Spatio-temporal epidemiology: principles and opportunities

Effects of climate and environmental factors on childhood and adolescent asthma: A systematic review based on spatial and temporal analysis evidence

BACKGROUND

Asthma is a prevalent chronic respiratory disease among children, influenced by various climate and environmental factors. Despite its prevalence, the specific effects of these factors on asthma remain unclear. This study aims to systematically assess the epidemiological evidence using spatial and temporal methods on the impact of climate and environmental factors on childhood asthma.

METHODS

A systematic review was conducted to analyse the impact of climate and environmental factors on childhood asthma and wheezing, focusing on spatial and temporal trends. Searches were carried out in PubMed, Embase, and CINAHL databases for studies published from January 2000 to April 2024, using key search terms 'asthma/wheezing', 'extreme weather, 'green space', 'air pollution' and 'spatial or temporal analyses".

RESULTS

The systematic review analysed 28 studies, with six employing spatial and 22 using temporal analysis methods; however, none incorporated spatio-temporal analysis in their models. The findings reveal that extreme weather events, including heatwaves and heavy rainfall, elevate childhood asthma risks across various climates, with significant effects observed during summer and winter months. Dust storms in arid and subtropical regions are linked to immediate spikes in hospital admissions due to asthma exacerbations. The effects of green spaces on childhood asthma are mixed, with some studies indicating protective effects while others suggest increased risks, influenced by local environmental factors. Air pollutants such as PM2.5, NO2, and ozone can exacerbate asthma symptoms and along with other environmental factors, contribute to seasonal effects. High temperatures generally correlate with increased asthma risks, though the effects vary by age, sex, and climate.

CONCLUSION

Future research should integrate spatial and temporal methods to better understand the effects of environmental and climate changes on childhood asthma.

The power of interactive maps for communicating spatio-temporal data to health professionals

While more and more health-related data is being produced and published every day, few of it is being prepared in a way that would be beneficial for daily use outside the scientific realm. Interactive visualizations that can slice and condense enormous amounts of multi-dimensional data into easy-to-digest portions are a promising tool that has been under-utilized for health-related topics. Here we present two case studies for how interactive maps can be utilized to make raw health data accessible to different target audiences: i) the European Notifiable Diseases Interactive Geovisualization (ENDIG) which aims to communicate the implementation status of disease surveillance systems across the European Union to public health experts and decision makers, and ii) the Zoonotic Infection Risk in Twente-Achterhoek Map (ZIRTA map), which aims to communicate information about zoonotic diseases and their regional occurrence to general practitioners and other healthcare providers tasked with diagnosing infectious diseases on a daily basis. With these two examples, we demonstrate that relatively straight-forward interactive visualization approaches that are already widely used elsewhere can be of benefit for the realm of public health.

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning

Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collapse amidst 'Big Data', prompt interpretable machine-learning (iML) approaches. Predicting dengue incidence and mortality in the Philippines, a data-limited yet high-burden country, the mlr3 universe of R packages was used to build and optimize ML models based on remotely sensed provincial and dekadal 3 NDVI and 9 rainfall features from 2016 to 2020. Between two tasks, models differ across four random forest-based learners and two clustering strategies. Among 16 candidates, rfsrc-year-case and ranger-year-death significantly perform best for predicting dengue incidence and mortality, respectively. Therefore, temporal clustering yields the best models, reflective of dengue seasonality. The two best models were subjected to tripartite global exploratory model analyses, which encompass model-agnostic post-hoc methods such as Permutation Feature Importance (PFI) and Accumulated Local Effects (ALE). PFI reveals that the models differ in their important explanatory aspect, rainfall for rfsrc-year-case and NDVI for ranger-year-death, among which long-term average (lta) features are most relevant. Trend-wise, ALE reveals that average incidence predictions are positively associated with 'Rain.lta', reflective of dengue cases peaking during the wet season. In contrast, those for mortality are negatively associated with 'NDVI.lta', reflective of urban spaces driving dengue-related deaths. By technologically addressing the challenges of the human-animal-ecosystem interface, this study adheres to the One Digital Health paradigm operationalized under Sustainable Development Goals (SDGs). Leveraging data digitization and predictive modeling for epidemiological research paves SDG 3, which prioritizes holistic health and well-being.

