Geospatial Health最新文献

The main objective of this paper was to model the relationship between married women's contraceptive use and the influence of their male partners. The study took place in Angola and Zambia, which stems from the fact that these countries ratified the Maputo Protocol that emphasises promotion of reproductive health among women. Most previous studies investigating women's progress towards the realisation of what is advocated in this protocol have overlooked the role of the male partners. Hence, it has become imperative to reduce this gap in the literature. This paper discusses the application of spatial multilevel modelling, which incorporates two levels of information based on the nature of the data available. This approach acknowledges the hypothesis that contraceptive use is a social phenomenon occurring within the geographical space and is therefore susceptible to autocorrelation. Findings confirm that the level of influence of male partners' exertion on women's contraceptive use is dependent on the situation in the country where it takes place as shown by various study variables analysed. The results indicate that socioeconomic and education factors play a major role, a phenomenon that calls for tailor-made reproductive health policies considering these aspects.

The COVID-19 outbreak has precipitated severe occurrences on a global scale. Hence, spatial analysis is crucial in determining the relationships and patterns of geospatial data. Moran's I and Geary's C are prominent methodologies used to measure the spatial autocorrelation of geographical data. Both measure the degree of similarity or dissimilarity between nearby locations based on attribute values in such a way that the selection of distance techniques and weight matrices significantly impact the spatial autocorrelation results. This paper aimed at carrying out the spatial epidemiological characteristics analysis of the pandemic comparing the results of Moran's I and Geary's C with different parameters to gain a comprehensive understanding of the spatial relationship of COVID-19 cases. We employed distance-based techniques, K-nearest neighbour, and Queen contiguity techniques to assess the sensitivity of the different parameter configurations for both Moran's I and Geary's C. The findings revealed that former provided more reliable and robust results compared to the latter, with consistent results of spatial autocorrelation (positive spatial autocorrelation). The distance weight of 0.05 using the Manhattan method of Moran's I is the recommended distance weight, as it outperformed other weight matrices (Moran's I = 0.0152, Z-value= 110.8844 and p-value=0.001).

Introduction: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as climate change, is an important public health issue, especially with regard to COPD.

Method: Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to investigate the spatial trajectory of COPD in the United States between 2001 and 2020. Global Moran's I was used to investigate spatial relationships utilising data from Terra satellite for night-time land surface temperatures (LSTnt), which served as an indicator of warming within the same time period across the United States. The forest-based classification and regression model (FCR) was applied to predict mortality rates.

Results: It was found that COPD mortality over the 20-year period was spatially clustered in certain counties. Moran's I statistic (I=0.18) showed that the COPD mortality rates increased with LSTnt, with the strongest spatial association in the eastern and south-eastern counties. The FCR model was able to predict mortality rates based on LSTnt values in the study area with a R2 value of 0.68.

Conclusion: Policymakers in the United States could use the findings of this study to develop long-term spatial and health-related strategies to reduce the vulnerability to global warming of patients with acute respiratory symptoms.

Despite national initiatives to enhance healthcare accessibility, unmet healthcare needs in South Korea remain notably high, particularly in specific regions. This study investigated the factors contributing to geographical disparities in unmet healthcare needs by employing spatial regression models to examine the spatial interactions between healthcare resources and unmet needs. Utilizing data from the 2020 Community Health Survey and Statistics Korea for 216 local government entities, excluding remote areas to ensure data consistency, we identified significant spatial clusters of unmet healthcare needs. These clusters are primarily located in non-metropolitan regions facing transportation barriers and limited healthcare infrastructure. Spatial regression analysis revealed that general hospitals and clinics are significantly associated with reduced unmet healthcare needs underscoring their critical role in mitigating regional disparities. In contrast, hospitals (≥30 beds) and convalescent hospitals did not exhibit significant effects, likely owing to their focus on specialised inpatient and long-term care services, which do not directly address immediate outpatient needs. These findings advance the understanding of how healthcare resource distribution impacts unmet needs at a regional level in South Korea and highlight the necessity for allocating general hospitals and clinics strategically to promote health equity. Based on these results, we recommend evidence- based policy interventions that optimise existing healthcare resources and strategically deploy new facilities in underserved regions. These insights provide valuable guidance for policymakers to reduce geographical health disparities and enhance overall public health outcomes.

