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

Examining geographical inequalities for malaria outcomes and spending on malaria in 40 malaria-endemic countries, 2010-2020

BACKGROUND

While substantial gains have been made in the fight against malaria over the past 20 years, malaria morbidity and mortality are marked by inequality. The equitable elimination of malaria within countries will be determined in part by greater spending on malaria interventions, and how those investments are allocated. This study aims to identify potential drivers of malaria outcome inequality and to demonstrate how spending through different mechanisms might lead to greater health equity.

METHODS

Using the Gini index, subnational estimates of malaria incidence and mortality rates from 2010 to 2020 were used to quantify the degree of inequality in malaria burden within countries with incidence rates above 5000 cases per 100,000 people in 2020. Estimates of Gini indices represent within-country distributions of disease burden, with high values corresponding to inequitable distributions of malaria burden within a country. Time series analyses were used to quantify associations of malaria inequality with malaria spending, controlling for country socioeconomic and population characteristics.

RESULTS

Between 2010 and 2020, varying levels of inequality in malaria burden within malaria-endemic countries was found. In 2020, values of the Gini index ranged from 0.06 to 0.73 for incidence, 0.07 to 0.73 for mortality, and 0.00 to 0.36 for case fatality. Greater total malaria spending, spending on health systems strengthening for malaria, healthcare access and quality, and national malaria incidence were associated with reductions in malaria outcomes inequality within countries. In addition, government expenditure on malaria, aggregated government and donor spending on treatment, and maternal educational attainment were also associated with changes in malaria outcome inequality among countries with the greatest malaria burden.

CONCLUSIONS

The findings from this study suggest that prioritizing health systems strengthening in malaria spending and malaria spending in general especially from governments will help to reduce inequality of the malaria burden within countries. Given heterogeneity in outcomes in countries currently fighting to control malaria, and the challenges in increasing both domestic and international funding allocated to control and eliminate malaria, the efficient targeting of limited resources is critical to attain global malaria eradication goals.

Understanding the factors associated with nurse employment in clinics: Experiences in Taiwan

BACKGROUND

The shortage and distribution of nurses affect healthcare access in aging societies. Limited research has explored the nursing workforce in clinics, which is vital for healthcare delivery. This study aimed to investigate the shortage and distribution of nurses in clinics in Taiwan, considering geographical, institutional, and specialty variations.

METHODS

We conducted a cross-sectional analysis of the national nursing workforce in Western medicine clinics in Taiwan using open government data. The nursing practice rate (NPR) was calculated. The ratio of clinics employing nurses (RCN) was determined by calculating the percentage of clinics with nurses in each category. A logistic regression model was fitted to examine the factors associated with nurse employment, including urbanization level and different specialty clinics. Adjusted odds ratios (OR) and 95% CIs were calculated.

RESULTS

The study revealed an overall NPR as low as 59.1%, and a decreasing trend with age. Among the 11 706 clinics in the study, nearly a quarter did not employ nurses, with an overall RCN of 72.3%. Urbanization level and clinic specialty were associated with nurse employment ( p < 0.05). After adjusting for urbanization level, the fitted regression model identified the top three specialties as plastic surgery (OR = 11.37, RCN = 96.8%), internal medicine (OR = 1.94, RCN = 84.1%), and orthopedics (OR = 1.89, RCN = 83.6%), while the bottom three were otolaryngology (OR = 0.59, RCN = 61.5%), psychiatry (OR = 0.49, RCN = 57.1%), and rehabilitation medicine (OR = 0.30, RCN = 45.2%). Nurses were more likely to be employed in areas at urbanization level 1 (OR = 1.17), 3 (OR = 1.37), and 6 (OR = 1.48), which represent highly urbanized areas within the urban, suburban, and rural categories, respectively, than in urbanization level 4.

CONCLUSION

Clinics in Taiwan showed nursing shortages and maldistribution, with 72.3% RCN and variations based on urbanization and specialty. These factors may be considered in nursing research conducted in other countries to inform future workforce planning.

Deprivation and Rurality Mediate Income Inequality's Association with Colorectal Cancer Outcomes

INTRODUCTION

Income inequality is associated with poor health outcomes, but its association with colorectal cancer is not well-studied. The authors aimed to determine the association between income inequality and colorectal cancer incidence/mortality in U.S. counties, and hypothesized that this association was mediated by deprivation.

