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The management of multidrug-resistant tuberculosis (MDR-TB) during pregnancy is challenging, yet no systematic synthesis of evidence has accurately measured treatment outcomes.
Kefyalew Alene BSc, MPH, PhD Head, Geospatial and Tuberculosis 0404705064 Kefyalew.alene@thekids.org.au Honorary Research Fellow Dr Kefyalew Alene
Globally, tuberculosis (TB) is the leading infectious cause of morbidity and mortality, with the risk of infection affected by both individual and ecological-level factors. While systematic reviews on individual-level factors exist, there are currently limited studies examining ecological-level factors associated with TB incidence and mortality. This study was conducted to identify ecological factors associated with TB incidence and mortality.
Drug-resistant tuberculosis (DR-TB) is a significant public health concern, often resulting in poor treatment outcomes. This study aims to identify predictors of poor treatment outcomes among patients with DR-TB in Hunan Province, China.
Cancer prevention and care efforts have been challenged by the COVID-19 pandemic and armed conflicts, resulting in a decline in the global Human Development Index (HDI), particularly in low- and middle-income countries. These challenges and subsequent shifts in health care priorities underscore the need to continuously monitor cancer outcome disparities and statistics globally to ensure delivery of equitable and optimal cancer prevention and care in uncertain times.
Under-five mortality declined in the last two decades in Ethiopia, but sub-national and local progress remains unclear. This study aimed to investigate the spatiotemporal distributions and ecological level factors of under-five mortality in Ethiopia. Data on under-five mortality were obtained from five different Ethiopian Demographic and Health Surveys conducted in 2000, 2005, 2011, 2016, and 2019. Environmental and healthcare access data were obtained from different publicly available sources.
By mapping land use under projections of socio-economic change, ecological changes can be predicted to inform conservation decision-making. We present a land use model that enables the fine-scale mapping of land use change under future scenarios.