Chlorinated organic contaminants in breast milk of New Zealand women.

Breast milk samples from 38 women in New Zealand were analyzed for organochlorine pesticides, polychlorinated biphenyls (PCBs), polychlorinated dibenzo-p-dioxins (PCDDs), and polychlorinated dibenzofurans (PCDFs) as part of a World Health Organization collaborative study of breast-milk contaminants. The women were recruited from two urban areas (Auckland and Christchurch) and two rural areas (Northland and North Canterbury) in the North and South Islands of New Zealand. The best predictor of contaminant concentrations in breast milk was found to be the age of the mother. Regional differences were found for hexachlorobenzene, dieldrin, and pp-DDE, reflecting historical use patterns. Urban-rural differences were found for several PCBs, PCDDs, and PCDFs when contaminant concentrations were calculated on a whole-milk basis. However, these differences could be attributed to variation in breast-milk fat concentrations between urban and rural mothers. Urban mothers had about 50% more breast-milk fat than rural mothers. Evidence suggests that breast-milk consumption by babies is regulated by caloric intake. Almost all of the caloric content of milk is in the fat fraction. This suggests that breast-milk contaminant levels calculated on a whole-milk basis do not necessarily reflect the relative levels of exposure of infants to these contaminants. However, the factors that influence breast-milk fat concentration deserve further study

Maternal residential pesticide use and risk of childhood leukemia in Costa Rica.

Evidence suggests that early-life exposure to pesticides inside the home may be associated with childhood leukemia, however data from Latin American countries are limited. We examined whether self-reported maternal residential pesticide use and nearby pesticide applications-before and after child's birth-were associated with acute lymphoblastic leukemia (ALL) in the Costa Rican Childhood Leukemia Study (CRCLS), a population-based case-control study (2001-2003). Cases (n = 251 ALL) were diagnosed between 1995 and 2000 (age <15 years at diagnosis) and were identified through the Costa Rican Cancer Registry and National Children's Hospital. Population controls (n = 577) were drawn from the National Birth Registry. We fitted unconditional logistic regression models adjusted for child sex, birth year, and socioeconomic status to estimate the exposure-outcome associations and also stratified by child sex. We observed that self-reported maternal insecticide use inside the home in the year before pregnancy, during pregnancy, and while breastfeeding was associated with increased odds of ALL among boys [adjusted Odds Ratio (aOR) = 1.63 (95% confidence interval [95% CI]: 1.05-2.53), 1.75 (1.13-2.73), and 1.75 (1.12-2.73), respectively. We also found evidence of exposure-response relationships between more frequent maternal insecticide use inside the home and increased odds of ALL among boys and girls combined. Maternal report of pesticide applications on farms or companies near the home during pregnancy and at any time period were also associated with ALL. Our study in Costa Rica highlights the need for education to minimize pesticide exposures inside and around the home, particularly during pregnancy and breastfeeding

Case-control study of arsenic in drinking water and lung cancer in California and Nevada.

Millions of people are exposed to arsenic in drinking water, which at high concentrations is known to cause lung cancer in humans. At lower concentrations, the risks are unknown. We enrolled 196 lung cancer cases and 359 controls matched on age and gender from western Nevada and Kings County, California in 2002-2005. After adjusting for age, sex, education, smoking and occupational exposures, odds ratios for arsenic concentrations ≥85 µg/L (median = 110 µg/L, mean = 173 µg/L, maximum = 1,460 µg/L) more than 40 years before enrollment were 1.39 (95% CI = 0.55-3.53) in all subjects and 1.61 (95% CI = 0.59-4.38) in smokers. Although odds ratios were greater than 1.0, these increases may have been due to chance given the small number of subjects exposed more than 40 years before enrollment. This study, designed before research in Chile suggested arsenic-related cancer latencies of 40 years or more, illustrates the enormous sample sizes needed to identify arsenic-related health effects in low-exposure countries with mobile populations like the U.S. Nonetheless, our findings suggest that concentrations near 100 µg/L are not associated with markedly high relative risks

Bio-mechanical risk factors for uterine prolapse among women living in the hills of west Nepal: A case-control study.

