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Evaluation of a Mobile Messaging Service in Improving Adherence (Text and /or Graphic/Audio) with Ensured Supply of Anti-seizure Medications in People with Epilepsy in Kilifi and Nairobi, Kenya, EPInA

Kenya, 2023 - 2024
Health and Well-Being (HaW)
Dr Symon Kariuki
Last modified November 13, 2024 Page views 18216 Documentation in PDF Metadata DDI/XML JSON

Identification

IDNO
DDI-KEN-APHRC-EPInA-2023-v1.0
Title
Evaluation of a Mobile Messaging Service in Improving Adherence (Text and /or Graphic/Audio) with Ensured Supply of Anti-seizure Medications in People with Epilepsy in Kilifi and Nairobi, Kenya, EPInA
Subtitle
EPInA
Country
Name Country code
Kenya KEN
Abstract
Improved outcomes for epilepsy treatment depend on a continuous supply and daily adherence to antiseizure medications (ASMs). In Kilifi County, the treatment gap which includes both the diagnostic and adherence gap, is greater than 70% and we have found interruption of supply of ASMs to peripheral clinics, distance from clinic and lack of availability of ASMs, to be barriers to adherence. In Nairobi County, factors such as environment hazards, lack of social amenities and correlates of poverty are preponderant in slums, but the prevalence of epilepsy has not been studied in such settings and consequently, the adherence gap remains unknown. Innovative mobile Health (mHealth) strategies including messages delivered by mobile phones have been used to ensure an adequate supply of drugs in health centres, and daily mobile messages have improved adherence to medication in Human Immunodeficiency Virus (HIV) programs, for example. Text messaging requires that the patient has access to a mobile phone and can understand the text message. Multimedia messaging, such as simple pictures or audio recordings, may improve understanding of the necessity to take medication, particularly in people who are illiterate, and we will explore this as an avenue to improve uptake.
We plan to randomize 1200 people with epilepsy at each site, from a defined area in Kilifi and Nairobi County, Kenya. They will be divided into four groups of 300 each, to receive either text, graphic/audio, both text and graphic/audio or messages on public health promotion not related to epilepsy e.g. use of bednets (for comparison). Our aim is to i) describe the perceptions and perspectives of people with epilepsy and their caregivers on the use of text and graphic/audio message reminders to improve adherence to ASMs, ii) compare the effectiveness of text versus graphic/audio messaging service in improving adherence in people with epilepsy and to engage the County Departments of Health through current ongoing training and capacity building studies to maintain supply of ASMs in peripheral clinics, iii) identify the factors associated with improvement in adherence, improved QoL and reduction in stigma among PWE and family members and, iv) conduct cost-effectiveness assessment for the roll out of the intervention. Besides medication-related messages, there will be other messages, from previous community-based feedback, selected to address stigmatization and improve quality of life. Blood-level monitoring and adherence questionnaires at baseline and during subsequent follow up visits will be used as measures of medication adherence. If found useful, this intervention may be applicable for self-managing other chronic conditions in under-resourced settings.

Version

Version Date
2024-06-04
Version Notes
N/A

Coverage

Geographic Coverage
National coverage (Kilifi and Nairobi Counties)
Unit of Analysis
Individual
Household
Universe
People with epilepsy in Kilifi and Nairobi, Kenya

Producers and sponsors

Authoring entity/Primary investigators
Agency Name Affiliation
Dr Symon Kariuki KEMRI-CGMRC
Producers
Name Affiliation Role
Prof Charles Newton KEMRI-CGMRC Co-principal investigator
Dr Mercy Atieno KEMRI-CGMRC Investigator
Mr. Gilbert Katana KEMRI-CGMRC Investigator
Ms Maria Mumbo KEMRI-CGMRC Investigator
Mr. Collins Kipkoech KEMRI-CGMRC Investigator
Ms Mary Bitta KEMRI-CGMRC Investigator
Dr Gershim Asiki APHRC Investigator
Dr Damazo Kadengye APHRC Investigator
Dr Fredrick Wekesah APHRC Investigator
Dr. Peter Otieno APHRC Investigator
Prof Sloane Mahone University of Oxford Investigator
Prof. Arjune Sen University of Oxford Investigator
Mr. Daniel Mwanga APHRC Investigator
Mr. Frank Ouma APHRC Data Documentation Specialist
Mr. Bonface Ingumba APHRC Data Governance Officer
Funding Agency/Sponsor
Name Role
University of Oxford Funder
Other Identifications/Acknowledgments
Name Affiliation Role
Dr Haji Musuko Kilifi County Collaborators
Dr Nadia Aliyan Kilifi County Collaborators
Dr Osman Miyanji Kenya Association for the Welfare of People with Epilepsy Collaborators
Patrick Ngechu Kenya Association for the Welfare of People with Epilepsy Collaborators

