Advances in spatial epidemiology and geographic information systems

Defining Spatial Epidemiology: A Systematic Review and Re-orientation

BACKGROUND

Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.

METHODS

We conducted a systematic review of studies indexed in PubMed that used the term "spatial epidemiolog*" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.

RESULTS

A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).

CONCLUSIONS

Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.

Mapping geographical inequalities of incomplete immunization in Ethiopia: a spatial with multilevel analysis

Background

Immunization is one of the most cost-effective interventions, averting 3.5-5 million deaths every year worldwide. However, incomplete immunization remains a major public health concern, particularly in Ethiopia. The objective of this study is to investigate the geographical inequalities and determinants of incomplete immunization in Ethiopia.

Methods

A secondary analysis of the mini-Ethiopian Demographic Health Survey (EDHS 2019) was performed, utilizing a weighted sample of 3,865 children aged 12-23 months. A spatial auto-correlation (Global Moran's I) statistic was computed using ArcGIS version 10.7.1 to assess the geographical distribution of incomplete immunization. Hot-spot (areas with a high proportion of incomplete immunization), and cold spot areas were identified through Getis-Ord Gi* hot spot analysis. Additionally, a Bernoulli probability-based spatial scan statistics was conducted in SaTScan version 9.6 software to determine purely statistically significant clusters of incomplete immunization. Finally, a multilevel fixed-effects logistic regression model was employed to identify factors determining the status of incomplete immunization.

Results

Overall, in Ethiopia, more than half (54%, 95% CI: 48-58%) of children aged 12-23 months were not fully immunized. The spatial analysis revealed that the distribution of incomplete immunization was highly clustered in certain areas of Ethiopia (Z-score value = 8.379419, p-value < 0.001). Hotspot areas of incomplete immunization were observed in the Afar, Somali, and southwestern parts of Ethiopia. The SaTScan spatial analysis detected a total of 55 statistically significant clusters of incomplete immunization, with the primary SaTScan cluster found in the Afar region (zones 1, 3, and 4), and the most likely secondary clusters detected in Jarar, Doola, Korahe, Shabelle, Nogob, and Afdar administrative zones of the Somali region of Ethiopia. Indeed, in the multilevel mixed-effect logistic regression analysis, the respondent's age (AOR: 0.92; 95% CI: 0.86-0.98), residence (AOR: 3.11, 95% CI: 1.36-7.14), living in a pastoralist region (AOR: 3.41; 95% CI: 1.29-9.00), educational status (AOR: 0.26; 95% CI: 0.08-0.88), place of delivery (AOR: 2.44; 95% CI: 1.15-5.16), and having PNC utilization status (AOR: 2.70; 95% CI: 1.4-5.29) were identified as significant predictors of incomplete immunization.

Conclusion and recommendation

In Ethiopia, incomplete immunization is not randomly distributed. Various factors at both individual and community levels significantly influence childhood immunization status in the country. It is crucial to reduce disparities in socio-demographic status through enhanced collaboration across multiple sectors and by bolstering the utilization of maternal health care services. This requires concerted efforts from stakeholders.

Factors Associated with Transfer Distance from Birth Hospital to Repair Hospital for First Surgical Repair among Infants with Myelomeningocele in California

OBJECTIVE

 The objective of our study was to examine factors associated with distance to care for first surgical repair among infants with myelomeningocele in California.

STUDY DESIGN

 A total of 677 eligible cases with complete geocoded data were identified for birth years 2006 to 2012 using data from the California Perinatal Quality Care Collaborative linked to hospital and vital records. The median distance from home to birth hospital among eligible infants was 9 miles, and from birth hospital to repair hospital was 15 miles. We limited our analysis to infants who lived close to the birth hospital, creating two study groups to examine transfer distance patterns: "lived close and had a short transfer" (i.e., lived <9 miles from birth hospital and traveled <15 miles from birth hospital to repair hospital; n = 92), and "lived close and had a long transfer" (i.e., lived <9 miles from birth hospital and traveled ≥15 miles from birth hospital to repair hospital; n = 96). Log-binomial regression was used to estimate crude and adjusted risk ratios (aRRs and 95% confidence intervals (CIs). Selected maternal, infant, and birth hospital characteristics were compared between the two groups.

RESULTS

 We found that low birth weight (aRR = 1.44; 95% CI = 1.04, 1.99) and preterm birth (aRR = 1.41; 95% CI = 1.01, 1.97) were positively associated, whereas initiating prenatal care early in the first trimester was inversely associated (aRR = 0.64; 95% CI = 0.46, 0.89) with transferring a longer distance (≥15 miles) from birth hospital to repair hospital. No significant associations were noted by maternal race-ethnicity, socioeconomic indicators, or the level of hospital care at the birth hospital.

CONCLUSION

 Our study identified selected infant factors associated with the distance to access surgical care for infants with myelomeningocele who had to transfer from birth hospital to repair hospital. Distance-based barriers to care should be identified and optimized when planning deliveries of at-risk infants in other populations.

KEY POINTS

· Low birth weight predicted long hospital transfer distance.. · Preterm birth was associated with transfer distance.. · Prenatal care was associated with transfer distance..

Prevalence and spatial distribution characteristics of human echinococcosis: A county-level modeling study in southern Xinjiang, China

Objectives

Human echinococcosis remains an important public health problem. The aim of this study was to analyze the prevalence and spatial distribution characteristics of human echinococcosis cases in southern Xinjiang, China from 2005 to 2021.

Methods

Human echinococcosis cases were collected from the National Infectious Disease Reporting System. Joinpoint regression analysis was performed to explore the trends. Spatial autocorrelation, hot spot analysis, as well as spatial-temporal clustering analysis were conducted to confirm the distribution and risk factors.

Results

A total of 4580 cases were reported in southern Xinjiang during 2005-2021, with a mean annual incidence of 2.56/100,000. Echinococcosis incidence showed an increasing trend from 2005 to 2017 (APC = 17.939, 95%CI: 13.985 to 22.029) and a decreasing trend from 2017 to 2021 (APC = -18.769, 95%CI: 28.157 to -8.154). Echinococcosis cases had a positive spatial autocorrelation in 2005-2021 (Moran's I = 0.19, P < 0.05). The disease hotspots were located in the east and west in these areas, then returned to the east clusters, including Hejing, Heshuo, Wuqia, Atushi, Aheqi, and Yanqi Hui Autonomous County. Meanwhile, spatial-temporal analysis identified the first cluster comprised of five counties (cities): Yanqi Hui Autonomous County, Korla City, Bohu County, Hejing County, and Heshuo County. And secondary clusters 1-3 are predominantly in Wushi County, Aheqi County, Keping County, Atushi City, Wuqia County and Cele County.

Conclusions

Our findings suggest that echinococcosis is still an important zoonotic parasitic disease in southern Xinjiang, yet it showed a certain degree of spatial clustering. It is crucial to implement comprehensive prevention and control measures to effectively combat the epidemic of echinococcosis.

Epidemiological Characteristics of Varicella in Anhui Province, China, 2012-2021: Surveillance Study

BACKGROUND

Varicella is a mild, self-limited disease caused by varicella-zoster virus (VZV) infection. Recently, the disease burden of varicella has been gradually increasing in China; however, the epidemiological characteristics of varicella have not been reported for Anhui Province.

OBJECTIVE

The aim of this study was to analyze the epidemiology of varicella in Anhui from 2012 to 2021, which can provide a basis for the future study and formulation of varicella prevention and control policies in the province.

