Background Approaches in malaria risk mapping continue to advance in scope with the advent of geostatistical techniques spanning both the spatial and temporal domains. A substantive review of the merits of the methods and covariates used to map malaria risk has not been undertaken. Therefore, this review aimed to systematically retrieve, summarise methods and examine covariates that have been used for mapping malaria risk in sub-Saharan Africa (SSA). Methods A systematic search of malaria risk mapping studies was conducted using PubMed, EBSCOhost, Web of Science and Scopus databases. The search was restricted to refereed studies published in English from January 1968 to April 2020. To ensure completeness, a manual search through the reference lists of selected studies was also undertaken. Two independent reviewers completed each of the review phases namely: identification of relevant studies based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, data extraction and methodological quality assessment using a validated scoring criterion. Results One hundred and seven studies met the inclusion criteria. The median quality score across studies was 12/16 (range: 7–16). Approximately half (44%) of the studies employed variable selection techniques prior to mapping with rainfall and temperature selected in over 50% of the studies. Malaria incidence (47%) and prevalence (35%) were the most commonly mapped outcomes, with Bayesian geostatistical models often (31%) the preferred approach to risk mapping. Additionally, 29% of the studies employed various spatial clustering methods to explore the geographical variation of malaria patterns, with Kulldorf scan statistic being the most common. Model validation was specified in 53 (50%) studies, with partitioning data into training and validation sets being the common approach. Conclusions Our review highlights the methodological diversity prominent in malaria risk mapping across SSA. To ensure reproducibility and quality science, best practices and transparent approaches should be adopted when selecting the statistical framework and covariates for malaria risk mapping. Findings underscore the need to periodically assess methods and covariates used in malaria risk mapping; to accommodate changes in data availability, data quality and innovation in statistical methodology.

The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD) in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran’s I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.

Leukemia, a group of malignant tumors, has been a significant public health concern due to its high incidence and mortality rates. This study aimed to provide an in-depth analysis of the global leukemia burden from 1990 to 2021 using the Global Burden of Disease (GBD) database, focusing on trends in incidence, mortality, and Disability-Adjusted Life Years (DALYs) across different regions, genders, and age groups including forecasting future trends. Data were sourced from the GBD study, utilizing the Global Health Data Exchange (GHDx) query tool. We employed descriptive, trend, cluster, and forecasting analyses using age-standardized rates (ASRs) and the Estimated Annual Percentage Change (EAPC) to quantify changes over time. Hierarchical clustering and forecasting models, including ARIMA and Exponential Smoothing (ES), were utilized to predict future trends. Notably, ARIMA and ES smoothing parameters were meticulously identified and estimated. The analysis of global leukemia burden from 1990 to 2021, as reflected by DALYs, indicates a downward trend, with the number of DALYs estimated to have decreased from 578,020 (401,241–797,570) in 1990 to 302,902 (206,475–421,952) in 2021, corresponding to an EAPC of -0.94 (-1.01—-0.88). Notably, it is important to highlight that there is variability in these estimates across different regions and demographic groups, which should be interpreted with caution due to potential data limitations. High-income regions generally showed a decreased leukemia burden, while some middle- and low-income countries exhibited an opposite trend. Males displayed higher leukemia incidence, mortality, and DALY rates compared to females. The oldest age groups, mainly those aged 95 and above, experienced the most significant changes, with the highest EAPC observed in this demographic. Geographical and Socio-demographic Index (SDI)–based analyses further highlighted the heterogeneity in leukemia burden. Additionally, forecasting models project a continued decrease in leukemia burden, emphasizing the importance of ongoing public health strategies. The study indicates overall progress in reducing the leukemia burden at a global level due to medical advancements. However, specific regions and demographic groups, particularly males and the elderly, continue to face challenges. Socioeconomic status significantly impacts healthcare outcomes, with a need for resource distribution and healthcare system strengthening in low-income areas. The findings call for nuanced public health strategies tailored to socioeconomic contexts and sustained research and healthcare infrastructure efforts.

