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Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study.

ABSTRACT: BACKGROUND:Previous studies have associated elevated mortality risk in central Appalachia with coal-mining activities, but few have explored how different non-coal factors influence the association within each county. Consequently, there is a knowledge gap in identifying effective ways to address health disparities in coal-mining counties. To specifically address this knowledge gap, this study estimated the effect of living in a coal-mining county on non-malignant respiratory diseases (NMRD) mortality, and defined this as "coal-county effect." We also investigated what factors may accentuate or attenuate the coal-county effect. METHODS:An ecological epidemiology protocol was designed to observe the characteristics of three populations and to identify the effects of coal-mining on community health. Records for seven coal-mining counties (n?=?19,692) were obtained with approvals from the Virginia Department of Health Office of Vital Statistics for the years 2005 to 2012. Also requested were records from three adjacent coal counties (n?=?10,425) to provide a geographic comparison. For a baseline comparison, records were requested for eleven tobacco-producing counties (n?=?27,800). We analyzed the association of 57,917 individual mortality records in Virginia with coal-mining county residency, county-level socioeconomic status, health access, behavioral risk factors, and coal production. The development of a two-level hierarchical model allowed the coal-county effect to vary by county-level characteristics. Wald tests detected sets of significant factors explaining the variation of impacts across counties. Furthermore, to illustrate how the model estimations help explain health disparities, two coal-mining county case studies were presented. RESULTS:The main result revealed that coal-mining county residency increased the probability of dying from NMRD. The coal-county effect was accentuated by surface coal mining, high smoking rates, decreasing health insurance coverage, and a shortage of doctors. In Virginia coal-mining regions, the average coal-county effect increased by 147% (p-value

SUBMITTER: Shi R

PROVIDER: S-EPMC6839055 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study.

Shi Ruoding R Meacham Susan S Davis George C GC You Wen W Sun Yu Y Goessl Cody C

BMC public health 20191108 1

<h4>Background</h4>Previous studies have associated elevated mortality risk in central Appalachia with coal-mining activities, but few have explored how different non-coal factors influence the association within each county. Consequently, there is a knowledge gap in identifying effective ways to address health disparities in coal-mining counties. To specifically address this knowledge gap, this study estimated the effect of living in a coal-mining county on non-malignant respiratory diseases (N ...[more]

PMID: 31703658

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Project description:ObjectivesAlthough nationally representative data are helpful in designing strategies and policies of programmes in a country, there is paucity of evidence with regard to trends and factors influencing utilisation of long-acting contraceptives (LACs). Thus, this study aimed to assess the trends and factors influencing LAC utilisation among contraceptive users in Ethiopia.DesignA repeated cross-sectional study.Setting and participantsThe Performance Monitoring and Accountability (PMA2020) national community-based survey data were used, and 2035 contraceptive users participated. To identify trends, proportions of LAC users were analysed using PMA data from round 1 in January 2014 to round 6 in July 2018.Main outcome measuresUsers using LAC methods or otherwise.ResultsThere was a difference in trends in LAC utilisation in the last 4.5 years. There was a 7% increase in the proportion of implant users, while there were no significant changes in utilisation of intrauterine device and female sterilisation. Women in the middle wealth quintile were 1.7 times more likely than those in the lowest quintile to use LAC, while contraceptive users who received recommendations from healthcare providers as well as those who made decisions jointly with healthcare providers were more likely to use LAC compared with those who decided on their own. Women with access to their desired method of contraception were less likely to use LAC, while those informed about intrauterine contraceptive device were more likely to use LAC compared with their counterparts. Women served at health posts, private hospitals and others (family planning clinics, pharmacies and non-governmental organisations) were less likely to use LAC compared with women served at public hospitals.ConclusionOverall the utilisation of LAC in Ethiopia is low. Therefore, much has to be done in terms of raising awareness about intrauterine device, how healthcare providers can help users in choosing contraceptive methods, and sharing of experiences between public hospitals and other family planning service delivery points.

