Despite a decrease in the prevalence of low birth weight (LBW) over time, its ongoing significance as a public health concern in Bangladesh remains evident. Low birth weight is believed to be a contributing factor to infant mortality, prolonged health complications, and vulnerability to non-communicable diseases. This study utilizes nationally representative data from the Multiple Indicator Cluster Surveys (MICS) conducted in 2012-2013 and 2019 to explore factors associated with birth weight. Modeling birth weight data considers interactions among factors, clustering in data, and spatial correlation. District-level maps are generated to identify high-risk areas for LBW. The average birth weight has shown a modest increase, rising from 2.93 kg in 2012-2013 to 2.96 kg in 2019. The study employs a regression tree, a popular machine learning algorithm, to discern essential interactions among potential determinants of birth weight. Findings from various models, including fixed effect, mixed effect, and spatial dependence models, highlight the significance of factors such as maternal age, household head\'s education, antenatal care, and few data-driven interactions influencing birth weight. District-specific maps reveal lower average birth weights in the southwestern region and selected northern districts, persisting across the two survey periods. Accounting for hierarchical structure and spatial autocorrelation improves model performance, particularly when fitting the most recent round of survey data. The study aims to inform policy formulation and targeted interventions at the district level by utilizing a machine learning technique and regression models to identify vulnerable groups of children requiring heightened attention.

Small-area methods are being used in spatial epidemiology to understand the effect of location on health and detect areas where the risk of a disease is significantly elevated. Disease mapping models relate the observed number of cases to an expected number of cases per area. Expected numbers are often calculated by internal standardization, which requires both accurate population numbers and disease rates per gender and/or age group. However, confidentiality issues or the absence of high-quality information about the characteristics of a population-at-risk can hamper those calculations. Based on methods in point process analysis for situations without accurate population data, we propose the use of a case-control approach in the context of lattice data, in which an unrelated, spatially unstructured disease is used as a control disease. We correct for the uncertainty in the estimation of the expected values, which arises by using the control-disease\'s observed number of cases as a representation of a fraction of the total population. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardized expected counts. The simulation study results confirm our findings for different spatial structures. We show that the proposed method can adequately address the problem of inaccurate or unavailable population data in disease mapping analysis.

Salmonella is one of the most important foodborne pathogens worldwide. Its main reservoirs are poultry and pigs, in which infection is endemic in many countries. Spain has one of the largest pig populations in the world. Even though Salmonella infection is commonly detected in pig farms, its spatial distribution at the national level is poorly understood. Here we aimed to report the spatial distribution of Salmonella-positive pig farms in Spain and investigate the presence of potential spatial trends over a 17-year period. For this, data on samples from pigs tested for Salmonella in 2002-2013, 2015, 2017, and 2019 as part of the Spanish Veterinary Antimicrobial Resistance Surveillance program, representing 3,730 farms were analyzed. The spatial distribution and clustering of Salmonella-positive pig farms at the province level were explored using spatial empirical Bayesian smoothing and global Moran\'s I, local Moran\'s I, and the Poisson model of the spatial scan statistics. Bayesian spatial regression using a reparameterized Besag-York-Mollié Poisson model (BYM2 model) was then performed to quantify the presence of spatially structured and unstructured effects while accounting for the effect of potential risk factors for Salmonella infection at the province level. The overall proportion of Salmonella-positive farms was 37.8% (95% confidence interval: 36.2-39.4). Clusters of positive farms were detected in the East and Northeast of Spain. The Bayesian spatial regression revealed a West-to-East increase in the risk of Salmonella infection at the province level, with 65.2% (50% highest density interval: 70-100.0%) of this spatial pattern being explained by the spatially structured component. Our results demonstrate the existence of a spatial variation in the risk of Salmonella infection in pig farms at the province level in Spain. This information can help to optimize risk-based Salmonella surveillance programs in Spain, although further research to identify farm-level factors explaining this pattern are needed.

