Geographical epidemiology, spatial analysis and geographical information systems: a multidisciplinary glossary

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.

The Impact of the Seasonal and Geographical Distribution of Tuberculosis in Sicily: A 6-Year Retrospective Study (2018-2023)

Background: Tuberculosis (TB) continues to be a major public health issue, with high mortality rates reported worldwide. It is worth noting that most of the hospitalizations for tuberculosis in the Sicilian region involve Italian-born individuals, underscoring the need to address this problem. Recent research on the geographic area and seasonality of infectious diseases, including tuberculosis, may aid in developing effective preventive measures. Objectives: This study aimed to evaluate the impact of the season and geographical area on tuberculosis disease prevalence in the Sicilian region. Methods: A retrospective study from January 2018 to May 2023 was conducted on patients with tuberculosis in the Sicilian region by analyzing computerized records on the Infectious Diseases Information System, currently named the Italian National Notification System (NSIS), of the Epidemiology Unit at Policlinico Paolo Giaccone University Hospital of Palermo and the Regional Reference Laboratory for Tuberculosis Surveillance and Control. Results: Eastern and Western Sicily were the geographical Sicilian areas with the highest frequency of patients with tuberculosis (52.2% and 42.6%, respectively). In comparison, Central Sicily had a significantly lower frequency of patients with tuberculosis (5.2%). Regarding the season, autumn was the season with the highest number of notification cases (28.9%), while spring was the season with the lowest frequency of patients with tuberculosis (19.7%). In autumn, we found significantly fewer patients with tuberculosis from Eastern Sicily (39.3%) and Central Sicily (1.5%), while Western Sicily had more patients with tuberculosis (59.3%). In spring, we found significantly more patients with tuberculosis from Eastern Sicily (64.1%), while Western and Central Sicily had significantly fewer patients with tuberculosis (23.9% and 12%, respectively). The presence of patients with tuberculosis did not significantly differ between geographical regions in summer and winter. Conclusions: Geographical area and seasonality significantly impact the distribution of tuberculosis cases in Sicily. These factors may be linked to different climatic conditions across the various geographical areas considered. Our findings suggest that climate can play a critical role in the spread of airborne infectious diseases, such as tuberculosis.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Spatial analysis of 10-year predicted risk and incident atherosclerotic cardiovascular disease: the CoLaus cohort

Whether cardiovascular risk scores geographically aggregate and inform on spatial development of atherosclerotic cardiovascular diseases (ASCVD) remains unknown. Our aim is to determine the spatial distribution of 10-year predicted cardiovascular risk and ASCVD, and to compare the overlap of the resulting spatial distributions. Using prospective data from the CoLaus|PsyCoLaus cohort study (2003-2021) we computed SCORE2 in participants free from ASCVD. Geographical distributions of predicted risk and events were determined using the Gi* Getis-Ord autocorrelation statistic. 6203 individuals (54% women, mean age 52.5 ± SD 10.7, ASCVD incidence rate 5.7%) were included. We identified clusters of high versus low predicted risk (4%, 6%, respectively) and ASCVD (5%, 5% respectively) at baseline. They persisted at follow-up. Overlap of SCORE2 and ASCVD clusters was marginal. Body-mass index and alcohol consumption explained most of the predicted risk distribution. For ASCVD, high clusters persisted or were reinforced after multivariate adjustment, while low incidence clusters were reduced, multifactorial determinants. Incidence rate of ASCVD was 2.5% higher (IC 95%, 1.4-3.7) in clusters of higher incidence of ASCVD. To develop up-to-date, geographically targeted prevention strategies, there is a need to study novel geographically risk factors affecting ASCVD and to update commonly used prediction models for a population approach.

Geospatial analysis of the hospitalisation rate of patients with rheumatoid arthritis in Hunan: a cross-sectional Chinese study

OBJECTIVE

Little is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China.

METHODS

A cross-sectional study was conducted among patients with RA admitted to hospitals in Hunan Province. Global Moran's I and local indicators of spatial association were used to explore the geospatial pattern of the HR of patients with RA. Generalised estimating equation analysis and geographically weighted regression were used to identify the potential influencing factors of the HR of patients with RA.

RESULTS

There were a total of 11 599 admissions, and the average HR was 1.57 per 10 000 population in Hunan. We detected different cluster patterns of the HR among patients with RA by local indicators of spatial association. Age, ethnicity, average temperature, average temperature range, average rainfall, regions, gross domestic product per capita, and doctors and hospitals per 10 000 people were risk factors for the HR. However, only average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. The increase in hospitals increased the probability of HR from east to west in Hunan with a positive coefficient, while temperature decreases increased the risk of HR from south to north negatively. Similarly, the growth of gross domestic product per capita decreased the probability of HR from southwest to northeast.

CONCLUSION

A non-random spatial distribution of the HR of patients with RA was demonstrated in Hunan, and average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. Our study indicated that spatial and geostatistics may be useful approaches for further study among patients with RA.

Geoepidemiological perspective on COVID-19 pandemic review, an insight into the global impact

The COVID-19 pandemic showed major impacts, on societies worldwide, challenging healthcare systems, economies, and daily life of people. Geoepidemiology, an emerging field that combines geography and epidemiology, has played a vital role in understanding and combatting the spread of the virus. This interdisciplinary approach has provided insights into the spatial patterns, risk factors, and transmission dynamics of the COVID-19 pandemic at different scales, from local communities to global populations. Spatial patterns have revealed variations in incidence rates, with urban-rural divides and regional hotspots playing significant roles. Cross-border transmission has highlighted the importance of travel restrictions and coordinated public health responses. Risk factors such as age, underlying health conditions, socioeconomic factors, occupation, demographics, and behavior have influenced vulnerability and outcomes. Geoepidemiology has also provided insights into the transmissibility and spread of COVID-19, emphasizing the importance of asymptomatic and pre-symptomatic transmission, super-spreading events, and the impact of variants. Geoepidemiology should be vital in understanding and responding to evolving new viral challenges of this and future pandemics.

Time series analysis of cutaneous leishmaniasis incidence in Shahroud based on ARIMA model

BACKGROUND

Leishmaniasis is a zoonotic disease and Iran is one of the ten countries with has the highest estimated cases of leishmaniasis. This study aimed to determine the time trend of cutaneous leishmaniasis (CL) incidence using the ARIMA model in Shahroud County, Semnan, Iran.

METHODS

In this study, 725 patients with leishmaniasis were selected in the Health Centers of Shahroud during 2009-2020. Demographic characteristics including; history of traveling, history of leishmaniasis, co-morbidity of other family members, history of treatment, underlying disease, and diagnostic measures were collected using the patients' information listed in the Health Ministry portal. The Box-Jenkins approach was applied to fit the SARIMA model for CL incidence from 2009 to 2020. All statistical analyses were done by using Minitab software version 14.

RESULTS

The mean age of patients was 28.2 ± 21.3 years. The highest and lowest annual incidence of leishmaniasis were in 2018 and 2017, respectively. The average ten-year incidence was 132 per 100,000 population. The highest and lowest incidence of the disease were 592 and 195 for 100,000 population in the years 2011 and 2017, respectively. The best model was SARIMA (3,1,1) (0,1,2)₄ (AIC: 324.3, BIC: 317.7 and RMSE: 0.167).

CONCLUSIONS

This study suggested that time series models would be useful tools for predicting cutaneous leishmaniasis incidence trends; therefore, the SARIMA model could be used in planning public health programs. It will predict the course of the disease in the coming years and run the solutions to reduce the cases of the disease.

