An ecological time series study of lagged associations between pesticide use and cancer incidence in Ukraine

Investigating Lagged County-Level Associations Between Agricultural Pesticide Application and Parkinson’s Disease Mortality in the Conterminous United States

Pesticide exposure is among the most studied environmental risk factors for Parkinson’s disease (PD), but most evidence comes from individual-level designs, and national ecological studies rarely account for confounding or for the long interval between exposure and disease. This study examined whether county-level agricultural pesticide application from 1998 to 2002 was associated with later countylevel PD mortality, and whether any association was robust to exposure definition, spatial structure, multivariable adjustment, and count-based modeling. County agricultural pesticide estimates from the U.S. Geological Survey EPest series were linked by Federal Information Processing Standard code to underlying-cause PD mortality (ICD-10 G20) from CDC WONDER for 2003–2007 and 2008–2012. In unadjusted and population-only models, log pesticide application was weakly and positively associated with age-adjusted PD mortality in 2008–2012 (r = .096; population-only β = 0.240, p = .002) and null in 2003–2007. This weak association was robust to normalizing exposure by land area and to removing high-use counties. However, it did not survive further scrutiny. Exposure and regression residuals showed strong positive spatial autocorrelation (Moran’s I ≈ 0.47 and ≈ 0.20, p = .001), violating the independence assumption; adjustment for population density, region, county age structure, median household income, and racial composition reduced the 2008–2012 coefficient to essentially zero (β = 0.003, p = .97; partial R² ≈ 0.000); and a negative-binomial count model with a population offset showed no significant association (incidence rate ratio ≈ 1.06, p = .14). The data therefore do not support a robust county-level association between agricultural pesticide application and later PD mortality. The apparent weak positive gradient is explained by geographic, demographic, and socioeconomic confounding. The study is ecological and hypothesis-generating; stronger inference will require longer exposure windows, incidence data, individual-level exposure, and chemical-specific histories.

Examining the Relationship Between Pesticide Use Levels and Infant Mortality: Correlational and Spatial Analysis by Province (2020–2024)

Numerous studies conducted worldwide have highlighted the harmful effects of excessive pesticide use in weed control on human health. The aim of this study is to examine the relationship between pesticide use levels and infant number of deaths in different provinces of Türkiye. Pesticide use reports from the Ministry of Agriculture and Forestry for the years 2020 and 2024, and infant mortality data by province from the Turkish Statistical Institute (TÜİK) were used. The analysis of the data was conducted utilising SPSS, Jamovi, and Jasp software. Spearman's correlation analysis was employed to ascertain the statistical relationship between the data. A p-value of less than 0.05 was considered to be significant, and the findings were visualised by creating geographic maps using QGIS and GeoDa software to demonstrate spatial overlap. The analyses revealed a moderate, positive, and statistically significant correlation between pesticide use at the provincial level and all infant mortality periods in both years examined. Geographic distribution maps demonstrated that the Aegean, Mediterranean, and Southeastern Anatolia regions, where pesticide use was most intensive, also exhibited the highest infant mortality rates, thereby providing spatial evidence for this correlation. An examination of the four-year period reveals a concurrent increase in pesticide utilisation and a decrease in infant number of deaths. The significant positive correlation obtained indicates that environmental pesticide exposure, along with other determinants such as socioeconomic status, is a potential risk factor contributing to infant mortality.

Impact of the atmospheric air pollution level and xenobiotic biotransformation systems on the incidence of breast cancer

An increase in formaldehyde concentration in the air is associated with a gain in breast cancer incidence, especially at the early stages, and it is advisable to strengthen formaldehyde monitoring, implement molecular epidemiological screening of these polymorphisms and active detection of breast cancer in genetically determined risk groups.

Spatial Autocorrelation and High-Risk Area Identification of Food Poisoning in Thailand, 2003–2022

Food poisoning represents a persistent public health burden in Thailand, yet provincial-level spatial clustering patterns have not been comprehensively characterized over extended time series. This retrospective analytical study utilized foodborne illness incidence data from the national disease surveillance system (Report 506) of the Department of Disease Control, Ministry of Public Health, covering all 77 provinces for the period 2003–2022 (n = 2,329,463 reported cases). Provincial incidence rates (per 100,000 population) were computed annually and linked to administrative boundary polygon data. Spatial autocorrelation was assessed using the Global Moran's I statistic, and spatial cluster analysis was performed using the Local Indicators of Spatial Association (LISA) with Queen contiguity first-order spatial weights in GeoDa (version 1.14.0). Global Moran's I ranged from 0.317 to 0.522 across all study years (all p < 0.05), indicating statistically significant positive spatial autocorrelation. High–High (H–H) clusters were consistently identified in the northeastern and northern regions, with the northeastern region demonstrating 9–14 provinces per year classified. Low–Low clusters predominated in the southern region throughout the study period. The spatial clustering patterns persisted across five-year sub-periods, suggesting stable geographically determined risk factors. Findings indicate that food poisoning in Thailand exhibits non-random, geographically structured distribution attributable to inter-related socioeconomic, dietary, and food-market environmental factors. These results provide evidence for spatially targeted surveillance and intervention strategies.

Residential pesticide exposure, mortality risk, and the potential mediating role of urinary metabolites: evidence from a nationally representative cohort

Trend and Epidemiological Mapping of Oral Cancer in The State of Ceará from 2017 to 2023: Ecological Time-Series Study

Introduction: Oral cancer is a global public health problem, characterized by high mortality rates and late diagnoses, with a negative impact on patients’ quality of life. In Brazil, policies such as the National Oral Health Policy (PNSB) and the “30- and 60-Day Laws” aim to improve early diagnosis and access to treatment, but significant challenges remain. Objective: To analyze the epidemiological behavior and perform a georeferenced mapping of oral cancer in Ceará between 2017 and 2023, considering sociodemographic aspects, tumor staging, and geographic distribution. Method: Ecological time-series study using secondary data from DATASUS. Variables included sex, age, tumor stage, time to treatment, and health regions. QGIS software was applied for geospatial mapping. Results: A higher prevalence was observed among men over 55 years old. In 2020, there was a significant increase in treatments initiated within 30 days (43.3%), but this rate declined in subsequent years, largely due to the impact of the COVID-19 pandemic. Between 2017 and 2019, many treatments were initiated after 60 days from diagnosis, highlighting difficulties in complying with the “60-Day Law.” The Fortaleza region concentrated most diagnoses, reflecting inequalities in access to specialized care. Awareness campaigns such as “Julho Verde” were associated with increased rates of early diagnosis. Conclusion: The study revealed both advances and persistent gaps in oral cancer care in Ceará, underscoring the need for a specific observatory to improve monitoring and ensure comprehensive oncological care.