Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

BACKGROUND: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. METHODS: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. RESULTS: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15-2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18-5.93 in males and 1.24-5.38 in females). Furthermore, bladder cancer in males (RR:1.32-2.77) and thyroid cancer in females (RR:1.88-3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23-5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31-3.31 in males and 1.33-4.13 in females), and prostate cancer in males (RR:1.22-2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26-5.25) in central areas. CONCLUSIONS: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Spatiotemporal dynamics and influencing factors of human brucellosis in Mainland China from 2005-2021.

BACKGROUND: Brucellosis poses a significant public health concern. This study explores the spatial and temporal dynamic evolution of human brucellosis in China and analyses the spatial heterogeneity of the influencing factors related to the incidence of human brucellosis at the provincial level. METHODS: The Join-point model, centre of gravity migration model and spatial autocorrelation analysis were employed to evaluate potential changes in the spatial and temporal distribution of human brucellosis in mainland China from 2005 to 2021. Ordinary Least Squares (OLS), Geographically Weighted Regression (GWR), and Multi-scale Geographically Weighted Regression (MGWR) models were constructed to analyze the spatial and temporal correlation between the incidence rate of human brucellosis and meteorological and social factors. RESULTS: From 2005 to 2021, human brucellosis in China showed a consistent upward trend. The incidence rate rose more rapidly in South, Central, and Southwest China, leading to a shift in the center of gravity from the North to the Southwest, as illustrated in the migration trajectory diagram. Strong spatial aggregation was observed. The MGWR model outperformed others. Spatio-temporal plots indicated that lower mean annual temperatures and increased beef, mutton, and milk production significantly correlated with higher brucellosis incidence. Cities like Guangxi and Guangdong were more affected by low temperatures, while Xinjiang and Tibet were influenced more by beef and milk production. Inner Mongolia and Heilongjiang were more affected by mutton production. Importantly, an increase in regional GDP and health expenditure exerted a notable protective effect against human brucellosis incidence. CONCLUSIONS: Human brucellosis remains a pervasive challenge. Meteorological and social factors significantly influence its incidence in a spatiotemporally specific manner. Tailored prevention strategies should be region-specific, providing valuable insights for effective brucellosis control measures.

Identifying emerging hot spots of road traffic injury severity using spatiotemporal methods: longitudinal analyses on major roads in Ghana from 2005 to 2020.

BACKGROUND: Although road traffic injuries and deaths have decreased globally, there is substantial national and sub-national heterogeneity, particularly in low- and middle-income countries (LMICs). Ghana is one of few countries in Africa collecting comprehensive, spatially detailed data on motor vehicle collisions (MVCs). This data is a critical step towards improving roadway safety, as accurate and reliable information is essential for devising targeted countermeasures. METHODS: Here, we analyze 16 years of police-report data using emerging hot spot analysis in ArcGIS to identify hot spots with trends of increasing injury severity (a weighted composite measure of MVCs, minor injuries, severe injuries, and deaths), and counts of injuries, severe injuries, and deaths along major roads in urban and rural areas of Ghana. RESULTS: We find injury severity index sums and minor injury counts are significantly decreasing over time in Ghana while severe injury and death counts are not, indicating the latter should be the focus for road safety efforts. We identify new, consecutive, intensifying, and persistent hot spots on 2.65% of urban roads and 4.37% of rural roads. Hot spots are intensifying in terms of severity and frequency on major roads in rural areas. CONCLUSIONS: A few key road sections, particularly in rural areas, show elevated levels of road traffic injury severity, warranting targeted interventions. Our method for evaluating spatiotemporal trends in MVC, road traffic injuries, and deaths in a LMIC includes sufficient detail for replication and adaptation in other countries, which is useful for targeting countermeasures and tracking progress.

Social and economic determinants of drug overdose deaths: a systematic review of spatial relationships.

