We are delighted to be attending the upcoming Global Symposium on Health Systems Research which will be held in Liverpool, UK on the 8-12 October 2018. The symposium is a great place to learn more about community health programmes and to showcase work from our project to an audience of researchers, policy makers and practitioners from around the world. As well as participating in the panel and posters below we are looking forward to catching up with colleagues from the Thematic Working Group on Supporting and Strengthening the Role of Community Health Workers in Health Systems Development. Do come to our session and meet the team!
Community Health Volunteers play a crucial role in linking (expectant) mothers, newborns and children to essential health services. There are challenges in measuring and improving the quality of services provided by Community Health Volunteers with most initiatives emphasising quality improvement at health facility rather than community level. This session will explore practical approaches to quality improvement (QI) in community health services, discuss what it takes to create a culture of quality and debate how to transition from externally funded quality improvement projects to embedded QI programmes within government health systems. Our panelists include researchers, implementers, policy makers and funders, who will draw on their wide-ranging expertise and experience of quality improvement in community health.
Regeru Njoroge Regeru, Kingsley Chikaphupha, Meghan Bruce Kumar, Lilian Otiso, Miriam Taegtmeyer
High-quality data are essential to monitor and evaluate the performance, quality, coverage and equity of community health programmes. However, data quality within Community Health Information Systems is rarely assessed formally. The aim of this study was to determine the quality of data reported by Community Health Volunteers (CHVs) in Kenya and Malawi, identifying factors that contribute positively or negatively to data quality. We also set out to explore the use of community-level health data in decision-making and identify interventions to strengthen Community Health Information Systems.
This was a cross-sectional mixed-methods study. We conducted Data Quality Assessments in eight purposively selected communities in Kenya and Malawi in 2017. For selected indicators, we recorded the values reported by individual CHVs in their primary data reporting tools between March-May 2016. We aggregated these to obtain monthly totals for each indicator. We then recorded the values reported for the same indicators by the CHVs’ supervisor in monthly supervisor summary reports for the same reporting period. Data verification ratios were calculated by dividing the aggregated totals from the CHVs’ tools by the values reported in the supervisor summary reports to measure the consistency in values. We also conducted 13 focus group discussions and 53 in-depth interviews with key actors in management of community-level health data in both countries. Data were coded and analysed with the support of Nvivo 11® to identify enablers and barriers to collecting and reporting high-quality data.
We found large discrepancies between the values reported by CHVs and their supervisors, indicating poor data quality. Data verification ratios ranged from 0.00 – 8.67. In some sites, there was no reporting at all. Factors affecting data quality included: unavailability of standard and thoughtfully-designed data reporting tools, lack of training and supportive supervision to regularly reinforce and ensure correct interpretation of health indicators, unreliable data management procedures and lack of feedback from primary healthcare facilities to CHVs. Respondents also reported unsystematic use of community-level heath data for decision-making that is often not documented.
Our findings of poor data quality have widespread implications for the analysis and use of community-level health data by policy- and decision-makers, undermining the quality of community health programmes. The most urgent interventions recommended to strengthen Community Health Information Systems are: provision of an adequate supply of standard data collection and reporting tools designed to suit CHVs; regular training, feedback and supportive supervision founded on locally-collected data; and regular Data Quality Assessments.
Lilian Otiso, Linet Okoth, Nelly Muturi, Robinson Karuga, Regeru Regeru, Meghan Bruce Kumar, Miriam Taegtmeyer
Critics of community health worker (CHW) programs often cite lack of data on quality of care, poor performance and limited effectiveness in improving health outcomes as a reason for not investing in scale-up. Quality improvement (QI) is an approach that has been used to strengthen these dimensions in health facilities through simple, robust approaches to local data use and analysis. Few studies have applied or assessed QI in CHW programs or its outcomes. We tested the feasibility and outcomes of implementing QI in community health programs in Kenya.
We implemented a QI intervention and evaluated it using a pre and post study design in community units in Nairobi, Kenya between October 2016 and July 2017. We developed a simplified QI curriculum, setting up four community work improvement teams (WITS), training and mentoring them on QI principles of problem identification and prioritization. The 8 to 12 member WITs consisted of CHWs, community members and link health facility staff from each unit. Quantitative data were collected through tracking CHW reporting rates. Qualitative data were collected through 26 in-depth interviews (IDIs) with WIT members and health workers and four focus group discussions (FGDs) with CHWs and community members at baseline and 12 IDIs and nine FGDs at endline to explore perspectives on QI in community health and its outcomes. Interviews were recorded, transcribed and analyzed using a thematic framework approach with the assistance of NVivo10 software.