Geo-epidemiological approach of the COVID-19 pandemic in France and in Europe for public health policies

The spread of the COVID-19 pandemic has shown great heterogeneity between countries that merits investigation. There is a need to better highlight the variability in the pandemic trajectories in different geographic areas. By using openly available data from 'GitHub' COVID-19 dataset for Europe and from the official dataset of France for the period 2020 to 2021, I present the three COVID-19 waves in France and Europe in maps. The epidemic trends across areas display different evolutions for different time periods. National and European public health authorities will be able to improve allocation of resources for more effective public health measures based on geo-epidemiological analyses.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

The Projection of Iran's Healthcare Expenditures By 2030: Evidence of a Time-Series Analysis

BACKGROUND

The projection of levels and composition of financial resources for the healthcare expenditure (HCE) and relevant trends can provide a basis for future health financing reforms. This study aimed to project Iran's HCEs by the sources of funds until 2030.

METHODS

The structural macro-econometric modeling in the EViews 9 software was employed to simulate and project Iran's HCE by the sources of funds (government health expenditure [GHCE], social security organization health expenditure [SOHCE], out-of-pocket [OOP] payments, and prepaid private health expenditure [PPHCE]). The behavioral equations were estimated by autoregressive distributed lag (ARDL) approach.

RESULTS

If there is a 5%-increase in Iran's oil revenues, the mean growth rate of gross domestic product (GDP) is about 2% until 2030. By this scenario, the total HCE (THCE), GHCE, SOHCE, OOP, and PPHCE increases about 30.5%, 25.9%, 34.4%, 31.2%, and 33.9%, respectively. Therefore, the THCE as a percentage of the GDP will increase from 9.6% in 2016 to 10.7% in 2030. It is predicted that Iran's THCE will cover 22.2%, 23.3%, 40%, and 14.5% by the government, social security organization (SSO), households OOP, and other private sources, respectively, in 2030.

CONCLUSION

Until 2030, Iran's health expenditures will grow faster than the GDP, government revenues, and non-health spending. Despite the increase in GHCE and total government expenditure, the share of the GHCE from THCE has a decreasing trend. OOP payments remain among the major sources of financing for Iran's HCE.

Power of Digital Economy to Drive Urban-Rural Integration: Intrinsic Mechanism and Spatial Effect, from Perspective of Multidimensional Integration

The consensus that the digital economy drives urban-rural integration has been gradually reached both in practice and theory. Besides, the way by which the digital economy drives urban-rural integration remains updated iteratively. The coming period is an important opportunity to break down the dualistic urban-rural structure and improve the urban-rural integration development. It is also a critical stage for China to promote the deep integration of the digital economy and the real economy. In this study, the intrinsic mechanism of the digital economy in driving the four dimensions of urban-rural integration was elaborated. An analysis was made of the spatial effects in 30 provinces (municipalities and autonomous regions) of China during 2011-2019 using Bivariate Global Moran's I and geographically and temporally weighted regression (GTWR) models. As revealed by the results: (1) the digital economy and the four dimensions of urban-rural integration advance steadily, in which the convergence degree of urban and rural resident consumption is comparatively higher; (2) there is a significant spatial auto-correlation between the digital economy and the four dimensions of urban-rural integration, with the influence gradually strengthened with time; (3) the digital economy exerts mainly positive impacts on the equivalent allocation of urban and rural factors, integration of three industries in urban and rural areas, and convergence degree of urban and rural resident consumption, but inhibits the equalization of urban and rural public services in nearly half research areas; (4) both digital equipment basis and user basis play a vital role in promoting the four dimensions of urban-rural integration.

National geographical pattern of COVID-19 hospitalization, case fatalities, and associated factors in patients covered by Iran Health Insurance Organization

Background

Understanding the Spatio-temporal distribution and interpersonal comparisons are important tools in etiological studies. This study was conducted to investigate the temporal and geographical distribution of COVID-19 hospitalized patients in the Iran Health Insurance Organization (IHIO) insured population (the second largest social health insurance organization) and the factors affecting their case fatality rate (CFR).

Methods

In this descriptive-analytical cross-sectional study, the demographic and clinical data of all insured of the IHIO who were hospitalized with COVID-19 in hospitals across the country until March 2021 was extracted from the comprehensive system of handling the inpatient documents of this organization. The Excel 2019 and GeoDA software were used for descriptive reporting and geographical distribution of variables. A multiple logistic regression model was used to estimate the Odds Ratio (OR) of death in patients with COVID-19 using STATA 14 software.

Results

During the first 14 months of the COVID-19 outbreak in Iran, 0.72% of the IHIO insured (303,887 individuals) were hospitalized with COVID-19. Hospitalization per 100,000 people varied from 192.51 in East Azerbaijan to 1,277.49 in Yazd province. The overall CFR in hospitalized patients was 14%. Tehran and Kohgiluyeh & BoyerAhmad provinces had the highest and lowest CFR with 19.39% and 5.19%, respectively. The highest odds of death were in those over 80 years old people (OR = 9.65), ICU-admitted (OR = 7.49), Hospitalized in governmental hospitals (OR = 2.08), Being a foreign national (OR = 1.45), hospitalized in November (OR = 1.47) and Residence in provinces such as Sistan & Baluchestan (OR = 1.47) and Razavi Khorasan (OR = 1.66) respectively. Furthermore, the odds of death were lower in females (OR = 0.81) than in males.

Conclusions

A sound understanding of the primary causes of COVID-19 death and severity in different groups can be the basis for developing programs focused on more vulnerable groups in order to manage the crisis more effectively and benefit from resources more efficiently.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13649-0.

Heterogeneity of the COVID-19 Pandemic in the United States of America: A Geo-Epidemiological Perspective

The spread of the COVID-19 pandemic has shown great heterogeneity between regions of countries, e. g., in the United States of America (USA). With the growing of the worldwide COVID-19 pandemic, there is a need to better highlight the variability in the trajectory of this disease in different worldwide geographic areas. Indeed, the epidemic trends across areas can display completely different evolution at a given time. Geo-epidemiological analyses using data, that are publicly available, could be a major topic to help governments and public administrations to implement health policies. Geo-epidemiological analyses could provide a basis for the implementation of relevant public health policies. With the COVID-19 pandemic, geo-epidemiological analyses can be readily utilized by policy interventions and USA public health authorities to highlight geographic areas of particular concern and enhance the allocation of resources.

Regional analysis of diabetic retinopathy and co-existing social and demographic factors in the overall population of Poland

INTRODUCTION

The aim of our study was to analyse the regional differences in diabetic retinopathy (DR) prevalence and its co-existing social and demographic factors in the overall population of Poland in the year 2017.

MATERIAL AND METHODS

Data from all levels of healthcare services at public and private institutions recorded in the National Health Fund database were evaluated. International Classification of Diseases codes were used to identify patients with type 1 and type 2 diabetes mellitus (DM) and with DR. Moran's I statistics and Spatial Autoregressive (SAR) model allowed us to understand the distribution of DR prevalence and its possible association with environmental and demographic exposures.

RESULTS

In total, 310,815 individuals with diabetic retinopathy (DR) were diagnosed in the year 2017 in Poland. Of them, 174,384 (56.11%) were women, 221,144 (71.15%) lived in urban areas, and 40,231 (12.94%) and 270,584 (87.06%) had type 1 and type 2 DM, respectively. The analysis of the SAR model showed that the significant factors for the occurrence of DR in particular counties were a higher level of average income and a higher number of ophthalmologic consultations per 10,000 adults.

CONCLUSIONS

The analyses of social, demographic, and systemic factors co-existing with DR revealed that level of income and access to ophthalmologic and diabetic services are crucial in DR prevalence in Poland.

