinal Mixed Methods Study to Support Community Driven Activity Design (USAID Nawiri Wave IV), NAWIRI WAVE IV
Subtitle
NAWIRI WAVE IV
Country
Name Country code
KENYA KEN
Abstract
Scientific abstract
Background: Acute malnutrition in infants and children less than 5 years is persistent in the arid and semi-arid lands (ASALs) of East Africa and the Sahel region despite years of investment. In the ASALs of Kenya, the situation is exacerbated by deep-rooted poverty and unequal access to basic services, sustained community conflicts, migration, poor seasonal rainfall/drought and other shocks. Nutrition specific and nutrition sensitive national and county level programs have either not been developed or not implemented effectively.

Objectives: To understand and map immediate, underlying, basic and systemic drivers of acute malnutrition for the development of overarching as well as micro-solutions for the sustainable reduction of persistent acute malnutrition (PAM) and inform pilot studies and Phase 2 (second phase of USAID Nawiri project implementation) activities in Turkana and Samburu Counties.

Methods: This study is a longitudinal mixed-methods observational cohort study of children less than 3 years and their mothers and/or caregivers in Samburu and Turkana Counties. Both quantitative and qualitative methods were utilized in the data collection processes. Data collection comenced in January 2021. Data analysis, learning and adapting was also ongoing so that results could inform pilots, theory of change (ToC) review and Phase 2 activities throughout the study.

Study outcomes: To develop new interventions, and to adapt and contextualize existing interventions to prevent global acute malnutrition (GAM); strengthen social and behavior change (SBC) strategies around maternal, infant and young child nutrition (MIYCN), water and sanitation (WASH), community health systems, gender dynamics, livelihoods and resilience, and to inform improvements of the current nutrition surveillance system.

Study duration: 24 months.
Summary budget: Total budget is KSH 140,400,000.00.
Lay summary:
The nutritional status of mothers and young children in Kenya's ASALs are strongly affected by deep-rooted poverty and unequal access to basic services, sustained community conflict, migration, poor seasonal rainfall/drought and other shocks. Inadequate women empowerment and limited control over household resources, high workload, domestic violence, insufficient household food security, inadequate social support, inadequate health services and an unhealthy environment, as well as inadequate dietary intake and high disease burden, are among other factors that contribute to poor maternal infant and young child feeding practice in these areas. Consequently, more than one in ten reproductive age women and 2-3 in ten young children in Turkana and in Samburu are undernourished. As such, this study aims to provide evidence for the appropriate policy and program design to improve the nutritional status of children and their mothers living in the two counties.

Version

Version Date
2023-07-10
Version Notes
In Wave 4 of data acollection. SOCIAL SAFETY NETS AND ECONOMIC SAFETY GUARDS section, Qn.7.2, Village Savings Groups was separated from Sacco/Cooperative/Village Savings Group and became a stand alone option as a copping mechanism

Coverage

Geographic Coverage
County coverage ( Turkana and Samburu )
Unit of Analysis
The unit of analysis are the sampled Households in Turkana and Samburu Counties who have children under 6 years
Universe
The survey covered households with children under 5 years and their mothers/caregivers

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
Dr. Estelle M. Sidze African Population and Health Research Center
Dr. Faith Thuita Research Triangle International
Producers
Name Affiliation Role
Dr. Chessa Lutter Research Triangle International Senior Nutrition Researcher and Senior Fellow
Dr. Valerie Flax Research Triangle International Senior Public Health Research Analyst
Mr. Brad Sagara Mercy Corps Deputy Director, Research and Learning
Dr. Elizabeth Kimani-Murage African Population and Health Research Center Senior Research Scientist
Dr. Dickson Amugsi African Population and Health Research Center Associate Research Scientist
Dr. Calistus Wilunda African Population and Health Research Center Project Manager
Mr. Albert Webale Research Triangle International Project Manager
Ms. Esther Anono African Population and Health Research Center Research Officer
Ms. Hazel Anyango African Population and Health Research Center Research Officer
Mr. Bonventure Mwangi African Population and Health Research Center Data analyst/manager
Ms. Anne Njeri African Population and Health Research Center Data Manager
Mr. Stephen Ilimo African Population and Health Research Center Field Coordinator - Turkana County
Ms. Gilian Chepkwony African Population and Health Research Center Field Coordinator - Samburu County
Mr. Bonface Ingumba African Population and Health Research Center Data Documentist
Funding Agency/Sponsor
Name
USAID through Food for Peace
Other Identifications/Acknowledgments
Name Affiliation Role
Philip Ebei Aemun CEC - Agriculture Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Charles Lokiyoto Ewoi CEC - Education Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Pauline Akai Lokuruka Chief Officer Education Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Paul Lokone Director Agriculture Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Alfred Emaniman Director Preventive and Promotive Health Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Lucas Edete County Community Health focal person Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Javan Manga Social Development Officer (Department Social Services) Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Fred Esinyen T/Central Nutrition Coordinator Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Dr. Jane Akale Deputy Director Veterinary services Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Josephat Lotwel County Drought Resilience Officer (NDMA) Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Dennis Mosioma NDMA Deputy Director of Information Turkana County – Stakeholder Consultation on NAWIRI Learning Agenda
Erastus Sinoti County Public Health Officer Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Delphina Kaaman County Nutrition Coordinator. Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Francis Koros Director Social Services Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Mary Bett County Agriculture Nutrition Officer Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Simon Lekartiwa County Livestock officer Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
James Kiptoon Sub County Public Health Officer Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Augustine Lenomouwapi County Community Strategy Focal Point Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Martin Thuranira County Director of Health Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda
Alex Leseketeti County NDMA Drought Coordinator Samburu County Stakeholder Consultation on NAWIRI’s Learning Agenda