OBJECTIVE:To investigate whether heavy load carrying, wearing a patuka, and body position at work are risk factors for uterine prolapse among Nepali women. METHODS:Community-based case-control study of 448 women (170 cases of uterine prolapse; 278 controls) aged 18-60 years in Kaski district, Nepal was conducted. Women diagnosed with uterine prolapse were cases. Two controls were recruited for each case, frequency-matched by residential area and age. Multivariate logistic regression was used to investigate associations between outcome and exposures. RESULTS:No association of heavy load carrying with uterine prolapse was observed; women who never used a patuka had lower odds of uterine prolapse (odds ratio = 0.18, 95% confidence interval = 0.05-0.71). Women working in a sitting position had higher odds than those working in a standing position (odds ratio = 2.94, 95% confidence interval = 1.74-4.96), as did women who mainly worked in a bending position (odds ratio = 2.45, 95% confidence interval = 1.12-5.34). Housewives were more prone to uterine prolapse than women engaged in farming (odds ratio = 2.13, 95% confidence interval = 1.31-3.47). CONCLUSION:Using a patuka, occupation, and body position during work were all associated with uterine prolapse. No association was found with heavy load carrying, although that might be attributable to the cross-sectional nature of study recruitment

The Impact of Heavy Load Carrying on Musculoskeletal Pain and Disability Among Women in Shinyanga Region, Tanzania.

BackgroundHeavy load carrying has been associated with musculoskeletal discomfort (MSD) and disability. However, there is a lack of research investigating this association in resource-constrained settings where heavy load carrying by women is common.ObjectivesWe assessed the impact of heavy load carrying on musculoskeletal pain and disability among women in Shinyanga Region, Tanzania, in an exploratory cross-sectional study.MethodsEligible participants were a convenience sample of women, at least 18 years of age, who passed a study recruitment site carrying a load. We collected information on load-carrying practices, including frequency and time spent carrying water, wood, agricultural products, coal, sand, or rocks, and measured the weight of the load carried at the time. Outcomes included self-reported MSDs, defined as experiencing pain lasting >3 days in the neck, head, back, knees, feet and/or ankles within the last 1 year, and related disability. Using multivariable logistic regression we assessed for associations between load carrying exposures and MSDs and disability.FindingsResults showed a high prevalence of MSDs across the body regions assessed and evidence to suggest a relationship of back pain and related disability with several measures of load-carrying, including duration, frequency, and weight. Multivariable analyses revealed associations of increased load carrying exposures with low back pain (LBP) and related disability, including statistically significant increases in odds of LBP with increasing weight, total duration of load carrying/week and cumulative loads/week.ConclusionsFindings indicate a substantial burden of MSDs and disability in this population of women who carry heavy loads daily. The extent of discomfort and disability increased with increasing exposure to various load-carrying measures, especially for LBP. Larger epidemiologic studies that definitively assess relationships of load carrying with MSDs and disability are warranted

Biomass Fuel Use and Cardiac Function in Nepali Women.

BackgroundExposure to household air pollution (HAP) from cooking with biomass fuel affects billions of people. We hypothesized that HAP from woodsmoke, compared to other household fuels, was associated with adverse cardiovascular outcomes, of which there have been few studies.MethodsA cross-sectional study was completed in 299 females aged 40-70 years in Kaski District, Nepal, during 2017-18. All participants underwent a standard 12-lead ECG, ankle and brachial systolic blood pressure measurement, and 2D color and Doppler echocardiography. Current stove type was confirmed by inspection. Blood pressure, height, and weight were measured using a standardized protocol. Hypertension was defined as ≥140/90 mmHg or prior diagnosis. Hemoglobin A1c (HbA1c) was obtained, with diabetes mellitus defined as a prior diagnosis or HbA1C ≥ 6.5%. We used adjusted linear and logistic multivariable regressions to examine the relationship of stove type with cardiac structure and function.ResultsThe majority of women primarily used liquified petroleum gas (LPG) stoves (65%), while 12% used biogas, and 23% used wood-burning cook-stoves. Prevalence of major cardiovascular risk factors was 35% with hypertension, 19% with diabetes mellitus, and 15% current smokers. After adjustment, compared to LPG, wood stove use was associated with increased indexed left atrial volume (β = 3.15, 95% CI 1.22 to 5.09) and increased indexed left ventricular end diastolic volume (β = 7.97, 95% CI 3.11 to 12.83). There was no association between stove type and systemic hypertension, left ventricular mass, systolic dysfunction, diastolic dysfunction, pulmonary hypertension, abnormal ankle-brachial index, or clinically significant ECG abnormalities.ConclusionBiomass fuel use was associated with increased indexed left atrial volume and increased indexed left ventricular diastolic volume in Nepali women, suggesting subclinical adverse cardiac remodeling from HAP in this cross-sectional study. We did not find evidence of an association with hypertension or typical cardiac sequelae of hypertension. Future studies to confirm these results are needed