Application and utility of geographic information systems in pharmacy specific health research: A scoping review

BACKGROUND

Geographic Information Systems (GIS) are considered essential tools to analyze spatially referenced health data.

OBJECTIVES

The purpose of this scoping review is to describe how GIS is used in pharmacy specific health research.

METHODS

During July 2020, the following databases were searched: EMBASE, MEDLINE (PubMed), Web of Science and Scopus. The search strategy included terms relating to spatial analysis and pharmacy. Studies were considered eligible if they involved the use of GIS and focused on pharmacies. A narrative and tabular synthesis of the results was carried out, structured around the spatial analysis methods utilized across studies, as well as the characteristics of pharmacies evaluated in studies.

RESULTS

After a review of 6967 sources, 48 studies were included in this review. Twenty-nine studies were conducted in the United States (60.4%) and thirty-six focused on accessibility (75.0%; n = 36). Twenty-two studies investigated the relationship between sociodemographic aspects of the population and the accessibility and availability of pharmacies (45.8%). Twelve studies (25.0%) performed distance analysis and six studies (12.5%) performed geostatistical analysis. Community pharmacies were the setting evaluated most frequently, with over-the-counter selling products being the most evaluated pharmacy variable (13.3%; n = 6). Population density (58.3%; n = 28), income indicators (43.8%; n = 21) and minority community composition rates (41.7%; n = 20) were the most used population variables.

CONCLUSIONS

GIS have been increasingly used in pharmacy specific health research. Generally, research has sought to identify potential barriers to access and their effects on the population. Future research may benefit by utilizing robust spatial methods and applications across countries outside of the United States. Doing so could help to confirm the impact of sociodemographic characteristics on the availability and/or accessibility of pharmacies globally.

An Assessment of Emergency Nurses' Perspectives on Nurse-Driven Human Immunodeficiency Virus Testing in the Emergency Department

INTRODUCTION

Engaging emergency clinicians in universal human immunodeficiency virus screening is paramount to achieving goals of reengaging human immunodeficiency virus-positive persons into care, identifying new human immunodeficiency virus cases, and linking them to care. The study aim was to identify beliefs and barriers towards opt-out human immunodeficiency virus testing among emergency nurses.

METHODS

A cross-sectional study used Qualtrics software to deliver a survey on a tablet device to emergency nurses in a private Level 1 trauma hospital in Houston, Texas during downtimes of their clinical shifts. The survey evaluated perspectives on human immunodeficiency virus screening and knowledge relative to rapid screening and human immunodeficiency virus prevalence rates locally and nationally.

RESULTS

Fifty emergency nurses were enrolled. Few nurses accurately identified human immunodeficiency virus prevalence rates at the local hospital and city level (10% and 42%, respectively). Most (54%) of nurses correctly estimated human immunodeficiency virus prevalence rates nationally. Nearly half of the nurses (42.0%) correctly predicted the cost of a rapid human immunodeficiency virus test with accuracy and most were willing to offer rapid human immunodeficiency virus testing all the time (60.0%). Eighty-eight percent of nurses were supportive of facilitating universal human immunodeficiency virus screening. However, 92.0% strongly supported human immunodeficiency virus testing for high risk patients only when compared to 80.0% support of testing for all eligible patients. Qualitative data revealed time constraints and follow-up concerns as barriers.

DISCUSSION

Emergency nurses reported barriers that sometimes prevented application of Centers for Disease Control and Prevention recommendations to human immunodeficiency virus screening. Strategies to overcome these barriers are instrumental to programmatic success. Solutions can corroborate the importance of emergency nurses to the nation's Ending the HIV Epidemic plan.

Combining Global Positioning System (GPS) with saliva collection among sexual minority adults: A feasibility study

BACKGROUND

This is the first study, of which we are aware, to evaluate the feasibility and accessibility of simultaneous use of Global Positioning System (GPS) and saliva collection for biomarker assessment as an objective measure of stress physiology among sexual minority (lesbian, gay, bisexual, queer, and other non-heterosexual identities) individuals. The principal motivation for pairing GPS and saliva collection was to investigate how characteristics of the built and social environments along with participants' daily activity paths affect stress. This can contribute to a better understanding of health and health behaviors in the sexual minority community.

METHODS

A convenience sample of enrolled participants (N = 124) from Houston, Texas was asked to complete questionnaires, carry with them a GPS unit daily, and collect and store 6 samples of saliva at specific times across the span of a day prior to a second visit around one week later.

RESULTS

Of 124 participants, 16 participants (12.90%) provided no useable GPS data and 98 (79.03%) provided at least 4 days of data. More than three-fourths (n = 98, 79.03%) also provided complete saliva samples.

CONCLUSIONS

Our results show that the simultaneous use of GPS and saliva collection to assess sexual minority individuals' activity paths and stress level is feasible.

Geographic Information Systems in Spatial Epidemiology: Unveiling New Horizons in Dental Public Health

Objectives:

Research on the role of environment and place in various aspects of dental public health using geographic information systems (GIS) is escalating rapidly. Yet, the understanding of GIS and the analytical tools that it offers are still vaguely understood. This narrative review therefore draws from the utilization of GIS in the dental public health research.

Materials and Methods:

Electronic databases such as Google Scholar, PUBMED, and Scopus were searched using terms “spatial epidemiology,” “GIS,” “geographic information systems,” “health geography,” “environment public health tracking,” “spatial distribution,” “disease mapping,” “geographic correlation studies,” “cartography,” “big data,” and “disease clustering” through December 2019.

Results:

This review builds upon the prospects of GIS application in various aspects of dental public health. Studies were classified as: (1) GIS for mapping of disease, population at risk, and risk factors; (2) GIS in geographic correlation studies; (3) GIS for gauging healthcare accessibility and spatial distribution of healthcare providers. We also identified the commonly used GIS analytical techniques in oral epidemiology.

Conclusions:

We anticipate that this review will spur advancement in the utilization of spatial analytical techniques and GIS in the dental public health research.

Geographical distribution of Anthrax using Geographic Information System (GIS) during 2010-2015 in Iran

Background: Anthrax is a zoonotic infectious disease that is still considered as a health problem in developing countries. Therefore, the aim of this study was to investigate the incidence and geographical distribution of anthrax using the Geographic Information System (GIS) and predict its incidence in Iran in 2021.

Methods: This study is descriptive analytical study. Information on anthrax was obtained from the Center for Communicable Diseases Control during 2010-2015. In the next step, ArcGIS 9.3 was used to prepare geographic maps of the disease incidence and frequency. Therefore, using the Raster Calculator tool, the disease prediction map was drawn.

Results: The highest incidence of anthrax during 2010-2015 was observed in the provinces of Kurdistan, North Khorasan, and Chaharmahal and Bakhtiari, respectively. The trend of the incidence of anthrax in Iran had increased from 2010 to 2013, while its incidence decreased in 2014. Based on the results of modeling in Iran, the provinces of Kurdistan, West Azarbaijan, Tehran, and Zanjan, respectively, with 37.16%, 33.83%, 16.78%, and 10.49% of their area (km²) had the highest risk of anthrax disease in the country in the year 2021.

Conclusion: Since the provinces of Kurdistan, West Azerbaijan, Tehran, and Zanjan are among the high-risk areas in the country in the coming years, the cooperation between the veterinary organization and the health care system and the vaccination of livestock in these areas can significantly help to control and prevent the disease.

The geographic distribution of un-immunized children in Ontario, Canada: Hotspot detection using Bayesian spatial analysis

BACKGROUND

In Ontario, Canada, little is currently known about the extent to which un-immunized children may cluster geographically. Our objectives were to: describe the geographic distribution of fully un-immunized children; identify geographic clusters (hotspots) of un-immunized children; and to characterize the contribution of spatial effects and covariates on hotspots, where found.

