The impact of earthquakes in Latin America on the continuity of HIV care: A retrospective observational cohort study

Objectives

As earthquakes occur frequently in Latin America and can cause significant disruptions in HIV care, we sought to analyze patterns of HIV care for adults at Latin American clinical sites experiencing a significant earthquake within the past two decades.

Study design

Retrospective clinical cohort study.

Methods

Adults receiving HIV care at sites experiencing at least a "moderate intensity" (Modified Mercalli scale) earthquake in the Caribbean, Central and South America network for HIV epidemiology (CCASAnet) contributed data from 2003 to 2017. Interrupted Time Series models were fit with discontinuities at site-specific earthquake dates (Sept. 16, 2015 in Chile; Apr. 18, 2014 and Sept. 19, 2017 in Mexico; and Aug. 15, 2007 in Peru) to assess clinical visit, CD4 measure, viral load lab, and ART initiation rates 3- and 6-months after versus before earthquakes.

Results

Comparing post-to pre-earthquake periods, there was a sharp drop in median visit (incidence rate ratio [IRR] = 0.79, 95% confidence interval [CI]: 0.68-0.91) and viral load lab (IRR = 0.78, 95% CI: 0.62-0.99) rates per week, using a 3-month window. CD4 measurement rates also decreased (IRR = 0.43; 95% CI: 0.37-0.51), though only using a 6-month window.

Conclusions

Given that earthquakes occur frequently in Latin America, disaster preparedness plans must be more broadly implemented to avoid disruptions in HIV care and attendant poor outcomes.

From erosion to epidemics: Understanding the overlapping vulnerability of hydrogeomorphic hotspots, malaria affliction, and poverty in Nigeria

Hydrogeomorphic changes, encompassing erosion, waterlogging, and siltation, disproportionately threaten impoverished rural communities. Yet, they are often marginalized in discussions of disasters. This oversight is especially concerning as vulnerable households with limited healthcare access are most susceptible to related diseases and displacement. However, our current understanding of how these risks intersect remains limited. We explore the complex relationships between hydrogeomorphic hazards, malaria incidence, and poverty in Nigeria. Through spatial analyses we expand traditional boundaries, incorporating factors such as healthcare access, migration patterns, dam locations, demographics, and wealth disparities into a unified framework. Our findings reveal a stark reality: most residents in hydrogeomorphic hotspots live in poverty (earnings per person ≤$1.25/day), face elevated malaria risks (80 % in malaria hotspots), reside near dams (59 %), and struggle with limited healthcare access. Moreover, exposure to hydrogeomorphic hotspots could double by 2080, affecting an estimated 5.8 million Nigerians. This forecast underscores the urgent need for increased support and targeted interventions to protect those living in poverty within these hazardous regions. In shedding light on these dynamics, we expose and emphasise the pressing urgency of the risks borne by the most vulnerable populations residing in these regions-communities often characterised by limited wealth and resilience.

Spatial analysis of HIV detection rates among Brazilian young men, 2007-2021

The main goal of this study was to spatially analyze the HIV epidemic scenario in young men in Brazil, 2007-2021. We used secondary data obtained from the Brazilian Information System for Notifiable Diseases. Individuals aged 15-29 years with permanent residence in Brazil who were diagnosed with HIV during the study period were included in the analysis. Municipality HIV age-adjusted detection rates were analyzed through spatial distribution, autocorrelation, and spatiotemporal risk analyses. During the study period, 108,392 HIV cases were reported in young Brazilian men. The HIV epidemic increased territorially in the northern, northeastern, midwestern, and southeastern regions but decreased in the southern region. Although the number of clusters comprising municipalities with high HIV detection rates (hotspots) decreased, new ones appeared, expanded, or stopped changing size. Hotspots and spatio-temporal risk zones (spatial areas with increased HIV detection in a specific period) comprised economically developed municipalities with high demographic density surrounded by less developed municipalities. The period of the spatiotemporal risk zones was between 2008 and 2021. Our study showed that HIV detection rates continue to increase among young Brazilian men, and health authorities should reinforce efforts mainly in capitals and surrounded municipalities in the north, northeast, southeast, and midwest regions.

A spatial autocorrelation analysis of road traffic crash by severity using Moran's I spatial statistics: A comparative study of Addis Ababa and Berlin cities

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

Outpatient geriatric health care in the German federal state of Mecklenburg-Western Pomerania: a population-based spatial analysis of claims data

BACKGROUND

Due to unidentified geriatric needs, elderly patients have a higher risk for developing chronic conditions and acute medical complications. Early geriatric screenings and assessments help to identify geriatric needs. Holistic and coordinated therapeutic approaches addressing those needs maintain the independence of elderly patients and avoid adverse effects. General practitioners are important for the timely identification of geriatric needs. The aims of this study are to examine the spatial distribution of the utilization of outpatient geriatric services in the very rural Federal State of Mecklenburg-Western Pomerania in the Northeast of Germany and to identify regional disparities.

METHODS

Geographical analysis and cartographic visualization of the spatial distribution of outpatient geriatric services of patients who are eligible to receive basic geriatric care (BGC) or specialized geriatric care (SGC) were carried out. Claims data of the Association of Statutory Health Insurance Physicians in Mecklenburg-Western Pomerania were analysed on the level of postcode areas for the quarter periods between 01/2014 and 04/2017. A Moran's I analysis was carried out to identify clusters of utilization rates.

RESULTS

Of all patients who were eligible for BGC in 2017, 58.3% (n = 129,283/221,654) received at least one BCG service. 77.2% (n = 73,442/95,171) of the patients who were eligible for SGC, received any geriatric service (BGC or SGC). 0.4% (n = 414/95,171) of the patients eligible for SGC, received SGC services. Among the postcode areas in the study region, the proportion of patients who received a basic geriatric assessment ranged from 3.4 to 86.7%. Several regions with statistically significant Clusters of utilization rates were identified.

CONCLUSIONS

The widely varying utilization rates and the local segregation of high and low rates indicate that the provision of outpatient geriatric care may depend to a large extent on local structures (e.g., multiprofessional, integrated networks or innovative projects or initiatives). The great overall variation in the provision of BGC services implicates that the identification of geriatric needs in GPs' practices should be more standardized. In order to reduce regional disparities in the provision of BGC and SGC services, innovative solutions and a promotion of specialized geriatric networks or healthcare providers are necessary.

Climate change and land cover effects on water yield in a subtropical watershed spanning the yungas-chaco transition of Argentina

The demand for mountain water resources is increasing, and their availability is threatened by climate change, emphasizing the urgency for effective protection and management. The upper Sali-Dulce watershed holds vital significance as it contributes the majority of the Sali-Dulce water resources, supporting a densely populated dry region in Northwestern Argentina, covering an area of 24,217 km2. However, the potential impact of climate change and land use/land cover change on water yield in this watershed remains uncertain. This study employs the InVEST Annual Water Yield model to analyze the average water yield in the watershed and evaluate its potential changes under future scenarios of climate and land use/land cover change. InVEST was calibrated using data from multiple river gauges located across the watershed, indicating satisfactory performance (R2 = 0.751, p-value = 0.0054). Precipitation and evapotranspiration were the most important variables explaining water yield in the area, followed by land use. Water yield showed a notable concentration in the montane area with 40% of the watershed accounting for 80% of the water yield, underscoring the importance of conserving natural land cover in this critical zone. Climate change scenarios project an increase in water yield ranging from 21 to 75%, while the effects of land cover change scenarios on water yield vary, with reforestation scenarios leading to reductions of up to 15% and expansions in non-irrigated agriculture resulting in increases of up to 40%. Additionally, water yield distribution may become more concentrated or dispersed, largely dependent on the type of land cover. The combined scenarios highlight the pivotal role of land cover in adapting to climate change. Our findings provide valuable insights for designing future studies and developing policies aimed at implementing effective adaptation strategies to climate change within the Salí-Dulce watershed.

'Nowhere and no one is safe': spatial analysis of damage to critical civilian infrastructure in the Gaza Strip during the first phase of the Israeli military campaign, 7 October to 22 November 2023

BACKGROUND

Since the Hamas attacks in Israel on 7 October 2023, the Israeli military has launched an assault in the Gaza Strip, which included over 12,000 targets struck and over 25,000 tons of incendiary munitions used by 2 November 2023. The objectives of this study include: (1) the descriptive and inferential spatial analysis of damage to critical civilian infrastructure (health, education, and water facilities) across the Gaza Strip during the first phase of the military campaign, defined as 7 October to 22 November 2023 and (2) the analysis of damage clustering around critical civilian infrastructure to explore broader questions about Israel's adherence to International Humanitarian Law (IHL).

