Geospatial evaluation of trade-offs between equity in physical access to healthcare and health systems efficiency.

INTRODUCTION: Decisions regarding the geographical placement of healthcare services require consideration of trade-offs between equity and efficiency, but few empirical assessments are available. We applied a novel geospatial framework to study these trade-offs in four African countries. METHODS: Geolocation data on population density (a surrogate for efficiency), health centres and cancer referral centres in Kenya, Malawi, Tanzania and Rwanda were obtained from online databases. Travel time to the closest facility (a surrogate for equity) was estimated with 1 km resolution using the Access Mod 5 least cost distance algorithm. We studied associations between district-level average population density and travel time to closest facility for each country using Pearson's correlation, and spatial autocorrelation using the Global Moran's I statistic. Geographical clusters of districts with inefficient resource allocation were identified using the bivariate local indicator of spatial autocorrelation. RESULTS: Population density was inversely associated with travel time for all countries and levels of the health system (Pearson's correlation range, health centres: -0.89 to -0.71; cancer referral centres: -0.92 to -0.43), favouring efficiency. For health centres, negative spatial autocorrelation (geographical clustering of dissimilar values of population density and travel time) was weaker in Rwanda (-0.310) and Tanzania (-0.292), countries with explicit policies supporting equitable access to rural healthcare, relative to Kenya (-0.579) and Malawi (-0.543). Stronger spatial autocorrelation was observed for cancer referral centres (Rwanda: -0.341; Tanzania: -0.259; Kenya: -0.595; Malawi: -0.666). Significant geographical clusters of sparsely populated districts with long travel times to care were identified across countries. CONCLUSION: Negative spatial correlations suggested that the geographical distribution of health services favoured efficiency over equity, but spatial autocorrelation measures revealed more equitable geographical distribution of facilities in certain countries. These findings suggest that even when prioritising efficiency, thoughtful decisions regarding geographical allocation could increase equitable physical access to services.

Organism burden, toxin concentration, and lactoferrin concentration do not distinguish between clinically significant and nonsignificant diarrhea in patients with Clostridium difficile.

Clostridium difficile infection is often overdiagnosed in patients with mild diarrhea. We evaluated 4 biomarkers as surrogates for clinically significant diarrhea (≥ 3 episodes in 24 hours) in 59 PCR-positive patients with and 59 PCR-positive patients without clinically significant diarrhea. Organism burden (median tcdB cycle threshold value, 26.9 versus 27.1, P=0.25) and toxin A and B concentrations (toxin A, median, 0 versus 0 ng/mL, P=0.42; toxin B, median, 0 versus 0 ng/mL, P=0.25) were not significantly different between patients with and without clinically significant diarrhea. Fecal lactoferrin concentrations were significantly increased in patients with clinically significant diarrhea (median, 99.0 versus 55.1 µg/mL, P=0.05); however, lactoferrin could not sufficiently classify patients into those with and without clinically significant diarrhea. Interventions that limit C. difficile testing to patients with clinically significant diarrhea are needed to improve the positive predictive value of C. difficile diagnostics.

An unsupervised learning method to identify reference intervals from a clinical database.

Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.