Evaluation of the malaria elimination programme in Muara Enim Regency: a qualitative study from Indonesia.

BACKGROUND: Malaria remains an enduring public health concern in Indonesia, exacerbated by its equatorial climate that fosters the proliferation of Anopheles mosquitoes. This study seeks to assess the performance of the malaria elimination programme comprehensively. METHODS: Between May and August 2022, a qualitative study was conducted in Muara Enim Regency, South Sumatra Province, involving 22 healthcare professionals from diverse backgrounds. These informants were strategically chosen for their pivotal roles in providing profound insights into various facets of the malaria elimination programme. This encompasses inputs such as human resources, budgetary allocation, and infrastructural support; processes like case identification and management, capacity enhancement, epidemiological surveillance, prevention measures, outbreak control, and enhanced communication and educational initiatives; and, notably, the programme's outcomes. Data were collected through 3-h Focus Group Discussions (FGDs) divided into two groups, each with 12 participants: healthcare professionals and programme managers. Additionally, in-depth interviews (IDIs) were conducted with ten informants. Employing the Input-Process-Output (IPO) model, this study meticulously analysed the healthcare system dynamics and the interventions' efficacy. RESULTS: The study unveiled many challenges during the input phase, including the absence of entomologists and a shortage of diagnostic tools. Despite these obstacles, it documented remarkable accomplishments in the output domain, marked by significant advancements in the distribution of mosquito nets and the successful implementation of the Early Warning System (EWS). Despite the adversities, the programme has made substantial strides towards malaria elimination. CONCLUSIONS: Urgent action is imperative to bolster the effectiveness of the malaria elimination programme. Key measures encompass augmenting the entomologist workforce, optimizing resource allocation, and ensuring stringent adherence to regional regulations. Addressing these concerns will enhance programme efficacy, yielding enduring public health benefits. This research substantially contributes to Indonesia's ongoing malaria elimination endeavours, furnishing actionable insights for programme enhancement. Consequently, this research holds significant importance for the malaria elimination drive.

Spatiotemporal Distribution of Malaria in the Kingdom of Saudi Arabia.

BACKGROUND: Malaria is a significant public health concern in the Kingdom of Saudi Arabia (KSA). This study aimed to investigate the spatiotemporal distribution of malaria in the KSA between 2017 and 2021. METHODS: A spatial analysis was conducted using data for malaria cases stratified by Plasmodium species reported by the Ministry of Health for the period 2017-2021. Covariate data such as environmental, socioeconomic, and demographic factors were assembled from different publicly available sources. RESULTS: A total of 13,852 cases were reported from 20 regions in the KSA during the study period. The study indicated a decline in the overall number of reported cases from 2715 in 2017 to 2616 in 2021, primarily driven by a decrease in Plasmodium falciparum infections. However, the number of Plasmodium vivax cases increased in 2021. Southern regions of the KSA remained at higher risk due to imported cases from neighboring Yemen. Socioeconomic and demographic factors, such as access to healthcare and education, were found to affect malaria transmission. Environmental factors, such as temperature and rainfall, were also identified as determinants of malaria risk. CONCLUSIONS: This study showed significant spatial variation in malaria cases in the KSA that was related to underlying socioeconomic status and environmental factors. The findings of this study highlight the need for continued efforts to control and eliminate malaria in the KSA, particularly in regions with higher risk of malaria.

The plastic health map: A systematic evidence map of human health studies on plastic-associated chemicals.