METHODS

We applied multi-temporal coherent change detection on Copernicus Sentinel 1-A Synthetic Aperture Radar (SAR) imagery to detect signals indicative of damage to the built environment through 22 November 2023. Specific locations of health, education, and water facilities were delineated using open-source building footprint and cross-checked with geocoded data from OCHA, OpenStreetMap, and Humanitarian OpenStreetMap Team. We then assessed the retrieval of damage at and with close proximity to sites of health, education, and water infrastructure in addition to designated evacuation corridors and civilian protection zones. The Global Moran's I autocorrelation inference statistic was used to determine whether health, education, and water facility infrastructure damage was spatially random or clustered.

RESULTS

During the period under investigation, in the entire Gaza Strip, 60.8% (n = 59) of health, 68.2% (n = 324) of education, and 42.1% (n = 64) of water facilities sustained infrastructure damage. Furthermore, 35.1% (n = 34) of health, 40.2% (n = 191) of education, and 36.8% (n = 56) of water facilities were functionally destroyed. Applying the Global Moran's I spatial inference statistic to facilities demonstrated a high degree of damage clustering for all three types of critical civilian infrastructure, with Z-scores indicating < 1% likelihood of cluster damage occurring by random chance.

CONCLUSION

Spatial statistical analysis suggests widespread damage to critical civilian infrastructure that should have been provided protection under IHL. These findings raise serious allegations about the violation of IHL, especially in light of Israeli officials' statements explicitly inciting violence and displacement and multiple widely reported acts of collective punishment.

Big Cities, Big Impacts? A spatial analysis of 3,335 ecological offsets in France since 2012

This paper assesses the French policy of mitigation hierarchy, with the aim of no net loss of biodiversity, by studying the geographical aspects of the application of the concept of ecological offsets in equivalence between losses and gains using spatialized data. We seek to know whether the dynamics of urban and interurban development (notably built-up and transport infrastructures) lead to a spatially integrated implementation of biodiversity offsets taking into account local characteristics and areas under pressure from land artificialization. Our main finding reveals that the majority of ecological offsets are generated by projects related to transport infrastructures (38%) and urban planning and construction projects (23%). However, if there are fewer, the ecological offsets of projects such as waste storage or energy development are mostly located in natural preserved areas, revealing a potential risk of non additionnality of offset measures and a risk that the private sector (through ecological offsets) will gradually replace the state in the protection of biodiversity. Our analysis also points out that despite the diversity of projects, habitats and protected species across France, there is a typical spatial layout profile of ecological offsets, pleading for a "one size fits all" offsetting in the French policy context of tenuous regulators' availability in time and competence level due to weakness of refresh training and downsizing of public services in the environment. This last result argues for a stronger control from environmental agencies between two tremendously tricky concepts of offsetting, the equivalence valuation methods and the adjustments coefficients (time delay and ecological risk), to drive ecological offsetting future decisions at local but above all regional- and national-level planning documents.

Exploring energy geography: Data insights on household consumption

Household energy consumption (HEC) varies across neighbourhoods and gender groups. This database provides raw and analysed data on HEC determinants and their estimated influence on HEC in 2707 residential neighbourhoods (Wijk) in the Netherlands in 2018. The raw data consists of 17 indicators on energy demand, socioeconomic characteristics, microclimate and buildings. The indicators are retrieved from and calculated based on open national and international datasets. The analysed data presents the local coefficients of the HEC determinants, the outcome of the geographically weighted regression model (GWR) presented in the related article [1].

Construction of China's food security evaluation index system and spatiotemporal evolution

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Association between Opisthorchis viverrini infection in cats and humans: Non-spatial and spatial analyses

Cats are recognized as significant reservoir hosts for human opisthorchiasis, particularly in areas with a high prevalence of infection. Despite this, the precise role of cats in the transmission of Opisthorchis viverrini between humans and felines remains unclear. This study investigates the association between these two hosts through both spatial and non-spatial analyses in the endemic Thanya sub-district of Thailand. A total of 105 owned cats were randomly sampled from 15 villages within the sub-district for stool examination. A questionnaire was administered to 66 cat owners to explore the human-pet relationship. Household locations were collected using GPS devices. Non-spatial analyses revealed a positive association between the two hosts (P= 0.011; OR 7, 95% CI: 1.6-30.9), highlighting two independent significant risk factors: cat owners consuming raw fish (P = 0.028; OR = 4.52, 95% CI: 1.25-19.45) and feeding cats raw fish (P = 0.011; OR = 16.41, 95% CI: 2.78-317.04) according to multivariate analysis. Spatial analysis provided further support to the non-spatial findings (p = 0.0123; OR = 3.45, 95% CI = 0.88-13.61). Multiple autologistic regression confirmed two significant risk factors: cat owners consuming raw fish (p = 0.054; OR = 3.37, 95% CI: 0.98-11.59) and feeding cats raw fish (p = 0.014; OR = 7.43, 95% CI: 1.49-37.05). Risk mapping identified the western part of the study site as a hotspot for O. viverrini infection. Hyper-endemic focusing revealed a union of human and cat buffers at 0.46 km², with an overlapping area of 0.22 km² (47.83%). This study underscores the impact of owners' behaviors, specifically consuming and feeding raw fish to cats, on the increased probability of infection in cats. It emphasizes the need for effective opisthorchiasis control through health education targeting cat owners in endemic areas.

"Characterization of residential proximity to sources of environmental carcinogens in clusters of Acute Lymphoblastic Leukemia in San Luis Potosi, Mexico"

BACKGROUND

Acute Lymphoblastic Leukemia (ALL) is the most prevalent neoplasia in children and teenagers in Mexico. Although epidemiological data supports that children's residence close to emissions from vehicular traffic or industrial processes increases the risk of ALL; and the IARC states that benzene, PAHs, and PM 2.5 are well-known environmental carcinogens, there is a gap in linking these carcinogenic hazards with the sources and their distribution from scenario perspective.

AIM

To identify ALL clusters in the population under 19 years of age and characterize the environment at the neighborhood level by integrating information on sources of carcinogenic exposure using spatial analysis techniques in the Metropolitan Area of San Luis Potosi, Mexico.

METHODS

Using the Kernel Density test, we designed an ecological study to identify ALL clusters from incident cases in the population under 19 years of age. A multicriteria analysis was conducted to characterize the risk at the community level from carcinogenic sources. A hierarchical cluster analysis was performed to characterize risk at the individual level based on carcinogenic source count within 1 km for each ALL case.

RESULTS

Eight clusters of carcinogenic sources were located within the five identified ALL clusters. The multicriteria analysis showed high-risk areas (by density of carcinogenic source) within ALL clusters.

CONCLUSIONS

This study has a limited source and amount of available data on ALL cases, so selection bias is present as well as the inability to rule out residual confounding factors, since covariates were not included. However, in this study, children living in environments with high vehicular density, gas stations, brick kilns, incinerators, commercial establishments burning biomass, or near industrial zones may be at higher risk for ALL.

Spatial Effects of Infiltrating T cells on Neighbouring Cancer Cells and Prognosis in Stage III CRC patients

Colorectal cancer (CRC) is one of the most frequently occurring cancers, but prognostic biomarkers identifying patients at risk of recurrence are still lacking. In this study, we aimed to investigate in more detail the spatial relationship between intratumoural T cells, cancer cells, and cancer cell hallmarks, as prognostic biomarkers in stage III colorectal cancer patients. We conducted multiplexed imaging of 56 protein markers at single cell resolution on resected fixed tissue from stage III CRC patients who received adjuvant 5-fluorouracil-based chemotherapy. Images underwent segmentation for tumour, stroma and immune cells, and cancer cell 'state' protein marker expression was quantified at a cellular level. We developed a Python package for estimation of spatial proximity, nearest neighbour analysis focusing on cancer cell - T cell interactions at single-cell level. In our discovery cohort (MSK), we processed 462 core samples (total number of cells: 1,669,228) from 221 adjuvant 5FU-treated stage III patients. The validation cohort (HV) consisted of 272 samples (total number of cells: 853,398) from 98 stage III CRC patients. While there were trends for an association between percentage of cytotoxic T cells (across the whole cancer core), it did not reach significance (Discovery cohort: p = 0.07, Validation cohort: p = 0.19). We next utilized our region-based nearest neighbourhood approach to determine the spatial relationships between cytotoxic T cells, helper T cells and cancer cell clusters. In the both cohorts, we found that lower distance between cytotoxic T cells, T helper cells and cancer cells was significantly associated with increased disease-free survival. An unsupervised trained model that clustered patients based on the median distance between immune cells and cancer cells, as well as protein expression profiles, successfully classified patients into low-risk and high-risk groups (Discovery cohort: p = 0.01, Validation cohort: p = 0.003).

