The Gini Coefficient as a useful measure of malaria inequality among populations

Abstract

BACKGROUND: Understanding inequality in infectious disease burden requires clear and unbiased indicators. The Gini coefficient, conventionally used as a macroeconomic descriptor of inequality, is potentially useful to quantify epidemiological heterogeneity. With a potential range from 0 (all populations equal) to 1 (populations having maximal differences), this coefficient is used here to show the extent and persistence of inequality of malaria infection burden at a wide variety of population levels. METHODS: We first applied the Gini coefficient to quantify variation among WHO world regions for malaria and other major global health problems. Malaria heterogeneity was then measured among countries within the geographical sub-region where burden is greatest, among the major administrative divisions in several of these countries, and among selected local communities. Data were analysed from previous research studies, national surveys, and global reports, and Gini coefficients were calculated together with confidence intervals using bootstrap resampling methods. RESULTS: Malaria showed a very high level of inequality among the world regions (Gini coefficient, G = 0.77, 95% CI 0.66-0.81), more extreme than for any of the other major global health challenges compared at this level. Within the most highly endemic geographical sub-region, there was substantial inequality in estimated malaria incidence among countries of West Africa, which did not decrease between 2010 (G = 0.28, 95% CI 0.19-0.36) and 2018 (G = 0.31, 0.22-0.39). There was a high level of sub-national variation in prevalence among states within Nigeria (G = 0.30, 95% CI 0.26-0.35), but more moderate variation within Ghana (G = 0.18, 95% CI 0.12-0.25) and Sierra Leone (G = 0.17, 95% CI 0.12-0.22). There was also significant inequality in prevalence among local village communities, generally more marked during dry seasons when there was lower mean prevalence. The Gini coefficient correlated strongly with the Coefficient of Variation which has no finite range. CONCLUSIONS: The Gini coefficient is a useful descriptor of epidemiological inequality at all population levels, with confidence intervals and interpretable bounds. Wider use of the coefficient would give broader understanding of malaria heterogeneity revealed by multiple types of studies, surveys and reports, providing more accessible insight from available data.

Quantifying spatial heterogeneity of malaria in the endemic Papua region of Indonesia: analysis of epidemiological surveillance data

“…This index has been shown to be a sensitive and intuitive measure to capture the heterogeneity of transmission intensity over space and time. 15 When compared to other potential measures of heterogeneity, such as the coefficient of variation, which can exceed 100% while being highly correlated with the Gini index, the Gini index, bounded between [0, 100]%, can facilitate interpretation and comparability across studies. 15 Along with visualising different levels of malaria intensity using geospatial maps, summarising heterogeneity into a single metric using the Gini index may be useful and complementary for tracking progress towards elimination.…”

“…15 Along with visualising different levels of malaria intensity using geospatial maps, summarising heterogeneity into a single metric using the Gini index may be useful and complementary for tracking progress towards elimination. 15…”

“…Although it is most commonly used in economics, the Gini index has been widely applied in a diverse range of research, including infectious-disease epidemiology. 15…”

Inequality indices to monitor geographic differences in incidence, mortality and fatality rates over time during the COVID-19 pandemic

“…The Gini index is also used to study the spread of infectious diseases. One article studies sexually transmitted diseases by investigating their distribution according to level of sexual activity, another one is concerned with assessing inequality between malaria prevalences among populations [10, 11]. Another field of GI’s application includes the analysis of the distribution of patient recruitment into clinical trials in critical care settings [12].…”

“…How does the distribution of sexually transmitted diseases depend on level of sexual activity [12]? How is the inequality between malaria prevalence among populations [13]? Inequality studies also concern the distribution of patient recruitment into clinical trials in different settings [14].…”