Neighborhood-level characteristics as effect modifiers on the efficacy of the MyPEEPS mobile intervention in same-sex attracted adolescent men

To estimate the effect of neighborhood-level modification on the efficacy of the MyPEEPS Mobile intervention on the reduction of condomless anal sex acts among same-sex attracted adolescent men. A series of generalized linear mixed model was used to examine if the effect of the MyPEEPS Mobile intervention on condomless anal sex acts was moderated by neighborhood-level factors using data from the 2019 American Community Survey US Census Bureau. "The magnitudes of intervention were significantly smaller at both 6- and 9-month follow-up among adolescents living in neighborhood with high proportions of Hispanic or Latino residents (IRR6M = 1.02, 95 % CI: 1.01, 1.02; IRR9M = 1.03, 95 % CI: 1.01, 1.05) and high proportions of families with income below the poverty level (IRR6M = 1.07, 95 % CI: 1.01, 1.12; IRR9M = 1.05, 95 % CI: 1.01, 1.10), which indicated that living in communities with a higher concentration of residents living under poverty or of Hispanic/and Latino ethnicity significantly modified the effective of program intervention on condomless sex among adolescent MSM. Understanding how neighborhood characteristics modify the effect of HIV prevention interventions may be useful in better targeting delivery and tailoring content of interventions based on neighborhood level characteristics such as the ones identified in this study.

Exploring Smoking Disparities and Sociodemographic Factors in a Peri-urban Landscape: A Census Tract-Level Analysis in McLennan County, Texas

Cigarette smoking significantly contributes to preventable illness, death, and economic costs. Despite overall reduction in national smoking rates, disparities persist between demographic groups and geographic regions. While some studies have explored urban-rural differences in smoking prevalence, gaps exist in understanding localized patterns. This study focuses on examining smoking rates and related factors at the census tract level in McLennan County, Texas, a county that contains a mixture of urban, peri-urban, and rural areas. This study uses census tract level aggregate sociodemographic, smoking, and health-related data from the American Community Survey and the PLACES Project City Health Dashboard. Geospatial analyses mapped co-occurrence of high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education. Multiple linear regression modeled associations between smoking and sociodemographic, and health-related factors. Geospatial analyses identified census tracts with co-occurring high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education level in McLennan County. Regression analyses identified that smoking rates were positively correlated with frequent physical distress (p < 0.0001) and negatively correlated with the proportion of routine medical check-ups (p < 0.0001) and the proportion living in poverty (p = 0.0002). This study found significant variations in smoking rates, physical and mental distress, medical check-ups, and sociodemographic factors between neighboring census tracts which geospatial analyses examining larger geographic units may have overlooked. Future research should focus on obtaining individual-level and community-level data to develop more targeted interventions sensitive to specific community contexts.

Exploring the geospatial epidemiology of breast cancer in Iran: identifying significant risk factors and spatial patterns for evidence-based prevention strategies

BACKGROUND

Breast Cancer (BC) is a formidable global health challenge, and Iran is no exception, with BC accounting for a significant proportion of women's malignancies. To gain deeper insights into the epidemiological characteristics of BC in Iran, this study employs advanced geospatial techniques and feature selection methods to identify significant risk factors and spatial patterns associated with BC incidence.

METHODS

Using rigorous statistical methods, geospatial data from Iran, including cancer-related, sociodemographic, healthcare infrastructure, environmental, and air quality data at the provincial level, were meticulously analyzed. Age-standardized incidence rates (ASR) are calculated, and different regression models are used to identify significant variables associated with BC incidence. Spatial analysis techniques, including global and local Moran's index, geographically weighted regression, and Emerging hotspot analysis, were utilized to examine geospatial patterns, identify clustering and hotspots, and assess spatiotemporal distribution of BC incidence.

RESULTS

The findings reveal that BC predominantly affects women (98.03%), with higher incidence rates among those aged 50 to 79. Isfahan (ASR = 26.1) and Yazd (ASR = 25.7) exhibit the highest rates. Significant predictors of BC incidence, such as marriage, tertiary education attainment rate, physician-to-population ratio, and PM2.5 air pollution, are identified through regression models.

CONCLUSION

The study's results provide valuable information for the development of evidence-based prevention strategies to reduce the burden of BC in Iran. The findings underscore the importance of early detection, health education campaigns, and targeted interventions in high-risk clusters and adjacent regions. The geospatial insights generated by this study have implications for policy-makers, researchers, and public health practitioners, facilitating the formulation of effective BC prevention strategies tailored to the unique epidemiological patterns in Iran.

SARS-CoV-2 in migrant worker dormitories: Geospatial epidemiology supporting outbreak management

Background

Migrant worker dormitories—residential complexes where 10–24 workers share living spaces—account for the majority of cases of SARS-CoV-2 infection in Singapore. To prevent overspill of transmission to the wider population, starting in early April 2020, residents were confined to their dormitories while measures were put in place to arrest the spread of infection. This descriptive study presents epidemiological data for a population of more than 60 000 migrant workers living in two barracks-style and four apartment-style dormitories located in western Singapore from April 3 to June 10, 2020.

Methods

Our report draws from data obtained over the first 50 days of outbreak management in order to describe SARS-CoV-2 transmission in high-density housing environments. Cumulative counts of SARS-CoV-2 cases and numbers of housing units affected were analyzed to report the harmonic means of harmonic means of doubling times and their 95% confidence intervals (CI).

Results

Multiple transmission peaks were identified involving at least 5467 cases of SARS-CoV-2 infection across six dormitories. Our geospatial heat maps gave an early indication of outbreak severity in affected buildings. We found that the number of cases of SARS-CoV-2 infection doubled every 1.56 days (95% CI 1.29–1.96) in barracks-style buildings. The corresponding doubling time for apartment-style buildings was 2.65 days (95% CI 2.01–3.87).

Conclusions

Geospatial epidemiology was useful in shaping outbreak management strategies in dormitories. Our results indicate that building design plays an integral role in transmission and should be considered in the prevention of future outbreaks.