Measuring the similarity of charts in graphical statistics

Figures used in statistics and other sciences play a vital role in understanding and analyzing the problems under study. Due to the complexity and diversity of these problems, figures such as cartograms, choropleth maps, or radar charts take various geometric forms. Their visual evaluation from the view of geometric similarity is essential but insufficient. This paper proposes and theoretically justifies new metrics based on graph theory. They make it possible to quickly determine the degree of similarity of the statistical figures used in the research procedure. The new metrics were used to 1. Determine the similarity of the domestic route networks of major U.S. airlines, 2. Determine the similarity of the distribution of votes cast in U.S. presidential election in each state in 2016 and 2020, 3. Compare radar charts of some countries, constructed based on the Global Competitiveness Index, 4. Analyze the similarity of neutrosophic double line graphs representing sets of approximate (neutrosophic) numbers. This improves analytical capabilities concerning various processes mapped with well-known types of statistical charts, such as choropleth maps, radar charts, etc.

Injury-severity analysis of crashes involving defective vehicles and accounting for the underlying socioeconomic mediators

Crashes occur from a combination of factors related to the driver, roadway, and vehicle factors. The impact of vehicles on road crashes is a critical consideration within road safety analysis, even though not much studies have been conducted in this area. This study assessed how various vehicle and other crash factors are significantly associated with crash outcomes. To do this, historical vehicle defect-related crashes were obtained for the state of Alabama from 2016 to 2020. After data cleaning, a crash injury severity model was developed using the random parameters multinomial logit with heterogeneity in means approach to account for possible unobserved heterogeneity in the data. A spatial analysis was further conducted to better understand vehicle defect crashes as a broader societal issue and potentially explore their connection with the socio-demographic characteristics of the drivers of these vehicles. The preliminary data analysis showed that brake and tire defects accounted for about 65% of the vehicle defects associated with the crashes. The model estimation results revealed that improper tread depth and headlight defects were associated with major injury outcomes, while brake defects were more associated with minor injuries. Also, crashes associated with speeding, drunk driving, failure to use seatbelts, and those that occurred on curved roads left with downgrades were likely to result in major injuries. Findings from the spatial analysis showed that postal codes with higher median incomes are more likely to record lower vehicle defect-related crashes, unlike those that have higher proportions of females and African Americans. The study's findings provide data-driven evidence for sustained safety campaigns, workshops, and training on basic vehicle maintenance practices in the low-income communities in the state.

Is low-dose computed tomography for lung cancer screening conveniently accessible in China? A spatial analysis based on cross-sectional survey

BACKGROUND

Regular Low-Dose Computed Tomography (LDCT) for lung cancer high-risk population has been proved to improve health outcomes and relieve disease burden efficiently for both individual and society. With geographical impedance becoming the major barrier preventing patients from getting timely healthcare service, this study incorporated health seeking behavior in estimating spatial accessibility of relative scarce LDCT resource in China, thus to provide real-world evidence for future government investment and policy making.

METHODS

Taking Sichuan Province in southwest China as the study area, a cross-sectional survey was first carried out to collect actual practice and preferences for seeking LDCT services. Using Computed Tomography (CT) registration data reported by owner institutions representing LDCT services capacity, and grided town-level high-risk population as demand, the Nearest Neighbor Method was then utilized to calculate spatial accessibility of LDCT services.

RESULTS

A total of 2,529 valid questionnaires were collected, with only 34.72% of the high-risk populations (746 individuals) followed the recommended annual screening. Participants preferred to travel to municipal-level and above institutions within 60 min for LDCT services. Currently, every thousand high-risk populations own 0.0845 CT scanners in Sichuan Province, with 96.95% able to access LDCT within 60 min and over half within 15 min. Urban areas generally showed better accessibility than rural areas, and the more developed eastern regions were better than the western regions with ethnic minority clusters.

CONCLUSIONS

Spatial access to LDCT services is generally convenient in Sichuan Province, but disparity exists between different regions and population groups. Improving LDCT capacity in county-level hospitals as well as promoting health education and policy guidance to the public can optimize efficiency of existing CT resources. Implementing mobile CT services and improving rural public transportation may alleviate emerging disparities in accessing early lung cancer detection.

Healthcare equity in focus: bridging gaps through a spatial analysis of healthcare facilities in Irbid, Jordan

In the Irbid Governorate, Jordan, equitable healthcare facility distribution is vital to ensuring healthcare accessibility and improving public health outcomes. This study investigated the spatial distribution, accessibility, and conformity of healthcare facilities to the Ministry of Health standards to identify areas requiring improvement. Using geographic information systems (GIS), three spatial analyses were conducted: nearest neighbor analysis, buffer analysis, and service area analysis. These analyses comprehensively assessed the healthcare landscape, revealing a random spatial distribution pattern of healthcare facilities; and indicating an absence of structured organization. The buffer analysis revealed concentrations in specific regions, while others were underserved. The Service Area Analysis revealed significant healthcare access challenges, especially in remote areas. The healthcare resource distribution of the Irbid governorate fell short of national and international standards, emphasizing the need for improvements. To address these disparities, policymakers and healthcare authorities should focus on equitably redistributing resources, tailoring allocation to local needs, improving remote area infrastructure, and refining government policies. Continuous monitoring and evaluation are imperative to ensure alignment with international standards and achieve healthcare equity. The insights from this case study provide valuable guidance for regions facing similar healthcare distribution challenges.

Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer

Despite advances in treatment, lung cancer survival rates remain low. A better understanding of the cellular heterogeneity and interplay of cancer-associated fibroblasts (CAFs) within the tumor microenvironment will support the development of personalized therapies. We report a spatially resolved single-cell imaging mass cytometry (IMC) analysis of CAFs in a non-small cell lung cancer cohort of 1,070 patients. We identify four prognostic patient groups based on 11 CAF phenotypes with distinct spatial distributions and show that CAFs are independent prognostic factors for patient survival. The presence of tumor-like CAFs is strongly correlated with poor prognosis. In contrast, inflammatory CAFs and interferon-response CAFs are associated with inflamed tumor microenvironments and higher patient survival. High density of matrix CAFs is correlated with low immune infiltration and is negatively correlated with patient survival. In summary, our data identify phenotypic and spatial features of CAFs that are associated with patient outcome in NSCLC.

Melanoma Registry of the Spanish Academy of Dermatology and Venereology (REGESMEL): Description and Data in its First Year of Operation

INTRODUCTION

The incidence of melanoma is rising in Spain. The prognostic stages of patients with melanoma are determined by various biological factors, such as tumor thickness, ulceration, or the presence of regional or distant metastases. The Spanish Academy of Dermatology and Venereology (AEDV) has encouraged the creation of a Spanish Melanoma Registry (REGESMEL) to evaluate other individual and health system-related factors that may impact the prognosis of patients with melanoma. The aim of this article is to introduce REGESMEL and provide basic descriptive data for its first year of operation.

METHODS

REGESMEL is a prospective, multicentre cohort of consecutive patients with invasive cutaneous melanoma that collects demographic and staging data as well as individual and healthcare-related baseline data. It also records the medical and surgical treatment received by patients.

RESULTS

A total of 450 cases of invasive cutaneous melanoma from 19 participant centres were included, with a predominance of thin melanomas≤1mm thick (54.7%), mainly located on the posterior trunk (35.2%). Selective sentinel lymph node biopsy was performed in 40.7% of cases. Most cases of melanoma were suspected by the patient (30.4%), or his/her dermatologist (29.6%). Patients received care mainly in public health centers (85.2%), with tele-dermatology resources being used in 21.6% of the cases.

CONCLUSIONS

The distribution of the pathological and demographic variables of melanoma cases is consistent with data from former studies. REGESMEL has already recruited patients from 15 Spanish provinces and given its potential representativeness, it renders the Registry as an important tool to address a wide range of research questions.

