APHRC Online Microdata Library
    Home / Central Data Catalog / HEALTH_AND_WELL-BEING / DDI-KEN-APHRC-IPUSH-ENDLINE-2022-V01
Health_and_Well-Being

Enhancing Universal Health Coverage in Kenya through Digital Innovations: A Financial and Health Diaries evaluation study of I-PUSH (ENDLINE), iPUSH

Kenya, 2021
Health and Well-Being (HaW)
Estelle M. Sidze, Wendy Janssens, Menno Pradhan
Last modified December 06, 2022 Page views 83827 Metadata DDI/XML JSON

Identification

IDNO
DDI-KEN-APHRC-IPUSH-ENDLINE-2022-v01
Title
Enhancing Universal Health Coverage in Kenya through Digital Innovations: A Financial and Health Diaries evaluation study of I-PUSH (ENDLINE), iPUSH
Subtitle
iPUSH
Country
Name Country code
Kenya KEN
Abstract
Background: The goal of Universal Health Coverage (UHC) is ensuring that populations have access to the health services they need without suffering financial hardship in paying for them. This is marred by significant resource shortages and service delivery gaps leading to lack of access to quality health services. In addition to this, poor populations suffer from more challenges like cost, which impedes access to healthcare. Saving for healthcare is an intervention that involves building capacities of individuals, families and communities to take up appropriate self-care, prevention, and care-seeking behavior in order to improve access to much needed healthcare. The Innovative Partnership for Universal Sustainable Healthcare (i-PUSH) program developed by African Medical and Research Foundation (AMREF) and PharmAccess, aims to empower low-income reproductive age women and their families by offering them the opportunity to save for health care on their mobile phones (“health wallet”), have subsidized health insurance, access upgraded quality healthcare, and improve their knowledge through digitally trained Community Health Volunteers (CHVs).

Objectives: To provide under-the-skin insights into the target populations in Kakamega, Kisumu and Nairobi; to evaluate the impact of i-PUSH program on maternal healthcare utilization, financial protection and women's empowerment; and to evaluate the impact of the LEAP training tool on CHVs and women's health literacy including their knowledge, behavior and uptake of respective services.

Study design: The study will use a three-pronged approach: year-long weekly Financial and Health Diaries, Baseline and Endline surveys, and qualitative interviews. The Diaries and the Surveys will be carried out in two Counties, Kakamega and Kisumu. The qualitative interviews will be carried out in the two counties and Nairobi as an additional county. The study will use a cluster randomized control trial design in Kakamega to evaluate the impact of i-PUSH program. In Kisumu, selected households will be followed-up to capture the health-seeking behavior, health insurance, and health expenditure pattern of households over time. The study will be initially for 17 months and will continue for three years subject to additional funding.

Version

Version Date
2022-01-12
Version Notes
na

Coverage

Geographic Coverage
Kakamega county
Unit of Analysis
The unit of analysis or observation that the study describes: individuals, households
Universe
Woman of reproductive age (18-49) who has at least one child below age 5 years that is living with her or pregnant.

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
Estelle M. Sidze African Population and Health Research Center
Wendy Janssens Vrije Universiteit Amsterdam
Menno Pradhan Vrije Universiteit Amsterdam
Producers
Name Affiliation
Amanuel Abajobir African Population and Health Research Center
Caroline Wainaina African Population and Health Research Center
Anne Njeri African Population and Health Research Center
Daniel Maina African Population and Health Research Center
Nelson Mbaya African Population and Health Research Center
Funding Agency/Sponsor
Name
Health Insurance Fund, and Amref and PharmAccess Amsterdam through the i-PUSH program (National Postcode Lottery)