Spatiotemporal Patterns of Hospitalizations Among Older Adults With Co-Presence of Cancer and Dementia in US Counties: 2013-2018

We assessed the spatiotemporal patterns of hospitalization with comorbid cancer and dementia. Using the 2013-2018 inpatient claims data for Medicare fee-for-service (FFS) beneficiaries, we calculated hospitalization rates by dividing the total admissions from individuals with the co-presence of a major cancer (breast, prostate, lung, and colorectal) and dementia diagnoses with the total counts of FFS beneficiaries aged 65 or older. We identified 22 hotspots with high hospitalization rates that showed heterogeneous spatial and temporal utilization patterns. The odds of a county being a hotspot increased significantly with the county-level percentage of dual Medicare-Medicaid beneficiaries (aOR 1.05; 95% CI: 1.04-1.07) and the prevalence of cancer (aOR 1.73; 95% CI: 1.59-1.89), while decreased significantly with increasing degree of rurality (aOR .82; 95% CI: .79-.85) and decreased yearly over time (aOR .72; 95% CI: .68-.75). The identified hotspots and factors at the county-level may help understand healthcare utilization patterns and assess resource allocation for this unique patient group.

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.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Evaluation of a smartphone-based methodology that integrates long-term tracking of mobility, place experiences, heart rate variability, and subjective well-being

This study presents MyGävle, a smartphone application that merge long-term tracking of mobility data, heart rate variability and subjective and objective well-being records. Developed to address the challenges faced in researching healthy and sustainable lifestyles, this app serves as a pioneering implementation of Real-life Long-term Methodology (ReaLM). After eight months' use by 257 participants from Gävle (Sweden), we evaluate the completeness, accuracy, validity, and consistency of all data collected. MyGävle produced remarkable results as a ReaLM method. On average, it precisely tracked participants daily locations for approximately 8 h and accurately collected heart-rate variability values throughout the day (12 h) and night (6 h). Participants reported 5115 subjective place experiences (ranging from 160 to 120 per week) and seasonal participation, although declining, is accurate. Our findings indicate that the amount of data collected through smartphone sensors, fitness wristbands and in-app questionnaires is consistent enough to be leveraged for integrated assessments of habits, environmental exposure, and subjective and physiological well-being. Yet, considerable variation exists across individuals; thus diagnostic analysis must precede use of these datasets in any particular research endeavors. By doing so we can maximise the potential of ReaLM research to delve into real life conditions conducive to healthy living habits while also considering broader sustainability goals.

Prevalence and risk factors of cysticercosis in cattle tracking

ABSTRACT The aim of this study was to identify the prevalence and the main risk factors related to the transmission of bovine cysticercosis based on tracking animals sent for slaughter and coming from properties located in the micro-region of Uberlândia, Minas Gerais, Brazil. The properties were previously evaluated for the occurrence of cysticercosis during post-mortem inspection in the 12 months prior to the beginning of the research, and those with animals with bovine cysticercosis found at least once during this period were considered positive. A cross-sectional study was carried out on 87 properties, from which 1024 bovine serum samples were collected. Indirect ELISA performed serological diagnosis and Immunoblot confirmed positive sera. The prevalence found in this study was 5.1% (95% CI = 3.74-6.42). The risk factors identified were cattle origin (RC = 4.9), grazing (RC = 6.4) and sewage destination on the property (RC = 3.6). These environmental factors suggest that sanitation control measures and the restriction of pastures beyond the property boundary can help prevent disease in the study area. A control system based on risk analysis was discussed and proposed as a strategy to control bovine cysticercosis in the Triângulo Mineiro region and other regions of the country.

A tutorial on the case time series design for small-area analysis

Background

The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.

Methods

This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.

Results

The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.

Conclusions

The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

A Systematic Review of Spatial and Spatio-temporal Analyses in Public Health Research in Korea

OBJECTIVES

Despite its advantages, it is not yet common practice in Korea for researchers to investigate disease associations using spatio-temporal analyses. In this study, we aimed to review health-related epidemiological research using spatio-temporal analyses and to observe methodological trends.

METHODS

Health-related studies that applied spatial or spatio-temporal methods were identified using 2 international databases (PubMed and Embase) and 4 Korean academic databases (KoreaMed, NDSL, DBpia, and RISS). Two reviewers extracted data to review the included studies. A search for relevant keywords yielded 5919 studies.