METHODS

Surveillance data were used to characterize the epidemiology of varicella in Anhui from 2012 to 2021 in terms of population, time, and space. Spatial autocorrelation of varicella was explored using the Moran index (Moran I). The Kulldorff space-time scan statistic was used to analyze the spatiotemporal aggregation of varicella.

RESULTS

A total of 276,115 cases of varicella were reported from 2012 to 2021 in Anhui, with an average annual incidence of 44.8 per 100,000, and the highest incidence was 81.2 per 100,000 in 2019. The male-to-female ratio of cases was approximately 1.26, which has been gradually decreasing in recent years. The population aged 5-14 years comprised the high-incidence group, although the incidence in the population 30 years and older has gradually increased. Students accounted for the majority of cases, and the proportion of cases in both home-reared children (aged 0-7 years who are not sent to nurseries, daycare centers, or school) and kindergarten children (aged 3-6 years) has changed slightly in recent years. There were two peaks of varicella incidence annually, except for 2020, and the incidence was typically higher in the winter peak than in summer. The incidence of varicella in southern Anhui was higher than that in northern Anhui. The average annual incidence at the county level ranged from 6.61 to 152.14 per 100,000, and the varicella epidemics in 2018-2021 were relatively severe. The spatial and temporal distribution of varicella in Anhui was not random, with a positive spatial autocorrelation found at the county level (Moran I=0.412). There were 11 districts or counties with high-high clusters, mainly distributed in the south of Anhui, and 3 districts or counties with high-low or low-high clusters. Space-time scan analysis identified five possible clusters of areas, and the most likely cluster was distributed in the southeastern region of Anhui.

CONCLUSIONS

This study comprehensively describes the epidemiology and changing trend of varicella in Anhui from 2012 to 2021. In the future, preventive and control measures should be strengthened for the key populations and regions of varicella.

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Temporal, spatial and space-time distribution of infections caused by five major enteric pathogens, Ontario, Canada, 2010-2017

AIMS

In Canada, enteric diseases pose substantial health and economic burdens. The distribution of these diseases is uneven across both geography and time and understanding these patterns is therefore important for the prevention of future outbreaks. We evaluated temporal, spatial and space-time clustering of laboratory-confirmed cases of Campylobacter spp. (n = 28,728), non-typhoidal Salmonella spp. (n = 22,640), Shiga toxin-producing Escherichia coli (STEC; n = 1340), Yersinia spp. (n = 1674) and Listeria monocytogenes (n = 471) infections, reported between 2010 and 2017 inclusive in Ontario, the most populous province in Canada (population ~ 13,500,000 in 2016).

METHODS AND RESULTS

For each enteric pathogen, we calculated the mean incidence rates (IRs) for Ontario's 35 public health unit (PHU) areas and visualized them using choropleth maps. We identified temporal, spatial and space-time high infection rate clusters using retrospective Poisson scan statistics. Campylobacter and Salmonella infections had the highest IRs, while Listeria infections had the lowest. Campylobacter, Salmonella, STEC and Listeria mostly clustered temporally in the spring/summer and sometimes extended into fall, while Yersinia showed a less clear seasonal pattern. The IR visualizations and spatial and space-time scan statistics showed geographic heterogeneity of infection rates with high infection rate clusters detected mainly in PHUs across the southwestern and central-western regions of Ontario for Campylobacter, Salmonella and STEC infections, and mainly in PHUs located in the central-eastern regions for Yersinia and Listeria. A high proportion of cases in some of the significant Salmonella, STEC and Listeria infection clusters were linked to disease outbreaks.

CONCLUSIONS

Results from this study will inform heightened public health surveillance, and prevention and control programmes, in populations and regions of high infection rates. Further research is needed to determine the pathogen-specific socioeconomic, environmental and agricultural risk factors that may be related to the temporal and spatial disease patterns we observed in our study.

Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer

PURPOSE

Gastric cancer (GC) ranks as the 7th most common cancer worldwide and a leading cause of cancer mortality. In Iran, stomach malignancies are the most common fatal cancers with higher than world average incidence. In recent years, methods like machine learning that provide the opportunity of merging health issues with computational power and learning capacity have caught considerable attention for prediction and diagnosis of diseases. In this study, we aimed to model GC data to find risk factors and identify GC cases in Golestan Cohort Study (GCS), using gradient boosting as a machine learning technique.

METHODS

Since the GC class (280) was smaller than not-GC (49,467), "Synthetic Minority Oversampling Technique" was used to balance the dataset. Seventy percent of the data was used to train the gradient boosting algorithm and find effective factors on gastric cancer, and the remaining 30% was used for accuracy assessment.

RESULTS

Our results indicated that out of 19 factors, age, social economical status, tea temperature, body mass index, gender, and education were the top six effective factors with impact rates of 0.24, 0.16, 0.13, 0.13, and 0.07, respectively. The trained model classified 70 out of 72 GC patients in the test set, correctly.

CONCLUSION

The results indicate that this model can effectively detect gastric cancer (GC) by utilizing important risk factors, thus avoiding the need for invasive procedures. The model's performance is reliable when provided with an adequate amount of input data, and as the dataset expands, its accuracy and generalization improve significantly. Overall, the trained system's success stems from its ability to identify risk factors and identify cancer patients.

Effect of urgency level on prehospital emergency transport times: a natural experiment

Accurate estimation of ambulance transport time from the scene of incident to arrival at the emergency department (ED) is important for effective resource management and emergency care system planning. Further, differences in transport times between different urgency levels highlight the benefits of ambulance transports with highest urgency level in a setting where ambulances are allowed to not follow standard traffic rules. The objective of the study is to compare ambulance urgency level on the differences in estimates of ambulance transport times generated by Google Maps and the observed transport times in a prehospital setting where emergency vehicles have their own traffic laws. The study was designed as a natural experiment and register study. Ambulance transports dispatched with different levels of urgency (Level A and B) were included in the Central Denmark Region (a mixed urban and rural area) from March 10 to June 11, 2021. Ambulance transports for highest urgency level were compared to lowest urgency level with Google Maps estimated transport times as reference. We analyzed 1981 highest urgency level and 8.958 lowest urgency level ambulance transports. Google Maps significantly overestimated the duration of transports operating at highest level of urgency (Level A) by 1.9 min/10 km (95% CI 1.8; 2.0) in average and 4.8 min/10 km (95% CI 3.9; 5.6) for the first driven 10 km. Contrary, Google Maps significantly underestimated the duration of transports operating at lowest level of urgency (Level B) by -1.8 min/10 km (95% CI -2.1; -1.5) in average and -4.4 min/10 km (95% CI -5.4; -3.5) for the first driven 10 km. Google Maps systematically overestimates transport times of ambulance transports driven with Level A, the highest level of urgency in a setting where ambulances are allowed to not follow standard traffic rules. The results highlight the benefit of using urgency Level A and provide valuable information for emergency care management.

Spatial Survival Model for COVID-19 in México

A spatial survival analysis was performed to identify some of the factors that influence the survival of patients with COVID-19 in the states of Guerrero, México, and Chihuahua. The data that we analyzed correspond to the period from 28 February 2020 to 24 November 2021. A Cox proportional hazards frailty model and a Cox proportional hazards model were fitted. For both models, the estimation of the parameters was carried out using the Bayesian approach. According to the DIC, WAIC, and LPML criteria, the spatial model was better. The analysis showed that the spatial effect influences the survival times of patients with COVID-19. The spatial survival analysis also revealed that age, gender, and the presence of comorbidities, which vary between states, and the development of pneumonia increase the risk of death from COVID-19.

Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy

Purpose

Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development.

Methods

We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters.

Results

The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters.