Typhoid fever is one of the common enteric fevers in developing countries, especially in emerging metropolitan areas in Indonesia. Yet, studies on spatial and temporal distribution of tyhoid fever are lacking. This study was conducted to analyze retrospective hospital-based data at the village level over the period 2017-2023 to understand the spatial and temporal variation of typhoid fever in Jakarta. Spatial analyses were performed by Moran's I and Local Indicators of Spatial Association (LISA) to examine spatial clustering of typhoid incidence and to identify high-risk villages for typhoid fever, respectively. Seasonal decomposition analysis was performed to investigate the seasonality of this infection. A total of 57,468 typhoid cases, resulting in a cumulative incidence of 533.99 per 100,000 people, were reported during the study period. The incidence was significantly clustered (I=0.548; p=0.001) at the village level across Jakarta. Statistically significant high-risk clusters were detected in the South and East of Jakarta that were heterogeneous over time. We identified seven persistent high-risk clusters in the eastern part of the city and two in the southern part. Moreover, the typhoid incidence showed a strong seasonality trend, significantly associated with monthly total rainfall (p=0.018). The study revealed a significant spatial variation with strong seasonality in typhoid incidence across the city suggesting a variation in transmission intensity and needs for effective public health interventions, especially in the high-risk areas. Improvement in water and sanitation facilities, hygiene awareness and surveillance are essential to help reduce typhoid transmission in Jakarta.

During the COVID-19 pandemic in 2021-2022, vaccination against this infection was crucial for Thailand's recovery. This research aimed to identify spatial patterns of association between the distribution and spread of the COVID-19 pandemic on the one hand and vaccine coverage, health service and socio-economic factors on the other. Univariate analysis using Getis-Ord GI* found strong clustering of the vaccine coverage, mostly in Eastern, Central, and Southern regions (Andaman coast), while bivariate analysis using Moran's I revealed significant positive spatial correlation vaccine coverage with the presence of COVID-19 patients (2021 = 0.273; 2022 = 0.273), Night Time Light (NTL) (2021 = 0.159; 2022 = 0.118) and medical personnel (2021 = 0.174; 2022 = 0.123). In addition, Local Indicators of Spatial Association (LISA) analysis found High-High clusters predominantly in the Eastern and Central regions. Areas with high economic growth (as reflected by high NTL) had greater COVID-19 vaccine coverage, likely due to better access to information and efficient transport systems in areas with stronger financial resources than elsewhere. These factors facilitated access to healthcare ensured presence of adequate personnel and enabled rapid distribution of the vaccine. Additionally, high rates of COVID-19 infections increased public awareness of infection risk leading to better vaccination uptake. Policymakers should prioritise vaccine distribution in high-risk and underserved areas to ensure equitable access. Additionally, increasing health workforce capacity is essential to improving service efficiency and readiness for future outbreaks.

Accessibility is an essential consideration in the design of public spaces, and commonly referred to as 'pedestrian accessibility' when walking is the primary mode of transportation. Computational methods, frequently coupled with Geographic Information systems (GIS), are increasingly available for assessing pedestrian accessibility using digital cartographic data such as road networks and digital terrain models. However, they often implicitly assume a level of mobility that may not be achievable by individuals with mobility impairments, e.g., wheelchair users. Therefore, it remains uncertain whether conventional pedestrian accessibility adequately approximates 'wheelchair accessibility,' and, if not, what computational resources would be required to evaluate it more accurately. We therefore designed a spatial database aimed at customizing mobility networks according to mobility limitations and compared the accessibility of a university campus for people with and without wheelchairs under various assumptions. The results showed there are clusters of locations either completely inaccessible or substantially less accessible for wheelchair users, indicating the presence of particular 'wheelchair coldspots', not only due to steep slopes and stairways but also arising from unforeseen consequences of aesthetic and safety enhancements, such as pebble pavements and raised sidewalks. It was found that a combination of simple spatial queries would help identifying potential locations for mobility aids such as ramps. These findings suggest that accessibility is not an invariant of a public space but experienced differently by different groups. Therefore, more comprehensive needs analysis and spatial database design are necessary to support inclusive design of healthier public spaces.