Many morbid-mortality atlases and small-area studies have been carried out over the last decade. However, the methods used to draw up such research, the interpretation of results and the conclusions published are often inaccurate. Often, the proliferation of this practice has led to inefficient decision-making, implementation of inappropriate health policies and negative impact on the advancement of scientific knowledge. This paper reviews the most frequent errors in the design, analysis and interpretation of small-area epidemiological studies and proposes a diagnostic evaluation test that should enable the scientific quality of published papers to be ascertained. Nine common mistakes in disease mapping methods are discussed. From this framework, and following the theory of diagnostic evaluation, a standardised test to evaluate the scientific quality of a small-area epidemiology study has been developed. Optimal quality is achieved with the maximum score (16 points), average with a score between 8 and 15 points, and low with a score of 7 or below. A systematic evaluation of scientific papers, together with an enhanced quality in future research, will contribute towards increased efficacy in epidemiological surveillance and in health planning based on the spatio-temporal analysis of ecological information.

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas‐guided computational framework based on brain disconnectome to assess the spatial–temporal patterns of WMH‐related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel‐wise disconnectome was computed by indirect mapping of individual WMHs onto population‐averaged tractography atlas. By performing chi‐square test, we discovered a spatial–temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH‐related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aβ and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease.

Vector-borne diseases (VBDs) pose significant global health threats, particularly in tropical and subtropical regions. Remote sensing (RS) and geospatial technologies offer valuable tools for monitoring environmental changes and predicting disease transmission patterns, thereby supporting proactive public health interventions. This study reviews the application of RS and geospatial methods in the prediction, monitoring and control of VBDs. A systematic approach was employed to analyse existing literature, focusing on RS platforms such as Landsat, MODIS and Sentinel-2, alongside geographical information systems and machine learning models used for predictive modelling. The review reveals that these technologies play a crucial role in identifying environmental drivers of disease dynamics, including temperature, precipitation and land-use changes. However, challenges remain in terms of data resolution, model generalizability and the integration of socio-economic factors into predictive frameworks. The integration of early warning systems and participatory surveillance is highlighted as a promising avenue for improving disease forecasting. The study emphasizes the need for enhanced data accessibility, cross-sector collaboration and the inclusion of socio-economic variables in future research to improve the scalability and accuracy of disease prediction models.

Abstract Background: Vitamin D deficiency is a growing global public health concern with important skeletal and extra-skeletal health consequences. Although sunlight exposure is the primary source of vitamin D, deficiency remains prevalent even in regions with abundant sunshine. This systematic review aimed to describe the temporal and spatial distribution of vitamin D deficiency worldwide as at 2019 using epidemiological and geospatial approaches. Methods: A systematic review of studies published between 1995 and 2019 was conducted using PubMed. Eligible studies included healthy adults aged ≥ 18 years and reported the prevalence of vitamin D deficiency, defined as serum 25-hydroxyvitamin D levels < 30 ng/mL (75 nmol/L). Studies involving children, pregnant women, individuals with comorbidities, or vitamin D supplementation were excluded. Data were extracted on sample size, number of deficiency cases, and prevalence with confidence intervals. Statistical analysis was performed using SPSS, while geographic distribution and predictive risk mapping were generated using ArcGIS 10.3 employing kriging interpolation. Results: A total of 144 studies from 46 countries were included, encompassing 357,089 participants, of whom 218,174 were vitamin D deficient, resulting in an overall prevalence of 51.3% (95% CI: 48.4–58.6). The highest prevalence was observed in Africa (65%) and the Middle East (63.1%), followed by Europe (56.1%), South America (53.5%), Asia (52.1%), and Australia (46.2%). North America had the lowest prevalence (23%). Geospatial analysis identified a high-risk belt extending across Europe, North Africa, Western Asia, and parts of South America. Publication trends increased markedly after 2004. Conclusion: Vitamin D deficiency is highly prevalent worldwide, including in sun-rich regions. Geospatial mapping highlights critical high-risk areas, emphasizing the need for targeted screening, prevention strategies, and public health interventions globally

National Health and Nutrition Examination Survey (NHANES) - National Cardiovascular Disease Surveillance System 1999–2000 to 2017–2018. The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This was one of the datasets provided by the National Cardiovascular Disease Surveillance System and presented on DHDSP’s Data, Trends, and Maps online tool. This tool was retired in April of 2024 and this dataset will not be updated. Contact dhdsprequests@cdc.gov if you need assistance with data previously included in this dataset. The data can be plotted as trends and stratified by age group, sex, and race/ethnicity. Updated 12/9/2025: Per a court order, HHS is required to restore this website to its version as of 12:00 AM on January 29, 2025.