Conclusions

Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.

EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization

The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as 'cluster embedding' methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiVECS, a tool which allows the user to perform cluster embedding and explore the results using interactive visualization. EpiVECS is available as a privacy preserving, in-browser open source web application at https://episphere.github.io/epivecs .

Global and local drivers of Echinococcus multilocularis infection in the western Balkan region

The cestode, Echinococcus multilocularis, is one of the most threatening parasitic challenges in the European Union. Despite the warming climate, the parasite intensively spread in Europe's colder and warmer regions. Little is known about the expansion of E. multilocularis in the Balkan region. Ordinary least squares, geographically weighted and multi-scale geographically weighted regressions were used to detect global and local drivers that influenced the prevalence in red foxes and golden jackals in the southwestern part of Hungary. Based on the study of 391 animals, the overall prevalence exceeded 18% (in fox 15.2%, in jackal 21.1%). The regression models revealed that the wetland had a global effect (β = 0.391, p = 0.006). In contrast, on the local scale, the mean annual precipitation (β = 0.285, p = 0.008) and the precipitation seasonality (β = - 0.211, p = 0.014) had statistically significant effects on the infection level. The geospatial models suggested that microclimatic effects might compensate for the disadvantages of a warmer Mediterranean climate. This study calls attention to fine-scale analysis and locally acting environmental factors, which can delay the expected epidemic fade-out. The findings of our study are suggested to consider in surveillance strategies.

Improving data capture of race and ethnicity for the Food and Drug Administration Sentinel database: a narrative review

PURPOSE

The U.S. Food and Drug Administration's Sentinel System is a national medical product safety surveillance system consisting of a large multisite distributed database of administrative claims supplemented by electronic health-care record data. The program seeks to improve data capture of race and ethnicity for pharmacoepidemiology studies.

METHODS

We conducted a narrative literature review of published research on data augmentation and imputation methods to improve race and ethnicity capture in U.S. health-care systems databases. We focused on methods with limited (five-digit ZIP codes only) or full patient identifiers available to link to external sources of self-reported data. We organized the literature by themes: (1) variation in data capture of self-reported data, (2) data augmentation from external sources of self-reported data, and (3) imputation methods, including Bayesian analysis and multiple regression.

RESULTS

Researchers reduced data missingness with high validity for Asian, Black, White, and Pacific Islander racial groups and Hispanic ethnicity. Native American and multiracial groups were difficult to validate due to relatively small sample sizes.

CONCLUSIONS

Limitations on accessible self-reported data for validation will dictate methods to improve race and ethnicity data capture. We recommend methods leveraging multiple sources that account for variations in geography, age, and sex.

Self-reported critical gaps in the essential knowledge and capacity of spatial epidemiology between the current university education and competency-oriented professional demands in preparing for a future pandemic among public health postgraduates in China: a nationwide cross-sectional survey

BACKGROUND

Spatial epidemiology plays an important role in public health. Yet, it is unclear whether the current university education in spatial epidemiology in China could meet the competency-oriented professional demands. This study aimed to understand the current situation of education and training, practical application, and potential demands in spatial epidemiology among public health postgraduates in China, and to assess the critical gaps in a future emerging infectious diseases (EID) pandemic preparedness and response.

METHODS

This study was divided into three parts. The first part was a comparative study on spatial epidemiology education in international public health postgraduate training. The second part was a cross-sectional survey conducted among public health professionals. The third part was a nationwide cross-sectional survey conducted among public health postgraduates at Chinese universities from October 2020 to February 2021. Data was collected by the WeChat-based questionnaire star survey system and analyzed using the SPSS software.

RESULTS

International education institutions had required public health postgraduates to master the essential knowledge and capacity of spatial epidemiology. A total of 198 public health professionals were surveyed, and they had a median of 4.00 (IQR 3.13-4.53) in demand degree of spatial epidemiology. A total of 1354 public health postgraduates were surveyed from 51 universities. Only 29.41% (15/51) of universities offered spatial epidemiology course. Around 8.05% (109/1354) of postgraduates had learned spatial epidemiology, and had a median of 1.05 (IQR 1.00-1.29) in learning degree and a median of 1.91 (IQR 1.05-2.78) in practical application degree of spatial epidemiology. To enhance professional capacity, 65.95% (893/1354) of postgraduates hoped that universities would deliver a credit-course of spatial epidemiology.

CONCLUSIONS

A huge unmet education and training demand in spatial epidemiology existed in the current education system of public health postgraduates in China. To enhance the competency-oriented professional capacity in preparedness and response to a future pandemic, it is urgent to incorporate the teaching and training of spatial epidemiology into the compulsory curriculum system of public health postgraduates in China.

Analysis of the cancer occurrence related to natural radioactivity in the Rio Grande do Norte State, Brazil

The state of Rio Grande do Norte, located in the Northeast region of Brazil, has areas of granites and pegmatites with minerals that have varying concentrations of uranium. Consequently, high concentrations of radon gas, a carcinogenic substance for humans, can occur. The present study aimed to assess the occurrence of cancer and its association with exposure to sources of natural radioactivity using geological and geophysical information in the aforementioned state. The spatial dependence of pulmonary, breast, stomach, leukemia, and skin cancer cases with the location of radioisotope sources were analyzed using geoprocessing tools. The geoprocessing analysis showed a differential pattern of uranium emission throughout the state, with the highest emission from areas with pegmatites outcrops. A spatial dependency of cancer cases was shown (Moran index: 0.43; p < 0.01). Moreover, a higher rate of natural radioactivity-cancer cases was associated with the high-intensity natural radioactivity areas: odds ratio:1.21 (95% CI 1.20; 1.23), following the same pattern when separately compared the different related types of cancer. These results highlight the importance of natural radioactivity as a public health problem in the Brazilian environmental scenario, confirming the need for further studies as the first toward understanding and implementing health management strategies mitigating the exposures, especially in areas of environmental risk.

Leveraging insect-specific viruses to elucidate mosquito population structure and dynamics

Several aspects of mosquito ecology that are important for vectored disease transmission and control have been difficult to measure at epidemiologically important scales in the field. In particular, the ability to describe mosquito population structure and movement rates has been hindered by difficulty in quantifying fine-scale genetic variation among populations. The mosquito virome represents a possible avenue for quantifying population structure and movement rates across multiple spatial scales. Mosquito viromes contain a diversity of viruses, including several insect-specific viruses (ISVs) and "core" viruses that have high prevalence across populations. To date, virome studies have focused on viral discovery and have only recently begun examining viral ecology. While nonpathogenic ISVs may be of little public health relevance themselves, they provide a possible route for quantifying mosquito population structure and dynamics. For example, vertically transmitted viruses could behave as a rapidly evolving extension of the host's genome. It should be possible to apply established analytical methods to appropriate viral phylogenies and incidence data to generate novel approaches for estimating mosquito population structure and dispersal over epidemiologically relevant timescales. By studying the virome through the lens of spatial and genomic epidemiology, it may be possible to investigate otherwise cryptic aspects of mosquito ecology. A better understanding of mosquito population structure and dynamics are key for understanding mosquito-borne disease ecology and methods based on ISVs could provide a powerful tool for informing mosquito control programs.

Spatial patterns of mortality in low birth weight infants at term and its determinants in the State of São Paulo, Brazil

OBJECTIVE

Low birth weight (LBW) is a public health problem strongly associated with infant mortality. This study aimed to identify the spatial distribution of infant mortality in newborns with LBW (750-2,500 g) at term (≥37 weeks of gestation), due to their being small for gestational age, analyzing its association with mother-related determinants, as well as to identify priority areas of mortality in the State of São Paulo, 2010-2019.

