Mortality among workers employed in the mining industry in the United States: A 29-year analysis of the National Health Interview Survey-Linked Mortality File, 1986-2014.

BACKGROUND: Working in the mining industry increases the risk of chronic diseases and mortality. We investigated overall and cause-specific mortality rates among workers employed in the mining sector in the United States. METHODS: We pooled 29 years of National Health Interview Survey (NHIS) public-use data from 1986 to 2014, with mortality follow-up until 31 December 2015. We grouped respondents into the mining and nonmining sectors based on the responses given at the time of the NHIS interview. We compared the overall and cause-specific mortality rates using standardized mortality ratios (SMR) and 95% confidence interval (CI) adjusted for the competing cause of death. RESULTS: From 1986 to 2014, an estimated 14 million deaths were recorded among subjects eligible for mortality follow-up. Of these, an estimated 50,000 deaths occurred among those working in the mining sector. A significantly higher overall mortality (SMR = 1.26, 95% CI: 1.17-1.36), and mortality from heart diseases (adjusted SMR = 1.56, 95% CI: 1.31-1.83), cancer (adjusted SMR = 1.30, 95% CI: 1.14-1.48) and unintentional injuries (adjusted SMR = 1.41, 95%CI: 1.03-1.85) were observed among those employed in the mining sector. When the analyses were restricted to men, only the SMRs for heart disease and cancer remained statistically significant. No elevated SMR for deaths from chronic lower respiratory disease was observed in the study. CONCLUSION: Workers employed in the mining sector have a significantly increased total death rate and death rates from heart disease, cancer, and unintentional injuries.

Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates.

PURPOSE: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates. METHODS: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics. FINDINGS: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates. CONCLUSIONS: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence.

Work-related asthma in the USA: nationally representative estimates with extended follow-up.

OBJECTIVE: We studied the associations of working in occupations with high asthma trigger exposures with the prevalence and incidence of asthma, and with ever reporting an asthma diagnosis throughout working life. METHODS: We used the nationally representative Panel Study of Income Dynamics (1968-2015; n=13 957; 205 498 person-years), with annual reports of occupation and asthma diagnoses across 48 years. We compared asthma outcomes in occupations likely to have asthma trigger exposures with those in occupations with limited trigger exposures. We estimated the prevalence ratios and the incidence risk ratios using log-binomial regression adjusted for age, sex, race/ethnicity, education, and current and past atopy and smoking, and accounting for the survey design and sampling weights. We calculated the attributable risk fractions and population attributable risks, and used multinomial logistic Markov models and microsimulation to estimate the percentage of people ever diagnosed with asthma during working life. RESULTS: The adjusted prevalence ratio comparing high-risk occupations with low-risk was 4.1 (95% CI 3.5 to 4.8); the adjusted risk ratio was 2.6 (CI 1.8 to 3.9). The attributable risk was 16.7% (CI 8.5 to 23.6); the population attributable risk was 11.3% (CI 5.0 to 17.2). In microsimulations, 14.9% (CI 13.4 to 16.3) with low trigger exposure risk reported asthma at least once, ages 18-65, compared with 23.9% (CI 22.3 to 26.0) with high exposure risk. CONCLUSION: Adults were more than twice as likely to report a new asthma diagnosis if their occupation involved asthma triggers. Work exposures to asthma triggers may cause or aggravate about 11% of all adult asthma and increase the risk of work-life asthma by 60%.

The Prevalence of Chronic Diseases Among Current and Ex-Miners in the United States.

OBJECTIVE: To determine and compare the prevalence and odds of chronic diseases among ex- and current miners. METHODS: Fourteen-year pooled data from the National Health Interview Survey between 2004 and 2017 were analyzed. Ex- and current miners ages 18 to 64 years were defined based on employment status at the time of National Health Interview Survey (NHIS) interview. We calculated age-adjusted prevalence rates and odds ratios of association of chronic diseases. The analysis was adjusted for respondent's age, sex, race, marital status, poverty-income ratio, health insurance, and smoking status. RESULTS: Ex-miners have significantly increased prevalence of most chronic diseases. The age-adjusted prevalence and the adjusted odds of heart disease, cancer, hypertension, diabetes, and psychological stress were significantly higher among ex-miners as compared with current miners. CONCLUSIONS: Ex-miners have worse health outcomes that may persist for years after leaving the mining industry.

Does longer duration of breastfeeding prevent childhood asthma in low-income families?