Sampling

Sampling Procedure
Sampling Strategy

Quantitative study
With regards to sample size, we followed Hemming et al. [61]'s study for sample size calculation by fixing the number of clusters per arm to be 12 clusters, and then estimate the cluster size and total sample size as well. In the current study, we assume that the current i-PUSH program could yield an effect size of 0.4 in terms of healthcare utilization with an intracluster correlation (ICC) of ?=0.014. The estimates of the ICC was derived from Geng et al. [62]'s study conducted in Nandi County where they used high-frequency data on diaries on health-seeking behaviors and financial expenditures over one year (October 2012-October 2013). The calculation of the ICC is based on healthcare utilization measured as visits to any formal health provider, regardless of any reported health symptoms. We also hypothesize a confidence interval of 95%, a margin-of-error of 5% and a power of 80%. The cluster size per arm, total women per clusters is therefore 10 and 12 women, respectively. Appendix 2 gives the details for sample size estimates.

Furthermore, for Kisumu, since we did not conduct any impact evaluation in that County, sample size estimates of 100 women was based on previous studies which used at least 100 participants for financial and health diaries data [62], [63]. We randomized at the level of the community units (CUs), proceeding as follows.

In Kakamega, we aligned RCT design with the i-PUSH expansion strategy in Kakamega. There are currently two options that were decided by AMREF and PharmAccess.
1) The i-PUSH program will expand to several new sub-counties; or 2) The i-PUSH program will expand to new Community Units within the existing four sub-counties where the program is currently implemented.

In either case, the treatment group consisted of randomly selected Community Units (CUs) in the catchment area of the clinics selected purposively by the program. Each treatment CU is served by a number of Community Health Workers (CHVs) who are trained on the i-PUSH program.

The control group consisted of CUs where the CHVs were not be trained by i-PUSH. We randomized at the level of the CU because within CUs, CHVs received the same training (hence cannot be assigned to treatment or control).
Option 1) In the new sub-counties: Out of a longlist of about 90 eligible CUs, approximately 80 CUs received training for the program. We randomly selected 10 CUs to be in the study control group (that were not be included) and 10 CUs in the study treatment group (after matching pairs of CUs); the remaining 70 CUs were included in the program but not in the study sample. Within CUs, we randomly selected 12 households for the dairies.

Option 2) In the existing sub-counties: Out of a list of at least 20 eligible CUs where i-Push has not been rolled out yet, the research team randomly selected 10 CUs to be in the study control group and 10 CUs to be in the study treatment group (after matching pairs of CUs); the remaining CUs in the existing sub-counties were included in the program but were not in the study sample.
Deviations from the Sample Design
na
Response Rate
na
Weighting
na

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date Cycle
2021-06-29 2021-07-15 Endline
Mode of data collection
Face-to-face [f2f]
Supervision
Trained field supervisors were situated in the field (Kakamega and Kisumu) to supervise real time data collection. During fieldwork, data quality for quantitative data was ensured by APHRC team leads through regular spot checks and sit-ins to approximately 5-10% of each field worker's daily work to verify authenticity of data collected.

The field supervisors certified the quality of the data through editing of the data before they were transferred to the database.

Data collection was done electronically using tablets/phones, with spot checks for quality control. Once the data collection was completed, all inconsistencies were resolved prior to data analysis.
An automated routine to check on the data completeness, correctness and consistency was also be run on 100% of the collected data. A discrepancy report was then generated that helped in following up on any inconsistencies or errors in the data with the responsible interviewer.
Type of Research Instrument
Endline
Background, household roster, socio demographics, education, health outcomes, health of children, health care utilization, food consumption, housing and assets, employment and income, women's empowerment, preffered clinics and mobile money, mental health, women's health (reproductive health experiences including family planning, previous and/or current pregnancy and uptake of respective services.), health knowledge, health of children, savings groups and cooperatives, Financial instruments (savings, loans/credit, insurance, M-Pesa)

Data Processing

Cleaning Operations
Data collection was done electronically using tablets/phones, with spot checks for quality control.
Other Processing
na

Data Appraisal

Estimates of Sampling Error
na

Data access

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-IPUSH-ENDLINE-2022-v01
Producers
Name Abbreviation Role
African population and health research center APHRC Documentation of the DDI
Date of Production
2022-01-12
Document version
Version 1.0 (January 2022)