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

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.

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

Malaria Journal ◽

10.1186/s12936-020-03489-x ◽

2020 ◽

Vol 19 (1) ◽

Author(s):

Jonathan Abeles ◽

David J. Conway

Keyword(s):

Global Health ◽

Confidence Intervals ◽

Gini Coefficient ◽

Malaria Incidence ◽

Health Problems ◽

World Health ◽

Potential Range ◽

The Gini Coefficient ◽

World Regions ◽

High Level

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.

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Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

10.1101/438127 ◽

2018 ◽

Author(s):

Sandro Gsteiger ◽

Nicola Low ◽

Pam Sonnenberg ◽

Catherine H Mercer ◽

Christian L Althaus

Keyword(s):

Sexual Activity ◽

Gini Coefficient ◽

Treatment Strategies ◽

Mycoplasma Genitalium ◽

Sti Prevention ◽

Sexually Transmitted ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Hpv Types ◽

Different Levels

AbstractObjectivesGini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time.MethodsWe used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999-2001; Natsal-3: 2010-2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients.ResultsGini coefficients for CT and MG were 0.33 (95% confidence interval (CI): 0.18-0.49) and 0.16 (95% CI: 0.02-0.36), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15-0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient.ConclusionsGini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.Key messagesThe Gini coefficient can be used to describe the distribution of STIs in a population, according to different levels of sexual activity.Gini coefficients for Chlamydia trachomatis (CT) and human papillomavirus (HPV) type 18 appear to be higher than for Mycoplasma genitalium and HPV 6, 11 and 16.Mathematical modelling suggests that CT screening interventions should reduce prevalence and increase the Gini coefficient, whilst condom use reduces prevalence without affecting the Gini coefficient.Changes in Gini coefficients over time could be used to assess the impact of STI prevention and treatment strategies.

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Frequency distribution of shoot weight of plants in populations ofTrifolium repenspersisting by clonal growth in grazed pastures

The Journal of Agricultural Science ◽

10.1017/s0021859600073895 ◽

1990 ◽

Vol 115 (1) ◽

pp. 41-47 ◽

Cited By ~ 4

Author(s):

M. J. M. Hay ◽

V. J. Thomas ◽

J. L. Brock

Keyword(s):

Gini Coefficient ◽

White Clover ◽

Clonal Growth ◽

Dry Weight ◽

Plant Size ◽

Size Hierarchy ◽

Gini Coefficients ◽

Shoot Dry Weight ◽

The Gini Coefficient ◽

Variable Nature

SUMMARYOver two years (1984/85 and 1986/87), monthly sampling of shoots of white clover plants compared the populations of white clover in mixed swards at Palmerston North, New Zealand, under set stocking, rotational grazing and a combination of both systems, at a common stocking rate of 22·5 ewe equivalents/ha.The frequency distributions of shoot (or stolon) dry weight per plant in each population over the study period was described by a log-normal model, which indicated that the populations consisted of many small individuals and few large individuals. Such inequality of shoot dry weight within populations is commonly termed size hierarchy; a statistic giving a measure of such size hierarchy is the Gini coefficient. The populations under different managements had similar Gini coefficients which differed little among seasons or between years. Lack of significant correlation between the Gini coefficient and mean shoot dry weight per plant of each population indicated that, in these white clover populations, size hierarchy was independent of mean plant size.These results were considered in relation to the clonal growth of white clover in grazed swards and it is suggested that the variable nature of death of older basal stolons makes an important contribution to the variability in size of individual plants and hence to size hierarchy. As size hierarchy, as assessed by Gini coefficients, was relatively stable in these populations over 3½ years, it appears that clonal growth of white clover incorporates sufficient variability within the growth and death processes at the individual plant level to maintain the size hierarchy, irrespective of variations in mean plant size of populations.

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Commentary: Measuring the shape of degree distributions

Network Science ◽

10.1017/nws.2013.10 ◽

2013 ◽

Vol 1 (2) ◽

pp. 213-225 ◽

Cited By ~ 12

Author(s):

JENNIFER M. BADHAM

Keyword(s):

Gini Coefficient ◽

Degree Distribution ◽

Fundamental Property ◽

Standard Measure ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Different Types ◽

Network Properties ◽

The Relationship

AbstractDegree distribution is a fundamental property of networks. While mean degree provides a standard measure of scale, there are several commonly used shape measures. Widespread use of a single shape measure would enable comparisons between networks and facilitate investigations about the relationship between degree distribution properties and other network features. This paper describes five candidate measures of heterogeneity and recommends the Gini coefficient. It has theoretical advantages over many of the previously proposed measures, is meaningful for the broad range of distribution shapes seen in different types of networks, and has several accessible interpretations. While this paper focuses on degree, the distribution of other node-based network properties could also be described with Gini coefficients.