Evaluating lake water quality with a GIS-based MCDA integrated approach: a case in Konya/Karapınar

Considering water quality is an essential requirement in terms of environmental planning and management. To protect and manage water resources effectively, it is necessary to develop an analytical decision-support system. In this study, a systematic approach was suggested to evaluate the lake water quality. The methodology includes the prediction of the values in different locations of the lakes from experimental data through inverse distance weighting (IDW) method, creation of maps by using Geographic Information System (GIS) integrated with analytic hierarchy process (AHP) from multi-criteria decision analysis (MCDA), reclassification into five class, combining the time-related spatial data into a single map to predict the whole lake water quality from the data of sampling points, and finally overlapping the final maps with topography/geology and land use. The proposed approach was verified and presented as case study for Meke and Acigol Lakes in Konya/Turkey which were affected by human and natural factors although they have ecological, hydromorphological, and socio-economic importance. In the proposed approach, categorizing water quality parameters as "hardness and minerals," "substrates and nutrients," "solids content," "metals," and "oil-grease" groups was helpful for AHP with the determined group weights of 0.484, 0.310, 0.029, and 0.046, respectively. Assigning weights within each group and then assigning weights between groups resulted in creating accurate final map. The proposed approach is flexible and applicable to any lake water quality data; even with a limited number of data, the whole lake water quality maps could be created for assessment.

Race, disadvantage, and violence: A spatial exploration of macrolevel covariates of police-involved homicides within and between US counties

Efforts to explore the macrolevel determinants of police-involved homicides have expanded in recent years due in part to increased scrutiny and media attention to such events, and increased data availability of these events through crowdsourced databases. However, little empirical research has examined the spatial determinants of such events. The present study extends the extant macrolevel research on police-involved homicides by employing an underutilized spatial econometric model, the spatial Durbin model (SDM), to assess the direct and indirect county effects of racial threat, economic threat, social disorganization, and community violence on police killings within and between US counties from 2013 through 2020. Results indicate a direct inverse relationship between racial threat and police-involved homicides, no support for economic threat, and a direct positive association with two measures of social disorganization. Additionally, we find firearm availability exhibits significant direct and indirect spatial dependence on focal county police-involved homicides, reflecting spatial spillover processes. In essence, as firearm availability in neighboring counties increases, police-involved homicides within a focal county increase. The implications of these findings for racial threat, economic threat, social disorganization, and community violence are discussed.

Characterization of indigenous populations of cannabis in Iran: a morphological and phenological study

BACKGROUND

Cannabis is a historically, culturally, and economically significant crop in human societies, owing to its versatile applications in both industry and medicine. Over many years, native cannabis populations have acclimated to the various environments found throughout Iran, resulting in rich genetic and phenotypic diversity. Examining phenotypic diversity within and between indigenous populations is crucial for effective plant breeding programs. This study aimed to classify indigenous cannabis populations in Iran to meet the needs of breeders and breeding programs in developing new cultivars.

RESULTS

Here, we assessed phenotypic diversity in 25 indigenous populations based on 12 phenological and 14 morphological traits in male and female plants. The extent of heritability for each parameter was estimated in both genders, and relationships between quantitative and time-based traits were explored. Principal component analysis (PCA) identified traits influencing population distinctions. Overall, populations were broadly classified into early, medium, and late flowering groups. The highest extent of heritability of phenological traits was found in Start Flower Formation Time in Individuals (SFFI) for females (0.91) Flowering Time 50% in Individuals (50% of bracts formed) (FT50I) for males (0.98). Populations IR7385 and IR2845 exhibited the highest commercial index (60%). Among male plants, the highest extent of Relative Growth Rate (RGR) was observed in the IR2845 population (0.122 g.g- 1.day- 1). Finally, populations were clustered into seven groups according to the morphological traits in female and male plants.

CONCLUSIONS

Overall, significant phenotypic diversity was observed among indigenous populations, emphasizing the potential for various applications. Early-flowering populations, with their high RGR and Harvest Index (HI), were found as promising options for inclusion in breeding programs. The findings provide valuable insights into harnessing the genetic diversity of indigenous cannabis for diverse purposes.

Spatial-temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

OBJECTIVE

This study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011-2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.

METHODS

This study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.

RESULTS

PCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.

CONCLUSION

PM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

Unsuccessful tuberculosis treatment outcomes across Brazil's geographical landscape before and during the COVID-19 pandemic: are we truly advancing toward the sustainable development/end TB goal?

BACKGROUND

Tuberculosis is one of the most significant infectious diseases for global public health. The reallocation of healthcare resources and the restrictions imposed by the COVID-19 pandemic have hindered access to TB diagnosis and treatment. Increases in unfavorable outcomes of the disease have been observed in Brazil. The objective of this study was to analyze the spatial distribution of unfavorable TB treatment outcomes in Brazil before and during the pandemic.

METHODS

An ecological study with spatial analysis was conducted with all 5569 municipalities in Brazil. All reported cases of tuberculosis between January 2010 and December 2021, as well as reported cases of COVID-19 from February 2020 to December 2021, were included. The outcomes studied encompass loss to follow-up, drug-resistant tuberculosis, and death. The Getis Ord GI* technique was employed to assess spatial association, and the Kernel density estimator was used to identify areas with concentrated increases or decreases in outcomes. Bivariate Local Moran's I was used to examine the spatial association between outcomes and COVID-19 incidence. The study was approved by the Research Ethics Committee of Ribeirão Preto Nursing School, University of São Paulo.

RESULTS

There were 134,394 cases of loss to follow-up, 10,270 cases of drug resistance, and 37,863 deaths. Clusters of high and low values were identified for all three outcomes, indicating significant changes in the spatial distribution patterns. Increases in concentrations were observed for lost to follow-up cases in the Southeast, while reductions occurred in the Northeast, South, and Midwest. Drug-resistant tuberculosis experienced an increase in the Southern and Southeastern regions and a decrease in the Northeast and South. TB-related deaths showed notable concentrations in the Midwest, Northeast, South, and Southeast. There was an increase in high occurrence clusters for deaths after 2020 and 2021 in the Northeast.

CONCLUSIONS

The pandemic has brought additional challenges, emphasizing the importance of enhancing efforts and disease control strategies, prioritizing early identification, treatment adherence, and follow-up. This commitment is vital for achieving the goal of tuberculosis elimination.

Unlocking biogas potential: Spatial analysis, economic viability, and climate resilience in southern regions of Khyber Pakhtunkhwa, Pakistan

Across the world, the growing embrace of anaerobic digestion for deriving energy from biogas presents substantial economic and environmental benefits. This strategic approach carries particular significance for Pakistan, which is actively striving to meet its renewable energy objectives. The study focuses on a comprehensive examination of the spatial distribution of biogas potential originating from livestock farm manure in the southern areas of Khyber Pakhtunkhwa. This factor plays a pivotal role in ascertaining the best location and economic feasibility of a bioenergy facility. The study employs a spatial analysis algorithm to estimate biogas production. By integrating livestock data, manure production metrics, and collection statistics, the algorithm provides a high-resolution assessment at 1 km interval. The analysis unveil a biogas potential of 1.40 billion cubic meters of bio-methane sourced from manure. District D.I.Khan emerges as the leading producer, generating 201.40 million cubic meters of bio-methane. Several maps illustrate optimal sites and potential capacities for biogas facilities utilizing manure. The study proposes the practicality of establishing 868 biogas plants within the surveyed geographic region. Collectively, these plants would generate a substantial 909.34 Megawatt electrical (Mwe), while individual capacities spanning from 120 Kilowatt electrical (Kwe) to an impressive 997 KWe. These findings offer a clear path for strategically siting biogas plants, a crucial component of the global battle against climate change. Moreover, the findings of this study have played a significant role in shaping guidelines aimed at mitigating methane emissions, promoting the generation of renewable energy, and advocating for sustainable waste management practices. These guidelines serve as invaluable tools for climate mitigation and adaptation endeavors.