METHODS

Infant mortality rate was analyzed in the division of neonatal mortality and postneonatal mortality of newborns with LBW at term. The empirical Bayesian method smoothed the rates, the univariate Moran index was used to measure the degree of spatial association between the municipalities, and the bivariate Moran index was employed to identify the existence of a spatial association between the rates and the selected determinants. Thematic maps of excess risk and local Moran were prepared to identify spatial clusters, adopting 5% as a significance level.

RESULTS

The excess risk map showed that more than 30% of the municipalities had rates above the state rate. High-risk clusters were identified in the southwest, southeast, and east regions, mainly among more developed municipalities. The determinants of adolescent mothers, mothers over 34 years of age, low education, human development index, social vulnerability index, gross domestic product, physicians, and pediatric beds showed a significant association with the rates evaluated.

CONCLUSIONS

Priority areas and significant determinants associated with reduced mortality in newborns with LBW were identified, suggesting the need for intervention measures to achieve the Sustainable Development Goal.

Optimizing allocation of colorectal cancer screening hospitals in Shanghai: a geospatial analysis

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

Unmatched spatially stratified controls: A simulation study examining efficiency and precision using spatially-diverse controls and generalized additive models

Unmatched spatially stratified random sampling (SSRS) of non-cases selects geographically balanced controls by dividing the study area into spatial strata and randomly selecting controls from all non-cases within each stratum. The performance of SSRS control selection was evaluated in a case study spatial analysis of preterm birth in Massachusetts. In a simulation study, we fit generalized additive models using controls selected by SSRS or simple random sample (SRS) designs. We compared mean squared error (MSE), bias, relative efficiency (RE), and statistically significant map results to the model results with all non-cases. SSRS designs had lower average MSE (0.0042-0.0044) and higher RE (77-80%) compared to SRS designs (MSE: 0.0072-0.0073; RE across designs: 71%). SSRS map results were more consistent across simulations, reliably identifying statistically significant areas. SSRS designs improved efficiency by selecting controls that are geographically distributed, particularly from low population density areas, and may be more appropriate for spatial analyses.

Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion

Objective

This study serves to ascertain trends of space and time for Japanese encephalitis (JE) transmission at the township-level and develop an innovative time series predictive model to predict the geographical spread of JE in Gansu Province, China.

Methods

We collected weekly data on JE from 2005 to 2019 at the township-level. Kriging interpolation maps were used to visualize the trend of the epidemic spread of JE, and linear regression models were used to calculate the monthly changes in minimum longitude and maximum latitude of emerging towns with JE to assess the speed of the epidemic's spread to the northwest. Additionally, we utilized a time series Seasonal Autoregressive Integrated Moving Average (SARIMA) model to dynamically predict the ongoing weekly number of JE emerging townships.

Results

The Kriging difference map revealed a significant trend of JE spread towards the northwest. Our regression model indicated that the rate of decrease in minimum longitude was approximately 0.64 km per month, while the rate of increase in maximum latitude was approximately 1.00 km per month. Furthermore, the SARIMA pattern (2,0,0)(2,0,1)₅₂ exhibited a better goodness-of-fit for predicting JE transmission, with an overall agreement of 93.27% to 94.23%.

Conclusion

Our study highlights the expansion of JE cases towards the northwest of Gansu, indicating the need for ongoing surveillance and control efforts. The use of the SARIMA model provides a valuable tool for predicting the trend of JE spatial dispersion, thereby improving early warning systems. Our findings suggest that the number of emerging townships can be used to predict the trend of JE spatial dispersion, providing crucial insights for future research on JE incidence.

The utility of Zip4 codes in spatial epidemiological analysis

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

Improving longitudinal research in geospatial health: An agenda

All aspects of public health research require longitudinal analyses to fully capture the dynamics of outcomes and risk factors such as ageing, human mobility, non-communicable diseases (NCDs), climate change, and endemic, emerging, and re-emerging infectious diseases. Studies in geospatial health are often limited to spatial and temporal cross sections. This generates uncertainty in the exposures and behavior of study populations. We discuss a research agenda, including key challenges and opportunities of working with longitudinal geospatial health data. Examples include accounting for residential and human mobility, recruiting new birth cohorts, geoimputation, international and interdisciplinary collaborations, spatial lifecourse studies, and qualitative and mixed-methods approaches.

The Current Distribution of Oncomelania hupensis Snails in the People's Republic of China Based on a Nationwide Survey

Schistosomiasis is a helminth infection caused by the genus Schistosoma, which is still a threat in tropical and sub-tropical areas. In the China, schistosomiasis caused by Schistosoma japonicum is mainly endemic to the Yangtze River valley. The amphibious snail Oncomelania hupensis (O. hupensis) is the unique intermediate host of S. japonicum; hence, snail control is a crucial approach in the process of schistosomiasis transmission control and elimination. In 2016, a nationwide snail survey was conducted involving all snail habitats recorded since 1950 in all endemic counties of 12 provinces. A total of 53,254 existing snail habitats (ESHs) were identified, presenting three clusters in Sichuan Basin, Dongting Lake, and Poyang Lake. The overall habitat area was 5.24 billion m², of which 3.58 billion m² were inhabited by O. hupensis. The area inhabited by snails (AIS) in Dongting and Poyang Lakes accounted for 76.53% of the population in the country. Three typical landscape types (marshland and lakes, mountains and hills, and plain water networks) existed in endemic areas, and marshland and lakes had a predominant share (3.38 billion m²) of the AIS. Among the 12 endemic provinces, Hunan had a share of nearly 50% of AIS, whereas Guangdong had no ESH. Ditches, dryland, paddy fields, marshland, and ponds are common habitat types of the ESH. Although the AIS of the marshland type accounted for 87.22% of the population in the whole country, ditches were the most common type (35,025 or 65.77%) of habitat. Six categories of vegetation for ESHs were identified. A total of 39,139 habitats were covered with weeds, accounting for 55.26% of the coverage of the area. Multiple vegetation types of snail habitats appeared in the 11 provinces, but one or two of these were mainly dominant. Systematic sampling showed that the presence of living snails was 17.88% among the 13.5 million sampling frames. The occurrence varied significantly by landscape, environment, and vegetation type. The median density of living snails in habitats was 0.50 per frame (0.33 m × 0.33 m), and the highest density was 40.01 per frame. Furthermore, two main clusters with high snail densities and spatial correlations indicated by hotspot analysis were identified: one in Hunan and Hubei, the other in Sichuan. This national survey is the first full-scale census on the distribution of O. hupensis, which is significant, as transmission interruption and elimination are truly becoming the immediate goal of schistosomiasis control in China. The study discerns the detailed geographic distribution of O. hupensis with the hotspots of snail density in China. It is beneficial to understand the status of the snail population in order to finally formulate further national control planning.

Spatial-temporal analysis of pulmonary tuberculosis in Hubei Province, China, 2011-2021

BACKGROUND

Pulmonary tuberculosis (PTB) is an infectious disease of major public health problem, China is one of the PTB high burden counties in the word. Hubei is one of the provinces having the highest notification rate of tuberculosis in China. This study analyzed the temporal and spatial distribution characteristics of PTB in Hubei province for targeted intervention on TB epidemics.

METHODS

The data on PTB cases were extracted from the National Tuberculosis Information Management System correspond to population in 103 counties of Hubei Province from 2011 to 2021. The effect of PTB control was measured by variation trend of bacteriologically confirmed PTB notification rate and total PTB notification rate. Time series, spatial autonomic correlation and spatial-temporal scanning methods were used to identify the temporal trends and spatial patterns at county level of Hubei.

