Small Area Analysis of Population Health: An Examination of the Value of Facility-Based Data in Ghana

Abstract

Background There is a growing demand for reliable small area health statistics by governments, researchers, health managers and programmes due to continuous surge in communicable and non-communicable diseases in developing countries. Countries’ routine health information systems data (facility-based data) have been identified as a key system building block of primary health care which produces information in greater detail to meet the high data demand. However, the huge amount of facility-based data collected daily, aggregated and reported monthly into the national health information systems have rarely been used for evidence-based decision-making. Instead, household surveys have become the primary source of data for local area public health decision-making. Household surveys have been designed to measure indicators at the regional and national levels only, and therefore are unable to produce reliable estimates for small areas due to the small sample sizes. Furthermore, household surveys are mostly conducted once every 5 years or more, therefore health managers and policy-makers must wait up to five years or above for Demographic and Health Surveys or Multiple Indicator Cluster Surveys to be conducted to enable them to assess what progress has been made or be able to monitor implementation of programmes and interventions which must be conducted on monthly, quarterly and yearly basis. Poor health facility reporting, inadequacy of methods, inadequacy of population denominators for calculating population-based rates and lack of external consistency checks were among the key constraints identified to be responsible for the limited use of health facility-based data for public health decision-making. Objective The main objective of this study was to explore the usage of geospatial and statistical techniques to address issues associated with limited use of facility-based data and to evaluate its value for small area evidence-based decision-making. University of Ghana http://ugspace.ug.edu.gh xvii Method This study is both a descriptive cross-sectional and an exploratory one. It made use of a number of datasets including: health facility visits data for malaria and diarrhoea recorded by all health facilities offering outpatient services in Greater Accra Region (GAR) in 2014, population and housing census data, Ghana demographic and health survey data (GDHS), climate data, health facility locations and base map of GAR. Completeness of facility reporting rate of the region and the districts were determined. Multiple Imputation by Chain Equation was used to fill the data gap created by non reporting facilities. Health facilities locations, public and private, have been geo-coded and linked to the enumeration boundaries of 2010 population census data to provide the denominators for small area disease rates calculations. Annual cumulative health facility visit rates for malaria and diarrhoea, calculated as health facility visits per 1,000 of the population for districts and catchment levels, was the main dependent variable for the spatial regression analysis. This study used geospatial tools such as choropleth map, hotspot analysis, and spatial autocorrelation (Moran’s I) to visualize and explore patterns in the facility-based data. Ordinary Least Squares (OLS) fit, and Geographically Weighted Regression models (GWR) were used to analyse spatial relationships between independent variables (household predictors of malaria and diarrhoea extracted from 2010 PHC data) and health facility visit rates for malaria and diarrhoea. The statistical analyses were performed using Microsoft Excel version 16 and Stata software package, version 14.4. The geospatial analyses were performed using GWR4 (Newcastle University, UK) and ArcGIS ( version 10.4.1, ESRI Inc., Redlands, USA). Results The study has found out that about 49% of the health facilities in Greater Accra Region in 2014 did not report outpatient data to District Health Information Management System 2 (DHIMS2), and the majority of these facilities were privately owned or a quasi-government. University of Ghana http://ugspace.ug.edu.gh xviii Completeness of facility reporting rate for the region was 53.1%. Half of the districts in GAR recorded completeness of facility reporting rate below the 80% limit set by WHO, suggesting poor facility reporting for the region as a whole. The overall annual cumulative health facility visits due to malaria and diarrhoea for Greater Accra Region in 2014 were 880,735 and 233,003 respectively. These included 44% and 43% imputed health facilityvisits due to malaria and diarrhoea respectively due to non-reporting facilities. A data consistency check has revealed a statiatically significant difference between 2014 DHIMS2 and GDHS under-5 year old health facility visits due to malaria and diarrhoea. There were far fewer health facility visits in total featured in DHIMS2 than one would estimate based on the GDHS, and the difference (for under-5s as a whole) was highly unlikely to be due to sampling error. Significant spatial variations were observed in the distribution of health facility visits due to malaria and diarrhoea at district and catchment levels. The rate ranges between 577 visits per 1,000 population to 90 visits per 1,000 populations for malaria and that of diarrhoea ranges between 118 visits per 1,000 population to 16 visits per 1,000 populations. For all age groups above 14years, more females visited the medical facilities (public and private) with malaria episodes than their male counterparts. Similar trend was observed for HFV due to diarrhoea. The local GWR model for health facility visit rates for malaria improved the Ordinary Least Square model fit considerably. Locally weighted ��2 value (goodness of fit statistics) indicate how well the GWR model replicates the HFVR for malaria around proportion of persons living within house walls prone to malaria and proportion of persons within the lowest wealth quintile. ��2 value by catchment area varies between 10% to 46% with a mean value of 30%. Health facility visit rates for malaria was found to vary spatially over Greater Accra Region. GWR results indicated that proportion of persons living in walls prone to malaria and University of Ghana http://ugspace.ug.edu.gh proportion of persons within the lowest wealth quintile were significantly spatially non-stationary – evidence that spatial heterogeneity exists in these relationships. Conclusions The result of the study suggested that Greater Accra Region has poor facility reporting. Nonetheless, with the use of geospatial and statistical techniques this data was enhanced to produce plausible population-based indicators comparable to those found in literature for public health decision-making. The study used health facility visits due to malaria and diarrhoea to demonstrate the various methodological stages involved in enhancing the value of facility-based data. The study showed variations in health facility visits due to malaria and diarrhoea by age and sex groups. Spatial variations were also observed at district and catchment levels in health facility visit rates for malaria and diarrhoea. Disaggregation of the data to catchment levels has shown further variations in health facility visits which were obscured at district levels. The study revealed that modelled risk factors for malaria vary geographically over short distances and can be used to focus interventions at the population most at need. The majority of the findings in this study agree with literature and is a confirmation that DHIMS2 data can generate plausible indicators that can be used for evidence-based decision making. This study also demonstrates that facility-based data with its current challenges can be manipulated with geostatistical techniques to generate plausible population-based indicators for small area evidence-based decision. It is hoped that the findings of this study will encourage public health practitioners and other researchers to have more confidence in using routine facility-based data (DHIMS2) for effective health sector planning and decision-making