UNASSIGNED: Lung cancer is considered as a common cause of cancer mortality. The disease represents the second and third causes of deaths from cancer among Iranian women and men, respectively. The present study aimed to evaluate the spatial variations in relative risk of lung cancer mortality in Iran and its relation to common risk factors between men and women and specific risk factors among women.
UNASSIGNED: In this ecological study, the lung cancer mortality data were analyzed in Iran during 2011-2014. Besag, York, and Mollie\'s (BYM) model and shared component model (SCM) were used to compare the spatial variations of the relative risk of lung cancer mortality by applying OpenBUGS version 3.2.3 and R version 3.6.1.
UNASSIGNED: The median age for death due to lung cancer in Iran is 74 years. During 2011-2014, the age-standardized lung cancer mortality rates among men and women were 12 and 5 per 100,000 individuals, respectively. In addition, almost similar spatial patterns were observed for both men and women. Further, risk factors, which are shared between men and women, were considered as the main cause of variation of lung cancer mortality relative risk in the regions under study for both men and women. The highest impact of the women-specific risk factors was estimated in northeastern and southwestern of the country while the lowest was related to Gilan province in northern part of Iran.
UNASSIGNED: Based on the spatial pattern, lung cancer risk factors are at relatively high levels in most parts of Iran, especially in the northwest of the country. Regarding the women, the high-risk regions were considerably extended. Further, the highest concentration of the specific risk factors among women was observed in the eastern, central, and southwestern parts. The smoking effect, and the second-smoking effect and environmental pollutions could play more significant roles for men and women, respectively.

Arsenic in drinking water impacts health. Highest levels of arsenic have been historically observed in Taiwan and Bangladesh but the contaminant has been affecting the health of people globally. Strong associations have been confirmed between exposure to high-levels of arsenic in drinking water and a wide range of diseases, including cancer. However, at lower levels of exposure, especially near the current World Health Organization regulatory limit (10μg/L), this association is inconsistent as the effects are mostly extrapolated from high exposure studies. This ecological study used Bayesian inference to model the relative risk of bladder and kidney cancer at these lower concentrations-0-2μg/L; 2-5μg/L and; ≥5μg/L of arsenic-in 864 bladder and 525 kidney cancers diagnosed in the study area, Nova Scotia, Canada between 1998 and 2010. The model included proxy measures of lifestyle (e.g. smoking) and accounted for spatial dependencies. Overall, bladder cancer risk was 16% (2-5μg/L) and 18% (≥5μg/L) greater than that of the referent group (<2μg/L), with posterior probabilities of 88% and 93% for these risks being above 1. Effect sizes for kidney cancer were 5% (2-5μg/L) and 14% (≥5μg/L) above that of the referent group (<2μg/L), with probabilities of 61% and 84%. High-risk areas were common in southwestern areas, where higher arsenic-levels are associated with the local geology. The study suggests an increased bladder cancer, and potentially kidney cancer, risk from exposure to drinking water arsenic-levels within the current the World Health Organization maximum acceptable concentration.

Globally, Cancer is the ever-increasing health problem and most common cause of medical deaths. In Libya, it is an important health concern, especially in the setting of an aging population and limited healthcare facilities. Therefore, the goal of this research is to map of the county’ cancer incidence rate using the Bayesian method and identify the high-risk regions (for the first time in a decade). In the field of disease mapping, very little has been done to address the issue of analyzing sparse cancer diseases in Libya. Standardized Morbidity Ratio or SMR is known as a traditional approach to measure the relative risk of the disease, which is the ratio of observed and expected number of accounts in a region that has the greatest uncertainty if the disease is rare or small geographical region. Therefore, to solve some of SMR’s problems, we used statistical smoothing or Bayesian models to estimate the relative risk for stomach cancer incidence in Libya in 2007 based on the BYM model. This research begins with a short offer of the SMR and Bayesian model with BYM model, which we applied to stomach cancer incidence in Libya. We compared all of the results using maps and tables. We found that BYM model is potentially beneficial, because it gives better relative risk estimates compared to SMR method. As well as, it has can overcome the classical method problem when there is no observed stomach cancer in a region.