METHODS

Our analytic cohort consisted of Ontario students aged 7-17 years in the 2016-2017 school year. We defined students as un-immunized if they had zero doses of any vaccine and a non-medical exemption recorded in Ontario's registry. We calculated unadjusted proportions of un-immunized students by Census Subdivision (CSD) and then used a sequential approach to identify hotspots starting first with hotspot identification at the CSD level and then probed identified hotspots further by Dissemination Area (DA) and including covariates. Hotspots were identified using the Besag-York-Mollie Bayesian spatial model and were defined as areas with >95% probability of having two times the proportion of un-immunized students, relative to the province overall.

RESULTS

We identified 15,208 (0.94%) un-immunized children within our cohort consisting of more than 1.61 million students. Unadjusted proportions of un-immunized students varied greatly by geography, ranging from 0% to 21.5% by CSD. We identified 16 hotspot CSDs which clustered in five distinct areas, all of which were located in southern Ontario. The contribution of covariates and spatial effects on the risk of having un-immunized students varied greatly across hotspot areas.

CONCLUSIONS

Although the provincial proportion (0.94%) of un-immunized students is small, geographical clustering of such students is evident in Ontario and in some areas presents an important risk for future outbreaks. Further qualitative work within these hotspot areas would be a helpful next step to better characterize the factors associated with vaccine refusal in these communities.

A spatial analysis of the COVID-19 period prevalence in U.S. counties through June 28, 2020: where geography matters?

PURPOSE

This study aims to understand how spatial structures, the interconnections between counties, matter in understanding the coronavirus disease 2019 (COVID-19) period prevalence across the United States.

METHODS

We assemble a county-level data set that contains COVID-19-confirmed cases through June 28, 2020, and various sociodemographic measures from multiple sources. In addition to an aspatial regression model, we conduct spatial lag, spatial error, and spatial autoregressive combined models to systematically examine the role of spatial structure in shaping geographical disparities in the COVID-19 period prevalence.

RESULTS

The aspatial ordinary least squares regression model tends to overestimate the COVID-19 period prevalence among counties with low observed rates, but this issue can be effectively addressed by spatial modeling. Spatial models can better estimate the period prevalence for counties, especially along the Atlantic coasts and through the Black Belt. Overall, the model fit among counties along both coasts is generally good with little variability evident, but in the Plain states, the model fit is conspicuous in its heterogeneity across counties.

CONCLUSIONS

Spatial models can help partially explain the geographic disparities in the COVID-19 period prevalence. These models reveal spatial variability in the model fit including identifying regions of the country where the fit is heterogeneous and worth closer attention in the immediate short term.

Spatio-temporal modeling of visceral leishmaniasis in Midwest Brazil: An ecological study of 18-years data (2001-2018)

Visceral leishmaniasis (VL) is a neglected vector-borne disease associated with socioeconomic and environmental issues. In Brazil, epidemics of VL have occurred in major cities since 1980. Applied models for medical and epidemiological research have been used to assess the distribution and characteristics of disease endpoints and identify and characterize potential risk factors. This study described the demographic features of VL and modeled the spatio-temporal distribution of human VL cases and their relationship with underlying predicitve factors using generalized additive models. We conducted an ecological study covering an 18-year period from the first report of an autochthonous case of VL in Campo Grande, state of Mato Grosso do Sul, in 2001 to 2018. The urban area of the city has 74 neighborhoods, and they were the units of analysis of our work. Socioeconomic and demographic data available from Brazilian public databases were considered as covariables. A total of 1,855 VL cases were reported during the study period, with an annual mean incidence rate of 13.23 cases per 100,000 population and a cumulative crude incidence of 235.77 per 100,000 population. The results showed the rapid transition from epidemic to endemic and the centrifugal dispersal pattern of the disease. Moreover, the model highlighted that the urban quality of life index, which is calculated based on income, education, housing conditions, and environmental sanitation data, plays a role in VL occurrence. Our findings highlighted the potential for improving spatio-temporal segmentation of control measures and the cost-effectiveness of integrated disease management programs as soon as VL is difficult to control and prevent and has rapid geographical dispersion and increased incidence rates.

GlaucoMap - Distribution of Glaucoma Surgical Procedures in the United States

PURPOSE

To understand the distribution of trabeculectomies, glaucoma drainage implants (GDI) and micro-invasive glaucoma surgeries (MIGS) performed in the United States through geospatial mapping.

METHODS

We performed an observational cohort study to evaluate glaucoma surgeries in patients age ≥65. The most recently released data from Centers for Medicare Part B Carrier Summary Files were queried to determine the number of glaucoma surgeries performed per state during the year of 2017. We created choropleth maps, titled "GlaucoMap", to characterize the rates of various surgeries performed across the United States, defined as the number of procedures performed per 10,000 individuals. A chi-squared analysis was further used to evaluate differences in surgical preferences across geographic region. Standardized residuals (SR) were calculated to determine regional influences on surgical distribution.

RESULTS

There were 174,788 glaucoma surgeries performed: 22,862 trabeculectomies (13.1%), 19,991 GDI (11.4%) and 131,935 (75.5%) MIGS. The Northeast had the highest trabeculectomy rate, GDI was highest in the Southeast and MIGS were highest in the Southwest. There was a statistically significant difference in proportional use of conventional surgeries versus MIGS across various regions in the United States (p < 0.0001). Given the high trabeculectomy and GDI rates and relatively low MIGS adoption in the Southeast, we observed a +7.03 SR for conventional surgeries and -4.01 SR for MIGS. The Southwest and Western states had the highest MIGS rate and contributed +3.29 and +3.24 SR toward disproportional MIGS preference, respectively. The preference for conventional surgeries in the Northeast (SR = +2.93) and MIGS in the Midwest (SR = +0.99) also contribute to the overall differences in glaucoma surgeries across the United States.

CONCLUSION

GlaucoMap is useful for visualizing the distribution of glaucoma surgeries in the United States. The heterogeneity in surgical preferences points to regional differences in glaucoma management.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

A Mapping Review on Urban Landscape Factors of Dengue Retrieved from Earth Observation Data, GIS Techniques, and Survey Questionnaires

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

Does Land Use and Landscape Contribute to Self-Harm? A Sustainability Cities Framework

Self-harm has become one of the leading causes of mortality in developed countries. The overall rate for suicide in Canada is 11.3 per 100,000 according to Statistics Canada in 2015. Between 2000 and 2007 the lowest rates of suicide in Canada were in Ontario, one of the most urbanized regions in Canada. However, the interaction between land use, landscape and self-harm has not been significantly studied for urban cores. It is thus of relevance to understand the impacts of land-use and landscape on suicidal behavior. This paper takes a spatial analytical approach to assess the occurrence of self-harm along one of the densest urban cores in the country: Toronto. Individual self-harm data was gathered by the National Ambulatory Care System (NACRS) and geocoded into census tract divisions. Toronto’s urban landscape is quantified at spatial level through the calculation of its land use at different levels: (i) land use type, (ii) sprawl metrics relating to (a) dispersion and (b) sprawl/mix incidence; (iii) fragmentation metrics of (a) urban fragmentation and (b) density and (iv) demographics of (a) income and (b) age. A stepwise regression is built to understand the most influential factors leading to self-harm from this selection generating an explanatory model.

Household environmental tobacco smoke exposure in healthy young children in Hong Kong: Prevalence and risk factors

Background

Environmental tobacco smoke (ETS) exposure attributable respiratory illness burden is huge in paediatric population. Understanding the epidemiology of ETS exposure is important to guide health promotion planning. Therefore, we designed this study to determine the prevalence of household ETS exposure in healthy young children under 2 years of age in Hong Kong, and to explore risk factors associated with the exposure. Our secondary goal was to characterise children’s exposure profile to maternal smoking.

