Exploring approaches to weighting estimates of facility readiness to provide health services used for estimating input-adjusted effective coverage: a case study using data from Tanzania

ABSTRACT

The ideal approach for calculating effective coverage of health services using ecological linking requires accounting for variability in facility readiness to provide health services and patient volume by incorporating adjustments for facility type into estimates of facility readiness and weighting facility readiness estimates by service-specific caseload. The aim of this study is to compare the ideal caseload-weighted facility readiness approach to two alternative approaches: (1) facility-weighted readiness and (2) observation-weighted readiness to assess the suitability of each as a proxy for caseload-weighted facility readiness. We utilised the 2014–2015 Tanzania Service Provision Assessment along with routine health information system data to calculate facility readiness estimates using the three approaches. We then conducted equivalence testing, using the caseload-weighted estimates as the ideal approach and comparing with the facility-weighted estimates and observation-weighted estimates to test for equivalence. Comparing the facility-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 58% of the estimates met the requirements for equivalence. In addition, the facility-weighted readiness estimates consistently underestimated, by a small percentage, facility readiness as compared to the caseload-weighted readiness estimates. Comparing the observation-weighted readiness estimates to the caseload-weighted readiness estimates, we found that 64% of the estimates met the requirements for equivalence. We found that, in this setting, both facility-weighted readiness and observation-weighted readiness may be reasonable proxies for caseload-weighted readiness. However, in a setting with more variability in facility readiness or larger differences in facility readiness between low caseload and high caseload facilities, the observation-weighted approach would be a better option than the facility-weighted approach. While the methods compared showed equivalence, our results suggest that selecting the best method for weighting readiness estimates will require assessing data availability alongside knowledge of the country context.

KEYWORDS:

• Coverage
• quality of care
• household survey
• health facility assessment
• LMIC
• linking methods

Responsible Editor

Jennifer Stewart Williams

Responsible Editor

Jennifer Stewart Williams

Acknowledgements

The authors wish to acknowledge the Bill & Melinda Gates Foundation for their support of this project.

Author’s contributions

AS, EC, and MKM contributed to conceptualising the paper and analysis. DM, DN, KYM, and SS performed data extraction and management. AS drafted the manuscript, with critical review and revision from all authors.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethics and consent

This is a secondary analysis and as such did not involve human subjects research.

Paper context

Effective coverage is increasingly used to monitor universal health coverage, often by linking household surveys and health facility assessments. This paper provides a comparison of three methods for calculating weighted facility readiness for effective coverage and concludes that in Tanzania the three methods yield equivalent estimates. However, in a setting with more variability in readiness, facility-weighting may underestimate readiness. Insights from this study serve to advance best practices in methodologies for generating effective coverage estimates.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the Improving Measurement and Programme Design grant [OPP1172551] from the Bill & Melinda Gates Foundation.