Sampling

Sampling Procedure
SAMBURU

The study sample was population-based, with stratification by sub-counties grouped into three survey zones (Central, North, and East) reflecting administrative sub-counties used in the Samburu Standardized Monitoring and Assessment of Relief and Transitions (SMART) Surveys.
The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 699 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in June and July 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Wave 2 data collection was carried out in November-December 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022 and Wave 5 in March-April 2023. Wave 6 data collection is will be carried out in August-September 2023.

TURKANA

The study sample was population-based, with stratification by sub-counties grouped into four survey zones (Central, North, West, and South) reflecting administrative sub-counties used in the Turkana SMART Surveys.
The study used mixed-method techniques with quantitative and qualitative data collection. The quantitative component included a household survey and a caregiver survey and covered 1,211 households. The qualitative data yielded rich and in-depth insights that will be triangulated with the quantitative survey findings in a companion report.

The baseline data collection was carried out in May and June 2021 following a full household listing operation in the county to establish the sampling frame of households with children under 3 years. Anthropometric data were collected from all under-5 children in the sampled households. Wave 2 data collection was carried out in October-November 2021, Wave 3 in March-April 2022, Wave 4 in September-October 2022 and Wave 5 in March-April 2023. Wave 6 data collection will be in August-September 2023.
Deviations from the Sample Design
During Wave 4 of data collection, there was no deviation from the sample design. All the sampled households were accessed both in Turkana and Samburu
Response Rate
TURKANA

94.7% response rate

SAMBURU

93.7% response rate
Weighting
Sample weights were calculated for each of the data files. We used the MEASURE Demographic and Health Survey (DHS) program document as a guide to calculate sampling weights for both households and mothers or caregivers. We first computed the design weight of a sampling unit (household or mother/caregiver), defined as the inverse of the overall probability with which the sampling unit was selected in the sample. The final sampling weight of a sampling unit was derived from the computed design weight correcting for nonresponse.

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date Cycle
2022-10-10 2022-11-23 WAVE 4
Mode of data collection
Face-to-face [f2f]
Supervision
Field operations were supervised through two layers: a day-to-day supervision by team leaders, and a weekly review of activities and data quality by the data coordination team including the two research officers assigned to each of the counties, the data analyst, the software programmer, and the post-doctoral research scientist. A weekly report on issues arising from the field and discrepancies observed in data would be shared with the research team, including the co-principal investigators and co-investigators who advised on necessary actions to be taken.

Field workers were supervised by carefully trained supervisors; with a ratio of five field workers per supervisor. Supervisors reported to the field coordinator. During fieldwork, data quality l was enhanced through regular spot checks and sit-ins on approximately 5-10% of each field interviewer's daily work to verify the authenticity of data collected.

The field coordinator certified the data quality through the editing of the same before they were transferred to the database. Once the data collection is completed, all inconsistencies were resolved prior to data analysis.

An automated routine to check on the data completeness, correctness and consistency was run on 100% of the collected data. A discrepancy report were then generated to enable resolution of any inconsistencies or errors in the data with the responsible interviewer.
Type of Research Instrument
In Wave 4, one questionnaire with three different sections (Household section, Mother/caregiver section and Child section was administered in each sampled household to Mother/caregiver

Household section collected various information on Household democraphics, Household Food insecurity coping strategies, water,hygiene and sanitation(WASH), Household shocks experienced, Social safety nets and economic safety guards, Household food insecurity experience scale(FIES), Interventions and services received by households,

Mother/caregiver section included,Mothers/caregivers information,Pregnancy and antenatal care, Family planning, Women's minimum dietary diversity, Gender, women empowerment, violence and community conflict, Psychological wellbeing.

Child section includes Infant and young child feeding practices, Supplementation and consuption of iron-rich or iron-fortified foods, Caregiving practices, Food safety, hygiene and sanitation practices, Child immunization, health and health-seeking practices, Acute Malnutrition screening, Anthropometric measurements.