Reporting rates increased from 64% to 94% among CHWs in the units with reported improvements in quality of data collected and utilization for feedback to the community during dialogue days. Participants found the QI approach simple, acceptable and aligned to their work. At endline, WITs were able to demonstrate understanding and application of QI principles: root cause analysis for problem identification and prioritization to develop action plans. Reported changes in health service outcomes were increased utilization of mother and child services in facilities due to improved referrals and reduction of tuberculosis and immunization defaulters from facilities. Community members appreciated that WITS provided an opportunity for communities to give feedback to facilities on quality of services.
We demonstrate that CHWs can learn and apply complex QI concepts in a rigorous manner once they are adapted to their context. By applying QI, CHWs are able to dramatically improve reporting, community engagement with the health system, efficiency and performance by focusing on priority issues which improve health outcomes.
Nelly Muturi, Maryline Mireku, Regeru Regeru, Linet Okoth, Vicki Doyle, Miriam Taegtmeyer and Lilian Otiso
Quality of care is a key driver of improved health outcomes and effective health service delivery and is evidenced by client satisfaction. In Kenya, there is a growing need and focus on Quality Improvement (QI) at community level to improve uptake of health services. However, there is little evidence on mechanisms to track the quality of services delivered by close-to-community (CTC) health service providers. We sought to capture experiences of using a community home follow up tool to measure the quality of services provided by Community Health Volunteers (CHVs) at household level as a component of a community QI intervention in nine Community Health Units (CHUs) in Nairobi County, Kenya.
The community follow up tool was a program tool introduced to be used by community work improvement teams (WITS) to inform areas of improvement of CHVs work. The tool assessed basic information (household), Maternal Newborn and Child Health (MNCH) activities, communication and referral at household level. The tool was administered by community members not involved in health service provision selected by WITs. Households from where data was collected were purposively selected by the Community Health Extension Worker (CHEW) as having received a CHV visit addressing maternal and child health issues as reported in the previous month. At least two households for every CHV were visited. Qualitative data (nine in-depth interviews and seven focus group discussions) were collected among selected WIT members to find out the ease of use of the tool and quality problems identified from the tool findings.
A majority of the respondents reported the tool to be easy to use and the data collected easily analyzed and interpreted. Challenges related to the use of the tool included arrangement of questions and language (English) instead of local languages. Quality gaps identified included the time CHVs took to conduct household visits (some were reported to take less than ten minutes in a household affecting the quality of services offered), gaps in CHV referral follow up and inaccuracy of the data collected by CHVs.
The Community Follow-Up tool can successfully be used to measure satisfaction with community health services provided by CHVs and can highlight priority areas for quality improvement. Furthermore, using the tool provides an opportunity to identify gaps in CHV capacity allowing support supervision and training to be tailored to their needs with the goal of achieving universal health coverage.
Vicki Doyle, Lilian Otiso, Linet Okoth, Regeru Regeru, Nelly Muturi, Maryline Mireku, Anthony Mwaniki, Michael Kimani, Lynda Keeru, Carol Ngunu, Judy Macharia, Meghan Bruce Kumar, Miriam Taegtmeyer
Kenya is in the process of scaling-up community health programmes with commitment from national and county governments and non-governmental stakeholders. But there are risks that rapid scale-up within a devolved context will compromise quality of services unless quality improvement (QI) approaches are embedded into community health systems at the onset. This requires simple, innovative capacity building and learning approaches for front line workers.
We supported the establishment of QI teams for community health services through training in QI methods, interspersed with periods of implementation and team coaching. An additional support element involved creating a space for collective sense making through hosting ‘learning events’. Nairobi County hosted the first event in October 2017 with 120 participants, twenty-four QI teams, two additional counties and participants from National MoH Departments for Community Health and Health Standards. A key aim of the event was to make it as interactive as possible and ensure all voices, from community members to policy makers could be heard. We used a wide-range of learning and sharing methods including plenary presentation and discussions, hand-designed poster presentations, panel discussion, peer assessment, QI awards and World Café. We purposely minimised the use of technology and PowerPoint presentations.
Using a range of methods provided multiple avenues to engage participants. Community members and health volunteers took a prominent role in describing the practical reality of their work on the ground to policy makers and managers. Involving senior MoH officials, paired with community workers in judging team documentation and poster presentations provided them with insights and in-depth understanding of the work and achievements of community-based teams. Powerful interactions between QI teams from different sub-counties gave a sense of friendly-competition and healthy debate around how to engage communities and improve quality within existing resources. Whilst reported improvements in quality and performance of community health services were variable, teams were able to demonstrate that QI at community level is feasible and can have positive impacts.
The ‘learning event’ methodology demonstrated the importance of creating a space for shared learning and advocacy. Ultimately success of QI at community level will depend on how well community structures, support and tools truly embed into the health system. Shared learning, rewarding best practice and advocacy are fundamental to this process and can be achieved with modest resource investment. QI teams are spearheading a quality revolution in Kenya, starting where it matters most, with the community.