Project description:Background: In subcontinental underground mines, coal mining is carried out manually and requires many laborers to practice traditional means of coal excavation. Each task of this occupation disturbs workers' musculoskeletal order. In order to propose and practice possible ergonomic interventions, it is necessary to know what tasks (drilling and blasting, coal cutting, dumping, transporting, timbering and supporting, loading and unloading) cause disorder in either upper limbs, lower limbs, or both. Methods: To this end, R-programming, version R 3.1.2 and SPSS, software 20, were used to calculate data obtained by studying 260 workers (working at different tasks of coal mining) from 20 mines of four districts of Punjab, Pakistan. In addition, a Standard Nordic Musculoskeletal Questionnaire (SNMQ) and Rapid Upper Limb Assessment (RULA) sheet were used to collect data and to analyze postures respectively. Results: In multi regression models, significance of the five tasks for upper and lower limb disorder is 0.00, which means that task based prevalence of upper and lower limb disorders are common in underground coal mines. The results of the multiple bar chart showed that 96 coal cutters got upper limb disorders and 82 got lower limb disorders. The task of timbering and supporting was shown to be dangerous for the lower limbs and relatively less dangerous for the upper limbs, with 25 workers reporting pain in their lower limbs, and 19 workers reporting pain in their upper limbs. Documented on the RULA sheet, all tasks got the maximum possible score (7), meaning that each of these tasks pose a threat to the posture of 100% of workers. The majority of participants (182) fell in the age group of 26 to 35 years. Of those workers, 131 reported pain in the lower limbs and slight discomfort (128) in the upper limbs. The significance value of age was 0.00 for upper limb disorder and was 0.012 for lower limb disorder. Frequency graphs show age in direct proportion to severity of pain while in inverse proportion with number of repetitions performed per min. Conclusions: All findings infer that each task of underground coal mining inflicts different levels of disorder in a workers' musculoskeletal structure of the upper and lower limbs. It highlighted the need for urgent intervention in postural aspects of each task.

Project description:This study is to investigate the prevalence of work-related musculoskeletal disorders (MSDs) and the influencing factors among coal mine workers employed in on-site operations. The job burnout scale and MSD scale were implemented to investigate a random sample of 1,500 coal mine workers working in on-site operations in Xinjiang, China. In total, 1,325 valid questionnaires were collected, with a recovery rate of 88.33% (1,325/1,500). The rate of job burnout was 90%, of which 39.8% were categorized as mild burnout, 43.8% as moderate burnout, and 6.4% as severe burnout; the average job burnout score was 50.77 ± 11.93. The annual prevalence of MSDs was 65.6%, with the highest annual prevalence in the waist (50.7%), followed by the neck, shoulder, and knee, and the lowest prevalence in the elbow (18.8%). Of the areas of the body affected by work-related MSDs, the highest proportion of requests for leave of absence was related to the waist, accounting for 25.7% of requests, while the lowest proportion (13.4%) was related to the wrist. In addition, the incidence of MSDs increased with the years of service. The lowest incidence of MSDs was associated with the two-shift and three-group working pattern. The prevalence of MSDs in the neck and waist was higher in women than in men. The prevalence of MSDs in various body parts increased with the years of service. Moreover, multiple logistic regression indicated that three shifts with four groups (OR = 1.096, 95% CI: 0.832-1.445), working more than 10 years (OR = 3.396, 95% CI: 2.369-5.748), working more than 20 years (OR = 3.008, 95% CI: 1.419-6.337), significant bending (OR = 2.062, 95% CI: 1.400-3.038), forward neck tilting (OR = 1.572, 95% CI: 1.071-2.281), maximum force operation within a short period of time (OR = 1.7222, 95% CI: 1.164-2.547), repeated movement of upper arms or fingers (OR = 1.495, 95% CI: 1.034-2.161), slip or fall incidents (OR = 1.124, 95% CI: 1.039-1.216), work under conditions of cold or temperature variations (OR = 1.911, 95% CI: 1.342-2.720), mild burnout (OR = 1.492, 95% CI: 1.016-2.191), moderate burnout (OR = 1.852, 95% CI: 1.267-2.708), and severe burnout (OR = 2.001, 95% CI: 1.145-3.496) were risk factors for MSDs. In conclusion, there is a high annual prevalence of MSDs among the coal mine workers employed in on-site operations in Xinjiang, China. Measures to reduce this prevalence are required.