PURPOSE: To synthesize the available evidence on the extent to which area-level socioeconomic conditions are associated with drug overdose deaths in the United States. METHODS: We performed a systematic review (in MEDLINE, EMBASE, PsychINFO, Web of Science, EconLit) for papers published prior to July 2022. Eligible studies quantitatively estimated the association between an area-level measure of socioeconomic conditions and drug overdose deaths in the US, and were published in English. We assessed study quality using the Effective Public Health Practice Project Quality Assessment Tool. The protocol was preregistered at Prospero (CRD42019121317). RESULTS: We identified 28 studies that estimated area-level effects of socioeconomic conditions on drug overdose deaths in the US. Studies were scored as having moderate to serious risk of bias attributed to both confounding and in analysis. Socioeconomic conditions and drug overdose death rates were moderately associated, and this was a consistent finding across a large number of measures and differences in study designs (e.g., cross-sectional versus longitudinal), years of data analyzed, and primary unit of analysis (e.g., ZIP code, county, state). CONCLUSIONS: This review highlights the evidence for area-level socioeconomic conditions are an important factor underlying the geospatial distribution of drug overdose deaths in the US and the need to understand the mechanisms underlying these associations to inform future policy recommendations. The current evidence base suggests that, at least in the United States, employment, income, and poverty interventions may be effective targets for preventing drug overdose mortality rates.

Intracounty modeling of COVID-19 infection with human mobility: Assessing spatial heterogeneity with business traffic, age, and race.

The COVID-19 pandemic is a global threat presenting health, economic, and social challenges that continue to escalate. Metapopulation epidemic modeling studies in the susceptible-exposed-infectious-removed (SEIR) style have played important roles in informing public health policy making to mitigate the spread of COVID-19. These models typically rely on a key assumption on the homogeneity of the population. This assumption certainly cannot be expected to hold true in real situations; various geographic, socioeconomic, and cultural environments affect the behaviors that drive the spread of COVID-19 in different communities. What's more, variation of intracounty environments creates spatial heterogeneity of transmission in different regions. To address this issue, we develop a human mobility flow-augmented stochastic SEIR-style epidemic modeling framework with the ability to distinguish different regions and their corresponding behaviors. This modeling framework is then combined with data assimilation and machine learning techniques to reconstruct the historical growth trajectories of COVID-19 confirmed cases in two counties in Wisconsin. The associations between the spread of COVID-19 and business foot traffic, race and ethnicity, and age structure are then investigated. The results reveal that, in a college town (Dane County), the most important heterogeneity is age structure, while, in a large city area (Milwaukee County), racial and ethnic heterogeneity becomes more apparent. Scenario studies further indicate a strong response of the spread rate to various reopening policies, which suggests that policy makers may need to take these heterogeneities into account very carefully when designing policies for mitigating the ongoing spread of COVID-19 and reopening.

The spatial signature of Plasmodium vivax and Plasmodium falciparum infections: quantifying the clustering of infections in cross-sectional surveys and cohort studies.

BACKGROUND: Over the last decades, enormous successes have been achieved in reducing malaria burden globally. In Latin America, South East Asia, and the Western Pacific, many countries now pursue the goal of malaria elimination by 2030. It is widely acknowledged that Plasmodium spp. infections cluster spatially so that interventions need to be spatially informed, e.g. spatially targeted reactive case detection strategies. Here, the spatial signature method is introduced as a tool to quantify the distance around an index infection within which other infections significantly cluster. METHODS: Data were considered from cross-sectional surveys from Brazil, Thailand, Cambodia, and Solomon Islands, conducted between 2012 and 2018. Household locations were recorded by GPS and finger-prick blood samples from participants were tested for Plasmodium infection by PCR. Cohort studies from Brazil and Thailand with monthly sampling over a year from 2013 until 2014 were also included. The prevalence of PCR-confirmed infections was calculated at increasing distance around index infections (and growing time intervals in the cohort studies). Statistical significance was defined as prevalence outside of a 95%-quantile interval of a bootstrap null distribution after random re-allocation of locations of infections. RESULTS: Prevalence of Plasmodium vivax and Plasmodium falciparum infections was elevated in close proximity around index infections and decreased with distance in most study sites, e.g. from 21.3% at 0 km to the global study prevalence of 6.4% for P. vivax in the Cambodian survey. In the cohort studies, the clustering decreased with longer time windows. The distance from index infections to a 50% reduction of prevalence ranged from 25 m to 3175 m, tending to shorter distances at lower global study prevalence. CONCLUSIONS: The spatial signatures of P. vivax and P. falciparum infections demonstrate spatial clustering across a diverse set of study sites, quantifying the distance within which the clustering occurs. The method offers a novel tool in malaria epidemiology, potentially informing reactive intervention strategies regarding radius choices of operations around detected infections and thus strengthening malaria elimination endeavours.

Understanding the role of environmental and socioeconomic factors in the geographic variation of breast cancer risk in the US-wide Sister Study.