RESULTS

Of the studies that were initially found, 150 were ultimately included based on the eligibility criteria. In terms of the research topic, 5 categories with 11 subcategories were identified: chronic diseases (n=31, 20.7%), infectious diseases (n=27, 18.0%), health-related topics (including service utilization, equity, and behavior) (n=47, 31.3%), mental health (n=15, 10.0%), and cancer (n=7, 4.7%). Compared to the period between 2000 and 2010, more studies published between 2011 and 2020 were found to use 2 or more spatial analysis techniques (35.6% of included studies), and the number of studies on mapping increased 6-fold.

CONCLUSIONS

Further spatio-temporal analysis-related studies with point data are needed to provide insights and evidence to support policy decision-making for the prevention and control of infectious and chronic diseases using advances in spatial techniques.

Space-time clustering and temporal trends of hospitalizations due to pulmonary tuberculosis: potential strategy for assessing health care policies

BACKGROUND

Tuberculosis (TB) causes pressure on healthcare resources, especially in terms of hospital admissions, despite being considered an ambulatory care-sensitive condition for which timely and effective care in ambulatory setting could prevent the need for hospitalization. Our objectives were to describe the spatial and temporal variation in pulmonary tuberculosis (PTB) hospitalizations, identify critical geographic areas at municipality level and characterize clusters of PTB hospitalizations to help the development of tailored disease management strategies that could improve TB control.

METHODS

Ecologic study using sociodemographic, geographical and clinical information of PTB hospitalization cases from continental Portuguese public hospitals, between 2002 and 2016. Descriptive statistics, spatiotemporal cluster analysis and temporal trends were conducted.

RESULTS

The space-time analysis identified five clusters of higher rates of PTB hospitalizations (2002-16), including the two major cities in the country (Lisboa and Porto). Globally, we observed a -7.2% mean annual percentage change in rate with only one of the identified clusters (out of six) with a positive trend (+4.34%). In the more recent period (2011-16) was obtained a mean annual percentage change in rate of -8.12% with only one cluster identified with an increase trend (+9.53%).

CONCLUSIONS

Our results show that space-time clustering and temporal trends analysis can be an invaluable resource to monitor the dynamic of the disease and contribute to the design of more effective, focused interventions. Interventions such as enhancing the detection of active and latent infection, improving monitoring and evaluation of treatment outcomes or adjusting the network of healthcare providers should be tailored to the specific needs of the critical areas identified.

Rurality and Origin-Destination Trajectories of Medical School Application and Matriculation in the United States

Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin-destination (O-D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural-urban gap in medical school applications, and reduce physician shortages in rural areas.

Socioeconomic Disparities in Colon Cancer Survival: Revisiting Neighborhood Poverty Using Residential Histories

BACKGROUND

Residential histories linked to cancer registry data provide new opportunities to examine cancer outcomes by neighborhood socioeconomic status (SES). We examined differences in regional stage colon cancer survival estimates comparing models using a single neighborhood SES at diagnosis to models using neighborhood SES from residential histories.

METHODS

We linked regional stage colon cancers from the New Jersey State Cancer Registry diagnosed from 2006 to 2011 to LexisNexis administrative data to obtain residential histories. We defined neighborhood SES as census tract poverty based on location at diagnosis and across the follow-up period through 31 December 2016 based on residential histories (average, time-weighted average, time-varying). Using Cox proportional hazards regression, we estimated associations between colon cancer and census tract poverty measurements (continuous and categorical), adjusted for age, sex, race/ethnicity, regional substage, and mover status.

RESULTS

Sixty-five percent of the sample was nonmovers (one census tract); 35% (movers) changed tract at least once. Cases from tracts with >20% poverty changed residential tracts more often (42%) than cases from tracts with <5% poverty (32%). Hazard ratios (HRs) were generally similar in strength and direction across census tract poverty measurements. In time-varying models, cases in the highest poverty category (>20%) had a 30% higher risk of regional stage colon cancer death than cases in the lowest category (<5%) (95% confidence interval [CI] = 1.04, 1.63).

CONCLUSION

Residential changes after regional stage colon cancer diagnosis may be associated with a higher risk of colon cancer death among cases in high-poverty areas. This has important implications for postdiagnostic access to care for treatment and follow-up surveillance. See video abstract: http://links.lww.com/EDE/B705.