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Analyzing the Relationship between the PM2.5 Concentration and the Gini Coefficient Using the Grey Model

Journal of Mathematics ◽

10.1155/2021/8222993 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Lifeng Wu ◽

Kai Cai ◽

Yan Chen

Keyword(s):

Gini Coefficient ◽

Northwest China ◽

Grey Model ◽

Effective Control ◽

Grey Prediction ◽

Gini Coefficients ◽

The Gini Coefficient ◽

The Rich ◽

The Relationship ◽

Pm2.5 Concentration

To explore the relationship between the PM2.5 concentration and the gap between the rich and the poor, the PM2.5 concentration in 26 provincial regions of China is predicted by using the Gini coefficient as the independent variable. The nonequigap fractional grey prediction model (CFNGM (1, 1)) is used for data fitting and predicting. The validity of the model is verified by comparing with the traditional nonequidistant grey model. The predicting results show that the PM2.5 concentration in many provinces of China presents a roughly downward trend. In the past nine years, the Gini coefficients have declined in more than 70% of the 26 provinces. However, the development of the Gini coefficient in Northwest China fluctuates greatly and even has an upward trend in recent years. According to the predictive results, reasonable suggestions can be put forward for the effective control of PM2.5 emission in China.

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Impact of incarceration on tuberculosis incidence and its interaction with income distribution inequality in Brazil

Transactions of the Royal Society of Tropical Medicine and Hygiene ◽

10.1093/trstmh/trz088 ◽

2019 ◽

Author(s):

Daniele Maria Pelissari ◽

Fredi Alexander Diaz-Quijano

Keyword(s):

Income Distribution ◽

Gini Coefficient ◽

Prison Population ◽

Population Attributable Fraction ◽

Multiple Regression Model ◽

Incidence Rates ◽

Negative Interaction ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Socio Economic Factors

Abstract Background Deteriorated conditions in the non-prison population can lead to an approximation of its tuberculosis (TB) risk to that in the prison population. We evaluated the association between incarceration and TB incidence rate and its interaction with population income distribution inequality in Brazilian municipalities (2013–2015). Methods We included 954 municipalities with at least one prison. Interaction between the Gini coefficient and prison exposure was analysed in a multiple regression model. We estimated the fraction of TB in the population attributable fraction (PAF) to exposure to prisons according the Gini coefficient. Results Compared with the non-prison population, the prisoners had 22.07 times (95% confidence interval [CI] 20.38 to 23.89) the risk of TB in municipalities where the Gini coefficient was <0.60 and 14.96 times (95% CI 11.00 to 18.92) the risk where the Gini coefficient was ≥0.60. A negative interaction in the multiplicative scale was explained by a higher TB incidence in the non-prison population in municipalities with a Gini coefficient ≥0.60. The PAF ranged from 50.06% to 5.19% in municipalities with Gini coefficients <0.40 and ≥0.60, respectively. Conclusions Interventions to reduce prison exposure would have an ostensible impact in population TB incidence rates mainly in settings with lower Gini coefficients. In those with extreme inequality in income distribution, strategies focused on mitigating the effects of socio-economic factors should also be prioritized.

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Gini coefficients for measuring the distribution of sexually transmitted infections among individuals with different levels of sexual activity

PeerJ ◽

10.7717/peerj.8434 ◽

2020 ◽

Vol 8 ◽

pp. e8434 ◽

Cited By ~ 1

Author(s):

Sandro Gsteiger ◽

Nicola Low ◽

Pam Sonnenberg ◽

Catherine H. Mercer ◽

Christian L. Althaus

Keyword(s):

Sexual Activity ◽

Sexually Transmitted Infections ◽

Gini Coefficient ◽

Transmission Model ◽

Sti Prevention ◽

Sexually Transmitted ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Hpv Types ◽

Different Levels

Objectives Gini coefficients have been used to describe the distribution of Chlamydia trachomatis (CT) infections among individuals with different levels of sexual activity. The objectives of this study were to investigate Gini coefficients for different sexually transmitted infections (STIs), and to determine how STI control interventions might affect the Gini coefficient over time. Methods We used population-based data for sexually experienced women from two British National Surveys of Sexual Attitudes and Lifestyles (Natsal-2: 1999–2001; Natsal-3: 2010–2012) to calculate Gini coefficients for CT, Mycoplasma genitalium (MG), and human papillomavirus (HPV) types 6, 11, 16 and 18. We applied bootstrap methods to assess uncertainty and to compare Gini coefficients for different STIs. We then used a mathematical model of STI transmission to study how control interventions affect Gini coefficients. Results Gini coefficients for CT and MG were 0.33 (95% CI [0.18–0.49]) and 0.16 (95% CI [0.02–0.36]), respectively. The relatively small coefficient for MG suggests a longer infectious duration compared with CT. The coefficients for HPV types 6, 11, 16 and 18 ranged from 0.15 to 0.38. During the decade between Natsal-2 and Natsal-3, the Gini coefficient for CT did not change. The transmission model shows that higher STI treatment rates are expected to reduce prevalence and increase the Gini coefficient of STIs. In contrast, increased condom use reduces STI prevalence but does not affect the Gini coefficient. Conclusions Gini coefficients for STIs can help us to understand the distribution of STIs in the population, according to level of sexual activity, and could be used to inform STI prevention and treatment strategies.

