Geospatial Health最新文献

This ecological study identified an aggregation of urban neighbourhoods spatial patterns in the cumulative new case detection rate (NCDR) of leprosy in the municipality of Rondonópolis, central Brazil, as well as intra-urban socioeconomic differences underlying this distribution. Scan statistics of all leprosy cases reported in the area from 2011 to 2017 were used to investigate spatial and spatiotemporal clusters of the disease at the neighbourhood level. The associations between the log of the smoothed NCDR and demographic, socioeconomic, and structural characteristics were explored by comparing multivariate models based on ordinary least squares (OLS) regression, spatial lag, spatial error, and geographically weighted regression (GWR). Leprosy cases were observed in 84.1% of the neighbourhoods of Rondonópolis, where 848 new cases of leprosy were reported corresponding to a cumulative NCDR of 57.9 cases/100,000 inhabitants. Spatial and spatiotemporal high-risk clusters were identified in western and northern neighbourhoods, whereas central and southern areas comprised low-risk areas. The GWR model was selected as the most appropriate modelling strategy (adjusted R²: 0.305; AIC: 242.85). By mapping the GWR coefficients, we identified that low literacy rate and low mean monthly nominal income per household were associated with a high NCDR of leprosy, especially in the neighbourhoods located within high-risk areas. In conclusion, leprosy presented a heterogeneous and peripheral spatial distribution at the neighbourhood level, which seems to have been shaped by intra-urban differences related to deprivation and poor living conditions. This information should be considered by decision-makers while implementing surveillance measures aimed at leprosy control.

Anaemia is a common public-health problem affecting about two-thirds of pregnant women in developing countries. Spacetime cluster analysis of anemia cases is important for publichealth policymakers to design evidence-based intervention strategies. This study discovered the potential space-time clusters of anemia in pregnant women in Khyber Pakhtunkhwa Province, Pakistan, from 2014 to 2020 using space-time scan statistic (SatScan). The results show that the most likely cluster of anemia was seen in the rural areas in the eastern part of the province covering five districts from 2017 to 2019. However, three secondary clusters in the West and one in the North were still active, signifying important targets of interest for public-health interventions. The potential anemia clusters in the province's rural areas might be associated with the lack of nutritional education in women and lack of access to sufficient diet due to financial constraints.

Leptospirosis is neglected in many tropical developing countries, including Indonesia. Our research on this zoonotic disease aimed to investigate epidemiological features and spatial clustering of recent leptospirosis outbreaks in Pangandaran, West Java. The study analysed data on leptospirosis notifications between September 2022 and May 2023. Global Moran I and local indicator for spatial association (LISA) were applied. Comparative analysis was performed to characterise the identified hotspots of leptospirosis relative to its neighbourhoods. A total of 172 reported leptospirosis in 40 villages from 9 sub-districts in Pangandaran District were analysed. Of these, 132 cases (76.7%) were male. The median age was 49 years (interquartile range [IQR]: 34-59 years). Severe outcomes including renal failure, lung failure, and hepatic necrosis were reported in up to 5% of the cases. A total of 30 patients died, resulting in the case fatality rate (CFR) of 17.4%. Moran's I analysis showed significant spatial autocorrelation (I=0.293; p=0.002) and LISA results identified 7 High-High clusters (hotspots) in the Southwest, with the total population at risk at 26,184 people. The hotspots had more cases among older individuals (median age: 51, IQR: 36-61 years; p<0.001), more farmers (79%, p=0.001) and more evidence of the presence of rats (p=0.02). A comprehensive One Health intervention should be targeted towards these high-risk areas to control the transmission of leptospirosis. More empirical evidence is needed to understand the role of climate, animals and sociodemographic characteristics on the transmission of leptospirosis in the area studied.

This study examined the incongruence of travel distance between the nearest provider and the provider that pregnant woman actually chose to visit. Using a dataset of South Carolina claims including rural and urban areas for the period 2014-2018 based on live births of 27,290 pregnant women, we compared the travel distance and travel time for two providers of health: the nearest facility and the main one for the area in question. The number of the former type was counted for every case. The mean travel distance/time to the nearest provider was 3.2 miles (5.2 km) and 5.0 minutes, while that to the main (predominant) provider was 23.0 miles (37.0 km) and 31.7 minutes. Only 21.6% of pregnant women chose one of the closest facilities as their provider. The mean travel distance and time to the nearest provider for women in rural areas were more than twice that for urban women but only 1.2 times for the main provider. Rural women had one third fewer providers situated closer than the main in comparison to number available for urban women. Thus, we conclude that proximity is not the only factor associated with access to healthcare. While evaluating geographic access, the number of available health providers within the mean travel distance or time would be a better indicator of proximate access.