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012–2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

Spatial analysis of vaccination against Hepatitis B in pregnant women in an urban Brazilian area

The objective of this article is to analyze the spatial distribution of Hepatitis B vaccine (HBVAC) of pregnant women. This is a cross-sectional study carried with 266 puerperae. The HBVAC record was obtained through the prenatal care booklet. The spatial scanning technique was used to detect a cluster of risk for the presence or absence of an HBVAC record. After this cluster identification, the individual and environmental variables were compared between the Coverage Areas of Basic Health Units (CAs-BHUs). The mean prevalence of non-HBVAC was 88.34%. Scan spatial scan analysis observed a cluster of a high prevalence of puerperae with a HBVAC record. Comparative analyses have shown that paid work and the number of prenatal visits are positively associated with an HBVAC record. Given the above, this work brings a reflection on possible disparities with other CAs-BHUs, besides the influence of the environmental perspective. It should be emphasized that the vaccination situation is influenced not only by factors intrinsic to the individuals. However, in this study, the results indicate that individual variables are predominantly mandatory in the decision of HBVAC uptake among pregnant women.

Using geographically weighted regression for social inequality analysis: association between mentally unhealthy days (MUDs) and socioeconomic status (SES) in U.S. counties

This research explores geographic variability of factors on social inequality related to mental health in the United States using county-level data in 2014. First, we account for complex design factors in Behavioural Risk Factor Surveillance System (BRFSS) data such as clustering, stratification, and sample weight using Complex Samples General Linear Model (CSGLM). Then, three variables are used in the model as indicators of social inequality, low socioeconomic status (SES): unemployment, education status, and social association status. A geographically weighted regression analysis is applied to examine the spatial variations in the associations of mentally unhealthy days (MUDs) with the indicators of SES in the United States. The results demonstrate that unemployment and education level show global positive and negative influences respectively on MUDs. Social association status ranged from positive to negative across the United States, implying some geographic clustering. These findings suggest that social and health policies should be adjusted to address the different effects of indicators of social inequality on mental health across different social characteristics of communities to more effectively manage mental health problems.

Complexity-Based Spatial Hierarchical Clustering for Malaria Prediction

Targeted intervention and resource allocation are essential in effective control of infectious diseases, particularly those like malaria that tend to occur in remote areas. Disease prediction models can help support targeted intervention, particularly if they have fine spatial resolution. But, choosing an appropriate resolution is a difficult problem since choice of spatial scale can have a significant impact on accuracy of predictive models. In this paper, we introduce a new approach to spatial clustering for disease prediction we call complexity-based spatial hierarchical clustering. The technique seeks to find spatially compact clusters that have time series that can be well characterized by models of low complexity. We evaluate our approach with 2 years of malaria case data from Tak Province in northern Thailand. We show that the technique's use of reduction in Akaike information criterion (AIC) and Bayesian information criterion (BIC) as clustering criteria leads to rapid improvement in predictability and significantly better predictability than clustering based only on minimizing spatial intra-cluster distance for the entire range of cluster sizes over a variety of predictive models and prediction horizons.

An Ecological Study on the Spatially Varying Relationship between County-Level Suicide Rates and Altitude in the United States

Suicide is a serious but preventable public health issue. Several previous studies have revealed a positive association between altitude and suicide rates at the county level in the contiguous United States. We assessed the association between suicide rates and altitude using a cross-county ecological study design. Data on suicide rates were obtained from a Web-based Injury Statistics Query and Reporting System (WISQARS), maintained by the U.S. National Center for Injury Prevention and Control (NCIPC). Altitude data were collected from the United States Geological Survey (USGS). We employed an ordinary least square (OLS) regression to model the association between altitude and suicide rates in 3064 counties in the contiguous U.S. We conducted a geographically weighted regression (GWR) to examine the spatially varying relationship between suicide rates and altitude after controlling for several well-established covariates. A significant positive association between altitude and suicide rates (average county rates between 2008 and 2014) was found in the dataset in the OLS model (R² = 0.483, p < 0.001). Our GWR model fitted the data better, as indicated by an improved R² (average: 0.62; range: 0.21–0.64) and a lower Akaike Information Criteria (AIC) value (13,593.68 vs. 14,432.14 in the OLS model). The GWR model also significantly reduced the spatial autocorrelation, as indicated by Moran’s I test statistic (Moran’s I = 0.171; z = 33.656; p < 0.001 vs. Moran’s I = 0.323; z = 63.526; p < 0.001 in the OLS model). In addition, a stronger positive relationship was detected in areas of the northern regions, northern plain regions, and southeastern regions in the U.S. Our study confirmed a varying overall positive relationship between altitude and suicide. Future research may consider controlling more predictor variables in regression models, such as firearm ownership, religion, and access to mental health services.