OBJECTIVE: The objective of this study was to examine the relationship of breastfeeding duration with childhood asthma among low-income families in Karachi, Pakistan. METHODS: Mothers/caregivers of 200 children with asthma and an equal number of children without asthma were interviewed about breastfeeding duration. Based on the responses, 6 different binary variables were constructed: breastfeeding 3 months or less, 6 months or less, 9 months or less, 12 months or less, 18 months or less, and 24 months or less. Asthma status of the child was determined by clinical examination by a primary care physician. Data was analyzed using multiple logistic regression method, adjusted for age and sex of the child, household income, parental ethnicity, number of older siblings, family history of asthma or hay fever, presence of mold, parental smoking, number of people in the household, and body mass index of the child. RESULTS: The average duration of breastfeeding was 21.4 months (SD = 7.33 months). Breastfeeding for at least 24 months was associated with increased odds of asthma (aOR = 1.77, 95%CI: 0.99, 3.16). Whereas breastfeeding for 12 months or less, and to some extent 18 months or less, was protective against childhood asthma. There was some evidence this protective effect may be delayed in children with a family history of asthma or hay fever. CONCLUSIONS: This study found breastfeeding for 12 months or less may have a protective effect against asthma. The protective effect weans down after 18 months, and if continued 24 months or more may place the child at-risk of asthma.

Prenatal exposure to secondhand smoke may increase the risk of postpartum depressive symptoms.

BACKGROUND: The harmful effects of smoking during pregnancy on occurrence of postpartum depressive symptoms (PPDS) have been well studied, but there is little research on the association of secondhand smoke (SHS) exposure during pregnancy with PPDS. This study aimed to explore the relationship between prenatal exposure to SHS during pregnancy and PPDS. METHODS: The authors analyzed data from 6884 women who participated in the North Carolina Pregnancy Risk Assessment and Monitoring System survey (2004-08). Data on the exposure (prenatal SHS), outcome (PPDS) and covariates were obtained from self-reported questionnaires. Univariable and multivariable logistic regression was used for data analysis. RESULTS: The prevalence of PPDS was 16.5%. In the unadjusted analysis, women exposed to SHS during pregnancy had nearly twice the odds of PPDS than the unexposed (odds ratio, OR = 1.90, 95% confidence interval, CI: 1.61-2.26). After adjusting for potential confounders, the association between SHS and PPDS was weakened but remained statistically significant (OR = 1.49, 95% CI: 1.23-1.80). A dose-response relationship was not evident in the analyses. Maternal smoking during pregnancy did not appear to modify the association between SHS and PPDS. CONCLUSIONS: The current study identified a positive association between SHS and PPDS.

Risk factors of smoking among health care professionals.

BACKGROUND: Health care professionals (HCPs) play a vital role at various levels of smoking cessation programs to reduce tobacco use in general populations. However, their smoking habits may limit their ability to intervene with their patients who smoke. AIMS: To determine prevalence rates of current smoking and examine sociodemographic and work-related factors associated with current smoking among HCPs. METHODS: A statewide survey was conducted in a representative sample of 4 groups of HCPs, physicians, nurses, respiratory therapists, and occupational therapists, with active licenses in Texas in 2003. Weighted prevalence estimates of current smoking rates were computed. Survey logistic regression analysis was used to investigate associations between individual characteristics and current smoking. RESULTS: There were 3600 HCPs included in the analysis. The overall prevalence of current smoking was 9% with the highest prevalence (16%) recorded among respiratory therapists and the lowest prevalence (3%) recorded among physicians. In the multivariable logistic regression analysis non-Hispanic white HCPs were almost 4 times as likely to be current smokers (adjusted odds ratio= 3.95, 95% confidence interval = 1.24-12.59) and those working for 50 hours or more per week were twice as likely to be current smokers (adjusted odds ratio = 2.07, 95% CI = 1.20-3.57). Compared with physicians, all other HCP groups had significantly greater odds of currently smoking. HCPs who were female and resided or worked in rural settings had higher smoking rates in univariable analysis only (P < .05). CONCLUSIONS: Smoking is prevalent among certain HCP groups. Working long hours was identified as an important occupational factor associated with current smoking.

Occupational exposures and asthma in health-care workers: comparison of self-reports with a workplace-specific job exposure matrix.