Project description:BackgroundPrevious studies on the impact of social distancing on COVID-19 mortality in the United States have predominantly examined this relationship at the national level and have not separated COVID-19 deaths in nursing homes from total COVID-19 deaths. This approach may obscure differences in social distancing behaviors by county in addition to the actual effectiveness of social distancing in preventing COVID-19 deaths.ObjectiveThis study aimed to determine the influence of county-level social distancing behavior on COVID-19 mortality (deaths per 100,000 people) across US counties over the period of the implementation of stay-at-home orders in most US states (March-May 2020).MethodsUsing social distancing data from tracked mobile phones in all US counties, we estimated the relationship between social distancing (average proportion of mobile phone usage outside of home between March and May 2020) and COVID-19 mortality (when the state in which the county is located reported its first confirmed case of COVID-19 and up to May 31, 2020) with a mixed-effects negative binomial model while distinguishing COVID-19 deaths in nursing homes from total COVID-19 deaths and accounting for social distancing- and COVID-19-related factors (including the period between the report of the first confirmed case of COVID-19 and May 31, 2020; population density; social vulnerability; and hospital resource availability). Results from the mixed-effects negative binomial model were then used to generate marginal effects at the mean, which helped separate the influence of social distancing on COVID-19 deaths from other covariates while calculating COVID-19 deaths per 100,000 people.ResultsWe observed that a 1% increase in average mobile phone usage outside of home between March and May 2020 led to a significant increase in COVID-19 mortality by a factor of 1.18 (P<.001), while every 1% increase in the average proportion of mobile phone usage outside of home in February 2020 was found to significantly decrease COVID-19 mortality by a factor of 0.90 (P<.001).ConclusionsAs stay-at-home orders have been lifted in many US states, continued adherence to other social distancing measures, such as avoiding large gatherings and maintaining physical distance in public, are key to preventing additional COVID-19 deaths in counties across the country.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) the causal agent for COVID-19, is a communicable disease spread through close contact. It is known to disproportionately impact certain communities due to both biological susceptibility and inequitable exposure. In this study, we investigate the most important health, social, and environmental factors impacting the early phases (before July, 2020) of per capita COVID-19 transmission and per capita all-cause mortality in US counties. We aggregate county-level physical and mental health, environmental pollution, access to health care, demographic characteristics, vulnerable population scores, and other epidemiological data to create a large feature set to analyze per capita COVID-19 outcomes. Because of the high-dimensionality, multicollinearity, and unknown interactions of the data, we use ensemble machine learning and marginal prediction methods to identify the most salient factors associated with several COVID-19 outbreak measure. Our variable importance results show that measures of ethnicity, public transportation and preventable diseases are the strongest predictors for both per capita COVID-19 incidence and mortality. Specifically, the CDC measures for minority populations, CDC measures for limited English, and proportion of Black- and/or African-American individuals in a county were the most important features for per capita COVID-19 cases within a month after the pandemic started in a county and also at the latest date examined. For per capita all-cause mortality at day 100 and total to date, we find that public transportation use and proportion of Black- and/or African-American individuals in a county are the strongest predictors. The methods predict that, keeping all other factors fixed, a 10% increase in public transportation use, all other factors remaining fixed at the observed values, is associated with increases mortality at day 100 of 2012 individuals (95% CI [1972, 2356]) and likewise a 10% increase in the proportion of Black- and/or African-American individuals in a county is associated with increases total deaths at end of study of 2067 (95% CI [1189, 2654]). Using data until the end of study, the same metric suggests ethnicity has double the association as the next most important factors, which are location, disease prevalence, and transit factors. Our findings shed light on societal patterns that have been reported and experienced in the U.S. by using robust methods to understand the features most responsible for transmission and sectors of society most vulnerable to infection and mortality. In particular, our results provide evidence of the disproportionate impact of the COVID-19 pandemic on minority populations. Our results suggest that mitigation measures, including how vaccines are distributed, could have the greatest impact if they are given with priority to the highest risk communities.

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Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study.

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Factors influencing high respiratory mortality in coal-mining counties: a repeated cross-sectional study.

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