Spatio-temporal determinants of mental health and well-being: advances in geographically-explicit ecological momentary assessment (GEMA)

PURPOSE

Overview of geographically explicit momentary assessment research, applied to the study of mental health and well-being, which allows for cross-validation, extension, and enrichment of research on place and health.

METHODS

Building on the historical foundations of both ecological momentary assessment and geographic momentary assessment research, this review explores their emerging synergy into a more generalized and powerful research framework.

RESULTS

Geographically explicit momentary assessment methods are rapidly advancing across a number of complimentary literatures that intersect but have not yet converged. Key contributions from these areas reveal tremendous potential for transdisciplinary and translational science.

CONCLUSIONS

Mobile communication devices are revolutionizing research on mental health and well-being by physically linking momentary experience sampling to objective measures of socio-ecological context in time and place. Methodological standards are not well-established and will be required for transdisciplinary collaboration and scientific inference moving forward.

Use of attribute association error probability estimates to evaluate quality of medical record geocodes

BACKGROUND

The utility of patient attributes associated with the spatiotemporal analysis of medical records lies not just in their values but also the strength of association between them. Estimating the extent to which a hierarchy of conditional probability exists between patient attribute associations such as patient identifying fields, patient and date of diagnosis, and patient and address at diagnosis is fundamental to estimating the strength of association between patient and geocode, and patient and enumeration area. We propose a hierarchy for the attribute associations within medical records that enable spatiotemporal relationships. We also present a set of metrics that store attribute association error probability (AAEP), to estimate error probability for all attribute associations upon which certainty in a patient geocode depends.

METHODS

A series of experiments were undertaken to understand how error estimation could be operationalized within health data and what levels of AAEP in real data reveal themselves using these methods. Specifically, the goals of this evaluation were to (1) assess if the concept of our error assessment techniques could be implemented by a population-based cancer registry; (2) apply the techniques to real data from a large health data agency and characterize the observed levels of AAEP; and (3) demonstrate how detected AAEP might impact spatiotemporal health research.

RESULTS

We present an evaluation of AAEP metrics generated for cancer cases in a North Carolina county. We show examples of how we estimated AAEP for selected attribute associations and circumstances. We demonstrate the distribution of AAEP in our case sample across attribute associations, and demonstrate ways in which disease registry specific operations influence the prevalence of AAEP estimates for specific attribute associations.

CONCLUSIONS

The effort to detect and store estimates of AAEP is worthwhile because of the increase in confidence fostered by the attribute association level approach to the assessment of uncertainty in patient geocodes, relative to existing geocoding related uncertainty metrics.

Unmanned aircraft systems for studying spatial abundance of ungulates: relevance to spatial epidemiology

Complex ecological and epidemiological systems require multidisciplinary and innovative research. Low cost unmanned aircraft systems (UAS) can provide information on the spatial pattern of hosts’ distribution and abundance, which is crucial as regards modelling the determinants of disease transmission and persistence on a fine spatial scale. In this context we have studied the spatial epidemiology of tuberculosis (TB) in the ungulate community of Doñana National Park (South-western Spain) by modelling species host (red deer, fallow deer and cattle) abundance at fine spatial scale. The use of UAS high-resolution images has allowed us to collect data to model the environmental determinants of host abundance, and in a further step to evaluate their relationships with the spatial risk of TB throughout the ungulate community. We discuss the ecological, epidemiological and logistic conditions under which UAS may contribute to study the wildlife/livestock sanitary interface, where the spatial aggregation of hosts becomes crucial. These findings are relevant for planning and implementing research, fundamentally when managing disease in multi-host systems, and focusing on risky areas. Therefore, managers should prioritize the implementation of control strategies to reduce disease of conservation, economic and social relevance.

Spatio-temporal variability of bovine tuberculosis eradication in Spain (2006-2011)

In this study we analyzed the space-time variation of the risk of bovine tuberculosis (bTB) in cattle between 2006 and 2011. The results indicated that at country level, there were no significant temporal changes between years, but, at county level bTB evolution was more heterogeneous. In some counties, between some years, the prevalence and the incidence of the disease was higher as compared to the global rate in the rest of the counties of Spain. The analysis of potential risk factors indicated that both, a large number of movements from counties with high incidence (>1%), and presence of bullfighting cattle herds increased bTB risk. Red deer abundance, number of goats and number of mixed cattle-goat farms were not significantly associated with the prevalence/incidence of bTB.

