Quantifying the validity of routine neonatal healthcare data in the Greater Accra Region, Ghana

PLoS One. 2014 Aug 21;9(8):e104053. doi: 10.1371/journal.pone.0104053. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Female
  • Ghana
  • Humans
  • Infant Care / statistics & numerical data*
  • Infant, Newborn
  • Pregnancy
  • Prenatal Care
  • Research Design / standards*