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Quantifying the Validity of Routine Neonatal Healthcare Data in the Greater Accra Region,Ghana
Authors:Gbenga A Kayode  Mary Amoakoh-Coleman  Charles Brown-Davies  Diederick E Grobbee  Irene Akua Agyepong  Evelyn Ansah  Kerstin Klipstein-Grobusch
Institution:1. Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.; 2. Ghana Health Service, Greater Accra Region, Accra, Ghana.; 3. School of Public Health, University of Ghana, Legon, Accra, Ghana.; 4. Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.; UCL, United Kingdom,
Abstract:

Objectives

The District Health Information Management System–2 (DHIMS–2) is the database for storing health service data in Ghana, and similar to other low and middle income countries, paper-based data collection is being used by the Ghana Health Service. As the DHIMS-2 database has not been validated before this study aimed to evaluate its validity.

Methods

Seven out of ten districts in the Greater Accra Region were randomly sampled; the district hospital and a polyclinic in each district were recruited for validation. Seven pre-specified neonatal health indicators were considered for validation: antenatal registrants, deliveries, total births, live birth, stillbirth, low birthweight, and neonatal death. Data were extracted on these health indicators from the primary data (hospital paper-registers) recorded from January to March 2012. We examined all the data captured during this period as these data have been uploaded to the DHIMS-2 database. The differences between the values of the health indicators obtained from the primary data and that of the facility and DHIMS–2 database were used to assess the accuracy of the database while its completeness was estimated by the percentage of missing data in the primary data.

Results

About 41,000 data were assessed and in almost all the districts, the error rates of the DHIMS-2 data were less than 2.1% while the percentages of missing data were below 2%. At the regional level, almost all the health indicators had an error rate below 1% while the overall error rate of the DHIMS-2 database was 0.68% (95% C I = 0.61–0.75) and the percentage of missing data was 3.1% (95% C I = 2.96–3.24).

Conclusion

This study demonstrated that the percentage of missing data in the DHIMS-2 database was negligible while its accuracy was close to the acceptable range for high quality data.
Keywords:
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