METHODS

The authors performed a cross-sectional study of U.S. counties from 2015-2019 using statewide cancer registries and the Centers for Disease Control and Prevention WONDER database. Generalized linear negative binomial regression was performed in 2024 to estimate the association between Gini coefficient (income inequality) and colorectal cancer incidence/mortality using incidence rate ratios (IRRs) for the entire cohort and stratified by rurality.

RESULTS

A total of 697,981 colorectal cancer cases were diagnosed in the 5-year study period. On adjusted regression, for every 0.1 higher Gini coefficient, there was an 11% higher risk of both colorectal cancer incidence and mortality (IRR 1.11, 95%CI 1.03,1.19 and IRR 1.11, 95%CI 1.05, 1.18 respectively). The association between income inequality and incidence/mortality peaked in more rural counties, however there was not an overall dose-dependent relationship between rurality and these associations. Deprivation mediated the association between income inequality and colorectal cancer incidence (indirect effect B coefficient 0.088, p<0.001) and mortality (B coefficient 0.088, p<0.001). The magnitude and direction of the direct, indirect, and total effects differed in each rurality strata.

CONCLUSIONS

Much of income inequality's association with colorectal cancer outcomes operates through deprivation. Rural counties have a stronger association between higher income inequality and higher mortality, which works in tandem with deprivation.

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Finding critical points and correlation length exponents using finite size scaling of Gini index

The order parameter for a continuous transition shows diverging fluctuation near the critical point. Here we show, through numerical simulations and scaling arguments, that the inequality (or variability) between the values of an order parameter, measured near a critical point, is independent of the system size. Quantification of such variability through the Gini index (g) therefore leads to a scaling form g=G[|F-F_{c}|N^{1/dν}], where F denotes the driving parameter for the transition (e.g., temperature T for ferromagnetic to paramagnetic transition, or lattice occupation probability p in percolation), N is the system size, d is the spatial dimension and ν is the correlation length exponent. We demonstrate the scaling for the Ising model in two and three dimensions, site percolation on square lattice, and the fiber bundle model of fracture.

Integrated biomarker profiling of the metabolome associated with type 2 diabetes mellitus among Tibetan in China

INTRODUCTION

Metabolomic signatures of type 2 diabetes mellitus (T2DM) in Tibetan Chinese population, a group with high diabetes burden, remain largely unclear. Identifying the serum metabolite profile of Tibetan T2DM (T-T2DM) individuals may provide novel insights into early T2DM diagnosis and intervention.

METHODS

Hence, we conducted untargeted metabolomics analysis of plasma samples from a retrospective cohort study with 100 healthy controls and 100 T-T2DM patients by using liquid chromatography-mass spectrometry.

RESULTS

The T-T2DM group had significant metabolic alterations that are distinct from known diabetes risk indicators, such as body mass index, fasting plasma glucose, and glycosylated hemoglobin levels. The optimal metabolite panels for predicting T-T2DM were selected using a tenfold cross-validation random forest classification model. Compared with the clinical features, the metabolite prediction model provided a better predictive value. We also analyzed the correlation of metabolites with clinical indices and found 10 metabolites that were independently predictive of T-T2DM.

CONCLUSION

By using the metabolites identified in this study, we may provide stable and accurate biomarkers for early T-T2DM warning and diagnosis. Our study also provides a rich and open-access data resource for optimizing T-T2DM management.

Malaria parasite density and detailed qualitative microscopy enhances large-scale profiling of infection endemicity in Nigeria