Mapping of population growth influence on land use transformation from 1994 to 2015 in Madaba Governorate, Jordan

The Madaba Governorate, as the second-largest wheat producer in Jordan, holds a crucial position in safeguarding regional food security. Its evolving landscape, marked by changes in land use, presents environmental and socio-economic challenges that necessitate sustainable urban planning and land management practices. This study delves into the intricate relationship between the conversion of agricultural lands into urban areas and the concurrent rise in population within the Madaba Governorate. Utilizing a Markov model, this research employs land use and land cover (LULC) data from 1994, 2004, and 2015 to project future changes in 2025 and 2035 with an impressive 80% accuracy (kappa coefficient). The findings reveal a projected 6% increase in urban areas over the next decade and a notable 11.81% decline in rural lands, signifying a substantial urbanization trend. In response to these population-driven LULC dynamics, there is an urgent need for the implementation of sustainable land use planning and management solutions. Given the constraints of limited water resources in the region, this study also places emphasis on water resource management. Recommendations include measures such as restricting urban sprawl, preserving agricultural lands, managing population growth, and implementing water conservation strategies. These insights provide invaluable information for stakeholders in the Madaba Governorate, including policymakers and land use planners, fostering a comprehensive understanding of the complex interplay between regional water resources, population expansion, and land use changes.

Individual and community-level factors associated with women's utilization of postnatal care services in Uganda, 2016: a multilevel and spatial analysis

BACKGROUND

Over time, Uganda has experienced high levels of maternal mortality (435 deaths per 100,000 live births in 2006 to 336 deaths per 100,000 live births in 2016). The persistence of high levels of maternal mortality jeopardizes the achievement of Sustainable Development Goal (SDG) 3.1, which calls for reducing maternal mortality to 70 deaths per 100,000 live births by 2030. Conversely, the utilization of postnatal care (PNC) services in Uganda remained very low and has varied across regions. This study examined the individual and community-level factors influencing women's utilization of postnatal care services in Uganda.

METHODS

Secondary data from the 2016 Uganda Demographic and Health Survey (UDHS) were used in this study. The study population consisted of women aged 15 to 49 who reported giving birth in the five years preceding the 2016 UDHS survey. The factors associated with postnatal care services were identified using multilevel binary logistic regression and spatial analysis.

RESULTS

The result shows that the prevalence of postnatal care service utilization in Uganda was low (58.3%) compared to the World Health Organization (WHO) target of 100%. The univariate analysis shows that 13.7% of women were adolescents, 79% were of higher parity, and 70.4% had primary/no formal education, of which 76.6% resided in rural areas. On the other hand, the multilevel analysis results showed that women aged 20-29 years and 30-39 years were also found to be more likely to use PNC services (AOR = 1.2, 95% CI: 1.01-1.47). Women who received quality ANC (AOR = 2.1, 95% CI: 1.78-2.36) were more likely to use postnatal care services than their counterparts. At the community level, women who lived in media-saturated communities were more likely to use postnatal care services (AOR = 1.3, 95% CI: 1.01-1.65). The spatial analysis found that the Central, Eastern, and Northern regions were the areas of hotspots in the utilization of postnatal care services.

CONCLUSION

This study found that age, parity, level of education, place of residence, employment status, quality of the content of antenatal care, and community media saturation were the predictors of postnatal care service utilization. The spatial analysis showed that the spatial distributions of postnatal care service utilization were significantly varied across Uganda. The government must expand access to various forms of media throughout the country to increase PNC utilization.

Tunable PhenoCycler imaging of the murine pre-clinical tumour microenvironments

BACKGROUND

The tumour microenvironment (TME) consists of tumour-supportive immune cells, endothelial cells, and fibroblasts. PhenoCycler, a high-plex single cell spatial biology imaging platform, is used to characterize the complexity of the TME. Researchers worldwide harvest and bank tissues from mouse models which are employed to model a plethora of human disease. With the explosion of interest in spatial biology, these panoplies of archival tissues provide a valuable resource to answer new questions. Here, we describe our protocols for developing tunable PhenoCycler multiplexed imaging panels and describe our open-source data analysis pipeline. Using these protocols, we used PhenoCycler to spatially resolve the TME of 8 routinely employed pre-clinical models of lymphoma, breast cancer, and melanoma preserved as FFPE.

RESULTS

Our data reveal distinct TMEs in the different cancer models that were imaged and show that cell-cell contacts differ depending on the tumour type examined. For instance, we found that the immune infiltration in a murine model of melanoma is altered in cellular organization in melanomas that become resistant to αPD-1 therapy, with depletions in a number of cell-cell interactions.

CONCLUSIONS

This work presents a valuable resource study seamlessly adaptable to any field of research involving murine models. The methodology described allows researchers to address newly formed hypotheses using archival materials, bypassing the new to perform new mouse studies.

Spatial association of homicide rate with violence, sociodemographic, and public security factors: global burden of disease study 2018 for municipalities in Brazil

OBJECTIVES

To analyse spatial-temporal changes and spatial association of homicide rates with violence, sociodemographic, public security and human rights indicators in Brazilian municipalities.

STUDY DESIGN

An ecological study using homicide estimates from the Global Burden of Disease and population from the Brazilian Ministry of Health, 2000 to 2018. The explanatory variables come from the systems of mortality, notifications of violence and security, and the Brazilian Institute of Geography and Statistics.

METHODS

Moran indices and maps identified clusters of high and low risk for homicides in three trienniums (p < 0.05). Multivariate linear and spatial regressions estimated explanatory factors' contributions for the last triennium.

RESULTS

Municipalities with high rates of homicides (>34/100,000) doubled, reaching 21.5 %. Those rates were concentrated in big cities, and increased in smaller municipalities. Increases in critical areas were found in the Northeast and North regions: more than 40 % in the states of Sergipe, Bahia, Ceará, Rio Grande do Norte and Roraima. Decreases occurred in the Southeast and Midwest regions: more than 35 % in São Paulo and Rio de Janeiro states. The spatial model, with an 18.9 % higher R2 (0.706), showed a positive association for records of violence, Blacks, low-level education, municipalities >50,000 inhabitants and municipalities with homicide and municipal police.

CONCLUSIONS

An increase in and the interiorisation of homicide risk areas in Brazil was observed, with displacement among regions (from the Southeast to the North/Northeast). The level of violence was the main explanatory factor for homicides. Territorial space proved to be important to understand and prevent lethal crime.

Spatiotemporal analysis of national carbon emission and regional carbon simulation in China

Land use and land cover (LULC) will cause large flows of carbon sources and sinks. As the world's largest carbon emitter with a complicated LULC, China's carbon emissions have profound implications for its ecological environment and future development. In this paper, we account for the land-use changes and carbon emissions of 30 Chinese provinces and cities in China from 2000 to 2020. Furthermore, the spatial correlation of carbon emissions among the study areas is explored. Four typical regions with spatial association (Beijing, Hebei, Sichuan, and Anhui) are selected, and their land-use change trends in 2025 and 2030 are simulated to predict the total carbon emissions in the future. The results show that the distribution of land-use in China is mainly cultivated and woodland, but the growth of urban built-up (UBL) land area indirectly leads to the continuous increase of carbon emissions. Total carbon emissions have increased over the past two decades, albeit at a slower growth rate, with some provinces experiencing no further growth. In the typical regional carbon emission simulation, it is found that the carbon emissions of the four provinces would show a downward trend in the future. The main reason is the reduction in indirect carbon emissions from fossil energy in UBL, while the other part is the influx of carbon sinks due to grassland, woodland, etc. We recommended that future carbon reduction measures should focus and prioritize controlling fossil energy and mitigating carbon emissions from UBL. Simultaneously, the significant contribution of forests and other land types as carbon sinks should be acknowledged to better implement China's carbon neutral commitment.

Spatial distribution and determinants of tuberculosis incidence in Mozambique: A nationwide Bayesian disease mapping study

INTRODUCTION

Mozambique is a high-burden country for tuberculosis (TB). International studies show that TB is a disease that tends to cluster in specific regions, and different risk factors (HIV prevalence, migration, overcrowding, poverty, house condition, temperature, altitude, undernutrition, urbanization, and inadequate access to TB diagnosis and treatment) are reported in the literature to be associated with TB incidence. Although Mozambique has a higher burden of TB, the spatial distribution, and determinants of TB incidence at the sub-national level have not been studied yet for the whole country. Therefore, we aimed to analyze the spatial distribution and determinants of tuberculosis incidence across all 154 districts of Mozambique and identify the hotspot areas.

METHOD

We conducted an ecological study with the district as our unit of analysis, where we included all cases of tuberculosis diagnosed in Mozambique between 2016 and 2020. We obtained the data from the Mozambique Ministry of Health and other publicly available open sources. The predictor variables were selected based on the literature review and data availability at the district level in Mozambique. The parameters were estimated through Bayesian hierarchical Poisson regression models using Markov Chain Monte Carlo simulation.