OBJECTIVE: To describe the geographic pattern of breast cancer incidence in a nationwide prospective cohort and investigate whether environmental exposures and/or neighborhood socioeconomic status explain observed geographic disparities. METHODS: Using accelerated failure time models with a spatial random effect term, we mapped the health region-level association between residential location and breast cancer incidence for 44,707 participants in the Sister Study after controlling for established individual-level breast cancer risk factors. We performed a variable selection process to select environmental exposures [i.e., ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5), PM2.5 chemical composition, outdoor light at night (LAN), ambient noise, ultraviolet radiation, and greenspace] and neighborhood-level factors [i.e., population density and area deprivation index (ADI)] that predicted breast cancer incidence and quantified the spatial variation explained by the selected factors. We also considered whether the geographic pattern and predictors were similar when restricting to estrogen receptor-positive (ER+) tumors. RESULTS: We observed a spatial patterning in the incidence of overall breast cancer (Moran's I = 16.7, p < 0.05) and ER+ breast cancer (Moran's I = 13.2, p < 0.05), with a lower risk observed in the South and Southeast and a greater risk in the Northwest and certain areas of the Midwest and Northeast. NO2, LAN, and ADI explained 21.4% of the spatial variation in overall breast cancer incidence whereas NO2, PM2.5 chemical composition, LAN, greenspace, and ADI together explained 63.3% of the spatial variation in ER+ breast cancer incidence. CONCLUSIONS: Our findings provide additional evidence for a role of environmental exposures in breast cancer incidence and suggest that geographic-based risk factors may vary according to breast cancer subtype. Our findings support the need for additional research to quantify the relative contributions of geographic-based risk factors for breast cancer.

Examining the association between safe drinking water act violations and adverse birth outcomes in Virginia.

In 1974, the United States established the Safe Drinking Water Act (SDWA) to protect consumers from potential exposure to drinking water contaminants associated with health risks. Each contaminant is assigned a health-based standard meant to reflect the maximum level at which an adverse human health outcome is unlikely; measurements beyond that level have greater potential to result in adverse health outcomes. Although there is extensive research on human health implications following water contaminant exposure, few studies have specifically examined associations between fetal health and municipal drinking water violations. Therefore, the objective of this study is to assess whether SDWA drinking water violations are associated with fetal health outcomes, including preterm birth (PTB), low birth weight (LBW), and term-low birth weight (tLBW), in the Commonwealth of Virginia. Singleton births (n = 665,984) occurring between 2007 and 2015 in Virginia were geocoded and assigned to a corresponding estimated water service area. Health-based (HB) and monitoring and reporting (MR) violations for 12 contaminants were acquired from the US EPA Safe Drinking Water Information System, with exposure defined at the approximate service area level to limit exposure misclassification. A logistic regression model for each birth outcome assessed potential relationships with SDWA violations. When examining the association between individual MR violations and birth outcomes, Nitrate-Nitrite (OR = 1.10; 95% CI = 1.02, 1.18, P = 0.01) was positively associated with PTB and the total coliform rule was negatively associated with tLBW (OR = 0.93; 95% CI = 0.87, 1.00, P = 0.04). These findings indicate that a lack of regular monitoring and reporting by water providers (resulting in monitoring and reporting violations) may be concealing health-based violations as these health concerns cannot be revealed without testing, suggesting a need for additional technical, managerial, and financial support to enable often-underfunded water systems to adhere to monitoring and reporting requirements meant to protect public health.

Understanding the spread of infectious diseases in edge areas of hotspots: dengue epidemics in tropical metropolitan regions.

Identifying clusters or hotspots from disease maps is critical in research and practice. Hotspots have been shown to have a higher potential for transmission risk and may be the source of infections, making them a priority for controlling epidemics. However, the role of edge areas of hotspots in disease transmission remains unclear. This study aims to investigate the role of edge areas in disease transmission by examining whether disease incidence rate growth is higher in the edges of disease hotspots during outbreaks. Our data is based on the three most severe dengue epidemic years in Kaohsiung city, Taiwan, from 1998 to 2020. We employed conditional autoregressive (CAR) models and Bayesian areal Wombling methods to identify significant edge areas of hotspots based on the extent of risk difference between adjacent areas. The difference-in-difference (DID) estimator in spatial panel models measures the growth rate of risk by comparing the incidence rate between two groups (hotspots and edge areas) over two time periods. Our results show that in years characterized by exceptionally large-scale outbreaks, the edge areas of hotspots have a more significant increase in disease risk than hotspots, leading to a higher risk of disease transmission and potential disease foci. This finding explains the geographic diffusion mechanism of epidemics, a pattern mixed with expansion and relocation, indicating that the edge areas play an essential role. The study highlights the importance of considering edge areas of hotspots in disease transmission. Furthermore, it provides valuable insights for policymakers and health authorities in designing effective interventions to control large-scale disease outbreaks.