The questionnaire is provided as external sources

Data Processing

Cleaning Operations
Data quality monitoring processes and checks were implemented throughout the data collection process, during the time of developing the data collection tools (through built-in quality control in the tablet-based platform), during training of fieldworkers, in real time during data collection (routine monitoring by the research team and periodic cross-checks against the protocols), and during the data cleaning process. During fieldwork, data quality was enhanced through regular spot checks and sit-ins by supervisors to verify the authenticity of data collected. Data were then reviewed and certified by the field coordinator before they were transferred to the server.

The quantitative data were collected using SurveyCTO, a survey platform for electronic data collection that has in-built skips and quality checks. Using this software increased efficiency and reduced the time needed for cleaning the data. In addition, the platform supported offline data capturing for regions with slow or no internet connectivity and data transmission when the internet became available. Fieldwork was conducted by trained fieldworkers using digital tablets with the questionnaire loaded in SurveyCTO. The questionnaire included the following modules: (1) identification and tracking, (2) demographics and household composition, (3) anthropometry of children <5 years and mothers, (4) socioeconomics, (5) household food security, (6) WASH, (7) health-seeking behavior, (8) MIYCN, (9) shock experience/exposure, and (10) shock preparedness and response. Data were uploaded from the tablets onto a secure African Population and Health Research Center (APHRC) server after each day of data collection. Data were synchronized automatically to a server when the tablet was in a location with network coverage. The uploaded data were then checked for quality daily by a data manager and a team dedicated to coordinate field procedures and at the APHRC head office in Nairobi.
Other Processing
N/A

Data Appraisal

Estimates of Sampling Error
Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during the implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically. If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the 2005-2006 MICS sample is the result of a multi-stage stratified design and consequently needs to use more complex formulae. The STATA complex samples module has been used to calculate sampling errors for the 2005-2006 MICS. Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Data access

Contact
Name Email URI
African Population and Health Research Center datarequests@aphrc.org/info@aphrc.org aphrc.org
Conditions
APHRC data access condition

All non-APHRC staff seeking to use data generated at the Center must obtain written approval to use the data from the Director of Research.
This form is developed to assess applications for data use and facilitate responsible sharing of data with external partners/collaborators/researchers. By entering into this agreement, the undersigned agrees to use these data only for the purpose for which they were obtained and to abide by the conditions outlined below:

1.Data Ownership:
The data remain the property of APHRC; any unauthorized reproduction and sharing of the data is strictly prohibited. The user will, therefore, not release nor permit others to use or release the data to any other person without the written authorization from the Center.

2.Purpose:
The provided data must be used for the purpose specified in the Data Request Form; any other use not specified in the form must receive additional or separate authorization.

3.Respondent Identifiers:
The Center is committed to protecting the identity of the respondents who provide information in its research. All analytical data sets (both qualitative and quantitative) released by the Data Unit MUST are stripped of respondent identifiers to protect the identity of the respondents. By accepting to use APHRC data, the user is pledging that he/she will not, under any circumstance, regenerate the identifiers or permit others to use the data to learn the identity of any individual, household or community included in any data set.

4.Confidentiality pledge:
The user will not use nor permit others to use the data to report any information in the data sets that could identify, directly or by inference, individuals or households.

5.Reporting of errors or inconsistencies:
The user will promptly notify the Head of the Statistics and Survey Unit any errors discovered in the data as soon as the errors are discovered.

6.Publications resulting from APHRC data:
The Center requires external collaborators to work with APHRC staff on all publications resulting from its data. In order to facilitate this, lead authors should send a detailed concept note of the paper (including the background, rationale, data, analytical methods, and preliminary findings) to the Principle Investigator (or Theme Leader) for the project (with a copy to the Director of Research), who will circulate the abstract to concerned researchers for possible expression of interest in participating in the publication as co-authors. Any exception to the involvement of APHRC staff should be approved by the Director of Research, APHRC.

7.Security:
The user will take responsibility for the security of the data by ensuring that the data are used and stored in a secure environment where access is password protected. This will ensure that non-authorized people should not have access to the data.

8.Loss of privilege to use data:
In the event that APHRC determines that the data user is in violation of the conditions for using the data, or if the user wishes to cancel this agreement, the user will destroy the data files provided to him/her. APHRC retains the right to revoke this agreement or informs publishers to withhold publication of any work based wholly or in part on its data if the conditions for using the data are violated.

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.
Citation requirement
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

Metadata production

Document ID
DDI-KEN-APHRC-NAWIRI-2023-W4-v10
Producers
Name Abbreviation Role
African Population and Health Research Center APHRC DDI Documentation
Date of Production
2023-07-10
Document version
Version 1.1 (July 2023)