RESULTS

A total of 436,955 cases were included in this study. The total PTB notification rate decreased significantly from 81.66 per 100,000 population in 2011 to 52.25 per 100,000 population in 2021. The peak of PTB notification occurred in late spring and early summer annually. This disease was spatially clustering with Global Moran's I values ranged from 0.34 to 0.63 (P< 0.01). Local spatial autocorrelation analysis indicated that the hot spots are mainly distributed in the southwest and southeast of Hubei Province. Using the SaTScan 10.0.2 software, results from the staged spatial-temporal analysis identified sixteen clusters.

CONCLUSIONS

This study identified seasonal patterns and spatial-temporal clusters of PTB cases in Hubei province. High-risk areas in southwestern Hubei still exist, and need to focus on and take targeted control and prevention measures.

Spatiotemporal and seasonality analysis of sheep and goat pox (SGP) disease outbreaks in Turkey between 2010 and 2019

Sheep and goat pox (SGP) is a highly infectious disease with a high case fatality rate. It causes serious economic losses and decreases productivity in infected facilities and contact areas. As in many countries of the world, SGP outbreaks reported from Turkey to the World Organization for Animal Health (OIE) continue to threaten animal health. Therefore, studies that will guide the production of effective policies to prevent and control SGP are extremely important. This study aims at evaluating the spatiotemporal distribution of SGP outbreaks by geographical information system (GIS)-based analyses. In accordance with this purpose, spatiotemporal scan analyses were applied to reveal the spatiotemporal distribution pattern and transmission of SGP outbreaks reported in Turkey between 2010 and 2019. Space-time cluster analysis revealed 4 several clusters, indicating geographic areas at the highest risk. Spatiotemporal clusters were 6 to 11 times more likely to be exposed to SGP than the general distribution. The average spatiotemporal density of outbreaks in clusters was estimated as 0.20 ± 0.07 outbreaks per 1000 km² per month. Seasonal analysis and time series analysis showed similar findings. The seasonality of SGP was mainly defined in the winter (from December to February) when the seasonal adjusted factor (SAF) was at a peak of 504.6. In addition, February had the highest SAF with 7.1. Directional distribution analysis showed that the transmission of SGP was oriented between northeast (NE)-southwest (SW) and northwest (NW)-southeast (SE) and that distribution was changed every 2 years. These findings present a basis for the effective monitoring and prevention of SGP and provide valuable information to policymakers.

Space-time cluster detection techniques for infectious diseases: A systematic review

BACKGROUND

Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives.

METHODS

We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion.

RESULTS

Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability.

CONCLUSION

This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Road environment characteristics and fatal crash injury during the rush and non-rush hour periods in the U.S: Model testing and cluster analysis

This study aims to assess the relationship between county-level fatal crash injuries and road environmental characteristics at all times of the day and during the rush and non-rush hour periods. We merged eleven-year (2010 - 2020) data from the Fatality Analysis Reporting System. The outcome variable was the county-level fatal crash injury counts. The predictor variables were measures of road types, junction types and work zone, and weather types. We tested the predictiveness of two nested negative binomial models and adjudged that a nested spatial negative binomial regression model outperformed the non-spatial negative binomial model. The median county crash mortality rates at all times of the day and during the rush and non-rush hour periods were 18.4, 7.7, and 10.4 per 100,000 population, respectively. Fatal crash injury rate ratios were significantly elevated on interstates and highways at all times of the day - rush and non-rush hour periods inclusive. Intersections, driveways, and ramps on highways were associated with elevated fatal crash injury rate ratios. Clusters of high fatal crash injury rates were observed in counties located in Montana, Nevada, Colorado, Kansas, New Mexico, Oklahoma, Texas, Arkansas, Mississippi, Alabama, Georgia, and Nevada. The built and natural road environment factors are associated with county-level fatal crash injuries during the rush and non-rush hour periods. Understanding the association of road environment characteristics and the cluster distribution of fatal crash injuries may inform areas in need of focused intervention.

Spatial Distribution and Temporal Trends of Dietary Niacin Intake in Chinese Residents ≥ 5 Years of Age between 1991 and 2018

Limited knowledge exists on trends in niacin consumption and the prevalence of inadequate intakes in China. Understanding trends and the spatial distribution of the prevalence of inadequate niacin intake is crucial to identifying high-risk areas and sub-populations. The dietary intakes of niacin between 1991 and 2018 were analyzed using the China Health and Nutrition Survey (CHNS) data. The estimated average requirement cut point was applied to estimate inadequacy. The geographic information system's ordinary kriging method was used to estimate the spatial distribution of the prevalence of inadequate niacin intakes. However, between 1991 and 2018, the prevalence of inadequate niacin intake increased from 13.00% to 28.40% in females and from 17.75% to 29.46% in males. Additionally, the geographically significant clusters of high and low prevalence were identified and remained stable over almost three decades. The high prevalence of insufficient niacin intake was more pronounced in Henan and Shandong over 27 years. Further, effective and tailored nutrition interventions are required to address inadequate niacin intake in China.

Okwaraji Y, Tigabu Z, Persson LÅ, Alemu K. Spatial distribution of common childhood illnesses, healthcare utilisation and associated factors in Ethiopia: Evidence from 2016 Ethiopian Demographic and Health Survey

INTRODUCTION

Childhood illnesses, such as acute respiratory illness, fever, and diarrhoea, continue to be public health problems in low-income countries. Detecting spatial variations of common childhood illnesses and service utilisation is essential for identifying inequities and call for targeted actions. This study aimed to assess the geographical distribution and associated factors for common childhood illnesses and service utilisation across Ethiopia based on the 2016 Demographic and Health Survey.

METHODS

The sample was selected using a two-stage stratified sampling process. A total of 10,417 children under five years were included in this analysis. We linked data on their common illnesses during the last two weeks and healthcare utilisation were linked to Global Positioning System (GPS) information of their local area. The spatial data were created in ArcGIS10.1 for each study cluster. We applied a spatial autocorrelation model with Moran's index to determine the spatial clustering of the prevalence of childhood illnesses and healthcare utilisation. Ordinary Least Square (OLS) analysis was done to assess the association between selected explanatory variables and sick child health services utilisation. Hot and cold spot clusters for high or low utilisation were identified using Getis-Ord Gi*. Kriging interpolation was done to predict sick child healthcare utilisation in areas where study samples were not drawn. All statistical analyses were performed using Excel, STATA, and ArcGIS.

RESULTS

Overall, 23% (95CI: 21, 25) of children under five years had some illness during the last two weeks before the survey. Of these, 38% (95%CI: 34, 41) sought care from an appropriate provider. Illnesses and service utilisation were not randomly distributed across the country with a Moran's index 0.111, Z-score 6.22, P<0.001, and Moran's index = 0.0804, Z-score 4.498, P< 0.001, respectively. Wealth and reported distance to health facilities were associated with service utilisation. Prevalence of common childhood illnesses was higher in the North, while service utilisation was more likely to be on a low level in the Eastern, South-western, and the Northern parts of the country.

CONCLUSION

Our study provided evidence of geographic clustering of common childhood illnesses and health service utilisation when the child was sick. Areas with low service utilisation for childhood illnesses need priority, including actions to counteract barriers such as poverty and long distances to services.

Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas

BACKGROUND

The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used.

OBJECTIVES

This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps.

METHODS

The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure.

RESULTS

231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies.

CONCLUSIONS

Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.

Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information

BACKGROUND

Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects.

