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Using a Decision-Support Smartphone application to enhance Community Health Volunteers’ effectiveness in reducing Maternal complications and reducing Newborn Deaths in the informal settlements of Nairobi, Kenya, Community Health Volunteers’ Decision Suppo

Kenya, 2016 - 2020
Health and Well-Being (HaW)
Pauline Bakibinga
Last modified April 01, 2021 Page views 487981 Documentation in PDF Interactive tools Metadata DDI/XML JSON

Identification

IDNO
KEN_APHRC_CHVDSS_2016_v01
Title
Using a Decision-Support Smartphone application to enhance Community Health Volunteers’ effectiveness in reducing Maternal complications and reducing Newborn Deaths in the informal settlements of Nairobi, Kenya, Community Health Volunteers’ Decision Suppo
Subtitle
Community Health Volunteers’ Decision Support System (CHV DSS) intervention project.
Country
Name Country code
Kenya KEN
Abstract
Improving maternal and newborn survival remain major aspirations for many countries in the global south. Slum settlements, a result of rapid urbanization in many developing countries including Kenya, exhibit high levels of maternal and neonatal mortality. There are limited referral mechanisms for sick neonates and their mothers from the community to health care facilities with ability to provide adequate care. In this study we specifically plan to assess the added value of having community health volunteers (CHVs) use smartphones to identify and track mothers and children in a bid to reduce pregnancy-related complications and newborn deaths.

Objectives:
To develop and validate a decision-support algorithm within an m-health solution in improving maternal and newborn health outcomes in the urban slums of Kamukunji sub-County in Nairobi, Kenya.

Study Design: Quasi-experimental, difference in differences, with a control.
We propose to implement an innovative, m-health application known as mPAMANECH which uses dynamic mobile phone and web-portal solutions, to enable Community Health Volunteers (CHVs) make timely decisions on the best course of action in their management of mothers and newborns at community level. The application is based on existing guidelines and protocols in use by CHVs. Currently, CHVs, trained over a five-day period, conduct weekly home visits and make decisions (counsel, treat, refer) from memory or using unwieldy manual tools, and thus prone to making errors. mPAMANECH will have an in-built algorithm that makes it easier, faster and more likely for CHVs to make the right management decision. We will work with a network of community units-CUs and selected CHVs and maternity centres to pilot test the tool.

Study Duration: 21 months

Version

Version Date
2020-02-04
Version Notes
NA

Coverage

Geographic Coverage
Kamukunji and Embakasi sub-counties of Nairobi, Kenya
Unit of Analysis
Quantitative study

The project will target households with women of reproductive age (15-49 years) in the informal settlements of Kamukunji. To understand the nature of health seeking behaviours of the women of reproductive age, the population based survey will obtain data from pregnant women and those with children under one year of age. Mothers with children under one year were chosen because they would have a more recent experience with childbirth. The CHVs will be expected to continue covering the households under their care. These data will be available in the system as part of the CHVs' reporting roles. However, for this project, emphasis will be placed on pregnant women and those with neonates; these data will also be available and analysed from within the system. The project will also target health care providers in five selected health facilities as well as the sub-County Health Management teams of Kamukunji, Embakasi, Makadara and Nairobi City County.
A quantitative survey will be conducted focusing mainly on maternal and newborn health and family planning services.


Qualitative study

We will use focus group discussions (FGDs) and in-depth interviews among the direct project beneficiaries and CHVs, and key informant interviews with key actors (sCHMTs, health providers, CHVs and community leaders). Participants will be purposively selected to represent the different stakeholders as well as different health service user categories (users and non-users).
Data from the quantitative survey will be used to identify women who have or not used specific MNH services and these will be approached to participate in the focus group discussions or in-depth interviews. Other respondents will be identified based on their position in the community and their role in the project.
Universe
The project targeted households with women of reproductive age (15-49 years) in the informal settlements of Kamukunji and Embakasi and also healthcare providers in five selected health facilities as well as the subcounty Health Management teams of Kamukunji, Embakasi, Makadara and Nairobi City County.