RESULTS

A total of 512 877 people were diagnosed with tuberculosis in Mozambique during our five-year study period. We found high variability in the spatial distribution of tuberculosis incidence across the country. Sixty-two districts out of 154 were identified as hotspot areas. The districts with the highest incidence rate were concentrated in the south and the country's central regions. In contrast, those with lower incidence rates were mainly in the north. In the multivariate analysis, we found that TB incidence was positively associated with the prevalence of HIV (RR: 1.23; 95 % CrI 1.13 to 1.34) and negatively associated with the annual average temperature (RR: 0.83; 95 % CrI 0.74 to 0.94).

CONCLUSION

The incidence of tuberculosis is unevenly distributed across the country. Lower average temperature and high HIV prevalence seem to increase TB incidence. Targeting interventions in higher-risk areas and strengthening collaboration between HIV and TB programs is paramount to ending tuberculosis in Mozambique, as established by the WHO's End TB strategy and the Sustainable Development Goals.

Comparison of the transportation network for public transit and private vehicles in Shanghai: An accessibility approach

This study examines the differences in service level and coverage of public transit (PT) and private vehicles (PV) with multi-source data in Shanghai. To construct computable networks and address visual results, the constrained shortest path algorithm and a spatial grid accessibility model are employed to seek the optimal path for travelers to city key points. Travel time ratio of PV and PT is applied to reflect the competitiveness of the two modes over different areas of Shanghai. Results show that for PV, although the average travel time meets the needs of car travel, 51 % of the population cannot get to graded city centers within 45 min. In addition, the PV accessibility gradually weakens from the central city to the outside, highways and expressways may be feasible solutions. For PT, half of the population can't reach any city key points within two transfers, and almost all of these people live in the suburbs. Less than 30 % of the population can reach the city key points within 1 h, of which rail transit contributes more than conventional buses. Furthermore, the travel accessibility of PV is much better than that of PT. The average travel time ratio in all comparable grids is 2.04 for hubs, and 2.10 for graded city centers. For travels to graded city centers, the travel time ratio of suburbs is 35 % higher than that of central city, indicating that the inequity distribution of public transportation resources is worse in the suburbs than in the central city. This study also measures equity performance of groups based on spatial location and income level, and we find out that more core locations and higher income lead to higher accessibility. The gap among groups is significant, with a Gini coefficient over 0.5.

Association of spatial proximity to fixed-site syringe services programs with HCV serostatus and injection equipment sharing practices among people who inject drugs in rural New England, United States

BACKGROUND

Hepatitis C virus (HCV) disproportionately affects rural communities, where health services are geographically dispersed. It remains unknown whether proximity to a syringe services program (SSP) is associated with HCV infection among rural people who inject drugs (PWID).

METHODS

Data are from a cross-sectional sample of adults who reported injecting drugs in the past 30 days recruited from rural counties in New Hampshire, Vermont, and Massachusetts (2018-2019). We calculated the road network distance between each participant's address and the nearest fixed-site SSP, categorized as ≤ 1 mile, 1-3 miles, 3-10 miles, and > 10 miles. Staff performed HCV antibody tests and a survey assessed past 30-day injection equipment sharing practices: borrowing used syringes, borrowing other used injection equipment, and backloading. Mixed effects modified Poisson regression estimated prevalence ratios (aPR) and 95% confidence intervals (95% CI). Analyses were also stratified by means of transportation.

RESULTS

Among 330 PWID, 25% lived ≤ 1 mile of the nearest SSP, 17% lived 1-3 miles of an SSP, 12% lived 3-10 miles of an SSP, and 46% lived > 10 miles from an SSP. In multivariable models, compared to PWID who lived within 1 mile of an SSP, those who lived 3 to 10 miles away had a higher prevalence of HCV seropositivity (aPR: 1.25, 95% CI 1.06-1.46), borrowing other used injection equipment (aPR: 1.23, 95% CI 1.04-1.46), and backloading (aPR: 1.48, 95% CI 1.17-1.88). Similar results were observed for PWID living > 10 miles from an SSP: aPR [HCV]: 1.19, 95% CI 1.01-1.40; aPR [borrowing other used equipment]:1.45, 95% CI 1.29-1.63; and aPR [backloading]: 1.59, 95% CI 1.13-2.24. Associations between living 1 to 3 miles of an SSP and each outcome did not reach statistical significance. When stratified by means of transportation, associations between distance to SSP and each outcome (except borrowing other used injection equipment) were only observed among PWID who traveled by other means (versus traveled by automobile).

CONCLUSIONS

Among PWID in rural New England, living farther from a fixed-site SSP was associated with a higher prevalence of HCV seropositivity, borrowing other used injection equipment, and backloading, reinforcing the need to increase SSP accessibility in rural areas. Means of transportation may modify this relationship.

Ecosystem health evaluation based on land use change-case study of the riparian zone of the Yangtze River in Jiangsu Province, China

Evaluating the ecosystem health of riparian zones is helpful for decision-makers to formulate appropriate management measures. However, there are few methods for such evaluation which account for both the human requirements and ecological aspects of riparian zones. To address this, we created a Pressure-State(Vigor-Organization-Resilience)-Response framework for evaluating the ecosystem health of the riparian zone of the Yangtze River in Jiangsu Province, a region experiencing intense land use changes. Evaluation indicators, including land use change and ecosystem services, were selected. The comprehensive index method was used to calculate the evaluation indicators of ecosystem health, namely pressure, state, and response, and the comprehensive evaluation indicator itself. Using the cold and hot spot analysis, we also analyzed the spatial heterogeneity of ecosystem health in the riparian zone, constructed an ecological management pattern, and proposed corresponding management and protection measures. The results show that (1) from 2010 to 2020, construction land in the study area increased by more than 20%, and all studied land types underwent some degree of conversion to construction land, with cultivated land and water bodies being the main focus of conversion. (2) In 2020, the average ecosystem health in the riparian zone was normal, with a spatial distribution characterized by "high dispersion and low clustering"; and (3) according to the results of the ecosystem health evaluation and cold and hot spot analysis, key areas for stronger ecological protection were identified and, based on this, a number of management recommendations were proposed.

Modeling and digital mapping of soil quality indicators in different land uses (a case study: Ravansar-Sanjabi Plain, Kermanshah)

Identifying the spatial variability of soil quality indices is necessary to manage soil resources on a watershed scale. This study aimed to identify suitable indices for soil quality assessment at the watershed scale using various soil characteristics and modeling approaches. Another objective was to map soil quality variability in a representative area in the Qarasu watershed in Kermanshah province, west of Iran. Latin hypercube sampling method using the auxiliary variables used to select 163 sampling points based on land use, soil, and topographical variability in an area of about 57 thousand hectares. In the field operations, soil profiles were described, and samples were taken from different soil profile horizons. Soil properties such as texture, pH, salinity, available water, equivalent calcium carbonate, saturation percentage, soil organic carbon, nitrogen, available phosphorous, potassium, Fe, Zn, Cu and Mn, and soil aggregate stability (mean weight diameter (MWD), geometric mean diametric (GMD), and stable aggregates larger than 0.25 mm (WAS)) measured in the laboratory. Soil quality indices (productivity index (PI), soil quality index (SQI) and reduced dimension soil quality index using principal component analysis (SQI-PCA)) were calculated for each point using the measured soil properties. Soil quality indices were simulated using modeling with the random forest and support vector machine methods and auxiliary variables. Results showed that the range of soil characteristics and integrated soil quality indices was very high across the study area. Soil organic carbon percent varied from about 0.19 to 8.44%. The range of changes in PI in the study area was more than SQI and SQI-PCA indices. Quantities of all soil quality indices were higher in forest and rangeland than in agricultural lands. The spatial estimation accuracy of the SQI-PCA was higher than other soil quality indices and converged well with land use changes compared to PI and SQI.

A district-level geospatial analysis of anaemia prevalence among rural men in India, 2019-21

BACKGROUND

Despite its considerable impact on health and productivity, anemia among men has received limited attention. In a country as diverse as India, characterized by extensive geographic variations, there is a pressing need to investigate the nuanced spatial patterns of anemia prevalence among men. The identification of specific hotspots holds critical implications for policymaking, especially in rural areas, where a substantial portion of India's population resides.

METHODS

The study conducted an analysis on a sample of 61,481 rural men from 707 districts of India, utilizing data from the National Family Health Survey-5 (2019-21). Various analytical techniques, including Moran's I, univariate LISA (Local Indicators of Spatial Association), bivariate LISA, and spatial regression models such as SLM (Spatial Lag Model), and SEM (Spatial Error Model) were employed to examine the geographic patterns and spatial correlates of anaemia prevalence in the study population.

