Geospatial Health最新文献

Melioidosis, a bacterial, infectious disease contracted from contaminated soil or water, is a public health problem identified in tropical regions and endemic several regions of Thailand. Surveillance and prevention are important for determining its distribution patterns and mapping its risk, which have been analysed in the present study. Case reports in Thailand were collected from 1 January 2016 to 31 December 2020. Spatial autocorrelation was analyzed using Moran's I and univariate local Moran's I. Spatial point data of melioidosis incidence were calculated, with riskmapping interpolation performed by Kriging. It was highest in 2016, at 32.37 cases per 100,000 people, and lowest in 2020, at 10.83 cases per 100,000 people. General observations revealed that its incidence decreased slightly from 2016 to 2018 and drastically in 2019 and 2020. The Moran's I values for melioidosis incidence exhibited a random spatial pattern in 2016 and clustered distribution from 2017 to 2020. The risk and variance maps show interval values. These findings may contribute to the monitoring and surveillance of melioidosis outbreaks.

Few studies have considered the impacts of sample size and sample ratio of presence and absence points on the results of random forest (RF) testing. We applied this technique for the prediction of the spatial distribution of snail habitats based on a total of 15,000 sample points (5,000 presence samples and 10,000 control points). RF models were built using seven different sample ratios (1:1, 1:2, 1:3, 1:4, 2:1, 3:1, and 4:1) and the optimal ratio was identified via the Area Under the Curve (AUC) statistic. The impact of sample size was compared by RF models under the optimal ratio and the optimal sample size. When the sample size was small, the sampling ratios of 1:1, 1:2 and 1:3 were significantly better than the sample ratios of 4:1 and 3:1 at all four levels of sample sizes (p<0.01) and there was no significant difference among the ratios of 1:1, 1:2 and 1:3 (p>0.05). The sample ratio of 1:2 appeared to be optimal for a relatively large sample size with the lowest quartile deviation. In addition, increasing the sample size produced a higher AUC and a smaller slope and the most suitable sample size found in this study was 2400 (AUC=0.96). This study provides a feasible idea to select an appropriate sample size and sample ratio for ecological niche modelling (ENM) and also provides a scientific basis for the selection of samples to accurately identify and predict snail habitat distributions.

Screening programmes are important for early diagnosis and treatment of colorectal cancer (CRC) but they are not equally efficient in all locations. Depending on which hospital people belong to, they often are not willing to follow up even after a positive result, resulting in a lower-than-expected overall detection rate. Improved allocation of health resources would increase the program's efficiency and assist hospital accessibility. A target population exceeding 70,000 people and 18 local hospitals were included in the investigation of an optimization plan based on a locationallocation model. We calculated the hospital service areas and the accessibility for people in communities to CRC-screening hospitals using the Huff Model and the Two-Step Floating Catchment Area (2SFCA) approach. We found that only 28.2% of the residents with initially a positive screening result had chosen followup with colonoscopy and significant geographical differences in spatial accessibility to healthcare services indeed exist. The lowest accessibility was found in the Southeast, including the Zhangjiang, Jichang and Laogang communities with the best accessibility mainly distributed near the city centre of Lujiazui; the latter also had relatively a high level of what is called "ineffective screening" as it represents wasteful resource allocation. It is recommended that Hudong Hospital should be chosen instead of Punan Hospital as the optimization, which can improve the service population of each hospital and the populations served per colonoscope. Based on our results, changes in hospital configuration in colorectal cancer screening programme are needed to achieve adequate population coverage and equitable facility accessibility. Planning of medical services should be based on the spatial distribution trends of the population served.

We aimed to explore the district-level temporal dynamics and sub-district level geographical variations of colorectal cancer (CRC) incidence in the Special Region of Yogyakarta Province. We performed a cross-sectional study using data from the Yogyakarta population-based cancer registry (PBCR) comprised of 1,593 CRC cases diagnosed in 2008-2019. The age-standardized rates (ASRs) were determined using 2014 population data. The temporal trend and geographical distribution of cases were analysed using joinpoint regression and Moran's I statistics. During 2008-2019, CRC incidence increased by 13.44% annually. Joinpoints were identified in 2014 and 2017, which were also the periods when annual percentage change (APC) was the highest throughout the observation periods (18.84). Significant APC changes were observed in all districts, with the highest in Kota Yogyakarta (15.57). The ASR of CRC incidence per 100,000 person- years was 7.03 in Sleman, 9.20 in Kota Yogyakarta, and 7.07 in Bantul district. We found a regional variation of CRC ASR with a concentrated pattern of hotspots in the central sub-districts of the catchment areas and a significant positive spatial autocorrelation of CRC incidence rates in the province (I=0.581, p<0.001). The analysis identified four high-high clusters sub-districts in the central catchment areas. This is the first Indonesian study reported from PBCR data, showing an increased annual CRC incidence during an extensive observation period in the Yogyakarta region. A heterogeneous distribution map of CRC incidence is included. These findings may serve as basis for CRC screening implementation and healthcare services improvement.