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
Pauline Bakibinga APHRC
Producers
Name Affiliation Role
Eva Kamande APHRC lead research officer in charge of the day-to-day operation of the study including the piloting of research instruments, recruitment, training and supervision of field workers. She will coordinate the field work during data collection and participate in the data analysis and writing.
Abdhalah Ziraba APHRC provide intellectual guidance to the design and realization of study, including the development and materialisation of tools and analysis plans and interpretation of results.
Elizabeth Kimani-Murage APHRC support and ensure the scientific rigor and project implementation
Catherine Kyobutungi APHRC providing overall intellectual guidance, policy engagement, data analysis and interpretation and will participate in scientific writing.
Funding Agency/Sponsor
Name
County Innovation Challenge Fund for Kenya (UK Department for International Development)
Other Identifications/Acknowledgments
Name Role
The communities of Kamukunji and Embakasi Study communiity
Sub-counties (Kamukunji and Embakasi) Administravie Support
The field workers Project activities
County (Nairobi) health management teams adminstravie support

Sampling

Sampling Procedure
To measure feasibility and acceptability, all the CHVs in the intervention site will be assessed, in addition to an audit of the functionality of the system. We will measure the percentage time for which the system is down on a monthly basis (Numerator: Number of times (in minutes) when the system is down. Denominator: Total active time in a month. Down time defined as 30 minutes of hanging and Active time as time without hitches), proportion of CHVs effectively using the decision support system (Within the system, on a quarterly basis we will be able to generate reports on decisions, correct or otherwise, made by the CHVs. These will also be compared to the control site that will only have a paper based system of data collection), and the experiences of the CHVs and the mothers with the mobile based system. These will be compared to the investment.


To answer the question on effect of the system on utilisation of services, the data above will be triangulated with data from the mPAMANECH application which already has an integrated data collection module, the data generated by the CHVs and participating health facilities will be retrieved, cleaned and analyzed to derive estimates of the main outcome of interest - correct referral practices. In addition, these data will be triangulated with other sources such as the CHV monthly reports and health facility HMIS. A system of random numbers generated using STATA will be used to select the respondents based on a sampling frame that is going to be informed by an updated household register in the selected community units.


Qualitative study
We will use focus group discussions (FGDs) and in-depth interviews among the direct project beneficiaries and CHVs, and key informant interviews with key actors (sCHMTs, health providers, CHVs and community leaders). Participants will be purposively selected to represent the different stakeholders as well as different health service user categories (users and non-users).
Data from the quantitative survey will be used to identify women who have or not used specific MNH services and these will be approached to participate in the focus group discussions or in-depth interviews. Other respondents will be identified based on their position in the community and their role in the project.

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date Cycle
2016-11-12 2020-12-02 01
Mode of data collection
Face-to-face [f2f]
Supervision
A total of 31 interviewers were shortlisted and underwent a one-week training covering project objectives, ethics in research and a comprehensive review of the data collection tools. The training consisted of a detailed, question-by-question explanation of the questionnaires/interview guides, demonstration of interviewing techniques through role-plays, group discussions, procedures for seeking informed consent, a collection of data using tablets, troubleshooting, and field logistics. Piloting of the instruments was conducted in Shauri Moyo in Kamukunji sub-county to ensure trainees grasped the questions and that the data collection tools captured as expected. Six team leaders were selected based on their experience in the conduct of surveys and their leadership abilities. The team leaders received additional training in the management of data collection, team dynamics, survey planning and logistics, observing interviews, and spot-checking for data quality. Each team leader was assigned a team of four to five field interviewers (FIs). They also assisted in revising the tools based on outcomes of the pilot study. To ensure validity, validation checks, constraints or skips were embedded in the Open Data Kit (ODK) software during development of the quantitative tool.

The electronic form was programmed to not save forms with missing data and implausible, out-of-range values. The quality control team tested it to ensure consistency and question flow before the tool's implementation. To further ensure data quality, during fieldwork, each team leader conducted regular spot checks and sit-ins to 10% of each FI's work and verified authenticity on 100% of the FIs' daily output before synchronizing the collected data with a master database in the APHRC head office. Furthermore, the research team held weekly meetings with the field team, addressingany inconsistencies or errors in the data with the responsible interviewer. An automated routine to check on the data completeness, correctness and consistency ran on 100% of the collected data. Data was exported to STATA for advanced cleaning and analysis.

Qualitative data was tape-recorded and transcribed verbatim. Transcribed files were saved in Microsoft Word.
Type of Research Instrument
Quantitative questionnaires:
Household surveys of women of reproductive age and newborns to determine changes in health care utilization patterns (if any) for maternal and newborn health services, interactions with CHVs, and referral patterns will be conducted. Structured questionnaires will be used to collect data from selected respondents by specially trained interviewers. The questionnaires will be designed in English and translated to Kiswahili. Each interview will take approximately 40-60 minutes and will seek information with regard to pregnancy history (past and present), antenatal care, delivery, postnatal care and family planning (for women in reproductive age); morbidity and care seeking behaviours, breastfeeding practices, and vaccination (for children under five). For both study groups, data will be collected on the frequency and quality of interactions with CHVs and providers at the selected health facilities. Identification on the questionnaire refers to the ID of the household. All quantitative data will be collected using mobile phones with the questionnaire pre-loaded. These interviews will be conducted during the day. In instances where the identified respondent will not be available, for one reason or the other, the interviewer will book an appointment with the respondent on the appropriate another day and time when the interview can be conducted.