Methods

A secondary analysis was performed based on the data collected from our 2013–2014 territory-wide cross-sectional pneumococcal carriage surveillance study, with a sample size of 1541. We conducted descriptive analysis for exposure prevalence, univariate and multivariate analysis for identification of risk factors.

Results

1541 children (mean age: 11.2 ± 6.4 months, male: 50.7%) were included in the analysis. The overall prevalence of current household ETS exposure was 31.5%, prevalence of prenatal and postnatal maternal smoking was 3.5% and 1.6% respectively. Independent factors associated with children’s ETS exposure were: never breastfed (AOR: 1.48, 95% CI: 1.13–1.93, p = 0.004); prenatal maternal smoking (AOR: 7.46, 95% CI: 2.73–20.39, p< 0.001); overcrowding of household living place (AOR: 3.17, 95% CI: 2.02–4.96, P< 0.001); lower household income (AOR: 1.34, 95% CI: 1.04–1.72, p = 0.02). Interestingly, children residing in Kowloon (AOR: 1.66, 95% CI: 1.19–2.33, p = 0.003) and New Territories West (AOR: 1.54, 95% CI: 1.11–2.15, p = 0.01) were associated with exposure compared with children residing in Hong Kong Island.

Conclusion

Exposure to household ETS is prevalent among Hong Kong young children, particularly in children with maternal unfavourable behaviour and lower socioeconomic status. The identified risk factors should be considered while tobacco control interventions and legislations are planned.

Protocol, rationale and design of PEOPLE (Post ExpOsure Prophylaxis for LEprosy in the Comoros and Madagascar): a cluster randomized trial on effectiveness of different modalities of implementation of post-exposure prophylaxis of leprosy contacts

Background

Leprosy is an ancient infectious disease with a global annual incidence that has plateaued above 200,000 new cases since over a decade. New strategies are required to overcome this stalemate. Post-exposure prophylaxis (PEP) with a single dose of Rifampicin (SDR) has conditionally been recommended by the World Health Organization (WHO), based on a randomized-controlled-trial in Bangladesh. More evidence is required. The Post ExpOsure Prophylaxis for Leprosy (PEOPLE) trial will assess effectiveness of different modalities of PEP on the Comoros and Madagascar.

Methods

PEOPLE is a cluster-randomized trial with villages selected on previous leprosy-incidence and randomly allocated to four arms. Four annual door-to-door surveys will be performed in all arms. All consenting permanent residents will be screened for leprosy. Leprosy patients will be treated according to international guidelines and eligible contacts will be provided with SDR-PEP.

Arm-1 is the comparator in which no PEP will be provided. In arms 2, 3 and 4, SDR-PEP will be provided at double the regular dose (20 mg/kg) to eligible contacts aged two years and above. In arm 2 all household-members of incident leprosy patients are eligible. In arm 3 not only household-members but also neighbourhood contacts living within 100-m of an incident case are eligible. In arm 4 such neighbourhood contacts are only eligible if they test positive to anti-PGL-I, a serological marker. Incidence rate ratios calculated between the comparator arm 1 and each of the intervention arms will constitute the primary outcome.

Discussion

Different trials on PEP have yielded varying results. The pivotal COLEP trial in Bangladesh showed a 57% reduction in incidence over a two-year period post-intervention without any rebound in the following years. A study in a high-incidence setting in Indonesia showed no effect of PEP provided to close contacts but a major effect of PEP provided as a blanket measure to an entire island population. High background incidence could be the reason of the lack of effect of PEP provided to individual contacts. The PEOPLE trial will assess effectiveness of PEP in a high incidence setting and will compare three different approaches, to identify who benefits most from PEP.

Trial registration

Clinicaltrials.Gov. NCT03662022. Initial Protocol Version 1.2, 27-Aug-2018.

Lessons Learned From the Environmental Public Health Tracking Sub-County Data Pilot Project

OBJECTIVE

Small area data are key to better understanding the complex relationships between environmental health, health outcomes, and risk factors at a local level. In 2014, the Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program (Tracking Program) conducted the Sub-County Data Pilot Project with grantees to consider integration of sub-county data into the National Environmental Public Health Tracking Network (Tracking Network).

DESIGN

The Tracking Program and grantees developed sub-county-level data for several data sets during this pilot project, working to standardize processes for submitting data and creating required geographies. Grantees documented challenges they encountered during the pilot project and documented decisions.

RESULTS

This article covers the challenges revealed during the project. It includes insights into geocoding, aggregation, population estimates, and data stability and provides recommendations for moving forward.

CONCLUSION

National standards for generating, analyzing, and sharing sub-county data should be established to build a system of sub-county data that allow for comparison of outcomes, geographies, and time. Increasing the availability and accessibility of small area data will not only enhance the Tracking Network's capabilities but also contribute to an improved understanding of environmental health and informed decision making at a local level.

Geographic variation in cardiometabolic risk distribution: A cross-sectional study of 256,525 adult residents in the Illawarra-Shoalhaven region of the NSW, Australia

INTRODUCTION

Metabolic risk factors for cardiovascular disease (CVD) warrant significant public health concern globally. This study aims to utilise the regional database of a major laboratory network to describe the geographic distribution pattern of eight different cardiometabolic risk factors (CMRFs), which in turn can potentially generate hypotheses for future research into locality specific preventive approaches.

METHOD

A cross-sectional design utilising de-identified laboratory data on eight CMRFs including fasting blood sugar level (FBSL); glycated haemoglobin (HbA1c); total cholesterol (TC); high density lipoprotein (HDL); albumin creatinine ratio (ACR); estimated glomerular filtration rate (eGFR); body mass index (BMI); and diabetes mellitus (DM) status was used to undertake descriptive and spatial analyses. CMRF test results were dichotomised into 'higher risk' and 'lower risk' values based on existing risk definitions. Australian Census Statistical Area Level 1 (SA1) were used as the geographic units of analysis, and an Empirical Bayes (EB) approach was used to smooth rates at SA1 level. Choropleth maps demonstrating the distribution of CMRFs rates at SA1 level were produced. Spatial clustering of CMRFs was assessed using Global Moran's I test and Local Indicators of Spatial Autocorrelation (LISA).

RESULTS

A total of 1,132,016 test data derived from 256,525 individuals revealed significant geographic variation in the distribution of 'higher risk' CMRF findings. The populated eastern seaboard of the study region demonstrated the highest rates of CMRFs. Global Moran's I values were significant and positive at SA1 level for all CMRFs. The highest spatial autocorrelation strength was found among obesity rates (0.328), and the lowest for albuminuria (0.028). LISA tests identified significant High-High (HH) and Low-Low (LL) spatial clusters of CMRFs, with LL predominantly in the less populated northern, central and southern regions of the study area.

CONCLUSION

The study describes a range of CMRFs with different distributions in the study region. The results allow generation of hypotheses to test in future research concerning location specific population health approaches.

A Spatiotemporal Analysis of HIV-Associated Mortality in Rural Western Kenya 2011-2015

Background:

Reliable data on the HIV epidemic is critical for the measurement of the impact of HIV response and for the implementation of further interventions.

Methods:

We used mortality data from the Kombewa health and demographic surveillance systems (HDSS) from January 1, 2011 to December 31, 2015 to examine the space–time pattern of HIV-associated mortality. HIV mortality rate was calculated per 1000 persons living with HIV (for comparison with regional and national averages) and per 1000 person-years (p-y) for comparison with data from other HDSS sites. We used the Optimized Hot Spot Analysis to examine whether HIV-associated deaths would form statistically significant local aggregation in the 5-year period. P-value of <0.05 and <0.01 was considered significant.