As found in the health studies literature, the levels of climate association between epidemiological diseases have been found to vary across regions. Therefore, it seems reasonable to allow for the possibility that relationships might vary spatially within regions. We implemented the geographically weighted random forest (GWRF) machine learning method to analyze ecological disease patterns caused by spatially non-stationary processes using a malaria incidence dataset for Rwanda. We first compared the geographically weighted regression (WGR), the global random forest (GRF), and the geographically weighted random forest (GWRF) to examine the spatial non-stationarity in the non-linear relationships between malaria incidence and their risk factors. We used the Gaussian areal kriging model to disaggregate the malaria incidence at the local administrative cell level to understand the relationships at a fine scale since the model goodness of fit was not satisfactory to explain malaria incidence due to the limited number of sample values. Our results show that in terms of the coefficients of determination and prediction accuracy, the geographical random forest model performs better than the GWR and the global random forest model. The coefficients of determination of the geographically weighted regression (R2), the global RF (R2), and the GWRF (R2) were 4.74, 0.76, and 0.79, respectively. The GWRF algorithm achieves the best result and reveals that risk factors (rainfall, land surface temperature, elevation, and air temperature) have a strong non-linear relationship with the spatial distribution of malaria incidence rates, which could have implications for supporting local initiatives for malaria elimination in Rwanda.

Exploratory disease maps are designed to identify risk factors of disease and guide appropriate responses to disease and helpseeking behaviour. However, when produced using aggregatelevel administrative units, as is standard practice, disease maps may mislead users due to the Modifiable Areal Unit Problem (MAUP). Smoothed maps of fine-resolution data mitigate the MAUP but may still obscure spatial patterns and features. To investigate these issues, we mapped rates of Mental Health- Related Emergency Department (MHED) presentations in Perth, Western Australia, in 2018/19 using Australian Bureau of Statistics (ABS) Statistical Areas Level 2 (SA2) boundaries and a recent spatial smoothing technique: the Overlay Aggregation Method (OAM). Then, we investigated local variation in rates within high-rate regions delineated using both approaches. The SA2- and OAM-based maps identified two and five high-rate regions, respectively, with the latter not conforming to SA2 boundaries. Meanwhile, both sets of high-rate regions were found to comprise a select number of localised areas with exceptionally high rates. These results demonstrate how, due to the MAUP, disease maps that are produced using aggregate-level administrative units are unreliable as a basis for delineating geographic regions of interest for targeted interventions. Instead, reliance on such maps to guide responses may compromise the efficient and equitable delivery of healthcare. Detailed investigation of local variation in rates within high-rate regions identified using both administrative units and smoothing is required to improve hypothesis generation and the design of healthcare responses.

Falling has become the first and second cause of death due to injury among urban and rural residents in China. This mortality is considerably higher in the southern part of the country than in the North. We collected the rate of mortality due to falling for 2013 and 2017 by province, age structure and population density, taking topography, precipitation and temperature into account. 2013 was used as the first year of the study since this year marks the expansion of the mortality surveillance system from 161 counties to 605 counties making available data more representative. A geographically weighted regression was used to evaluate the relationship between mortality and the geographic risk factors. High levels of precipitation, steep topography and uneven land surfaces as well as a higher quantile of the population aged above 80 years in southern China are believed to have led to the significantly higher number of falling compared with that in the North. Indeed, when evaluated by geographically weighted regression, the factors mentioned found a difference between the South and the North with regard to falling of 81% and 76% for the years 2013 and 2017, respectively. Interaction effects were observed between geographic risk factors and falling that, apart from the age factor, could be explained by topographic and climatic differences. The roads in the South are more difficult to negotiate on foot, particularly when it rains, which increases the probability of falling. In summary, the higher mortality due to falling in southern China emphasizes the need to apply more adaptive and effective measures in rainy and mountainous region to reduce this kind of risk.

Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran's I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.

Researchers in many fields have discovered the advantage of using geographical information systems (GIS), spatial statistics and computer modelling, but these techniques are only sparingly applied in archaeological research. Writing 30 years ago, Castleford (1992) noted the considerable potential of GIS, but he also felt that its then atemporal structure was a serious flaw. It is clear that the study of dynamic processes suffers if past events cannot be linked to each other, or to the present, but today's powerful tools have overcome this drawback. Importantly, with location and time as key indices, hypotheses about early human population dynamics can be tested and visualized in ways that can potentially reveal hidden relationships and patterns. [...].