Sepsis is a significant global health issue causing organ failure and high mortality. The number of sepsis cases has recently increased in Thailand making it crucial to comprehend the factors behind these infections. This study focuses on exploring the spatial autocorrelation between socio-economic factors and health service factors on the one hand and sepsis mortality on the other. We applied global Moran's I, local indicators of spatial association (LISA) and spatial regression to examine the relationship between these variables. Based on univariate Moran's I scatter plots, sepsis mortality in all 77 provinces in Thailand were shown to exhibit a positive spatial autocorrelation that reached a significant value (0.311). The hotspots/ high-high (HH) clusters of sepsis mortality were mostly located in the central region of the country, while the coldspots/low-low (LL) clusters were observed in the north-eastern region. Bivariate Moran's I indicated a spatial autocorrelation between various factors and sepsis mortality, while the LISA analysis revealed 7 HH clusters and 5 LL clusters associated with population density. Additionally, there were 6 HH and 4 LL clusters in areas with the lowest average temperature, 4 HH and 2 LL clusters in areas with the highest average temperature, 8 HH and 5 LL clusters associated with night-time light and 6 HH and 5 LL clusters associated with pharmacy density. The spatial regression models conducted in this study determined that the spatial error model (SEM) provided the best fit, while the parameter estimation results revealed that several factors, including population density, average lowest and highest temperature, night-time light and pharmacy density, were positively correlated with sepsis mortality. The coefficient of determination (R2) indicated that the SEM model explained 56.4% of the variation in sepsis mortality. Furthermore, based on the Akaike Information Index (AIC), the SEM model slightly outperformed the spatial lag model (SLM) with an AIC value of 518.1 compared to 520.

This study aimed to investigate the geospatial distribution of normal reference values of Interleukin 4 (IL-4) in healthy Chinese adults and to provide a basis for the development of standard references. IL-4 values of 5,221 healthy adults from 64 cities in China were collected and analyzed for a potential correlation with 24 topographical, climatic and soil factors. Seven of these factors were extracted and used to build a back propagation (BP) neural network model that was used to predict IL-4 reference values in healthy individuals from 2,317 observation sites nationwide. The predicted values were tested for normality and geographic distribution by analytic Kriging interpolation to map the geographic distribution of IL-4 reference values in healthy Chinese subjects. The results showed that IL-4 values generally decreased and then increased from the South to the North. We concluded that the BP neural network model applies to this approach, where certain geographical factors determine levels of various biochemical and immunological standards in healthy adults in regions with different topography, climate and soil indices.

Indonesia needs to lower its high infectious disease rate. This requires reliable data and following their temporal changes across provinces. We investigated the benefits of surveying the epidemiological situation with the imax biclustering algorithm using secondary data from a recent national scale survey of main infectious diseases from the National Basic Health Research (Riskesdas) covering 34 provinces in Indonesia. Hierarchical and k-means clustering can only handle one data source, but BCBimax biclustering can cluster rows and columns in a data matrix. Several experiments determined the best row and column threshold values, which is crucial for a useful result. The percentages of Indonesia's seven most common infectious diseases (ARI, pneumonia, diarrhoea, tuberculosis (TB), hepatitis, malaria, and filariasis) were ordered by province to form groups without considering proximity because clusters are usually far apart. ARI, pneumonia, and diarrhoea were divided into toddler and adult infections, making 10 target diseases instead of seven. The set of biclusters formed based on the presence and level of these diseases included 7 diseases with moderate to high disease levels, 5 diseases (formed by 2 clusters), 3 diseases, 2 diseases, and a final order that only included adult diarrhoea. In 6 of 8 clusters, diarrhea was the most prevalent infectious disease in Indonesia, making its eradication a priority. Direct person-to-person infections like ARI, pneumonia, TB, and diarrhoea were found in 4-6 of 8 clusters. These diseases are more common and spread faster than vector-borne diseases like malaria and filariasis, making them more important.

Under-5 mortality rate (U5MR) is a key indicator of child health and overall development. In Thailand, despite significant steps made in child health, disparities in U5MR persist across different provinces. We examined various socio-economic variables, health service availability and environmental factors impacting U5MR in Thailand to model their influences through spatial analysis. Global and Local Moran's I statistics for spatial autocorrelation of U5MR and its related factors were used on secondary data from the Ministry of Public Health, National Centers for Environmental Information, National Statistical Office, and the Office of the National Economic and Social Development Council in Thailand. The relationships between U5MR and these factors were modelled using ordinary least squares (OLS) estimation, spatial lag model (SLM) and spatial error model (SEM). There were significant spatial disparities in U5MR across Thailand. Factors such as low birth weight, unemployment rate, and proportion of land use for agricultural purposes exhibited significant positive spatial autocorrelation, directly influencing U5MR, while average years of education, community organizations, number of beds for inpatients per 1,000 population, and exclusive breastfeeding practices acted as protective factors against U5MR (R2 of SEM = 0.588).The findings underscore the need for comprehensive, multi-sectoral strategies to address the U5MR disparities in Thailand. Policy interventions should consider improving socioeconomic conditions, healthcare quality, health accessibility, and environmental health in high U5M areas. Overall, this study provides valuable insights into the spatial distribution of U5MR and its associated factors, which highlights the need for tailored and localized health policies and interventions.

In the Article titled "Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)." published May 5th, 2021, in Vol. 16(1) of Geospatial Health, an author's name was misspelled. The seventh author's name should be "Alamgir".   Reference: Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MS J, Ali I, Khalil A, 2021. Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020). Geospatial Health, 16:961. https://doi.org/10.4081/gh.2021.961.

In the Article titled "Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach." published November 6th, 2017, in Vol 12(2) of Geospatial Health, an author's name was misspelled. The fifth author's name should be "Alamgir". Reference: Ullah S, Daud H, Dass SC, Khan HN, Khalil A, 2017. Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach. Geospatial Health, 12:567. https://doi.org/10.4081/gh.2017.567.