Results:

The HIV-associated mortality rate over the 5-year period was 9.8 per 1000 persons living with HIV (PLHIV). Mortality declined from 11.6 per 1000 PLHIV in 2011 to 7.3 per 1000 PLHIV by the end of 2015. The rates of HIV were highest among infants [hazard ratio (HR) = 2.39 (<0.001)]. Tuberculosis mortality rates were highest in the age group 5–14 years [HR = 2.29 (0.002)] and the age group 50–64 years [HR = 1.18 (0.531)]. The overall trend in HIV-associated mortality showed a decline from 1.8 per 1000 p-y in 2011 to 1.3 per 1000 p-y by the end of 2015. The hotspot analysis showed that 20.0% of the study area (72 km²) was detected as hotspots (Z = 2.382–3.143, P ≤ 0.001) and 4.2% of the study area as cold spots (15 km²).

Conclusions:

HIV attributable death in the HDSS population is substantial, although it is lower than both the national and the regional estimates.

Exploring and visualizing the small-area-level socioeconomic factors, alcohol availability and built environment influences of alcohol expenditure for the City of Toronto: a spatial analysis approach

INTRODUCTION

Many Canadians continue to drink alcohol in excess of the recommended low-risk guidelines. In this study, we visualized the geographic variation of licensed premises alcohol expenditures in Toronto and examined the effects of area-level socioeconomic characteristics, alcohol availability and built environment influences on alcohol expenditures at the Dissemination Area (DA) level.

METHODS

Dissemination Area average total household expenditures on alcohol from licensed premises, from the 2010 Survey of Household Spending, was the main outcome variable. Moran's I and Local Moran's I were used to quantify geographic variation and determine hot spots and cold spots of expenditure. We used DA-level socioeconomic characteristics from the 2006 Census of Canada, and the density of licensed premises and other built environment characteristics from the 2008 DMTI Spatial and 2010 CanMap datasets to predict alcohol expenditures in multivariate spatial regression models.

RESULTS

The results indicated that the most significant area-level predictors of alcohol expenditure were the percentage of individuals in management or finance occupations and the percentage with postsecondary education (one-unit increases associated with 78.6% and 35.0% increases in expenditures respectively). Presence of subway lines in the immediate and neighbouring areas was also significant (one-unit increases resulted in 5% and 28% increases respectively). Alcohol outlet density was also positively associated with alcohol expenditures.

CONCLUSION

The associations identified between licensed premises alcohol expenditures and small-area-level characteristics highlight the potential importance of small-area-level factors in understanding alcohol use. Understanding the small-area-level characteristics of expenditures and geographic variation of alcohol expenditures may provide avenues for alcohol use reduction initiatives and policies.

Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in Brazil

BACKGROUND

Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil.

METHODS

We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area's HDI (each one of the components - longevity, education, and income) with TB spatial distribution.

RESULTS

During the study period (2011-2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036).

CONCLUSIONS

We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs.

Spatial Clustering of Suicide and Associated Community Characteristics, Idaho, 2010-2014

Introduction

In 2015, Idaho had the fifth highest suicide rate in the United States. Little is known about the characteristics of areas in Idaho with high suicide rates. To aid suicide prevention efforts in the state, we sought to identify and characterize spatial clusters of suicide.

Methods

We obtained population data from the 2010 US Census and the 2010–2014 American Community Survey, analyzed data on suicides from death certificates, and used a discrete Poisson model in SaTScan to identify spatial clusters of suicide. We used logistic regression to examine associations between suicide clustering and population characteristics.

Results

We found 2 clusters of suicide during 2010–2014 that accounted for 70 (4.7%) of 1,501 suicides in Idaho. Areas within clusters were positively associated with the following population characteristics: median age ≤31.1 years versus >31.1 years (multivariable-adjusted odds ratio [aOR] = 2.4; 95% confidence interval [CI], 1.04–5.6), >53% female vs ≤53% female (aOR = 2.7; 95% CI, 1.3–5.8; P = .01), >1% American Indian/Alaska Native vs ≤1% American Indian/Alaska Native (aOR = 2.9; 95% CI, 1.4–6.3), and >30% never married vs ≤30% never married (aOR = 3.4; 95% CI, 1.5–8.0; P = .004).

Conclusion

Idaho suicide prevention programs should consider using results to target prevention efforts to communities with disproportionately high suicide rates.

Spatiotemporal patterns of malaria at cross-boundaries area in Menoreh Hills, Java, Indonesia

Background

Comprehensive reports of malaria in Menoreh Hills, Central Java, Indonesia, a unique district cross-boundaries area under three districts and two provinces have been published previously. However, no study was performed to identify the hotspots of malaria in this cross-boundaries area, Kaligesing and Bagelen Subdistricts in Purworejo, Jawa Tengah Province and Kokap Subdistrict in Kulon Progo, Yogyakarta Province, using a longitudinal spatial data.

Methods

Monthly reports of malaria cases at primary health centres during 2005–2015 were collected and processed with ArcGIS and SaTScan to identify the malaria distribution at the village level. Malaria distribution was analysed using global spatial autocorrelation (Moran index) in ArcGIS. Cluster analysis was conducted using SaTScan purely spatial clustering and purely temporal clustering. Cluster characteristics resulted from three different approach were compared and analysed.

Results

During the last 11 years, 3812 malaria cases were reported and the number of high case incidence (HCI) villages were increased continuously. Malaria spatial distribution in Menoreh Hills was clustered spatially. Using three different approaches of time period ranges, consistent conclusion was found i.e. most likely clusters always occurred in the Purworejo district while the secondary clusters appeared later in the cross-boundaries districts.

Conclusion

Spatiotemporal analysis of an 11 years surveillance data showed that hotspots of malaria cases in Menoreh Hills were continuously located in Purworejo district. The success of malaria elimination in the cross boundaries area of Menoreh Hills might be depended on the success in malaria case management and surveillance in this hotspot area.

Geographical inequalities in acute myocardial infarction beyond neighbourhood-level and individual-level sociodemographic characteristics: a Danish 10-year nationwide population-based cohort study

OBJECTIVE

This study examined whether geographical patterns in incident acute myocardial infarction (AMI) were explained by neighbourhood-level and individual-level sociodemographic characteristics.

DESIGN

An open cohort study design of AMI-free adults (age ≥30 years) with a residential location in Denmark in 2005-2014 was used based on nationwide administrative population and health register data linked by the unique personal identification number. Poisson regression of AMI incidence rates (IRs) with a geographical random effect component was performed using a Bayesian approach. The analysis included neighbourhood-level variables on income, ethnic composition, population density and population turnover and accounted for individual-level age, sex, calendar year, cohabitation status, income and education.

SETTING

Residents in Denmark (2005-2014).

PARTICIPANTS

The study population included 4 128 079 persons (33 907 796 person-years at risk) out of whom 98 265 experienced an incident AMI.

OUTCOME MEASURE

Incident AMI registered in the National Patient Register or the Register of Causes of Death.

RESULTS

Including individual and neighbourhood sociodemographic characteristics in the model decreased the variation in IRs of AMI. However, living in certain areas was associated with up to 40% increased IRs of AMI in the adjusted model and accounting for sociodemographic characteristics only moderately changed the geographical disease patterns.

CONCLUSIONS

Differences in sociodemographic characteristics of the neighbourhood and individuals explained part, but not all of the geographical inequalities in incident AMI. Prevention strategies should address the confirmed social inequalities in incident AMI, but also target the areas with a heavy disease burden to enable efficient allocation of prevention resources.

