Article

Expanded Quality Management Using Information Power (EQUIP): protocol for a quasi-experimental study to improve maternal and newborn health in Tanzania and Uganda

Implementation Science (Impact Factor: 3.47). 04/2014; 9(1):41. DOI: 10.1186/1748-5908-9-41
Source: PubMed

ABSTRACT Maternal and newborn mortality remain unacceptably high in sub-Saharan Africa. Tanzania and Uganda are committed to reduce maternal and newborn mortality, but progress has been limited and many essential interventions are unavailable in primary and referral facilities. Quality management has the potential to overcome low implementation levels by assisting teams of health workers and others finding local solutions to problems in delivering quality care and the underutilization of health services by the community. Existing evidence of the effect of quality management on health worker performance in these contexts has important limitations, and the feasibility of expanding quality management to the community level is unknown. We aim to assess quality management at the district, facility, and community levels, supported by information from high-quality, continuous surveys, and report effects of the quality management intervention on the utilization and quality of services in Tanzania and Uganda.
In Uganda and Tanzania, the Expanded Quality Management Using Information Power (EQUIP) intervention is implemented in one intervention district and evaluated using a plausibility design with one non-randomly selected comparison district. The quality management approach is based on the collaborative model for improvement, in which groups of quality improvement teams test new implementation strategies (change ideas) and periodically meet to share results and identify the best strategies. The teams use locally-generated community and health facility data to monitor improvements. In addition, data from continuous health facility and household surveys are used to guide prioritization and decision making by quality improvement teams as well as for evaluation of the intervention. These data include input, process, output, coverage, implementation practice, and client satisfaction indicators in both intervention and comparison districts. Thus, intervention districts receive quality management and continuous surveys, and comparison districts-only continuous surveys.
EQUIP is a district-scale, proof-of-concept study that evaluates a quality management approach for maternal and newborn health including communities, health facilities, and district health managers, supported by high-quality data from independent continuous household and health facility surveys. The study will generate robust evidence about the effectiveness of quality management and will inform future nationwide implementation approaches for health system strengthening in low-resource settings.Trial registration: PACTR201311000681314.

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Available from: Ulrika Baker, Dec 15, 2014
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    ABSTRACT: Maternal and newborn mortality remain unacceptably high in sub-Saharan Africa. Tanzania and Uganda are committed to reduce maternal and newborn mortality, but progress has been limited and many essential interventions are unavailable in primary and referral facilities. Quality management has the potential to overcome low implementation levels by assisting teams of health workers and others finding local solutions to problems in delivering quality care and the underutilization of health services by the community. Existing evidence of the effect of quality management on health worker performance in these contexts has important limitations, and the feasibility of expanding quality management to the community level is unknown. We aim to assess quality management at the district, facility, and community levels, supported by information from high-quality, continuous surveys, and report effects of the quality management intervention on the utilization and quality of services in Tanzania and Uganda. In Uganda and Tanzania, the Expanded Quality Management Using Information Power (EQUIP) intervention is implemented in one intervention district and evaluated using a plausibility design with one non-randomly selected comparison district. The quality management approach is based on the collaborative model for improvement, in which groups of quality improvement teams test new implementation strategies (change ideas) and periodically meet to share results and identify the best strategies. The teams use locally-generated community and health facility data to monitor improvements. In addition, data from continuous health facility and household surveys are used to guide prioritization and decision making by quality improvement teams as well as for evaluation of the intervention. These data include input, process, output, coverage, implementation practice, and client satisfaction indicators in both intervention and comparison districts. Thus, intervention districts receive quality management and continuous surveys, and comparison districts-only continuous surveys. EQUIP is a district-scale, proof-of-concept study that evaluates a quality management approach for maternal and newborn health including communities, health facilities, and district health managers, supported by high-quality data from independent continuous household and health facility surveys. The study will generate robust evidence about the effectiveness of quality management and will inform future nationwide implementation approaches for health system strengthening in low-resource settings.Trial registration: PACTR201311000681314.
    Implementation Science 04/2014; 9(1):41. DOI:10.1186/1748-5908-9-41 · 3.47 Impact Factor
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    ABSTRACT: Background The lack of high quality timely data for evidence-informed decision making at the district level presents a challenge to improving maternal and newborn survival in low income settings. To address this problem, the EQUIP project (Expanded Quality Management using Information Power) implemented a continuous household and health facility survey for continuous feedback of data in two districts each in Tanzania and Uganda as part of a quality improvement innovation for mothers and newborns.Methods Within EQUIP, continuous survey data were used for quality improvement (intervention districts) and for effect evaluation (intervention and comparison districts). Over 30 months of intervention (November 2011 to April 2014), EQUIP conducted continuous cross-sectional household and health facility surveys using 24 independent probability samples of household clusters to represent each district each month, and repeat censuses of all government health facilities. Using repeat samples in this way allowed data to be aggregated at six four-monthly intervals to track progress over time for evaluation, and for continuous feedback to quality improvement teams in intervention districts.In both countries, one continuous survey team of eight people was employed to complete approximately 7,200 household and 200 facility interviews in year one. Data were collected using personal digital assistants. After every four months, routine tabulations of indicators were produced and synthesized to report cards for use by the quality improvement teams.ResultsThe first 12 months were implemented as planned. Completion of household interviews was 96% in Tanzania and 91% in Uganda. Indicators across the continuum of care were tabulated every four months, results discussed by quality improvement teams, and report cards generated to support their work.Conclusions The EQUIP continuous surveys were feasible to implement as a method to continuously generate and report on demand and supply side indicators for maternal and newborn health; they have potential to be expanded to include other health topics. Documenting the design and implementation of a continuous data collection and feedback mechanism for prospective description, quality improvement, and evaluation in a low-income setting potentially represents a new paradigm that places equal weight on data systems for course correction, as well as evaluation.
    Implementation Science 08/2014; 9(1):112. DOI:10.1186/s13012-014-0112-1 · 3.47 Impact Factor