Prediction-Powered Inference

We introduce prediction-powered inference \unicode{x2013} a framework for performing valid statistical inference when an experimental data set is supplemented with predictions from a machine-learning system. Our framework yields provably valid conclusions without making any assumptions on the machine-learning algorithm that supplies the predictions. Higher accuracy of the predictions translates to smaller confidence intervals, permitting more powerful inference. Prediction-powered inference yields simple algorithms for computing valid confidence intervals for statistical objects such as means, quantiles, and linear and logistic regression coefficients. We demonstrate the benefits of prediction-powered inference with data sets from proteomics, genomics, electronic voting, remote sensing, census analysis, and ecology.Comment: Code is available at https://github.com/aangelopoulos/prediction-powered-inferenc

Cooking practices, air quality, and the acceptability of advanced cookstoves in Haryana, India: an exploratory study to inform large-scale interventions.

BackgroundIn India, approximately 66% of households rely on dung or woody biomass as fuels for cooking. These fuels are burned under inefficient conditions, leading to household air pollution (HAP) and exposure to smoke containing toxic substances. Large-scale intervention efforts need to be informed by careful piloting to address multiple methodological and sociocultural issues. This exploratory study provides preliminary data for such an exercise from Palwal District, Haryana, India.MethodsTraditional cooking practices were assessed through semi-structured interviews in participating households. Philips and Oorja, two brands of commercially available advanced cookstoves with small blowers to improve combustion, were deployed in these households. Concentrations of particulate matter (PM) with a diameter <2.5 μm (PM2.5) and carbon monoxide (CO) related to traditional stove use were measured using real-time and integrated personal, microenvironmental samplers for optimizing protocols to evaluate exposure reduction. Qualitative data on acceptability of advanced stoves and objective measures of stove usage were also collected.ResultsTwenty-eight of the thirty-two participating households had outdoor primary cooking spaces. Twenty households had liquefied petroleum gas (LPG) but preferred traditional stoves as the cost of LPG was higher and because meals cooked on traditional stoves were perceived to taste better. Kitchen area concentrations and kitchen personal concentrations assessed during cooking events were very high, with respective mean PM2.5 concentrations of 468 and 718 µg/m3. Twenty-four hour outdoor concentrations averaged 400 µg/m3. Twenty-four hour personal CO concentrations ranged between 0.82 and 5.27 ppm. The Philips stove was used more often and for more hours than the Oorja.ConclusionsThe high PM and CO concentrations reinforce the need for interventions that reduce HAP exposure in the aforementioned community. Of the two stoves tested, participants expressed satisfaction with the Philips brand as it met the local criteria for usability. Further understanding of how the introduction of an advanced stove influences patterns of household energy use is needed. The preliminary data provided here would be useful for designing feasibility and/or pilot studies aimed at intervention efforts locally and nationally

Class-Conditional Conformal Prediction with Many Classes

Standard conformal prediction methods provide a marginal coverage guarantee, which means that for a random test point, the conformal prediction set contains the true label with a user-specified probability. In many classification problems, we would like to obtain a stronger guarantee--that for test points of a specific class, the prediction set contains the true label with the same user-chosen probability. For the latter goal, existing conformal prediction methods do not work well when there is a limited amount of labeled data per class, as is often the case in real applications where the number of classes is large. We propose a method called clustered conformal prediction that clusters together classes having "similar" conformal scores and performs conformal prediction at the cluster level. Based on empirical evaluation across four image data sets with many (up to 1000) classes, we find that clustered conformal typically outperforms existing methods in terms of class-conditional coverage and set size metrics