METHODS

This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality.

RESULTS

Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations.

CONCLUSIONS

This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.

Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives

With the rapid development of the internet, the application of internet search data has been seen as a novel data source to offer timely infectious disease surveillance intelligence. Moreover, the advancements in internet search data, which include rich information at both space and time scales, enable investigators to sufficiently consider the spatiotemporal uncertainty, which can benefit researchers to better monitor infectious diseases and epidemics. In the present study, we present the necessary groundwork and critical appraisal of the use of internet search data and spatiotemporal analysis approaches in infectious disease surveillance by updating the current stage of knowledge on them. The study also provides future directions for researchers to investigate the combination of internet search data with the spatiotemporal analysis in infectious disease surveillance. Internet search data demonstrate a promising potential to offer timely epidemic intelligence, which can be seen as the prerequisite for improving infectious disease surveillance.

Population flow based spatial-temporal eigenvector filtering modeling for exploring effects of health risk factors on COVID-19

The COVID-19 pandemic has had great impact on human health and social economy. Several studies examined spatial and temporal patterns of health risk factors associated with COVID-19, but population flow spillover effect has not been sufficiently considered. In this paper, a population flow-based spatial-temporal eigenvector filtering model (FLOW-ESTF) was developed to consider spatial-temporal patterns and population flow connectivity simultaneously. The proposed FLOW-ESTF method efficiently improved model prediction accuracy, which could help the government aware of the infection risk level and to make suitable control policies. The selected population flow spatial-temporal eigenvector contributed most to modeling and the visualization of corresponding eigenvector set helped to explore the underlying spatial-temporal patterns and pandemic transmission nodes. The model coefficients could reflect how health risk factors contribute the modeling of state-level COVID-19 weekly increased cases and how their influence changed through time, which could help people and government to better aware the potential health risks and to adjust control measures at different stage. The extracted population flow spatial-temporal eigenvector not only represents influence of population flow and its spillover effects but also represents some possible omitted health risk factors. This could provide an efficient path to solve the problem of spatial and temporal autocorrelation in COVID-19 modeling and an intuitive way to discover underlying spatial patterns, which will partially compensate for the problems of insufficient consideration of potential risk variables and missing data.

Multiscale analysis of cancer service areas in the United States

The purpose of delineating Cancer Service Areas (CSAs) is to define a reliable unit of analysis, more meaningful than geopolitical units such as states and counties, for examining geographic variations of the cancer care markets using geographic information systems (GIS). This study aims to provide a multiscale analysis of the U.S. cancer care markets based on the 2014-2015 Medicare claims of cancer-directed surgery, chemotherapy, and radiation. The CSAs are delineated by a scale-flexible network community detection algorithm automated in GIS so that the patient flows are maximized within CSAs and minimized between them. The multiscale CSAs include those comparable in size to those 4 census regions, 9 divisions, 50 states, and also 39 global optimal CSAs that generates the highest modularity value. The CSAs are more effective in capturing the U.S. cancer care markets because of its higher localization index, lower cross-border utilizations, and shorter travel time. The first two comparisons reveal that only a few regions or divisions are representative of the underlying cancer care markets. The last two comparisons find that among the 39 CSAs, 54% CSAs comprise multiple states anchored by cities near inner state borders, 28% are single-state CSAs, and 18% are sub-state CSAs. Their (in)consistencies across state borders or within each state shed new light on where the intervention of cancer care delivery or the adjustment of cancer care costs are needed to meet the challenges in the U.S. cancer care system. The findings could guide stakeholders to target public health policies for more effective coordination of cancer care in improving outcomes and reducing unnecessary costs.

Use of Cross-sectional and Perspective Mapping to Spatially and Statistically Represent Inpatient Glucose Control

BACKGROUND

The use of inpatient location for the depiction of glycemic control is an alternative approach to the traditional analysis of hospital-derived glucometric data. Our aim was to develop a method of spatial representation and to test for corresponding statistical variation in inpatient glucose control data.

METHODS

Point-of-care blood glucose data from inpatients with diabetes mellitus were extracted. Calculations included patient-day weighted means (PDWMs) and percentage of patient hospital days with hypoglycemia. Results were overlaid onto hospital floor plans, and room numbers were used as geolocators to generate cross-sectional (2-dimensional) and perspective (3-dimensional) views of the data. Linear mixed and mixed-effects logistic regression models were used to compare the location effect and to assess statistical variation in the data after adjusting for age, sex, and severity of illness.

RESULTS

Visual inspection of cross-sectional and perspective maps demonstrated variation in glucometric outcomes across areas within the hospital. Statistical analysis confirmed significant variation between some hospital wings and floors.

CONCLUSIONS

Spatial depiction of glucometric data within the hospital could yield insights into hot spots of poor glycemic control. Future studies on how to operationalize this approach, and whether this method of analysis can drive changes in glycemic management practices, need to be conducted.

The Use of Spatial Analysis in Syphilis-Related Research: A Scoping Review Protocol (Preprint)

BACKGROUND

Latin America, Africa, and Asia have high incidences of syphilis. New approaches are needed to understand and reduce disease transmissibility. In health care, spatial analysis is important to map diseases and understand their epidemiologic aspects.

OBJECTIVE

The proposed scoping review will identify and map the use of spatial analysis as a tool for syphilis-related research in health care.

METHODS

This protocol was based on the Joanna Briggs Institute manual, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). We will conduct searches in Embase; Lilacs, via the Virtual Health Library (Biblioteca Virtual en Salud; BVS), in Portuguese and English; Medline/PubMed; Web of Science; Cumulative Index to Nursing and Allied Health Literature (CINAHL); and Scopus. Gray literature will be searched for in Google Scholar, the Digital Library of Theses and Dissertations, the Catalog of Theses and Dissertations of the Coordination of Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; CAPES), Open Access Theses and Dissertations, ProQuest Dissertations and Theses Global, and the Networked Digital Library of Theses and Dissertations. The main research question is "How has spatial analysis been used in syphilis-related research in health care?" Studies are included if they have the full text available, address syphilis, and use geographic information systems software and spatial analysis techniques, regardless of sample characteristics or size. Studies published as research articles, theses, dissertations, and government documents will also be considered, with no location, time, or language restrictions. Data will be extracted using a spreadsheet adapted from the Joanna Briggs Institute. Quantitative and qualitative data will be analyzed using descriptive statistics and a thematic analysis, respectively.

RESULTS

The results will be presented according to the PRISMA-ScR guidelines and will summarize the use of spatial analysis in syphilis-related research in health care in countries with different contexts, factors associated with spatial cluster formation, population health impacts, contributions to health systems, challenges, limitations, and possible research gaps. The results will guide future research and may be useful for health and safety professionals, managers, public policy makers, the general population, the academic community, and health professionals who work directly with people with syphilis. Data collection is projected to start in June 2023 and end in July 2023. Data analysis is scheduled to take place in August and September 2023. We expect to publish results in the final months of 2023.

CONCLUSIONS

The review may reveal where syphilis incidence has the highest incidence, which countries most use spatial analysis to study syphilis, and whether spatial analysis is applicable to syphilis in each continent, thereby contributing to discussion and knowledge dissemination on the use of spatial analysis as a tool for syphilis-related research in health care.

TRIAL REGISTRATION

Open Science Framework CNVXE; https://osf.io/cnvxe.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/43243.