Spatial clustering patterns of child weight status in a southeastern US county

Youth obesity is a major public health concern due to associated physical, social, and psychological health consequences. While rates and disparities of youth obesity levels are known, less research has explored spatial clustering patterns, associated correlates of spatial clustering, comparing patterns in urban and rural areas. Therefore, this study 1) examined spatial clustering of youth weight status, 2) investigated sociodemographic correlates of spatial clustering patterns, and 3) explored spatial patterns by level of urbanization. This study occurred in a southeastern US county (pop:474,266) in 2013. Trained physical education teachers collected height and weight for all 3^rd-5th grade youth (n = 13,469) and schools provided youth demographic attributes. BMI z-scores were calculated using standard procedures. Global Moran's Index and Anselin's Local Moran's I (LISA) were used detect global and local spatial clustering, respectively. To examine correlates of spatial clustering, BMI z-score residuals from a series of four linear regression models were spatially analyzed, mapped, and compared. SAS 9.4 and GeoDA were used for analyses; ArcGIS was used for mapping. Significant, positive global clustering (Index = 0.04,p < 0.001) was detected. LISA results showed that about 4.7% (n = 635) and 7.9% (n = 1058) of the sample were identified as high and low obesity localized spatial clusters (p < 0.01), respectively. Individual and neighborhood sociodemographic characteristics accounted for the majority of spatial clustering and differential patterns were observed by level of urbanization. Identifying geographic areas that contain significant spatial clusters is a powerful tool for understanding the location of and exploring contributing factors to youth obesity.

The university campus environment as a protective factor for intimate partner violence against women: An exploratory study

Some neighborhood characteristics linked to social disorganization theory have been related to intimate partner violence against women (IPVAW). The study of other neighborhood-level factors that may influence IPVAW risk, however, has received less attention. The aim of this study is to analyze the influence of university campuses on IPVAW risk. To conduct the study, IPVAW cases from 2011 to 2013 in the city of Valencia, Spain, were geocoded (n = 1,623). Census block groups were used as the neighborhood analysis unit. Distance between each census block group and the nearest university campus was measured. A Bayesian spatial model adjusted for census block group-level characteristics was performed. Results showed that the distance from a university campus was associated with an approximate 7% increase in IPVAW risk per kilometer. These results suggest that university campuses integrated in the city are related to IPVAW risk. Further research is needed to explain the mechanisms involved.

Comparison of the Clusters and Non-Clusters Areas of Attempted Suicide Cases in Hamadan Province, Western Iran: Findings from a Pilot Study (2016-2017)

BACKGROUND

Suicide behaviors are complex and multifactorial problems that in the most of the societies are considered as the public health challenge. However, its underlying reasons and spatial pattern remain unclear in Hamadan Province, western Iran.

STUDY DESIGN

Secondary analysis of existing data.

METHODS

We assessed the spatial pattern pre-city regarding some influencing factors by scan-statistics and logistic regression to detect clusters areas and its comparison with other areas for the period of 2016-2017. All of the registered cases of attempted suicide in a quality registry system of suicide in Sina (Farshchian) Hospital affiliated to Hamadan University of Medical Sciences, Hamadan, Iran were enrolled.

RESULTS

Two significant clusters were detected in study areas, formed with relative risk at 5.28 (P=0.001) and 6.33 (P=0.017), and with the centrality of Asadabad and Razan, respectively. Clusters and non-clusters areas were differed in terms of location (OR=0.15, 95%, CI: 0.07, 0.31), self-harms methods (OR=0.28, 95%, CI: 0.9, 0.88) and education. Residents of rural areas, illiterate people and non-drug user cases have more likely to be in a cluster.

CONCLUSIONS

Clusters were not formed equally among cities of Hamadan Province. Accordingly, we suggest the implementation of appropriate, long-term and evidence-based educations for high-risk and vulnerable groups through the intersectoral interventions in different parts of Hamadan Province (considering the cluster and non-clusters areas) to avert deaths and related injuries from attempted suicide.

Ovarian Cancer Incidence in the U.S. and Toxic Emissions from Pulp and Paper Plants: A Geospatial Analysis

Ovarian cancer is the fifth leading cause of female cancer mortality in the U.S. and accounts for five percent of all cancer deaths among women. No environmental risk factors for ovarian cancer have been confirmed. We previously reported that ovarian cancer incidence rates at the state level were significantly correlated with the extent of pulp and paper manufacturing. We evaluated that association using county-level data and advanced geospatial methods. Specifically, we investigated the relationship of spatial patterns of ovarian cancer incidence rates with toxic emissions from pulp and paper facilities using data from the Environmental Protection Agency’s Toxic Release Inventory (TRI). Geospatial analysis identified clusters of counties with high ovarian cancer incidence rates in south-central Iowa, Wisconsin, New York, Pennsylvania, Alabama, and Georgia. A bivariate local indicator of spatial autocorrelation (LISA) analysis confirmed that counties with high ovarian cancer rates were associated with counties with large numbers of pulp and paper mills. Regression analysis of state level data indicated a positive correlation between ovarian cancer and water pollutant emissions. A similar relationship was identified from the analysis of county-level data. These data support a possible role of water-borne pollutants from pulp and paper mills in the etiology of ovarian cancer.

Unlocking pandemic potential: prevalence and spatial patterns of key substitutions in avian influenza H5N1 in Egyptian isolates

Background

Avian influenza H5N1 has a high human case fatality rate, but is not yet well-adapted to human hosts. Amino acid substitutions currently circulating in avian populations may enhance viral fitness in, and thus viral adaptation to, human hosts. Substitutions which could increase the risk of a human pandemic (through changes to host specificity, virulence, replication ability, transmissibility, or drug susceptibility) are termed key substitutions (KS). Egypt represents the epicenter of human H5N1 infections, with more confirmed cases than any other country. To date, however, there have not been any spatial analyses of KS in Egypt.

Methods

Using 925 viral samples of H5N1 from Egypt, we aligned protein sequences and scanned for KS. We geocoded isolates using dasymetric mapping, then carried out geospatial hot spot analyses to identify spatial clusters of high KS detection rates. KS prevalence and spatial clusters were evaluated for all detected KS, as well as when stratified by phenotypic consequence.

Results

A total of 39 distinct KS were detected in the wild, including 17 not previously reported in Egypt. KS were detected in 874 samples (94.5%). Detection rates varied by viral protein with most KS observed in the surface hemagglutinin (HA) and neuraminidase (NA) proteins, as well as the interior non-structural 1 (NS1) protein. The most frequently detected KS were associated with increased viral binding to mammalian cells and virulence. Samples with high overall detection rates of KS exhibited statistically significant spatial clustering in two governorates in the northwestern Nile delta, Alexandria and Beheira.

Conclusions

KS provide a possible mechanism by which avian influenza H5N1 could evolve into a pandemic candidate. With numerous KS circulating in Egypt, and non-random spatial clustering of KS detection rates, these findings suggest the need for increased surveillance in these areas.

The contribution of area-level walkability to geographic variation in physical activity: a spatial analysis of 95,837 participants from the 45 and Up Study living in Sydney, Australia

BACKGROUND

Individual-level studies support a positive relation between walkable built environments and participation in moderate-intensity walking. However, the utility of this evidence for population-level planning is less clear as it is derived at much finer spatial scales than those used for regional programming. The aims of this study were to: evaluate if individual-level relations between walkability and walking to improve health manifest at population-level spatial scales; assess the specificity of area-level walkability for walking relative to other moderate and vigorous physical activity (MVPA); describe geographic variation in walking and other MVPA; and quantify the contribution of walkability to this variation.