BACKGROUND: The global production and use of plastic materials has increased dramatically since the 1960s and there is increasing evidence of human health impacts related to exposure to plastic-associated chemicals. There is, however, no comprehensive, regulatory, post-market monitoring for human health effects of plastic-associated chemicals or particles and it is unclear how many of these have been investigated for effects in humans, and therefore what the knowledge gaps are. OBJECTIVE: To create a systematic evidence map of peer-reviewed human studies investigating the potential effects of exposure to plastic-associated particles/chemicals on health to identify research gaps and provide recommendations for future research and regulation policy. METHODS: Medline and Embase databases were used to identify peer-reviewed primary human studies published in English from Jan 1960 - Jan 2022 that investigated relationships between exposures to included plastic-associated particles/chemicals measured and detected in bio-samples and human health outcomes. Plastic-associated particles/chemicals included are: micro and nanoplastics, due to their widespread occurrence and potential for human exposure; polymers, the main building blocks of plastic; plasticizers and flame retardants, the two most common types of plastic additives with the highest concentration ranges in plastic materials; and bisphenols and per- or polyfluoroalkyl substances, two chemical classes of known health concern that are common in plastics. We extracted metadata on the population and study characteristics (country, intergenerational, sex, age, general/special exposure risk status, study design), exposure (plastic-associated particle/chemical, multiple exposures), and health outcome measures (biochemical, physiological, and/or clinical), from which we produced the interactive database 'Plastic Health Map' and a narrative summary. RESULTS: We identified 100,949 unique articles, of which 3,587 met our inclusion criteria and were used to create a systematic evidence map. The Plastic Health Map with extracted metadata from included studies are freely available at https://osf.io/fhw7d/ and summary tables, plots and overall observations are included in this report. CONCLUSIONS: We present the first evidence map compiling human health research on a wide range of plastic-associated chemicals from several different chemical classes, in order to provide stakeholders, including researchers, regulators, and concerned individuals, with an efficient way to access published literature on the matter and determine knowledge gaps. We also provide examples of data clusters to facilitate systematic reviews and research gaps to help direct future research efforts. Extensive gaps are identified in the breadth of populations, exposures and outcomes addressed in studies of potential human health effects of plastic-associated chemicals. No studies of the human health effects of micro and/or nanoplastics were found, and no studies were found for 26/1,202 additives included in our search that are of known hazard concern and confirmed to be in active production. Few studies have addressed recent "substitution" chemicals for restricted additives such as organophosphate flame retardants, phthalate substitutes, and bisphenol analogues. We call for a paradigm shift in chemical regulation whereby new plastic chemicals are rigorously tested for safety before being introduced in consumer products, with ongoing post-introduction biomonitoring of their levels in humans and health effects throughout individuals' life span, including in old age and across generations.

Risk factors of malaria transmission in mining workers in Muara Enim, South Sumatra, Indonesia.

Eliminating malaria by 2030 is stated as goal three in the UN's Sustainable Development Goals (SDGs). However, malaria still remains a significant public health problem. This study aims to identify the factors determining malaria transmission in artisanal or small-scale miner (ASM) communities in three villages: Tanjung Agung, Tanjung Lalang, and Penyandingan, located in the Tanjung Enim District, Muara Enim, South Sumatra, Indonesia. Researchers conducted a cross-sectional study involving 92 participants from the study area. They used a logistic regression model to investigate the risk factors related to malaria occurrence. The multivariable analysis revealed that age (Adjusted Prevalence Ratio (APR) = 7.989 with 95% CI 1.724-37.002) and mosquito breeding (APR = 7.685 with 95% CI 1.502-39.309) were risk factors for malaria. On the other hand, higher education (APR = 0.104 with 95% CI 0.027-0.403), the use of mosquito repellent (APR = 0.138 with 95% CI 0.035-0.549), and the condition of house walls (APR = 0.145 with 95% CI 0.0414-0.511) were identified as protective factors. The current study highlights age and mosquito breeding sites as risk factors for malaria. Additionally, higher education, insect repellent use, and the condition of house walls are protective factors against malaria. Therefore, reducing risk factors and increasing protective measures through effective communication, information, and education are highly recommended to eliminate malaria in mining areas.

Mapping tuberculosis prevalence in Ethiopia using geospatial meta-analysis.

BACKGROUND: Reliable and detailed data on the prevalence of tuberculosis (TB) with sub-national estimates are scarce in Ethiopia. We address this knowledge gap by spatially predicting the national, sub-national and local prevalence of TB, and identifying drivers of TB prevalence across the country. METHODS: TB prevalence data were obtained from the Ethiopia national TB prevalence survey and from a comprehensive review of published reports. Geospatial covariates were obtained from publicly available sources. A random effects meta-analysis was used to estimate a pooled prevalence of TB at the national level, and model-based geostatistics were used to estimate the spatial variation of TB prevalence at sub-national and local levels. Within the MBG Plugin Framework, a logistic regression model was fitted to TB prevalence data using both fixed covariate effects and spatial random effects to identify drivers of TB and to predict the prevalence of TB. RESULTS: The overall pooled prevalence of TB in Ethiopia was 0.19% [95% confidence intervals (CI): 0.12%-0.28%]. There was a high degree of heterogeneity in the prevalence of TB (I2 96.4%, P <0.001), which varied by geographical locations, data collection periods and diagnostic methods. The highest prevalence of TB was observed in Dire Dawa (0.96%), Gambela (0.88%), Somali (0.42%), Addis Ababa (0.28%) and Afar (0.24%) regions. Nationally, there was a decline in TB prevalence from 0.18% in 2001 to 0.04% in 2009. However, prevalence increased back to 0.29% in 2014. Substantial spatial variation of TB prevalence was observed at a regional level, with a higher prevalence observed in the border regions, and at a local level within regions. The spatial distribution of TB prevalence was positively associated with population density. CONCLUSION: The results of this study showed that TB prevalence varied substantially at sub-national and local levels in Ethiopia. Spatial patterns were associated with population density. These results suggest that targeted interventions in high-risk areas may reduce the burden of TB in Ethiopia and additional data collection would be required to make further inferences on TB prevalence in areas that lack data.