The association between the food environment and adherence to healthy diet quality: the Maastricht Study.

OBJECTIVE: The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality. DESIGN: This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables. SETTING: The region of Maastricht including the surrounding food retailers in the Netherlands. PARTICIPANTS: 7367 subjects aged 40-75 years in the south of the Netherlands. RESULTS: No relationship was identified between either the FEHI (B = 0·62; 95 % CI = -2·54, 3·78) or individual food outlets, such as fast food (B = -0·07; 95 % CI = -0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = -0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = -3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages. CONCLUSION: The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.

Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States.

BACKGROUND: COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota. METHODS: COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters. RESULTS: County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed. CONCLUSION: Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

Understanding the burden of chronic back pain: a spatial microsimulation of chronic back pain at small area level across England.

PURPOSE: Chronic back pain (CBP) carries a significant burden. Understanding how and why CBP prevalence varies spatially, as well as the potential impact of policies to decrease CBP would prove valuable for public health planning. This study aims to simulate and map the prevalence of CBP at ward-level across England, identify associations which may explain spatial variation, and explore 'what-if' scenarios for the impact of policies to increase physical activity (PA) on CBP. METHODS: A two-stage static spatial microsimulation approach was used to simulate CBP prevalence in England, combining national-level CBP and PA data from the Health Survey for England with spatially disaggregated demographic data from the 2011 Census. The output was validated, mapped, and spatially analysed using geographically weighted regression. 'What-if' analysis assumed changes to individuals' moderate-to-vigorous physical activity (MVPA) levels. RESULTS: Large significant clusters of high CBP prevalence were found predominantly in coastal areas and low prevalence in cities. Univariate analysis found a strong positive correlation between physical inactivity and CBP prevalence at ward-level (R2 = 0.735; Coefficient = 0.857). The local model showed the relationship to be stronger in/around cities (R2 = 0.815; Coefficient: Mean = 0.833, SD = 0.234, Range = 0.073-2.623). Multivariate modelling showed this relationship was largely explained by confounders (R2 = 0.924; Coefficient: Mean = 0.070, SD = 0.001, Range = 0.069-0.072). 'What-if' analysis showed a detectable reduction in CBP prevalence for increases in MVPA of 30 and 60 min (- 2.71%; 1, 164, 056 cases). CONCLUSION: CBP prevalence varies at ward-level across England. At ward-level, physical inactivity is strongly positively correlated with CBP. This relationship is largely explained by geographic variation in confounders (the proportion of residents that are: over 60, in low-skilled jobs, female, pregnant, obese, smokers, white or black, disabled). Policies to increase PA by 30 min weekly MVPA will likely result in a significant reduction in CBP prevalence. To maximise their impact, policies could be tailored to areas of high prevalence, which are identified by this study.

Healthcare-associated infections by multidrug-resistant bacteria in Andalusia, Spain, 2014 to 2021.

BackgroundMultidrug-resistant (MDR) bacteria are among chief causes of healthcare-associated infections (HAIs). In Spain, studies addressing multidrug resistance based on epidemiological surveillance systems are lacking.AimIn this observational study, cases of HAIs by MDR bacteria notified to the epidemiological surveillance system of Andalusia, Spain, between 2014-2021, were investigated. Notified cases and their spatiotemporal distribution were described, with a focus on social determinants of health (SDoH).MethodsNew cases during the study period of HAIs caused by extended-spectrum ß-lactamase (ESBL)-/carbapenemase-producing Enterobacterales, MDR Acinectobacter baumannii, MDR Pseudomonas aeruginosa or meticillin resistant Staphylococcus aureus were considered. Among others, notification variables included sex and age, while socio-economic variables comprised several SDoH. Cases' spatial distribution across municipalities was assessed. The smooth standardised incidence ratio (sSIR) was obtained using a Bayesian spatial model. Association between municipalities' sSIR level and SDoH was evaluated by bivariate analysis.ResultsIn total, 6,389 cases with a median age of 68 years were notified; 61.4% were men (n = 3,921). The most frequent MDR bacteria were ESBL-producing Enterobacterales (2,812/6,389; 44.0%); the main agent was Klebsiella spp. (2,956/6,389; 46.3%). Between 2014 and 2021 case numbers appeared to increase. Overall, up to 15-fold differences in sSIR between municipalities were observed. In bivariate analysis, there appeared to be an association between municipalities' sSIR level and deprivation (p = 0.003).ConclusionThis study indicates that social factors should be considered when investigating HAIs by MDR bacteria. The case incidence heterogeneity between Andalusian municipalities might be explained by SDoH, but also possibly by under-notification. Automatising reporting may address the latter.

