Heavy metals drinking water contamination and health risk assessment among primary school children of Pakistan.

The purpose of this study was to characterize the concentrations of lead (Pb), cadmium (Cd), manganese (Mn), and Fe (Fe) in drinking water sources in primary schools in Sindh Province, Pakistan and to quantify potential health risks among those school children. We conducted a representative, cross-sectional study among 425 primary schools in Sindh province of Pakistan. We used risk assessment models to estimate the metal index, pollution index, lifetime cancer risk, and hazard quotient index. Across the 425 sampled schools, the levels of heavy metals in the drinking water often exceeded the WHO permissible limits (67% of schools exceeded Pb limit, 17% for Cd, 15% for Fe). The average incremental lifetime cancer risk (ILCR) for Pb exceeded tolerable limits in all of the districts under study. The findings, particularly for Pb, are of concern, as Pb may negatively influence children's growth, development, school performance, and long-term health.

Polycystic ovary syndrome and postpartum depression symptoms: a population-based cohort study.

BACKGROUND: Women with polycystic ovary syndrome are more likely to experience several pregnancy complications including hypertensive disorders, gestational diabetes mellitus, and preterm births than women without polycystic ovary syndrome. However, at present, there is limited research on whether polycystic ovary syndrome is associated with both anxiety and depression during pregnancy and whether this augments a woman's risk of postpartum depression, particularly among high-risk populations who have limited access to care. OBJECTIVE: Our primary objective was to assess the association between prepregnancy polycystic ovary syndrome and postpartum depression, considering important baseline confounding factors. Our secondary objective was to evaluate the mediating role of prenatal depression and anxiety on the association between polycystic ovary syndrome and postpartum depression. STUDY DESIGN: This study involved a population-based sample of 3906 postpartum (2-6 months) women who completed the Utah Pregnancy Risk Assessment Monitoring System Phase 8 questionnaire (2016-2018). Weighted adjusted prevalence ratios were used to assess the association between polycystic ovary syndrome and postpartum depression, considering potential confounding factors and assessing mediating effects of depression and anxiety experienced during pregnancy. RESULTS: Following the exclusion criteria, 8.2% of women reported clinical polycystic ovary syndrome and 19.1%, 6.2%, and 4.4% reported irregular periods and acne, irregular periods and hirsutism, and all 3 symptoms, respectively. Moreover, 17.7% and 23.5% reported experiencing prenatal depression and anxiety and 9.5% and 10.2% reported experiencing postpartum depressed mood and anhedonia, respectively. Clinical polycystic ovary syndrome was associated with a 1.76 higher adjusted prevalence ratio (95% confidence interval, 1.03-3.00) for postpartum depressed mood or anhedonia after taking into consideration age, prepregnancy body mass index, race/ethnicity, education, and marital status. A similar higher prevalence was seen for irregular periods and acne (adjusted prevalence ratio, 1.65; 95% confidence interval, 1.13-2.41), irregular periods and hirsutism (adjusted prevalence ratio, 1.40; 95% confidence interval, 0.82-2.40), and all 3 symptoms (adjusted prevalence ratio, 1.75; 95% confidence interval, 0.96-3.19) and postpartum depressed mood or anhedonia. Prenatal depression and anxiety mediated 20% and 32% of the effect of clinical polycystic ovary syndrome on postpartum depressed mood and anhedonia, respectively. CONCLUSION: Clinical polycystic ovary syndrome is associated with postpartum depressed mood and symptoms among this population-based sample inclusive of high-risk mothers. Prenatal depression and anxiety mediate this association, emphasizing the importance of prenatal psychological screening among women with polycystic ovary syndrome. An additional important clinical and public health implication of this study lies in the finding that nearly 20% of women in this population-based sample who reported at least 2 polycystic ovary syndrome symptoms (including at-risk women who may not have access to care) had not received a clinical diagnosis for polycystic ovary syndrome.

Spatial clusters of cancer incidence: analyzing 1940 census data linked to 1966-2017 cancer records.

PURPOSE: A life course perspective to cancer incidence is important for understanding effects of the environment during early life on later cancer risk. We assessed spatial clusters of cancer incidence based on early life location defined as 1940 US Census Enumeration District (ED). METHODS: A cohort of 260,585 individuals aged 0-40 years in 1940 was selected. Individuals were followed from 1940 to cancer diagnosis, death, or last residence in Utah. We geocoded ED centroids in Utah for the 1940 Census. Spatial scan statistics with purely spatial elliptic scanning window were used to identify spatial clusters of EDs with excess cancer rates across 26 cancer types, assuming a discrete Poisson model. RESULTS: Cancer was diagnosed in 66,904 (25.67%) individuals during follow-up across 892 EDs. Average follow-up was 50.9 years. We detected 15 clusters of excess risk for bladder, breast, cervix, colon, lung, melanoma, oral, ovary, prostate, and soft tissue cancers. An urban area had dense overlap of multiple cancer types, including two EDs at increased risk for five cancer types each. CONCLUSIONS: Early environments may contribute to cancer risk later in life. Life course perspectives applied to the study of cancer incidence can provide insights for increasing understanding of cancer etiology.

Fine Particulate Matter and Respiratory Healthcare Encounters among Survivors of Childhood Cancers.

Some chemotherapies that treat childhood cancers have pulmonary-toxic properties that increase risk for adverse respiratory-health outcomes. PM2.5 causes similar outcomes but its effect among pulmonary compromised cancer survivors is unknown. This case-crossover study identified the PM2.5-associated odds for primary-respiratory hospitalizations and emergency department visits among childhood cancer survivors in Utah. We compared risk among chemotherapy-treated survivors to a cancer-free sample. We calculated 3-day-average PM2.5 by ZIP code and county for event and control days. Conditional logistic regression estimated odds ratios. Models were stratified by cause of admission (infection, respiratory disease, asthma), previous chemotherapy, National Ambient Air Quality Standard (NAAQS), and other variables. Results are presented per 10 µg/m³ of PM2.5. 90% of events occurred at 3-day PM2.5 averages <35.4 µg/m³, the NAAQS 24-h standard. For survivors, PM2.5 was associated with respiratory hospitalizations (OR = 1.84, 95% CI = 1.13⁻3.00) and hospitalizations from respiratory infection (OR = 2.09, 95% CI = 1.06⁻4.14). Among chemotherapy-treated survivors, the PM2.5-associated odds of respiratory hospitalization (OR = 2.03, 95% CI = 1.14⁻3.61) were significantly higher than the cancer-free sample (OR = 0.84, 95% CI = 0.57⁻1.25). This is the first study to report significant associations between PM2.5 and respiratory healthcare encounters in childhood cancer survivors. Chemotherapy-treated survivors displayed the highest odds of hospitalization due to PM2.5 exposure and their risk is significantly higher than a cancer-free sample.

Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity.

OBJECTIVES: Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. METHODS: We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics. RESULTS: For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths. CONCLUSIONS: Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities-information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.