METHODS

Data on sufficient walking, sufficient MVPA, and high MVPA to improve health were analyzed for 95,837 Sydney respondents to the baseline survey of the 45 and Up Study between January 2006 and April 2010. We used conditional autoregressive models to create smoothed MVPA "disease maps" and assess relations between sufficient MVPA to improve health and area-level walkability adjusted for individual-level demographic, socioeconomic, and health factors, and area-level relative socioeconomic disadvantage.

RESULTS

Within-cohort prevalence of meeting recommendations for sufficient walking, sufficient MVPA, and high MVPA were 31.7 (95% CI 31.4-32.0), 69.4 (95% CI 69.1-69.7), and 56.1 (95% CI 55.8-56.4) percent. Prevalence of sufficient walking was increased by 1.20 (95% CrI 1.12-1.29) and 1.07 (95% CrI 1.01-1.13) for high and medium-high versus low walkability postal areas, and for sufficient MVPA by 1.05 (95% CrI 1.01-1.08) for high versus low walkability postal areas. Walkability was not related to high MVPA. Postal area walkability explained 65.8 and 47.4 percent of residual geographic variation in sufficient walking and sufficient MVPA not attributable to individual-level factors.

CONCLUSIONS

Walkability is associated with area-level prevalence and geographic variation in sufficient walking and sufficient MVPA to improve health in Sydney, Australia. Our study supports the use of walkability indexes at multiple spatial scales for informing population-level action to increase physical activity and the utility of spatial analysis for walkability research and planning.

A new approach to spatial identification of potential health hazards associated with childhood asthma

RESEARCH BACKGROUND

Childhood asthma is a chronic disease, known to be linked to prolonged exposure to air pollution. However, the identification of specific health hazards, associated with childhood asthma is not always straightforward, due to the presence of multiple sources of air pollution in urban areas. In this study, we test a novel approach to the spatial identification of environmental hazards that have the highest probability of association with the observed asthma morbidity patterns.

METHODS

The effect of a particular health hazard on population morbidity is expected to weaken with distance. To account for this effect, we rank potential health hazards based on the strength of association between the observed morbidity patterns and wind-direction weighted proximities to these locations. We validate this approach by applying it to a study of spatial patterns of childhood asthma in the Greater Haifa Metropolitan Area (GHMA) in Israel, characterised by multiple health hazards.

RESULTS

We identified a spot in the local industrial zone as the primary risk source for the observed asthma morbidity patterns. Multivariate regressions, controlling for socio-economic and geographic variables, revealed that the observed incidence rates of asthma tend to decline as a function of distance from the identified industrial location.

CONCLUSION

The proposed identification approach uses disease patterns as its main input, and can be used by researches as a preliminary risk assessment tool, in cases in which specific sources of locally elevated morbidity are unclear or cannot be identified by traditional methods.

Spatio-temporal analysis of small-area intestinal parasites infections in Ghana

Intestinal parasites infection is a major public health burden in low and middle-income countries. In Ghana, it is amongst the top five morbidities. In order to optimize scarce resources, reliable information on its geographical distribution is needed to guide periodic mass drug administration to populations of high risk. We analyzed district level morbidities of intestinal parasites between 2010 and 2014 using exploratory spatial analysis and geostatistics. We found a significantly positive Moran’s Index of spatial autocorrelation for each year, suggesting that adjoining districts have similar risk levels. Using local Moran’s Index, we found high-high clusters extending towards the Guinea and Sudan Savannah ecological zones, whereas low-low clusters extended within the semi-deciduous forest and transitional ecological zones. Variograms indicated that local and regional scale risk factors modulate the variation of intestinal parasites. Poisson kriging maps showed smoothed spatially varied distribution of intestinal parasites risk. These emphasize the need for a follow-up investigation into the exact determining factors modulating the observed patterns. The findings also underscored the potential of exploratory spatial analysis and geostatistics as tools for visualizing the spatial distribution of small area intestinal worms infections.

Geographic Variations in Cardiometabolic Risk Factors in Luxembourg

Cardiovascular disease (CVD) and associated behavioural and metabolic risk factors constitute a major public health concern at a global level. Many reports worldwide have documented different risk profiles for populations with demographic variations. The objective of this study was to examine geographic variations in the top leading cardio metabolic and behavioural risk factors in Luxembourg, in order to provide an overall picture of CVD burden across the country. The analysis conducted was based on data from the nationwide ORISCAV-LUX survey, including 1432 subjects, aged 18-69 years. A self-reported questionnaire, physical examination and blood sampling were performed. Age and sex-adjusted risk profile maps were generated using multivariate Bayesian geo-additive regression models, based on Markov Chain Monte Carlo techniques and were used to evaluate the significance of the spatial effects on the distribution of a range of cardio metabolic risk factors, namely smoking, high body mass index (BMI), high blood pressure, high fasting plasma glucose, alcohol use, high total cholesterol, low glomerular filtration rate, and physical inactivity. Higher prevalence of smoking was observed in the northern regions, higher overweight/obesity and abdominal obesity clustered in the central belt, whereas hypertension was spotted particularly in the southern part of the country. Maps revealed that subjects residing in Luxembourg canton were significantly less likely to be hypertensive or overweight/obese, whereas they were less likely to practice physical activity of ≥8000 Metabolic Equivalent of Task (MET)-min/week. These patterns were also observed at the municipality level in Luxembourg. Statistically, there were non-significant spatial patterns regarding smoking, diabetes, total serum cholesterol and low glomerular filtration rate risk distribution. This comprehensive risk profile mapping showed remarkable geographic variations in cardio metabolic and behavioural risk factors. Considering the prominent burden of CVD this research provides opportunities for tailored interventions and may help to better fight against this escalating public health problem.

Impact of iron fortification on the geospatial patterns of malaria and non-malaria infection risk among young children: a secondary spatial analysis of clinical trial data from Ghana

OBJECTIVES

Patterns of infection among children with varying levels of iron status in a malaria endemic area may vary spatially in ways requiring integrated infection and iron deficiency control programmes. The objective of this secondary analysis was to determine the geospatial factors associated with malaria and non-malaria infection status among young Ghanaian children at the end of a 5-month iron intervention trial.

DESIGN

Cluster-randomised controlled trial.

SETTING

Rural Ghana PARTICIPANTS: 1943 children (6-35 months of age) with geocoded compounds.

INTERVENTIONS

Point-of-use fortification with micronutrient powders containing vitamins and minerals with or without iron.

PRIMARY AND SECONDARY OUTCOME MEASURES

Generalised linear geostatistical models with a Matern spatial correlation function were used to analyse four infection response variables, defined using different combinations of inflammation (C-reactive protein, CRP >5 mg/L) and malaria parasitaemia. Analyses were also stratified by treatment group to assess the independent effects of the iron intervention.

RESULTS

The by-group and combined-group analyses both showed that baseline infection status was the most consistent predictor of endline infection risk, particularly when infection was defined using parasitaemia. In the No-iron group, age above 24 months and weight-for-length z-score at baseline were associated with high CRP at endline. Higher asset score was associated with a 12% decreased odds of endline infection, defined as CRP >5 mg/L and/or parasitaemia (OR 0.88, 95% credible interval 0.78 to 0.98), regardless of group. Maps of the predicted risk and spatial random effects showed a defined low-risk area around the District centre, regardless of how infection was defined.

CONCLUSION

In a clinical trial setting of iron fortification, where all children receive treated bed nets and access to malaria treatment, there may be geographical variation in the risk of infection with distinct high-risk and low-risk areas, particularly around municipal centres.

TRIAL REGISTRATION NUMBER

clinicaltrials.gov, NCT01001871.