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

BACKGROUND: Spatial epidemiology plays an important role in public health. Yet, it is unclear whether the current university education in spatial epidemiology in China could meet the competency-oriented professional demands. This study aimed to understand the current situation of education and training, practical application, and potential demands in spatial epidemiology among public health postgraduates in China, and to assess the critical gaps in a future emerging infectious diseases (EID) pandemic preparedness and response. METHODS: This study was divided into three parts. The first part was a comparative study on spatial epidemiology education in international public health postgraduate training. The second part was a cross-sectional survey conducted among public health professionals. The third part was a nationwide cross-sectional survey conducted among public health postgraduates at Chinese universities from October 2020 to February 2021. Data was collected by the WeChat-based questionnaire star survey system and analyzed using the SPSS software. RESULTS: International education institutions had required public health postgraduates to master the essential knowledge and capacity of spatial epidemiology. A total of 198 public health professionals were surveyed, and they had a median of 4.00 (IQR 3.13-4.53) in demand degree of spatial epidemiology. A total of 1354 public health postgraduates were surveyed from 51 universities. Only 29.41% (15/51) of universities offered spatial epidemiology course. Around 8.05% (109/1354) of postgraduates had learned spatial epidemiology, and had a median of 1.05 (IQR 1.00-1.29) in learning degree and a median of 1.91 (IQR 1.05-2.78) in practical application degree of spatial epidemiology. To enhance professional capacity, 65.95% (893/1354) of postgraduates hoped that universities would deliver a credit-course of spatial epidemiology. CONCLUSIONS: A huge unmet education and training demand in spatial epidemiology existed in the current education system of public health postgraduates in China. To enhance the competency-oriented professional capacity in preparedness and response to a future pandemic, it is urgent to incorporate the teaching and training of spatial epidemiology into the compulsory curriculum system of public health postgraduates in China.

Ambient PM2.5 exposures could increase risk of tuberculosis recurrence.

BACKGROUND: The effect of ambient PM2.5 on the incidence of tuberculosis (TB) has been investigated in epidemiological studies. However, they did not separately study new and relapsed TB infection and focused on relatively short-term effects of PM2.5. In this regard, we examined the associations of long-term PM2.5 exposures with both new and relapsed TB incidences in South Korea, where the disease burden of TB is greatest among high-income countries. METHODS: An area-level ecological study of 250 districts was conducted from 2015 to 2019. Age- and sex-standardized TB incidence ratios for each district and year were used as outcome variables, and their associations with PM2.5 concentrations for one to five-year average were examined. Negative binomial regression models incorporating spatiotemporal autocorrelation were employed using integrated nested Laplace approximations. Stratified analyses were conducted by type of TB (total, new, and relapsed cases). RESULTS: Districts with higher PM2.5 concentrations tended to have significantly higher TB recurrence rate. The relative risks per 10 µg/m3 PM2.5 increase were 1.218 (95% credible interval 1.051-1.411), 1.260 (1.039-1.527) and 1.473 (1.015-2.137) using the two, three and five-year average PM2.5 exposures, respectively. CONCLUSIONS: The results imply that interventions for reducing air pollution might help prevent TB recurrence.

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

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

Invited Commentary: Predicting Incidence Rates of Rare Cancers-Adding Epidemiologic and Spatial Contexts.

There are unique challenges to identifying causes of and developing strategies for prevention of rare cancers, driven by the difficulty in estimating incidence, prevalence, and survival due to small case numbers. Using a Poisson modeling approach, Salmerón et al. (Am J Epidemiol. 2022;191(3):487-498) built upon their previous work to estimate incidence rates of rare cancers in Europe using a Bayesian framework, establishing a uniform prior for a measure of variability for country-specific incidence rates. They offer a methodology with potential transferability to other settings with similar cancer surveillance infrastructure. However, the approach does not consider the spatiotemporal correlation of rare cancer case counts and other, potentially more appropriate nonnormal probability distributions. In this commentary, we discuss the implications of future work from cancer epidemiology and spatial epidemiology perspectives. We describe the possibility of developing prediction models tailored to each type of rare cancer; incorporating the spatial heterogeneity in at-risk populations, surveillance coverage, and risk factors in these predictions; and considering a modeling framework with which to address the inherent spatiotemporal components of these data. We note that extension of this methodology to estimate subcountry rates at provincial, state, or smaller geographic levels would be useful but would pose additional statistical challenges.