Once a vaccine against COVID-19 had been developed, distribution strategies were needed to vaccinate large numbers of the population as efficiently as possible. In this study we explored the geographical accessibility of vaccination centres and examined their optimal location. To achieve this, we used open-source data. For the analysis we assessed the centre-to-population ratio served to assess inequalities and examined the optimal number and location of centres needed to serve 50%, 70% and 85% of the population, while ensuring physical accessibility using a common mode of transportation, the bicycle. The Location Set Covering Problem (LSCP) model was used to determine the lowest number of vaccination centres needed and assess where these should be located for each Municipal Health Service (GGD) region in The Netherlands. Our analysis identified an unequal distribution of health centres by GGD region, with a primary concentration of vaccination locations in the central region of the Netherlands. GGD Region Noord en Oost Gelderland (N=34), Utrecht (N=29) and Hollands-Midden (N=26) had the highest numbers, while the lowest were found in West-Brabant (N=1), Brabant-Zuidoost (N=2), with Kennemerland, Hollands-Noorden, Groningen and Flevoland (N=3) each. The centre-to-population ratio ranged from 1 centre serving 22,000 people (Noord en Oost Gelderland) to 1 centre serving 672,000 people (West Brabant region). The location-allocation analysis identified several regions that would benefit by adding more centres, most of which would serve densely populated regions previously neglected by the existing vaccination strategy. The number of centres needed ranged from 110 to 322 to achieve 50% and 85% population coverage respectively. In conclusion, location-allocation models coupled with Geographic Information Systems (GIS) can aid decision-making efforts during mass vaccination efforts. To increase effectiveness, a nuanced distribution approach considering accessibility and coverage would be useful. The methodology presented here is valuable for aiding decisionmakers in providing optimized locally adapted crucial health services accessible for the population, such as vaccination centres.

Ensuring universal and equitable accessibility to healthcare services is crucial for fostering equal living conditions aligned with global and national objectives. This study examines disparities in accessing General Practitioner (GP) care within Lower Saxony and Bremen, Germany, using the two-step floating catchment area method for spatial analysis at street section level, incorporating various transportation modes. Findings are compared with needs-related planning guidelines to uncover spatial disparities and deviations between prescribed guidelines (target state) and empirical findings (actual state). The analysis reveals significant discrepancies, with over 50% of the population inadequately supplied due to accessibility or capacity issues, particularly in rural and some urban areas, challenging assumptions of sufficient urban healthcare provision. This is the first detailed analysis of primary care provision at this granular level in Lower Saxony, exposing substantial gaps between current GP care and planning targets. Fine-grained spatial analysis proves essential for revealing healthcare accessibility inequities and offers a roadmap for targeted policy interventions. Despite limitations, such as not fully capturing real-world dynamics or patient preferences, the study provides valuable insights into enhancing geographically equitable GP care. It contributes to the discourse on achieving equal living conditions through equitable healthcare accessibility, advocating a more refined, localised approach to healthcare planning, emphasizing the importance of detailed spatial analysis for informed decision-making and promoting health equity.

Equitable spatial accessibility to vaccination sites is essential for enhancing the effectiveness of infectious disease prevention and control. While traffic modes significantly influence the evaluation of spatial accessibility to vaccination sites, most existing studies measure it separately using homogeneous or single travel modes making it challenging to comprehensively understand the overall accessibility and support spatial optimization for vaccination sites. This study proposes to optimize the spatial distribution of vaccination sites based on heterogeneous travel modes in multiple scenarios by a hybrid travel time approach. This was done by first considering heterogeneous travel modes to measure spatial accessibility to vaccination sites followed by spatial optimization using hybrid travel time to determine the optimal configuration of vaccination sites across multiple scenarios. In the study area of Xiangtan, a prefecture-level city in east-central Hunan Province, China, spatial inequality in accessibility to COVID-19 vaccination sites were identified. The public in the Yuhu and Yuetang districts benefit from easy access to vaccination sites, and spatial accessibility within these areas is also equitable. By utilizing spatial optimization under the condition that the addition of a new site would not result in a comprehensive hybrid travel time increase exceeding 0.1%, up to 21 redundant sites were detected among the original ones and when newly added sites were considered, the optimal number of the optimized sites amounted to 124. These findings provide crucial spatial information to support for enhancing the efficiency of infectious disease prevention and control.