Dataset Information

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

First, the Gini coefficient was applied to quantify variation among World Health Organization 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 problems 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), contrasting with 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 standard 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.

SUBMITTER: Abeles J

PROVIDER: S-EPMC7709295 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

The Gini coefficient as a useful measure of malaria inequality among populations.

Abeles Jonathan J Conway David J DJ

Malaria journal 20201202 1

<h4>Background</h4>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 popula ...[more]

PMID: 33267885

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Project description:Background and aimsRoot hair density (i.e. the number of root hairs per unit root length) in Arabidopsis thaliana varies among individual plants in response to different nutrient stresses. The degree of such variation, defined as inequality, serves as a unique indicator of the uniformity of response within a plant population to nutrient availability.MethodsUsing the Gini coefficient (G) as an inequality index, the inequality of root hair density in Arabidopsis thaliana 'Columbia' was evaluated under conditions of nutrient stresses; in particular the effect of phosphorus and its interaction with ethylene.Key resultsWith decreasing phosphorus concentration, root hair density increased while inequality decreased logarithmically. The addition of the ethylene precursor 1-aminocyclopropane-1-carboxylate (ACC) under high phosphorus increased root hair density and decreased inequality by 7-fold. Inhibition of ethylene action with 1-methylcyclopropene (MCP) and silver thiosulphate (STS) under low phosphorus decreased root hair density, and increased inequality by 9-fold and 4-fold, respectively. The ethylene action inhibitors had little effect on root hair density under high phosphorus, but inequality increased 3-fold in the presence of MCP and decreased 2-fold in the presence of STS. Compared with the control, deficiencies in S, N and K increased inequality of root hair density, whereas deficiencies in P, Ca, B, Mn, Fe, Zn, Cu and Mg decreased inequality. In particular, the inequality of root hair density increased by over 2-fold under deficiencies of N or K, but decreased 14-fold under phosphorus deficiency.ConclusionsThe inequality analysis indicates a strong correlation between prevalent signals from the environment (i.e. phosphorus stress) and the response of the plant, and the role of ethylene in this response. As the environmental signals become stronger, an increasing proportion of individuals respond, resulting in a decrease in variation in responsiveness among individual plants as indicated by reduced inequality.

Project description:We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

Dataset Information

The Gini coefficient as a useful measure of malaria inequality among populations.

Background

Methods

Results

Conclusions

Publications

The Gini coefficient as a useful measure of malaria inequality among populations.

Similar Datasets

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