RESULTS

In rural India, three out of every ten men were found to be anemic. The univariate Moran's I value for anaemia was 0.66, indicating a substantial degree of spatial autocorrelation in anaemia prevalence across the districts in India. Cluster and outlier analysis identified five prominent 'hotspots' of anaemia prevalence across 97 districts, primarily concentrated in the eastern region (encompassing West Bengal, Jharkhand, and Odisha), the Dandakaranya region, the Madhya Pradesh-Maharashtra border, lower Assam, and select districts in Jammu and Kashmir. The results of SLM revealed significant positive association between anaemia prevalence at the district-level and several key factors including a higher proportion of Scheduled Tribes, men in the 49-54 years age group, men with limited or no formal education, individuals of the Muslim faith, economically disadvantaged men, and those who reported alcohol consumption.

CONCLUSIONS

Substantial spatial heterogeneity in anaemia prevalence among men in rural India suggests the need for region-specific targeted interventions to reduce the burden of anaemia among men in rural India and enhance the overall health of this population.

Societal knowledge, attitude, and practices towards dengue and associated factors in epidemic-hit areas: Geoinformation assisted empirical evidence

Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.

A modeling framework to assess the crop production potential of green roofs

The increase in food demand and limited opportunities to expand agricultural land pose a threat to local and global food security. Producing food in urban areas such as green roofs can help satisfy urban food demand and thus alleviate pressure on agricultural land. However, a modeling framework that simulates crop growth and production potential on green roofs at a city scale is missing. Here, we adapt the Aquacrop model to explore the growth potential of various types of crops on green roofs and apply it to suitable roof areas in the city of Amsterdam. Our modeling framework includes irrigation methods for water use on green roofs that are optimized according to various climate-driven scenarios of water availability. We find that cabbage has the maximum achievable crop yields ranging from 30.8 to 75.9 t ha-1 yr-1, while pea has the minimum achievable crop yields ranging from 1.7 to 6.4 t ha-1 yr-1. The potential suitable green roof area (i.e., roofs with a certain slope and bearing capacity) for Amsterdam is roughly 400 ha for crop production. This represents 16 % of the total rooftop areas of Amsterdam and can produce up to a total of 28 kt of crops on an annual basis. Our modeling framework can be easily applied to other cities to identify the crop growth potential of green roofs. Our results can help policymakers and urban planners find optimal planting strategies and contribute to shorter food supply chains.

Community-acquired Staphylococcus aureus skin and soft tissue infection risk assessment using hotspot analysis and risk maps: the case of California emergency departments

BACKGROUND

Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk.

METHODS

Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area.

RESULTS

Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods.

CONCLUSIONS

Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Geographically weighted random forests for macro-level crash frequency prediction

Machine learning models such as random forests (RF) have been widely applied in the field of road safety. RF is a prominent algorithm, overcoming the limitations of using a single decision tree such as overfitting and instability. However, the traditional RF is a global concept, and thus may fail to capture spatial variability. In macro-level analysis of road safety, the relationship between crash frequency and risk factors can vary spatially. To address this issue, we employ a modified RF algorithm, named geographically weighted random forest (GWRF). Based on the data from London at the level of Middle-super-output-area (MSOA), the predictive performances of RF and GWRF are compared using mean absolute error (MAE) and root mean square error (RMSE). Moreover, considering MSOAs are geographically connected with each other, several factors related to the discrepancies between adjacent zones are also included in the models. Our results indicate that GWRF outperforms the traditional RF and GWR when an appropriate bandwidth is selected. We further explore the effects of multicollinearity on model performance. The results show that prediction accuracy of GWRF models are not susceptible to the multicollinearity. However, the importance values of those variables with multicollinearity may reduce. Finally, and of equal importance, it is found that the importance of each explanatory variable varies across zones. The density of minor road makes the highest contribution to crash frequency in downtown area, while the crash frequency in peripheral area is more sensitive to the discrepancy of road environment between MSOAs. With such information, road safety interventions can be designed and implemented according to the locally important factors, avoiding thus general guidelines addressed for the entire city.

Geospatially clustered low COVID-19 vaccine rates among adolescents in socially vulnerable US counties

COVID-19 vaccinations are widely available across the United States (U.S.), yet little is known about the spatial clustering of COVID-19 vaccinations. This study aimed to test for geospatial clustering of COVID-19 vaccine rates among adolescents aged 12-17 across the U.S. counties and to compare these clustering patterns by sociodemographic characteristics. County-level data on COVID-19 vaccinations and sociodemographic characteristics were obtained from the COVID-19 Community Profile Report up to April 14, 2022. A total of 3,108 counties were included in the analysis. Global Moran's I statistic and Anselin Local Moran's analysis were used, and clustering patterns were compared to sociodemographic variables using t-tests. Counties with low COVID-19 vaccinated clusters were more likely, when compared to unclustered counties, to have higher numbers of individuals in poverty and uninsured individuals, and higher values of Social Vulnerability Index (SVI) and COVID-19 Community Vulnerability Index (CCVI). While high COVID-19 vaccinated clusters, compared to neighboring counties, had lower numbers of Black population, individuals in poverty, and uninsured individuals, and lower values of SVI and CCVI, but a higher number of Hispanic population. This study emphasizes the importance of addressing systemic barriers, such as poverty and lack of health insurance, which were found to be associated with low COVID-19 vaccination coverage.

Spatial analysis of human and livestock anthrax in Lai Chau province, Vietnam (2004-2021)

Anthrax is reported globally with varying disease intensity and seasonality among countries. In Vietnam, anthrax epidemiology and ecology remain understudied. We used historical data of human and livestock anthrax from 2004 to 2021 in Lai Chau province, to identify spatial clusters of human and livestock anthrax, describe epidemiological characteristics, and compare livestock anthrax vaccine coverage to human and livestock disease incidence. Local Moran's I (LISA) using spatial Bayes smoothed commune-level cumulative incidence (per 10,000) for the study period, epidemiological descriptive statistics, livestock vaccine coverage data, and annual incidence rates (per 10,000) at provincial level were used. LISA identified a human anthrax hotspot (high-high) in the southeast which did not overlap spatially with livestock anthrax hotspots in southeastern and northeastern communes. Most human cases were male, aged 15-59 years, handled sick animals, and/or consumed contaminated meat. Almost all cases were reported by grassroot health facilities with a delay of 6.3 days between exposure and case notification to the national surveillance system. 80 % of human cases were reported from June-October. The increase in disease incidence occurred shortly after livestock anthrax vaccine coverage decreased. This study informs vaccination strategy and targeted surveillance and control measures in newly identified high-risk areas and seasons of anthrax.

Spatial distribution of heavy metals in the sediments of River Ganges, India: Occurrence, contamination, source identification, seasonal variations, mapping, and ecological risk evaluation

Present study analyzed the seasonal and spatial distribution patterns, sources, and ecological risks of seven heavy metals (Cr, Fe, Ni, Cu, Zn, Cd and Pb) in the sediments of River Ganges, finding that the majority of concentrations were lithologic, except for Cd, which was significantly higher than background standards. Elevated values of geochemical indices viz. Igeo, CF, RI, Cd, mCd, HQ, mHQ, and PN suggest moderate to high ecological risk in the benthic environment and its organisms due to the synergistic effect of heavy metals. The PEC-Qmetals revealed 8-10 % toxicity in the upstream and downstream sites, due to the influence of agricultural activities. Multivariate statistical techniques (PCM and PCA) indicated that Cd and Pb predominantly originated from anthropogenic sources, while other metals primarily derived from geological background. These geochemical findings may help to understand the potential risks and recommend strategies to mitigate the effects of metallic contamination in river sediments.

Explore the spatial pattern of carbon emissions in urban functional zones: a case study of Pudong, Shanghai, China

It is crucial for the development of carbon reduction strategies to accurately examine the spatial distribution of carbon emissions. Limited by data availability and lack of industry segmentation, previous studies attempting to model spatial carbon emissions still suffer from significant uncertainty. Taking Pudong New Area as an example, with the help of multi-source data, this paper proposed a research framework for the amount calculation and spatial distribution simulation of its CO2 emissions at the scale of urban functional zones (UFZs). The methods used in this study were based on mapping relations among the locations of geographic entities and data of multiple sources, using the coefficient method recommended by the Intergovernmental Panel on Climate Change (IPCC) to calculate emissions. The results showed that the emission intensity of industrial zones and transport zones was much higher than that of other UFZs. In addition, Moran's I test indicated that there was a positive spatial autocorrelation in high emission zones, especially located in industrial zones. The spatial analysis of CO2 emissions at the UFZ scale deepened the consideration of spatial heterogeneity, which could contribute to the management of low carbon city and the optimal implementation of energy allocation.