BACKGROUND: Cystic echinococcosis (CE) is a zoonotic disease that presents a public health challenge and a socioeconomic burden on developing areas in the Middle East. This study used spatial methods to assess the distribution of surgically managed CE cases in an endemic region of north-eastern Iran.
METHODS: For the years 2001-2007, a case series of all 446 patients that were surgically treated for CE in a referral hospital in north-eastern Iran was evaluated. Patients seen at the referral hospital represent 35 counties in three provinces (Razavi Khorasan, North Khorasan, and South Khorasan). A Besag, York and Mollie (BYM) spatial model was used to produce smoothed standardized incidence ratios (SIRs) for surgically managed cases of CE for the 35 counties represented in this study.
RESULTS: Out of 446 surgically managed patients, 54% were male. County-level crude incidence rates ranged from 0 to 3.27 cases per 100,000 population. The highest smoothed SIR (3.46) was for Sarakhs County in the province of Razavi Khorasan, while the lowest smoothed SIR (0.05) was for Birjand County, located in the province of South Khorasan.
CONCLUSIONS: SIRs for CE were highest for the province of Razavi Khorasan, which has large ranching and agricultural industries. Additional studies are needed to better evaluate the role of climate, land cover, and livestock rearing on local Echinococcus granulosus transmission in Iran.

We investigated whether there might be an excess of breast and prostate cancer mortality among the population residing near Spanish industries, according to different categories of industrial groups. An ecologic study was designed to examine breast and prostate cancer mortality at a municipal level (period 1997-2006). Population exposure to pollution was estimated by means of distance from town of residence to industrial facilities. Using Besag-York-Mollié regression models with Integrated Nested Laplace approximations for Bayesian inference, we assessed the relative risk of dying from these tumors in 2-, 3-, 4-, and 5-km zones around installations, and analyzed the effect of category of industrial group. For all sectors combined, no excess risk was detected. However, excess risk of breast cancer mortality (relative risk, 95% credible interval) was detected near mines (1.10, 1.00-1.21 at 4 km), ceramic industries (1.05, 1.00-1.09 at 5 km), and ship building (1.12, 1.00-1.26 at 5 km), and excess risk of prostate cancer was detected near aquaculture for all distances analyzed (from 2.42, 1.53-3.63 at 2 km to 1.63, 1.07-2.36 at 5 km). Our findings do not support that residing in the vicinity of pollutant industries as a whole (all industrial sectors combined) is a risk factor for breast and prostate cancer mortality. However, isolated statistical associations found in our study with respect to specific industrial groups warrant further investigation.

We investigated whether there might be excess ovarian cancer mortality among women residing near Spanish industries, according to different categories of industrial groups and toxic substances. An ecologic study was designed to examine ovarian cancer mortality at a municipal level (period 1997-2006). Population exposure to pollution was estimated by means of distance from town to facility. Using Poisson regression models, we assessed the relative risk of dying from ovarian cancer in zones around installations, and analyzed the effect of industrial groups and pollutant substances. Excess ovarian cancer mortality was detected in the vicinity of all sectors combined, and, principally, near refineries, fertilizers plants, glass production, paper production, food/beverage sector, waste treatment plants, pharmaceutical industry and ceramic. Insofar as substances were concerned, statistically significant associations were observed for installations releasing metals and polycyclic aromatic chemicals. These results support that residing near industries could be a risk factor for ovarian cancer mortality.

Our objective was to investigate whether there might be excess cancer mortality in the vicinity of Spanish installations for the production of cement, lime, plaster, and magnesium oxide, according to different categories of industrial activity. An ecologic study was designed to examine municipal mortality due to 33 types of cancer (period 1997-2006) in Spain. Population exposure to pollution was estimated on the basis of distance from town to industrial facility. Using spatial Besag-York-Mollié regression models with integrated nested Laplace approximations for Bayesian inference, we assessed the relative risk of dying from cancer in a 5-km zone around installations, analyzed the effect of category of industrial activity according to the manufactured product, and conducted individual analyses within a 50-km radius of each installation. Excess all cancer mortality (relative risk, 95% credible interval) was detected in the vicinity of these installations as a whole (1.04, 1.01-1.07 in men; 1.03, 1.00-1.06 in women), and, principally, in the vicinity of cement installations (1.05, 1.01-1.09 in men). Special mention should be made of the results for tumors of colon-rectum in both sexes (1.07, 1.01-1.14 in men; 1.10, 1.03-1.16 in women), and pleura (1.71, 1.24-2.28), peritoneum (1.62, 1.15-2.20), gallbladder (1.21, 1.02-1.42), bladder (1.11, 1.03-1.20) and stomach (1.09, 1.00-1.18) in men in the vicinity of all such installations. Our results suggest an excess risk of dying from cancer, especially in colon-rectum, in towns near these industries.