With global progress towards malaria reduction stalling, further analysis of epidemiology is required, particularly in countries with the highest burden. National surveys have mostly analysed infection prevalence, while large-scale data on parasite density and different developmental forms rarely available. In Nigeria, the country with the largest burden globally, blood slide microscopy of children up to 5 years of age was conducted in the 2018 National Demographic and Health Survey, and parasite prevalence previously reported. In the current study, malaria parasite density measurements are reported and analysed for 7783 of the children sampled across the 36 states within the six geopolitical zones of the country. Asexual and sexual stages, and infections with different malaria parasite species are analysed. Across all states of Nigeria, there was a positive correlation between mean asexual parasite density within infected individuals and prevalence of infection in the community (Spearman's rho = 0.39, P = 0.02). Asexual parasite densities were highest in the northern geopolitical zones (geometric means > 2000 μL-1), extending the evidence of exceptionally high infection burden in many areas. Sexual parasite prevalence in each state was highly correlated with asexual parasite prevalence (Spearman's rho = 0.70, P < 0.001), although sexual parasite densities were low (geometric means < 100 μL-1 in all zones). Infants had lower parasite densities than children above 1 year of age, but there were no differences between male and female children. Most infections were of P. falciparum, which had higher asexual densities but lower sexual parasite densities than P. malariae or P. ovale mono-infections. However, mixed species infections had the highest asexual parasite densities. It is recommended that future large surveys in high burden countries measure parasite densities as well as developmental stages and species, to improve the quality of malaria epidemiology and tracking of future changes.

Unemployment and expenditure on health and education as mediators of the association between toothbrushing and global income inequalities

Objective

The study assessed the association of country-level income inequalities with the percentage of schoolchildren toothbrushing-at-least-twice-daily; and the mediating effect of country-level unemployment rate and governmental expenditure on health and education (EH&E).

Methods

This was an ecological study. The dependent variable was country-level toothbrushing-at-least-twice-daily among 11-15-year-old schoolchildren. Data for the period 2009 to 2019 were extracted from two global surveys about schoolchildren’s health and from manuscripts identified through a systematic search of three databases. The independent variable was country-level income inequalities measured by the Gini coefficient (GC) extracted from the Sustainable Development Report 2021. The mediators were the unemployment rate and EH&E. We stratified the sample by the level of GC and assessed the correlation between the dependent and independent variables in each stratum. Linear regression was used to assess the relations between the dependent and independent variables, and mediation path analysis was used to quantify the direct, indirect, and total effects.

Results

Data were available for 127 countries. The mean (SD) percentage of children who brushed-at-least-twice-daily was 67.3 (16.1), the mean (SD) GC = 41.4 (8.2), unemployment rate = 7.5 (4.7) and EH&E = 8.4 (3.3). The percentage of children brushing at-least-twice-daily had weak and non-significant correlation with GC that was positive in countries with the least inequality and negative for countries with higher levels of inequality. A greater percentage of schoolchildren brushing-at-least-twice-daily was significantly associated with higher GC (B = 0.76, 95%CI: 0.33, 1.18), greater EH&E (B = 1.67, 95%CI: 0.69, 2.64) and lower unemployment rate (B=-1.03, 95%CI: -1.71, -0.35). GC had a significant direct positive effect (B = 0.76, 95%CI: 0.33, 1.18), a significant indirect negative effect through unemployment and EH&E (B=-0.47, 95%CI: -0.79, -0.24) and a non-significant total positive effect (B = 0.29, 95%CI: -0.09, 0.67) on the percentage of schoolchildren brushing-at-least-twice-daily.

Conclusion

Unemployment and EH&E mediated the association between income inequality and toothbrushing. Country-level factors may indirectly impact toothbrushing.

Global burden of atrial fibrillation/flutter due to high systolic blood pressure from 1990 to 2019: estimates from the global burden of disease study 2019

Atrial fibrillation/atrial flutter (AF/AFL) has progressed to be a public health concern, and high systolic blood pressure (HSBP) remains the leading risk factor for AF/AFL. This study estimated the HSBP attributable AF/AFL burden based on the data from the Global Burden of Disease (GBD) study 2019. Numbers, age-standardized rates (ASR) of deaths, disability-adjusted life years (DALYs), and corresponding estimated annual percentage change (EAPC) were analyzed by age, sex, sociodemographic index (SDI), and locations. Gini coefficient was calculated to evaluate health inequality. Globally, HSBP-related AF/AFL caused 107 091 deaths and 3 337 876 DALYs in 2019, an increase of 142.5% and 105.9% from 1990, respectively. The corresponding mortality and DALYs ASR declined by 5.8% and 7.7%. High-income Asia Pacific experienced the greatest decrease in mortality and DALYs ASR, whereas the largest increase was observed in Andean Latin America. Almost half of the HSBP-related AF/AFL burden was carried by high and high-middle SDI regions, and it was experiencing a shift to lower SDI regions. A negative correlation was detected between EAPC and SDI. Females and elderly people tended to have a higher AF/AFL burden, whereas young adults (30-49 years old) experienced an annual increase in AF/AFL burden. The Gini index of DALYs rate decreased from 0.224 in 1990 to 0.183 in 2019. Despite improved inequality having been observed over the past decades, the HSBP-related AF/AFL burden varied across regions, sexes, and ages. Cost-effective preventive, diagnostic, and therapeutic tools are required to be implemented in less developed regions.