Decoding the impacts of contributory factors and addressing social disparities in crash frequency analysis

Understanding the relationship between social disparities and traffic crash frequency is essential for long-term transportation planning and policymaking. Few studies have systemically examined the influence of socioeconomic and infrastructure-related disparities in macro-level traffic crash frequency. This study provides a framework to spatially examine the relationships between crash rates and demographic and socioeconomic characteristics, as well as roadway infrastructure and traffic characteristics at the Census Block Groups (CBGs) level. Spatial autocorrelation analysis was first performed on the residual of the Ordinary Least Squares (OLS) model to identify whether non-stationarity exists. Then, the Geographically Weighted Regression (GWR) model and the Multiscale Geographically Weighted Regression (MGWR) model were applied to assess the impacts of these factors on crash rates spatially and statistically. Our findings indicate that MGWR outperforms both OLS and GWR in uncovering the spatial relationships between contributing factors and both fatal and injury (FI) crashes as well as property damage only (PDO) crashes. A thorough examination of local coefficient maps highlighted six pivotal variables that significantly influenced a majority of CBGs. Improving infrastructure, including pedestrian pathways and public transit facilities, in low-income areas can offer significant benefits. These findings and recommendations can inform the development of effective strategies for reducing crashes and guide the appropriate selection of modeling techniques for macro-level crash analysis.

Geo-Demographic and Socioeconomic Determinants of Diagnosed Hypertension among Urban Dwellers in Ibadan, Nigeria: A Community-based Study

Background

The relationship between diagnosed high blood pressure (HBP) and proximity to health facilities and noise sources is poorly understood. We investigated the relationship between proximity to noise sources, sociodemographic and economic factors, and diagnosed HBP in Ibadan, Nigeria.

Methods

We investigated 13,531 adults from the African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES) study in Ibadan. Using a Geographic Information System (GIS), the locations of healthcare facilities, pharmaceutical shops, bus stops, churches, and mosques were buffered at 100m intervals, and coordinates of persons diagnosed with HBP were overlaid on the buffered features. The number of persons with diagnosed HBP living at every 100m interval was estimated. Gender, occupation, marital status, educational status, type of housing, age, and income were used as predictor variables. Analysis was conducted using Spearman rank correlation and binary logistic regression at p<0.05.

Results

There was a significant inverse relationship between the number of persons diagnosed with HBP and distance from pharmaceutical shops (r=-0.818), churches (r=-0.818), mosques (r=-0.893) and major roads (r=-0.667). The odds of diagnosed HBP were higher among the unemployed (AOR=1.58, 95% CI: 1.11-2.24), currently married (AOR=1.45, CI: 1.11-1.89), and previously married (1.75, CI: 1.29-2.38). The odds of diagnosed HBP increased with educational level and age group.

Conclusion

Proximity to noise sources, being unemployed and educational level were associated with diagnosed HBP. Reduction in noise generation, transmission, and exposure could reduce the burden of hypertension in urban settings.

Impacts of land use/cover and slope on the spatial distribution and ecological risk of trace metals in soils affected by smelting emissions

The soil contamination around smelting sites shows high spatial heterogeneity. This study investigated the impacts of distance, land use/cover types, land slopes, wind direction, and soil properties on the distribution and ecological risk of trace metals in the soil around a copper smelter. The results demonstrated that the average concentrations of As, Cd, Cu, Pb, and Zn were 248.0, 16.8, 502.4, 885.6, and 250.2 g mg kg-1, respectively, higher than their background values. The hotspots of trace metals were primarily distributed in the soil of smelting production areas, runoff pollution areas, and areas in the dominant wind direction. The concentrations of trace metals decreased with the distance to the smelting production area. An exponential decay regression revealed that, depending on the metal species, the influence distances of smelting emissions on trace metals in soil ranged from 450 to 1000 m. Land use/cover types and land slopes significantly affected trace element concentrations in the soil around the smelter. High concentrations of trace metals were observed in farmland, grassland, and flatland areas. The average concentrations of trace metals in the soil decreased in the order of flat land > gentle slope > steep slope. Soil pH values were significantly positively correlated with Cd, Cu, Pb, Zn, and As, and SOM was significantly positively correlated with Cd, Pb, and Zn in the soil. Trace metals in the soil of the study area posed a significant ecological risk. The primary factors influencing the distribution of ecological risk, as determined by the Ctree analysis, were land slope, soil pH, and distance to the source. These results can support the rapid identification of high-risk sites and facilitate risk prevention and control around smelting sites.

Industrial air pollution and low birth weight in New Mexico, USA

In recent decades, the low birth weight (LBW) rate in New Mexico has consistently exceeded the Unites States average. Maternal exposure to air pollution during pregnancy may be a significant contributor to LBW in offspring. This study investigated the links between maternal residential exposure to air pollution from industrial sources and the risk of LBW in offspring. The analysis included 22,375 LBW cases and 233,340 controls. It focused on 14 common chemicals listed in the Toxic Release Inventory (TRI) and monitoring datasets, which have abundant monitoring samples. The Emission Weighted Proximity Model (EWPM) was used to calculate maternal air pollution exposure intensity. Adjusted odds ratios (adjORs) were calculated using binary logistic regressions to examine the association between maternal residential air pollution exposure and LBW, while controlling for potential confounders, such as the maternal age, race/ethnicity, gestational age, prenatal care, education level, consumption of alcohol during pregnancy, public health regions, child's sex, and the year of birth. Multiple comparison correction was applied using the False Discovery Rate approach. The results showed that maternal residential exposure to 1,2,4-trimethylbenzene, benzene, chlorine, ethylbenzene, and styrene had significant positive associations with LBW in offspring, with adjusted odds ratios ranging from 1.10 to 1.13. These five chemicals remained as significant risk factors after dividing the estimated exposure intensities into four categories. In addition, significant linear trends were found between LBW and maternal exposure to each of the five identified chemicals. Furthermore, 1,2,4-trimethylbenzene was identified as a risk factor to LBW for the first time. The findings of this study should be confirmed through additional epidemiological, biological, and toxicological studies.

Spatial methods for measuring access to health care

Access to health care is a universal human right and key indicator of health system performance. Spatial access encompasses geographic factors mediating with the accessibility and availability of health services. Equity of health service access is a global issue, which includes access to the specialized nursing workforce. Nursing research applying spatial methods is in its infancy. Given the use of spatial methods in health research is a rapidly developing field, it is timely to provide guidance to inspire greater application in cardiovascular research. Therefore, the objective of this methods paper is to provide an overview of spatial analysis methods to measure the accessibility and availability of health services, when to consider applying spatial methods, and steps to consider for application in cardiovascular nursing research.

Single-Cell and Spatial Transcriptome Analysis of Dermal Fibroblast Development in Perinatal Mouse Skin: Dynamic Lineage Differentiation and Key Driver Genes

Several single-cell RNA studies of developing mouse skin have elucidated the molecular and cellular processes involved in skin development. However, they have primarily focused on either the fetal or early postnatal period, leaving a gap in our understanding of skin development. In this study, we conducted a comprehensive time-series analysis by combining single-cell RNA-sequencing datasets collected at different stages of development (embryonic days 13.5, 14.5, and 16.5 and postnatal days 0, 2, and 4) and validated our findings through multipanel in situ spatial transcriptomics. Our analysis indicated that embryonic fibroblasts exhibit heterogeneity from a very early stage and that the rapid determination of each lineage occurs within days after birth. The expression of putative key driver genes, including Hey1, Ebf1, Runx3, and Sox11 for the dermal papilla trajectory; Lrrc15 for the dermal sheath trajectory; Zfp536 and Nrn1 for the papillary fibroblast trajectory; and Lrrn4cl and Mfap5 for the fascia fibroblast trajectory, was detected in the corresponding, spatially identified cell types. Finally, cell-to-cell interaction analysis indicated that the dermal papilla lineage is the primary source of the noncanonical Wnt pathway during skin development. Together, our study provides a transcriptomic reference for future research in the field of skin development and regeneration.