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Which Institutions Are More Relevant Than Others in Inequality Mitigation?

The Pakistan Development Review ◽

10.30541/v45i4iipp.893-912 ◽

2006 ◽

Vol 45 (4II) ◽

pp. 893-912

Author(s):

Dawood Mamoon

Keyword(s):

United States ◽

United Kingdom ◽

Central Europe ◽

Gini Coefficient ◽

The United States ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Industrialised Countries ◽

Increasing Trends ◽

Former Ussr

During the 1950s, 1960s and most of the 1970s inequality followed declining trends in the most developed and developing countries. However, the inequality trends have been reversed in most countries since the early 1980s. First, inequality started rising in the mid- to late- 1970s in the United States, United Kingdom, Australia and the New Zealand, which were the first among the OECD countries to adopt a neoliberal policy approach. In United Kingdom the increase in inequality was quite pronounced as the Gini coefficient of the distribution of net disposable income rose more than 30 percent between 1978 and 1991, which was twice as fast as that recorded in United States for the same period. The Scandinavian countries and the Netherlands were next to follow where inequality followed a U-shaped pattern. From 1970 to 80, Finland and France also experienced a halt in declining trends in inequality. In Italy inequality rose by 4 points between 1992 and 1995. In 1993 the Gini coefficient for Japan stood at 0.44, which is approximately the same as United States and far higher than the likes of Sweden and Denmark. Most of this increase in income inequality in these industrialised countries is explained by a rise in earnings inequality [Cornia, et al. (2004)]. Since 1989, inequality in the transition countries of Central Europe has also witnessed increasing trends but they remain modest when compared to former USSR and Southeastern Europe where the Gini coefficients rose on average by 10-20 points which is 304 times faster than the Gini in Central Europe. The rise in inequality in this region has been attributed to rise in returns to education following liberalisation [Rutkowski (1999)].

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The Impact of Centralization of Obstetric Care Resources in Japan on the Perinatal Mortality Rate

ISRN Obstetrics and Gynecology ◽

10.1155/2013/709616 ◽

2013 ◽

Vol 2013 ◽

pp. 1-5 ◽

Cited By ~ 2

Author(s):

Akira Sudo ◽

Yoshiki Kuroda

Keyword(s):

Mortality Rate ◽

Perinatal Mortality ◽

Gini Coefficient ◽

Obstetric Care ◽

Gini Coefficients ◽

The Gini Coefficient ◽

Perinatal Mortality Rate ◽

Care Facilities ◽

Per Capita ◽

The Impact

Objective. We investigated the effects of the centralization of obstetricians and obstetric care facilities on the perinatal mortality rate in Japan. Methods. We used the Gini coefficient as an index to represent the centralization of obstetricians and obstetric care facilities. The Gini coefficients were calculated for the number of obstetricians and obstetric care facilities of 47 prefectures using secondary medical care zones as units. To measure the effects of the centralization of obstetricians and obstetric care facilities on the outcomes (perinatal mortality rates), we performed multiple regression analysis using the perinatal mortality rate as the dependent variable. Results. Obstetric care facilities were more evenly distributed than obstetricians. The perinatal mortality rate was found to be significantly negatively correlated with the number of obstetricians per capita and the Gini coefficient of obstetric care facilities. The latter had a slightly stronger effect on the perinatal mortality rate. Conclusion. The centralization of obstetric care facilities can improve the perinatal mortality rate, even when increasing the number of obstetricians is difficult.

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Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling

Journal of Official Statistics ◽

10.2478/jos-2020-0013 ◽

2020 ◽

Vol 36 (2) ◽

pp. 237-249

Author(s):

Yves G. Berger ◽

İklim Gedik Balay

Keyword(s):

Confidence Interval ◽

Confidence Intervals ◽

Empirical Likelihood ◽

Simulation Study ◽

Gini Coefficient ◽

Probability Sampling ◽

Unequal Probability Sampling ◽

Unequal Probability ◽

The Gini Coefficient

AbstractWe propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct confidence intervals. Our simulation study shows the estimator proposed is usually less biased than customary estimators. The observed coverages of the empirical likelihood confidence interval proposed are also closer to the nominal value.

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