Historical redlining and the epidemiology of present-day firearm violence in the United States: A multi-city analysis.

Firearm violence is a major cause of morbidity, mortality, and racial health disparities in the United States. Previous studies have identified associations between historically racist housing discrimination (i.e., redlining practices) and firearm violence; however, these studies generally have been limited to a single city and have yet to provide sufficient evidence through which to determine the extent and dynamics of the impact of this relationship across the country. The aim of our study was (1) to estimate the association of historical redlining on both violent and firearm death across the country in nested models; and (2) to examine spatial non-stationarity to determine whether the impact of historical redlining on violent and firearm death was the same across the U.S. We used multilevel Bayesian conditional autoregressive Poisson models to determine the relationship between redlining as illustrated through Home Owners' Loan Corporation maps and 2019 violent and firearm deaths at the ZIP code-level nested within 21 cities across the U.S. We found that at the ZIP code level, there was a dose-responsive relationship between HOLC grading and the incidence of present-day firearm deaths. In general, redlined ZIP codes had higher relative incidence of firearm deaths. Associations were not stable across cities. For example, associations were relatively stronger in Baltimore, MD and weaker in Los Angeles, CA. This research reinforces the findings of previous studies examining the impact of redlining on firearm death across the extent of the entire country in 21 cities and claim that HOLC grades are associated with present-day violence.

Spatial analysis of HIV infection and the associated correlates among transgender persons in the United States.

Identifying the geographic hotspots of HIV infection among high-risk populations such as transgender people is critical to ending the HIV epidemic in the United States (U.S.). This study examined the spatial pattern of HIV positivity rate and the associated correlates among transgender persons in the 48 contiguous states and the District of Columbia in the U.S. The data source was the 2015 U.S. Transgender Survey (n = 27,715). We conducted spatial analyses, with state as the unit of analysis. We fitted a spatial lag regression model to assess demographic, social, and behavioral risk variables associated with HIV. The HIV positivity rate ranged by state from 0.5% to 17.1%, with a mean of 2.9%. There was a significant positive global spatial autocorrelation (global Moran's I = 0.42, p = 0.001). The identified spatial clusters of high values (hot spots i.e., states with high HIV positivity rates surrounded by states with similarly high rates) included five neighboring states (Arkansas, Louisiana, Mississippi, Alabama, and Tennessee) in the Southern region. HIV positivity rate was positively associated with the percentage of transgender persons who were non-Hispanic Black, had no high school education, living in poverty, and engaged in sex work. Structural interventions are needed to address education, poverty, racial discrimination, and sex work that predispose transgender persons to HIV.

Geographical distribution and space-time clustering of human illnesses with major Salmonella serotypes in Florida, USA, 2017-2018.

Nontyphoidal salmonellosis is the leading reported foodborne illness in Florida. Although the diversity of Salmonella serotypes circulating in Florida has been identified, the geographical characteristics of the major serotypes are poorly described. Here we examined the geospatial patterns of 803 whole-genome sequenced Salmonella isolates within seven major serotypes (Enteritidis, Newport, Javiana, Sandiego, Braenderup, Typhimurium and I 4,[5],12:i:-) with the metadata obtained from Florida Department of Health during 2017-2018. Geographically, the distribution of incidence rates varied distinctively between serotypes. Illnesses with Enteritidis and Newport serotypes were widespread in Florida. The incidence rate for Javiana was relatively higher in the north compared to the south. Typhimurium was concentrated in the northwest, while I 4,[5],12:i:-, the monophasic variant of Typhimurium was limited to the south. We also evaluated space-time clustering of isolates at the zip code level using scan statistic models. Space-time clusters were detected for each major serotype during 2017-2018. The multinomial scan statistic found the risk of illness with Javiana was higher in the north and southwest in the fall of 2017 compared to other major serotypes. This serotype-specific clustering analysis will assist in further unpacking the associations between distinct reservoirs and illnesses with major serotypes in Florida.