The authors compared self-reported occupational exposures with a workplace-specific job exposure matrix (JEM) in a 2004 survey of Texas health-care professionals (n = 3,650), by asthma status. Sensitivity, specificity, chance-corrected (kappa) and chance-independent (phi) agreement, and associations of self-reported exposures with asthma were compared with those for the JEM. Among asthmatics, the median sensitivity of self-reported exposures was 74% (range, 53-90); specificity was 64% (range, 27-74). For nonasthmatics, median sensitivity was 67% (range, 40-88) and specificity was 70% (range, 33-82). Sensitivity was higher among asthmatics for exposures involving perceptible odors. Specificity was higher among nonasthmatics for instrument cleaning and exposure to adhesives/solvents. Asthmatics showed better agreement with the JEM for patient-care-related cleaning (phi = 0.51 vs. 0.40); there was little difference for other exposures. In all cases, confidence intervals overlapped. Prevalence ratios were higher with self-reported exposures than with the JEM; differences were greatest for cleaning products, adhesives/solvents, and gases/vapors. However, confidence intervals overlapped with those obtained using the JEM. In asthma studies, differential reporting bias by health status should be taken into consideration. Findings favor using externally developed methods of exposure classification, although information gleaned from examining distributions of exposure self-reports, particularly among nondiseased persons, can provide useful information for improving the reliability of exposure ascertainment.

Association between personal exposure to volatile organic compounds and asthma among US adult population.

OBJECTIVES: There is growing concern about adverse respiratory health effects from exposure to indoor air pollution. The purpose of this study was to analyze association between exposure to volatile organic compounds (VOC) and asthma in adults. METHODS: This study utilized passive personal exposure data on ten VOC collected as part of the National Health and Nutrition Examination Survey (NHANES) 1999-2000. A total of 550 subjects who were of non-Hispanic whites, Mexican-Americans, or non-Hispanic Blacks race/ethnicity were included in this analysis. The primary outcome variable was physician-diagnosed asthma and the secondary outcome variable was presence of wheezing in the previous 12 months among those without physician-diagnosed asthma. Exploratory factor analysis was used to generate factor scores to group VOCs, which were included as indicator variables in the analyses. Associations between exposure to VOCs, physician-diagnosed asthma, and wheezing in the previous 12 months were evaluated using multiple logistic regression analyses. Odds ratios are for 1-U increase in level of exposure. RESULTS: Seven of the ten VOC variables loaded on two factors, "aromatic compounds" and "chlorinated hydrocarbons." The geometric mean concentration of VOCs varied from as low as 0.03 microg/m3 for trichloroethene to as high as 14.33 microg/m3 for toluene. Mexican-Americans had the highest personal exposures to benzene (geometric mean=2.38 microg/m3) as compared to non-Hispanic whites (geometric mean=1.15 microg/m3) and non-Hispanic Blacks (geometric mean=1.07 microg/m3). The odds of physician-diagnosed asthma were significantly higher among those exposed to aromatic compounds (Adjusted OR=1.63, 95% CI: 1.17-2.27). Among those subjects never diagnosed by a physician to have asthma, a significantly increased odds of one to two wheezing attacks were observed for aromatic compounds (Adjusted OR=1.68, 95% CI: 1.08-2.61) and chlorinated hydrocarbons (Adjusted OR=1.50, 95% CI: 1.01-2.23) as compared to no wheezing. No association with three wheezing attacks or more was observed in the study. CONCLUSION: In this cross-sectional study of a representative sample of the US population, environmental exposures to VOCs, especially aromatic compounds, were associated with adverse respiratory effects.

Patterns of alcohol drinking and its association with obesity: data from the Third National Health and Nutrition Examination Survey, 1988-1994.

BACKGROUND: Recent reports suggest that alcohol use may have a protective effect on obesity. This study explores association between obesity and alcohol consumption in the non-smoking U.S. adult population. METHODS: We analyzed data on a total of 8,236 respondents who participated in the Third National Health and Nutrition Examination Survey. Body mass index (weight-kg/height-m2) was derived from measured height and weight data and categorized into: normal weight, overweight, and obese. Alcohol consumption was measured using following measures: history of drinking, binge drinking, quantity of drinks/day, frequency of drinking, and average volume of drinks/week. RESULTS: Mean body mass index in this sample of non-smokers was 26.4 (95% CI: 26.1, 26.7). Approximately 46% of respondents were classified as current drinkers. Current drinkers had lower odds of obesity (Adjusted odds ratio = 0.73, 95% CI: 0.55, 0.97) as compared to non-drinkers. The odds of overweight and obesity were significantly greater among binge drinkers and those consuming four or more drinks/day. However, those who reported drinking one or two drinks per day had 0.46 (95% CI: 0.34, 0.62) and 0.59 (95% CI: 0.41, 0.86) times the odds of obesity, respectively. Similarly, the odds of obesity were significantly lower among those who reported drinking frequently and consuming less than five drinks per week. The association between overweight and other alcohol measures was less pronounced. CONCLUSION: The results suggest further exploring the possible role of moderate alcohol drinking in controlling body weight in adults.