Spatiotemporal mapping of malaria incidence in Sudan using routine surveillance data.

Malaria is a serious threat to global health, with over [Formula: see text] of the cases reported in 2020 by the World Health Organization in African countries, including Sudan. Sudan is a low-income country with a limited healthcare system and a substantial burden of malaria. The epidemiology of malaria in Sudan is rapidly changing due to factors including the rapidly developing resistance to drugs and insecticides among the parasites and vectors, respectively; the growing population living in humanitarian settings due to political instability; and the recent emergence of Anopheles stephensi in the country. These factors contribute to changes in the distribution of the parasites species as well as malaria vectors in Sudan, and the shifting patterns of malaria epidemiology underscore the need for investment in improved situational awareness, early preparedness, and a national prevention and control strategy that is updated, evidence based, and proactive. A key component of this strategy is accurate, high-resolution endemicity maps of species-specific malaria. Here, we present a spatiotemporal Bayesian model, developed in collaboration with the Sudanese Ministry of Health, that predicts a fine-scale (1 km [Formula: see text] 1 km) clinical incidence and seasonality profiles for Plasmodium falciparum and Plasmodium vivax across the country. We use monthly malaria case counts for both species collected via routine surveillance between January 2017 and December 2019, as well as a suite of high-resolution environmental covariates to inform our predictions. These epidemiological maps provide a useful resource for strategic planning and cost-effective implementation of malaria interventions, thus informing policymakers in Sudan to achieve success in malaria control and elimination.

Spatial codistribution of HIV, tuberculosis and malaria in Ethiopia.

BACKGROUND: HIV, tuberculosis (TB) and malaria are the three most important infectious diseases in Ethiopia, and sub-Saharan Africa. Understanding the spatial codistribution of these diseases is critical for designing geographically targeted and integrated disease control programmes. This study investigated the spatial overlap and drivers of HIV, TB and malaria prevalence in Ethiopia. METHODS: HIV, TB and malaria data were obtained from different nationwide prevalence surveys, and geospatial covariates were obtained from publicly available sources. A Bayesian model-based geostatistical framework was applied to each survey leveraging the strength of high-resolution spatial covariates to predict continuous disease-specific prevalence surfaces and their codistribution. RESULTS: The national prevalence was 1.54% (95% CI 1.40 to 1.70) for HIV, 0.39% (95% CI 0.34 to 0.45) for TB and 1.1% (95%CI 0.95 to 1.32) for malaria. Substantial subnational variation was predicted with the highest HIV prevalence estimated in Gambela (4.52%), Addis Ababa (3.52%) and Dire Dawa (2.67%) regions. TB prevalence was highest in Dire Dawa (0.96%) and Gambela (0.88%), while malaria was highest in Gambela (6.1%) and Benishangul-Gumuz (3.8%). Spatial overlap of their prevalence was observed in some parts of the country, mainly Gambela region. Spatial distribution of the diseases was significantly associated with healthcare access, demographic, and climatic factors. CONCLUSIONS: The national distribution of HIV, TB and malaria was highly focal in Ethiopia, with substantial variation at subnational and local levels. Spatial distribution of the diseases was significantly associated with healthcare access, demographic and climatic factors. Spatial overlap of HIV, TB and malaria prevalence was observed in some parts of the country. Integrated control programmes for these diseases should be targeted to these areas with high levels of co-endemicity.

Variation in excess all-cause mortality by age, sex, and province during the first wave of the COVID-19 pandemic in Italy.

Although previous evidence suggests that the infection fatality rate from COVID-19 varies by age and sex, and that transmission intensity varies geographically within countries, no study has yet explored the age-sex-space distribution of excess mortality associated with the COVID pandemic. By applying the principles of small-area estimation to existing model formulations for excess mortality, this study develops a novel method for assessing excess mortality across small populations and assesses the pattern of COVID excess mortality by province, year, week, age group, and sex in Italy from March through May 2020. We estimate that 53,200 excess deaths occurred across Italy during this time period, compared to just 35,500 deaths where COVID-19 was registered as the underlying cause of death. Out of the total excess mortality burden, 97% of excess deaths occurred among adults over age 60, and 68% of excess deaths were concentrated among adults over age 80. The burden of excess mortality was unevenly distributed across the country, with just three of Italy's 107 provinces accounting for 32% of all excess mortality. This method for estimating excess mortality can be adapted to other countries where COVID-19 diagnostic capacity is still insufficient, and could be incorporated into public health rapid response systems.