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

Background

As control efforts progress towards elimination, malaria is likely to become more spatially concentrated in few local areas. The purpose of this study was to quantify and characterise spatial heterogeneity in malaria transmission-intensity across highly endemic Indonesian Papua.

Methods

We analysed individual-level malaria surveillance data for nearly half a million cases (2019-2020) reported in the Papua and West Papua provinces and adapted the Gini index approach to quantify spatial heterogeneity at the district and health-unit levels. In this context, high Gini index implies disproportionately distributed malaria cases across the region. We showed malaria incidence trends and the spatial and temporal distribution of sociodemographic characteristics and aetiological parasites among cases.

Findings

While Papua province accounted for the majority of malaria cases reported in the region and had seen a rise in transmission since 2015, West Papua province had maintained a comparatively low incidence. We observed that Gini index estimates were high, particularly when the lower spatial scale of health units was evaluated. The Gini index appears to be inversely associated to annual parasite-incidence, as well as the proportions of vivax malaria, male sex, and adults.

Interpretation

This study suggests that areas with varying levels of transmission-intensities exhibited distinct characteristics. Malaria was distributed in a markedly disproportionate manner throughout the region, emphasising the need for spatially targeted interventions. Periodic quantification and characterisation of risk heterogeneity at various spatial levels using routine malaria surveillance data may aid in tracking progress towards elimination and guiding evidence-informed prioritisation of resource allocation.

Funding

The study was funded by the Australian Government Department of Foreign Affairs and Trade Indo-Pacific Centre for Health Security through the Strengthening Preparedness in the Asia-Pacific Region through Knowledge (SPARK) project.

Using Microsimulation Modeling to Inform EHE Implementation Strategies in Los Angeles County

BACKGROUND

Pre-exposure prophylaxis (PrEP) is essential to ending HIV. Yet, uptake remains uneven across racial and ethnic groups. We aimed to estimate the impacts of alternative PrEP implementation strategies in Los Angeles County.

SETTING

Men who have sex with men, residing in Los Angeles County.

METHODS

We developed a microsimulation model of HIV transmission, with inputs from key local stakeholders. With this model, we estimated the 15-year (2021-2035) health and racial and ethnic equity impacts of 3 PrEP implementation strategies involving coverage with 9000 additional PrEP units annually, above the Status-quo coverage level. Strategies included PrEP allocation equally (strategy 1), proportionally to HIV prevalence (strategy 2), and proportionally to HIV diagnosis rates (strategy 3), across racial and ethnic groups. We measured the degree of relative equalities in the distribution of the health impacts using the Gini index (G) which ranges from 0 (perfect equality, with all individuals across all groups receiving equal health benefits) to 1 (total inequality).

RESULTS

HIV prevalence was 21.3% in 2021 [Black (BMSM), 31.1%; Latino (LMSM), 18.3%, and White (WMSM), 20.7%] with relatively equal to reasonable distribution across groups (G, 0.28; 95% confidence interval [CI], 0.26 to 0.34). During 2021-2035, cumulative incident infections were highest under Status-quo (n = 24,584) and lowest under strategy 3 (n = 22,080). Status-quo infection risk declined over time among all groups but remained higher in 2035 for BMSM (incidence rate ratio, 4.76; 95% CI: 4.58 to 4.95), and LMSM (incidence rate ratio, 1.74; 95% CI: 1.69 to 1.80), with the health benefits equally to reasonably distributed across groups (G, 0.32; 95% CI: 0.28 to 0.35). Relative to Status-quo, all other strategies reduced BMSM-WMSM and BMSM-LMSM disparities, but none reduced LMSM-WMSM disparities by 2035. Compared to Status-quo, strategy 3 reduced the most both incident infections (% infections averted: overall, 10.2%; BMSM, 32.4%; LMSM, 3.8%; WMSM, 3.5%) and HIV racial inequalities (G reduction, 0.08; 95% CI: 0.02 to 0.14).