Disparities in Geographic Access to Cardiac Rehabilitation in Los Angeles County

Background Exercise-based cardiac rehabilitation (CR) is known to reduce morbidity and mortality for patients with cardiac conditions. Sociodemographic disparities in accessing CR persist and could be related to the distance between where patients live and where CR facilities are located. Our objective is to determine the association between sociodemographic characteristics and geographic proximity to CR facilities. Methods and Results We identified actively operating CR facilities across Los Angeles County and used multivariable Poisson regression to examine the association between sociodemographic characteristics of residential proximity to the nearest CR facility. We also calculated the proportion of residents per area lacking geographic proximity to CR facilities across sociodemographic characteristics, from which we calculated prevalence ratios. We found that racial and ethnic minorities, compared with non-Hispanic White individuals, more frequently live ≥5 miles from a CR facility. The greatest geographic disparity was seen for non-Hispanic Black individuals, with a 2.73 (95% CI, 2.66-2.79) prevalence ratio of living at least 5 miles from a CR facility. Notably, the municipal region with the largest proportion of census tracts comprising mostly non-White residents (those identifying as Hispanic or a race other than White), with median annual household income <$60 000, contained no CR facilities despite ranking among the county's highest in population density. Conclusions Racial, ethnic, and socioeconomic characteristics are significantly associated with lack of geographic proximity to a CR facility. Interventions targeting geographic as well as nongeographic factors may be needed to reduce disparities in access to exercise-based CR programs. Such interventions could increase the potential of CR to benefit patients at high risk for developing adverse cardiovascular outcomes.

Small-scale spatiotemporal epidemiology of notifiable infectious diseases in China: a systematic review

Background

The prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.

Methods

We searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China’s Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Results

A total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran’s I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.

Conclusions

Small-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07669-9.

County-level Differences in Liver-related Mortality, Waitlisting, and Liver Transplantation in the United States

BACKGROUND

Much of our understanding regarding geographic issues in transplantation is based on statistical techniques that do not formally account for geography and is based on obsolete boundaries such as donation service area.

METHODS

We applied spatial epidemiological techniques to analyze liver-related mortality and access to liver transplant services at the county level using data from the Centers for Disease Control and Prevention and Scientific Registry of Transplant Recipients from 2010 to 2018.

RESULTS

There was a significant negative spatial correlation between transplant rates and liver-related mortality at the county level (Moran's I, -0.319; P  = 0.001). Significant clusters were identified with high transplant rates and low liver-related mortality. Counties in geographic clusters with high ratios of liver transplants to liver-related deaths had more liver transplant centers within 150 nautical miles (6.7 versus 3.6 centers; P  < 0.001) compared with all other counties, as did counties in geographic clusters with high ratios of waitlist additions to liver-related deaths (8.5 versus 2.5 centers; P  < 0.001). The spatial correlation between waitlist mortality and overall liver-related mortality was positive (Moran's I, 0.060; P  = 0.001) but weaker. Several areas with high waitlist mortality had some of the lowest overall liver-related mortality in the country.

CONCLUSIONS

These data suggest that high waitlist mortality and allocation model for end-stage liver disease do not necessarily correlate with decreased access to transplant, whereas local transplant center density is associated with better access to waitlisting and transplant.

Epidemiological features and spatial-temporal clustering of visceral leishmaniasis in mainland China from 2019 to 2021

Background

Visceral leishmaniasis (VL) is a serious vector-borne disease in central and western China. In recent years, the number of VL cases increased gradually, particularly the mountain-type zoonotic visceral leishmaniasis (MT-ZVL). This study clarified the epidemiological features and spatial-temporal clustering of VL in China between 2019 and 2021, identified the risk areas for VL transmission, and provided scientific evidence for the prevention and control of VL.

Materials and methods

The information on VL cases in 2019–2021 was collected from the Infectious Disease Reporting Information Management System of the Chinese Center for Disease Control and Prevention. The epidemiological characteristics of VL cases were analyzed. The global Moran’s I and Getis-ORD Gi* statistical data were processed for spatial autocorrelation and hotspot analysis in ESRI ArcGIS software. Also, spatial-temporal clustering analysis was conducted with the retrospective space–time permutation scan statistics.

Results

A total of 608 VL cases were reported from 2019 to 2021, with 158, 213, and 237 cases reported each year, respectively. Of the 608 cases, there were 10 cases of anthroponotic visceral leishmaniasis (AVL), 20 cases of desert-type zoonotic visceral leishmaniasis (DT-ZVL), and 578 cases of MT-ZVL. The age of VL cases was mainly distributed in the group of subjects aged ≥ 15 years. Peasants and infants were the dominant high-risk population. The incidence peak season of VL occurred between March and May. The cases were mainly distributed in Shanxi (299 cases), Shaanxi (118 cases), and Gansu (106 cases) Provinces, accounting for 86.02% (523/608) of the total reported cases in China. Spatial analysis revealed that clustering of infection is mainly located in eastern Shanxi Province and Shaanxi–Shanxi border areas, as well as southern Gansu and northern Sichuan Province. In addition, new reemergence hotspots in Shanxi, Henan, and Hebei Provinces have been detected since 2020. Spatio-temporal clustering analysis revealed an increase in the degree of infection aggregation in eastern Shanxi Province and Shaanxi–Shanxi border areas.

Conclusion

The AVL and DT-ZVL were endemic at a lower level in western China, whereas MT-ZVL rebounded rapidly and showed a resurgence in historically endemic counties. The spatial-temporal clustering analysis displayed that the high-incidence areas of VL have shifted to central China, particularly in Shanxi and Shaanxi Provinces. Integrated mitigation strategies targeting high-risk populations are needed to control VL transmission in high-risk areas.

The epidemiology of diphtheria in Haiti, December 2014–June 2021: A spatial modeling analysis

Background

Haiti has been experiencing a resurgence of diphtheria since December 2014. Little is known about the factors contributing to the spread and persistence of the disease in the country. Geographic information systems (GIS) and spatial analysis were used to characterize the epidemiology of diphtheria in Haiti between December 2014 and June 2021.

Methods

Data for the study were collected from official and open-source databases. Choropleth maps were developed to understand spatial trends of diphtheria incidence in Haiti at the commune level, the third administrative division of the country. Spatial autocorrelation was assessed using the global Moran’s I. Local indicators of spatial association (LISA) were employed to detect areas with spatial dependence. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were built to identify factors associated with diphtheria incidence. The performance and fit of the models were compared using the adjusted r-squared (R²) and the corrected Akaike information criterion (AIC_c).

Results

From December 2014 to June 2021, the average annual incidence of confirmed diphtheria was 0.39 cases per 100,000 (range of annual incidence = 0.04–0.74 per 100,000). During the study period, diphtheria incidence presented weak but significant spatial autocorrelation (I = 0.18, p<0.001). Although diphtheria cases occurred throughout Haiti, nine communes were classified as disease hotspots. In the regression analyses, diphtheria incidence was positively associated with health facility density (number of facilities per 100,000 population) and degree of urbanization (proportion of urban population). Incidence was negatively associated with female literacy. The GWR model considerably improved model performance and fit compared to the OLS model, as indicated by the higher adjusted R² value (0.28 v 0.15) and lower AIC_c score (261.97 v 267.13).

Conclusion

This study demonstrates that GIS and spatial analysis can support the investigation of epidemiological patterns. Furthermore, it shows that diphtheria incidence exhibited spatial variability in Haiti. The disease hotspots and potential risk factors identified in this analysis could provide a basis for future public health interventions aimed at preventing and controlling diphtheria transmission.