The impact of spatial scales and spatial smoothing on the outcome of bayesian spatial model

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matérn correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.

Public health strategies for prevention and control of HSV-2 in persons who use drugs in the United States

BACKGROUND

Herpes simplex virus type 2 (HSV-2) affects HIV acquisition, transmission, and disease progression. Effective medications for genital herpes and for HIV/AIDS exist. Parenteral transmission of HIV among persons who inject drugs is decreasing. Reducing sexual transmission of HIV and HSV-2 among persons who use drugs (PWUD; i.e., heroin, cocaine, "speedball", crack, methamphetamine through injection or non-injection) necessitates relevant services.

METHODS

We reviewed HSV-2 sero-epidemiology and HSV-2/HIV associations in U.S.-based studies with PWUD and the general literature on HSV-2 prevention and treatment published between 1995 and 2012. We used the 6-factor Kass framework to assess relevant HSV-2 public health strategies and services in terms of their goals and effectiveness; identification of, and minimization of burdens and concerns; fair implementation; and fair balancing of benefits, burdens, and concerns.

RESULTS

Eleven studies provided HSV-2 serologic test results. High HSV-2 sero-prevalence (range across studies 38-75%) and higher sero-prevalence in HIV-infected PWUD (97-100% in females; 61-74% in males) were reported. Public health strategies for HSV-2 prevention and control in PWUD can include screening or testing; knowledge of HSV-2 status and partner disclosure; education, counseling, and psychosocial risk-reduction interventions; treatment for genital herpes; and HIV antiretroviral medications for HSV-2/HIV co-infected PWUD.

CONCLUSIONS

HSV-2 sero-prevalence is high among PWUD, necessitating research on development and implementation of science-based public health interventions for HSV-2 infection and HSV-2/HIV co-infections, including research on effectiveness and cost-effectiveness of such interventions, to inform development and implementation of services for PWUD.

Contours of risk: spatializing human behaviors to understand disease dynamics in changing landscapes

We echo viewpoints presented in recent publications from EcoHealth and other journals arguing for the need to understand linkages between human health, disease ecology, and landscape change. We underscore the importance of incorporating spatialities of human behaviors and perceptions in such analyses to further understandings of socio-ecological interactions mediating human health. We use Buruli ulcer, an emerging necrotizing skin infection and serious health concern in central Ghana, to illustrate our argument.

Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study

BACKGROUND

Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.

METHODS

National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM2.5 and NO2) and a chemical transport model (for O3). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.

RESULTS

Calibration of the national PM2.5 surface using annual spatiotemporal interpolation predicted historical PM2.5 measurement data best (R2 = 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO2 (R2 = 0.38) and O3 (R2 = 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM2.5, NO2 and O3 exposures of 11.3 μg/m3 (SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM2.5, NO2 and O3 exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.

CONCLUSIONS

We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments. For submission to: Environmental Health.

A review of spatial methods in epidemiology, 2000-2010

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000-2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.

Potential role of safer injection facilities in reducing HIV and hepatitis C infections and overdose mortality in the United States

BACKGROUND

Safer injection facilities (SIFs) reduce risks associated with injecting drugs, particularly public injection and overdose mortality. They exist in many countries, but do not exist in the United States. We assessed several ethical, operational, and public health considerations for establishing SIFs in the United States.

METHOD

We used the six-factor Kass framework (goals, effectiveness, concerns, minimization of concerns, fair implementation, and balancing of benefits and concerns), summarized needs of persons who inject drugs in the United States, and reviewed global evidence for SIFs.

RESULTS

SIFs offer a hygienic environment to inject drugs, provide sterile injection equipment at time of injection, and allow for safe disposal of used equipment. Injection of pre-obtained drugs, purchased by persons who inject drugs, happens in a facility where trained personnel provide on-site counseling and referral to addiction treatment and health care and intervene in overdose emergency situations. SIFs provide positive health benefits (reducing transmission of HIV and viral hepatitis, bacterial infections, and overdose mortality) without evidence for negative health or social consequences. SIFs serve most-at-risk persons, including those who inject in public or inject frequently, and those who do not use other public health programs. It is critical to address legal, ethical, and local concerns, develop and implement relevant policies and procedures, and assess individual- and community-level needs and benefits of SIFs given local epidemiologic data.

CONCLUSIONS

SIFs have the potential to reduce viral and bacterial infections and overdose mortality among those who engage in high-risk injection behaviors by offering unique public health services that are complementary to other interventions.