CONCLUSIONS

Microsimulation models developed with early, continuous stakeholder engagement and inputs yield powerful tools to guide policy implementation.

Geographical and temporal variation in reduction of malaria infection among children under 5 years of age throughout Nigeria

INTRODUCTION

Global progress in reducing malaria has stalled since 2015. Analysis of the situation is particularly needed in Nigeria, the country with by far the largest share of the burden, where approximately a quarter of all cases in the world are estimated to occur.

METHODS

We analysed data from three nationwide surveys (Malaria Indicator Surveys in 2010 and 2015 and a National Demographic and Health Survey in 2018), with malaria parasite prevalence in children under 5 years of age determined by sampling from all 36 states of Nigeria, and blood slide microscopy performed in the same accredited laboratory for all samples. Changes over time were evaluated by calculating prevalence ratio (PR) values with 95% CIs for each state, together with Mantel-Haenszel-adjusted PRs (PR_adj) for each of the six major geopolitical zones of the country.

RESULTS

Between 2010 and 2018, there were significant reductions in parasite prevalence in 25 states, but not in the remaining 11 states. Prevalence decreased most in southern zones of the country (South West PR_adj=0.53; South East PR_adj=0.59; South South PR_adj=0.51) and the North Central zone (PR_adj=0.36). Changes in the north were less marked, but were significant and indicated overall reductions by more than 20% (North-West PR_adj=0.74; North East PR_adj=0.70). Changes in the south occurred mostly between 2010 and 2015, whereas those in the north were more gradual and most continued after 2015. Recent changes were not correlated with survey-reported variation in use of preventive measures.

CONCLUSION

Reductions in malaria infection in children under 5 have occurred in most individual states in Nigeria since 2010, but substantial geographical variation in the timing and extent indicate challenges to be overcome to enable global malaria reduction.

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

BACKGROUND

It is of interest to explore the variability in how the COVID-19 pandemic evolved geographically during the first twelve months. To this end, we apply inequality indices over regions to incidences, infection related mortality, and infection fatality rates. If avoiding of inequality in health is an important political goal, a metric must be implemented to track geographical inequality over time.

METHODS

The relative and absolute Gini index as well as the Theil index are used to quantify inequality. Data are taken from international data bases. Absolute counts are transformed to rates adjusted for population size.

RESULTS

Comparing continents, the absolute Gini index shows an unfavorable development in four continents since February 2020. In contrast, the relative Gini as well as the Theil index support the interpretation of less inequality between European countries compared to other continents. Infection fatality rates within the EU as well as within the U.S. express comparable improvement towards more equality (as measured by both Gini indices).

CONCLUSIONS

The use of inequality indices to monitor changes in geographic inequality over time for key health indicators is a valuable tool to inform public health policies. The absolute and relative Gini index behave complementary and should be reported simultaneously in order to gain a meta-perspective on very complex dynamics.

Assessing Classic Maya multi-scalar household inequality in southern Belize

Inequality is present to varying degrees in all human societies, pre-modern and contemporary. For archaeological contexts, variation in house size reflects differences in labor investments and serves as a robust means to assess wealth across populations small and large. The Gini coefficient, which measures the degree of concentration in the distribution of units within a population, has been employed as a standardized metric to evaluate the extent of inequality. Here, we employ Gini coefficients to assess wealth inequality at four nested socio-spatial scales-the micro-region, the polity, the district, and the neighborhood-at two medium size, peripheral Classic Maya polities located in southern Belize. We then compare our findings to Gini coefficients for other Classic Maya polities in the Maya heartland and to contemporaneous polities across Mesoamerica. We see the patterning of wealth inequality across the polities as a consequence of variable access to networks of exchange. Different forms of governance played a role in the degree of wealth inequality in Mesoamerica. More autocratic Classic Maya polities, where principals exercised degrees of control over exclusionary exchange networks, maintained high degrees of wealth inequality compared to most other Mesoamerican states, which generally are characterized by more collective forms of governance. We examine how household wealth inequality was reproduced at peripheral Classic Maya polities, and illustrate that economic inequity trickled down to local socio-spatial units in this prehispanic context.

Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil

BACKGROUND

Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country.

METHODS

This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR).

FINDINGS

The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease.

DISCUSSION

Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.