Epidemiological Profile of a Human Hepatitis E Virus Outbreak in 2018, Chattogram, Bangladesh

Hepatitis E virus (HEV) is a waterborne zoonotic disease that can result in a high fatality rate in pregnant women and infants. In 2018, a large HEV outbreak emerged in Chattogram, Bangladesh, resulting in 2800 cases and a significant public health response to mitigate the transmission. While the source of the outbreak remained poorly understood, authorities suggested that possible risk factors for HEV infection included contamination of water supply, exacerbated by concurrent severe flooding events in the community. A cross-sectional study was conducted to investigate the distribution and risk factors for HEV seroprevalence between January and December 2018 in the Chattogram city area. A total of 505 blood samples were collected from symptomatic patients of 10 hospitals who met the case definition for an HEV infection. Standard ELISA tests were performed in all patients to identify anti-HEV antibodies. The size and location of HEV seroprevalence clusters within Chattogram were investigated using SaTScan. We investigated the association between risk of HEV infection and individual and environmentally lagged risk factors using Bernoulli generalised linear regression models. Our results indicate an overall HEV seroprevalence of 35% with significant variation according to sex, source of drinking water, and boiling of drinking water. A positive cross-correlation was found between HEV exposure and precipitation, modified normalised difference water index (MNDWI), and normalised difference vegetation index (NDVI). Our model indicated that risk of infection was associated with sex, age, source of drinking water, boiling of water, increased precipitation, and increased MNDWI. The results from this study indicate that source and boiling of drinking water and increased precipitation were critical drivers of the 2018 HEV outbreak. The communities at highest risk identified in our analyses should be targeted for investments in safe water infrastructure to reduce the likelihood of future HEV outbreaks in Chattogram.

Evaluating the geographic distribution of cervical cancer patients presenting to a multidisciplinary gynecologic oncology clinic in Gaborone, Botswana

OBJECTIVE

In Botswana, cervical cancer is the leading cause of cancer death for females. With limited resources, Botswana is challenged to ensure equitable access to advanced cancer care. Botswana's capital city, Gaborone, houses the only gynecologic oncology multi-disciplinary team (MDT) and the one chemoradiation facility in the country. We aimed to identify areas where fewer women were presenting to the MDT clinic for care.

METHODS

This cross-sectional study examined cervical cancer patients presenting to the MDT clinic between January 2015 and March 2020. Patients were geocoded to residential sub-districts to estimate age-standardized presentation rates. Global Moran's I and Anselin Local Moran's I tested the null hypothesis that presentation rates occurred randomly in Botswana. Community- and individual-level factors of patients living in sub-districts identified with higher (HH) and lower (LL) clusters of presentation rates were examined using ordinary least squares with a spatial weights matrix and multivariable logistic regression, respectively, with α level 0.05.

RESULTS

We studied 990 patients aged 22-95 (mean: 50.6). Presentation rates were found to be geographically clustered across the country (p = 0.01). Five sub-districts were identified as clusters, two high (HH) sub-district clusters and three low (LL) sub-district clusters (mean presentation rate: 35.5 and 11.3, respectively). Presentation rates decreased with increased travel distance (p = 0.033). Patients residing in LL sub-districts more often reported abnormal vaginal bleeding (aOR: 5.62, 95% CI: 1.31-24.15) compared to patients not residing in LL sub-districts. Patients in HH sub-districts were less likely to be living with HIV (aOR: 0.59; 95% CI: 0.38-0.90) and more likely to present with late-stage cancer (aOR: 1.78; 95%CI: 1.20-2.63) compared to patients not in HH sub-districts.

CONCLUSIONS

This study identified geographic clustering of cervical cancer patients presenting for care in Botswana and highlighted sub-districts with disproportionately lower presentation rates. Identified community- and individual level-factors associated with low presentation rates can inform strategies aimed at improving equitable access to cervical cancer care.

Utilizing Baidu Index to Investigate Seasonality, Spatial Distribution and Public Attention of Dry Eye Diseases in Chinese Mainland

Purpose

To explore the characteristics of spatial-temporal prevalence and public attention of dry eye diseases (DED) through Baidu Index (BI) based on infodemiology method.

Methods

The data about BI of DED were collected from Baidu search engine using "Dry eye diseases" as keyword. The spatial and temporal distribution of DED were analyzed through timeseries data decomposition as well as spatial autocorrelation and hotspot detection of BI about DED. The most popular related words and demographic characteristics were recorded to determine the public attention of DED.

Results

The trends of BI about DED in Chinese mainland had gradually increased over time with a rapid increase from 2012 to 2014 and in 2018. The results of timeseries decomposition indicated that there was seasonality in the distribution of BI about DED with the peak in winter, especially in northern regions. The geographic distribution demonstrated the search activities of DED was highest in the east of Chinese mainland while lowest in the west. The vast majority of people searching for DED were teenagers (20-29 years), with a predominance of females. Glaucoma, keratitis and conjunctivitis were the diseases most often confused with DED, and the artificial tears were the most common treatment for DED in Chinese mainland according to the BI about DED.

Conclusions

The analysis revealed the seasonality, geographic hotspots and public concern of DED through BI in Chinese mainland, which provided new insights into the epidemiology of DED.

The Geographic Context of Racial Disparities in Aggressive Endometrial Cancer Subtypes: Integrating Social and Environmental Aspects to Discern Biological Outcomes

The number of Endometrial Carcinoma (EC) diagnoses is projected to increase substantially in coming decades. Although most ECs have a favorable prognosis, the aggressive, non-endometrioid subtypes are disproportionately concentrated in Black women and spread rapidly, making treatment difficult and resulting in poor outcomes. Therefore, this study offers an exploratory spatial epidemiological investigation of EC patients within a U.S.-based health system's institutional cancer registry (n = 1748) to search for and study geographic patterns. Clinical, demographic, and geographic characteristics were compared by histotype using chi-square tests for categorical and t-tests for continuous variables. Multivariable logistic regression evaluated the impact of risks on these histotypes. Cox proportional hazard models measured risks in overall and cancer-specific death. Cluster detection indicated that patients with the EC non-endometrioid histotypes exhibit geographic clustering in their home address, such that congregate buildings can be identified for targeted outreach. Furthermore, living in a high social vulnerability area was independently associated with non-endometrioid histotypes, as continuous and categorical variables. This study provides a methodological framework for early, geographically targeted intervention; social vulnerability associations require further investigation. We have begun to fill the knowledge gap of geography in gynecologic cancers, and geographic clustering of aggressive tumors may enable targeted intervention to improve prognoses.

Spatial distribution and determinants of HIV prevalence among adults in urban Ethiopia: Findings from the Ethiopia Population-based HIV Impact Assessment Survey (2017–2018)

The design and evaluation of national HIV programs often rely on aggregated national data, which may obscure localized HIV epidemics. In Ethiopia, even though the national adult HIV prevalence has decreased, little information is available about local areas and subpopulations. To inform HIV prevention efforts for specific populations, we identified geographic locations and drivers of HIV transmission. We used data from adults aged 15–64 years who participated in the Ethiopian Population-based HIV Impact Assessment survey (October 2017–April 2018). Location-related information for the survey clusters was obtained from the 2007 Ethiopia population census. Spatial autocorrelation of HIV prevalence data were analyzed via a Global Moran’s I test. Geographically weighted regression analysis was used to show the relationship of covariates. The finding indicated that uncircumcised men in certain hotspot towns and divorced or widowed individuals in hotspot woredas/towns might have contributed to the average increase in HIV prevalence in the hotspot areas. Hotspot analysis findings indicated that, localized, context-specific intervention efforts tailored to at-risk populations, such as divorced or widowed women or uncircumcised men, could decrease HIV transmission and prevalence in urban Ethiopia.