ICTworks

The mixed-methods assessment of community health data in ‘Do you trust those data?’—a mixed-methods study assessing the quality of data reported by community health workers in Kenya and Malawi, found large discrepancies in selected community units in both Kenya and Malawi between the values reported by CHVs and those reported by their supervisors.

Overall only 15% of the data reported were consistent during the 3-month period.

Participants at various levels of the health information system raised concerns about the quality of community-level health data and its limited potential to contribute to planning. The study found multiple similarities underlying causes of this poor data quality in the two countries even though one has HSAs paid and integrated into the health system (Malawi) and one used volunteers (Kenya).

• Both reported unavailability of standard paper-based data collection and reporting tools and limited training for CHVs on maternal and child health issues as well as on data entry.
• This problem was exacerbated by parallel reporting requirements of vertical programmes, resulting in multiple tools and duplication of efforts that was particularly prominent in the Malawian context.
• In-depth exploration revealed that CHWs experience tensions at the interface between the formal health system and the community they serve and are affected by the social and cultural expectations of their role with reports of fabrication of data in both countries and evidence that CHW’s gender can affect reporting.

The value of a supportive approach to supervision as an enabler of data quality was stressed by community participants in both contexts, with examples of what this term meant to them in practice.

These findings are in keeping with descriptions of the low quality of community level health data in other sites in Kenya and Malawi as well as other LMICs. This study also conducted qualitative work that mirrored the need for supportive supervision, although fabrication of data and the importance of gender and cultural sensitivities did not emerge.

How to Improve CHW Data Collection

The WHO guidelines on health policy and system support to optimize community health worker programmes stress the importance of data quality, but in practice, widespread mistrust of community health data by decision-makers means its potential to bring about quality improvement will not be realized.

1. Program coordination and planning

CHWs are often burdened by clashing vertical programmes that fail to consider the wider workload and fail to integrate reporting and supervision structures. This results in CHWs prioritizing the reporting of certain data at the expense of other data, based on factors such as remuneration. In certain sites in Malawi, changing the language of data collection and reporting tools used by CHWs from English to the local language reduced the number of errors in data collection.

There are a growing number of examples of how high-quality data can underpin quality improvement efforts for community health services, with resultant impacts on health outcomes. In Ethiopia, Kenya, South Africa and Tanzania, quality improvement teams have successfully used local data to identify gaps in community health services and develop practical solutions.

In Malawi, training on data analysis and use has resulted in greater ownership by CHWs and facility health workers and the use of data for quality improvement, and in Rwanda and Zambia, the embedding of data quality assessments has had the same effect.  The quality of community health could be strengthened through paying adequate attention to the co-ordination and comprehensive planning of community health programmes.

2. CHW training and supervision

Training, supervision and the availability of registers are all required in order to strengthen community health information systems. In Ethiopia, Kenya, Malawi and Mali, it has been shown that with training targeted towards specific tasks, CHWs can collect accurate data.

It is during supervision meetings between CHWs and their supervisors that paper-based data reporting tools are submitted. Without such meetings, CHWs feel that there is no demand for their data and feel unsupported and demotivated.

The desire of CHVs for more supervision and feedback is a common finding across CHW programmes. Systematic reviews on the performance of CHWs have found that frequent supervision with supportive approaches and a focus on quality assurance/problem-solving, as well as continuous training, are effective ways to improve CHV performance.

Local supervisors can best understand the context in which CHVs work, creating an immediate opportunity for problem-solving and improved data quality.

A lightly edited synopsis of ‘Do you trust those data?’—a mixed-methods study assessing the quality of data reported by community health workers in Kenya and Malawi by Regeru Njoroge Regeru, Kingsley Chikaphupha, Meghan Bruce Kumar, Lilian Otiso, and Miriam Taegtmeyer