Since the mPAMANECH application already has an integrated data collection module, the data generated by the CHVs and participating health facilities will be retrieved, cleaned and analyzed to derive estimates of the main outcome of interest - correct referral practices. In addition, these data will be triangulated with other sources such as the CHV monthly reports and health facility HMIS.

Routine Health Management Information System (HMIS)+ CHV monthly reports:
reports from the preceding 12 months to determine whether there was a change in service utilization levels for ANC, PNC, delivery at the five health facilities, as well as admissions for premature and low birth weight, and sick newborns will be reviewed. From the various data sources we will also determine the number of adverse maternal and newborn outcomes such as emergency referrals for severe maternal complications, stillbirths, and neonatal deaths. HMIS data from the selected health facilities will be collated and quantitatively analysed to estimate attendance rates, case management practices against guidelines and uptake of different MNH services, referral patterns especially with regard to two-way referral by CHVs.

Qualitative questionnaires:
These will be used to collect and analyse data from key informant interviews (KIIs), in-depth interviews (IDIs) and focus group discussions (FGDs). The qualitative interviews will collect data on experiences (perceived quality, accessibility and affordability) regarding the existing maternal and newborn health and family planning services, effectiveness of referral system, and barriers to utilization of the health services. The FGD and IDI participants will be notified in advance the venue, day, and time that the interviews will take place and those available will participate in the discussions. The KII interviews will be conducted upon the availability of the selected respondents.

Data Processing

Cleaning Operations
Survey data: Quantitative data entered in the mobile phones by the field workers during the baseline and end line assessments will be synchronized with the master database in APHRC head office every day. Where data inconsistencies are noted, the office editor will inform the data manager who will then contact the field teams for clarifications and where need arises send back queries to the field teams for completion of incomplete data or correction of the inconsistencies. Clean data will be exported for analysis to statistical software (STATA version 11.0; StataCorp LP, USA) for advanced cleaning and analysis. To ensure validity, validation checks, constraints or skips were embedded in the Open Data Kit (ODK) software during development of the quantitative tool. The electronic form was programmed to not save forms with missing data and implausible, out-of-range values. The quality control team tested it to ensure consistency and question flow before the tool's implementation. To further ensure data quality, during fieldwork, each team leader conducted regular spot checks and sit-ins to 10% of each FI's work and verified authenticity on 100% of the FIs' daily output before synchronizing the collected data with a master database in the APHRC head office. Furthermore, the research team held weekly meetings with the field team, addressing any inconsistencies or errors in the data with the responsible interviewer. An automated routine to check on the data completeness, correctness and consistency ran on 100% of the collected data. Data was exported to STATA for advanced cleaning and analysis.


mPAMANECH data: Household data entered into the mobile phones by the CHVs, during their household visits will be relayed in real time to the APHRC server. Data inconsistencies will be followed up by the CHEWs during their monthly spot checks and review meetings.


Qualitative data (survey):
Detailed summaries of the in-depth interviews, key informant interviews and focus group discussions will be written up by the interviewers. Qualitative data will be transcribed and saved in Word format. Transcribed word files will be imported into NVIVO software (QSR International Pty Ltd) for coding and further analysis. Qualitative data was tape-recorded and transcribed verbatim. Transcribed files were saved in Microsoft Word.
Other Processing
NA

Data access

Contact
Name Email URI
African Population & Health Research Center info@aphrc.org www.aphrc.org
Conditions
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."(FOR STUDIES CONDUCTED IN KOROGOCHO AND VIWANDANI)
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
"African Population & Health Research Center (APHRC), Birth history Table (2002-2015)The specific study, Version 1.0 of the licensed public use dataset (May 2017)according to the specific study, provided by the APHRC. www.aphrc.org <http://www.aphrc.org>"

Metadata production

Document ID
KEN_APHRC_CHVDSS_2016_v01
Producers
Name Abbreviation Role
African Population and Health Reseach Centre APHRC Documentation of the DDI
Date of Production
2020-02-04
Document version
Version1.0 (February 2020)