Sampling

Sampling Procedure
Participants wererandomly selected from a database of over 3500 PWE. The selected participants were invited to the clinic for screening against the eligibility criteria and enrolment into the study. The study sample consisted of 1,200 people with epilepsy in each site. Out of the 1200 PWE, there were900 participants in the intervention arm (300 receiving text SMS, 300 receiving graphic/audio SMS, 300 receiving both text and graphic/audio) and 300 in the control arm (non-epilepsy related SMS)
Deviations from the Sample Design
N/A
Response Rate
For baseline surveys we enrolled 650 participants. The follow up surveys for participants enrolled to the SMS Trial achieved 582/650 (89% response rate) for first follow up (month 3); 587/650 (90% response rate) for second follow up (month 6) and 556/650 (85.5% response rate) for third follow up (month 12).
Weighting
N/A

Data Collection

Dates of Data Collection (YYYY/MM/DD)
Start date End date
2023-02-27 2024-09-26
Mode of data collection
Face-to-face [f2f]
Supervision
Field supervision was conducted in two primary ways:

- Daily oversight by the team leader and the research officer. Data quality checks were also performed daily by the team leader prior to data submission.

- To ensure comprehensive oversight, the senior management team, which included the research officer and the data manager, conducted a weekly review of field activities and overall data management. Additionally, project reports were developed and shared with the project team on a weekly basis.
Type of Research Instrument
The study administered 11 questionnaires to its participants: Screening tool, Socio-demographic tool, Clinical tool, Morisky Medication Adherence Scale (MMA), Laboratory Analysis Sample tool, Cost-effectiveness assessment tool administered to all participants, Paediatric tool for children, World Health Organization's Disability Assessment Schedule for both epileptic children and adults and World Health Organization's quality of life assessment for epileptic patients. For children, the questionnaire was administered to the mother or caretaker of the child. The questionnaires were designed collaboratively by the study investigators, project manager and the data manager and published in both English and Swahili languages. The Swahili translation was reviewed and any inconsistencies resolved. Each tool was reviewed by the lead Data Manager, Project Manager, and Principal Investigator(s) at APHRC. The English and Swahili questionnaires were both piloted as part of the survey pretest.
All questionnaires are provided as external resources.

Data Processing

Cleaning Operations
All staff involved in data processing were trained before they were allowed to access the production version of SurveyCTO. Data entry clerks, data quality assurance officer, data manager(s) and site investigator(s) had the data management plan and data entry training. Field interviewers administered socio-demographics questionnaires and administered consent to potential participants. Clinicians screened participants during recruitment for possible enrollment. The clinicians then administered the study tools, collected sociodemographic data and collected a blood sample where applicable. All study information were entered into the prescribed electronic data capture forms on SurveyCTO.

Many of the real-time data validation rules were in place to identify simple entry errors (e.g. If the data entry clerk selects an incorrect option in a multiple-choice field). Any field that must be entered in the CRF was marked as "Required" in the SurveyCTO database, and an alert appeared if the answer is left blank. Furthermore, branching logic ensured that only the appropriate fields appeared for data entry, based on previous responses recorded for the participant, so that all appropriate data was recorded for each participant. SurveyCTO also generated alerts if a value fell outside the expected range, entered in an incorrect format, or violated any one of the user-defined validation rules, which primarily tested for concordance across different CRFs.
The data entry team was able to modify any invalid response at the time of entry (e.g. enter data from a field erroneously left empty during initial entry), leaving a short description of the change in the SurveyCTO audit log. In the event that SurveyCTO flagged an alert which cannot be resolved by the data entry clerk (e.g. the information entered on the CRF violates a cross-check rule), they forwarded it to the clinicians/designee, who then resolved the query. The data entry clerk marked the record as "Incomplete" until the queries were resolved, then proceeded to complete data entry and marked the record as "Complete".

Edit check validations were defined during the development of the database to avoid data entry errors in real time. There were several real-time data validations in place. During entry, different alerts appeared in cases of invalid formats, missing values that should be entered during the visit (i.e., fields marked as "Required" that are not entered), invalid ranges, and multivariate cross-checks within and across CRFs in the same record.

Post-entry data cleaning was performed on a regular basis. All data was extracted from SurveyCTO into CSV format and imported into an external statistical package (STATA), where data cleaning was performed. At this stage, any inconsistences that were generated at the time of CRF completion by the clinicians or during data entry were identified and resolved. The code to identify these inconsistencies was developed in advance of any data cleaning activities; this ensured consistency in data cleaning efforts and allowed for a thorough consideration of potential errors and inconsistencies that needed to be checked. All data queries were raised in a data resolution workflow in SurveyCTO before any changes were made to the database.
Other Processing
N/A

Data Appraisal

Estimates of Sampling Error
N/A

Data access

Contact
Name Email URI
African Population and Health Researrch Center datarequests@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-EPInA-2023-v1.0
Producers
Name Abbreviation Role
African Population and Health Research Center APHRC DDI Documentation
Date of Production
2024-06-04
Document version
Version 1.0 (June,2024)