scholarly journals Misclassification and characterization of exposure to humidifier disinfectants using a questionnaire

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hyeonsu Ryu ◽  
Yoon-Hyeong Choi ◽  
Eunchae Kim ◽  
Jinhyeon Park ◽  
Seula Lee ◽  
...  
Keyword(s):  
Exposure Assessment ◽  
Clinical Diagnosis ◽  
Clinical Severity ◽  
Misclassification Error ◽  
Recall Bias ◽  
Exposure Misclassification ◽  
Face To Face ◽  
Adverse Health Effects ◽  
True Value ◽  
Error Bias

Abstract Background Lung disease caused by exposure to chemical substances such as polyhexamethylene guanidine (PHMG) used in humidifier disinfectants (HDs) has been identified in Korea. Several researchers reported that exposure classification using a questionnaire might not correlate with the clinical severity classes determined through clinical diagnosis. It was asserted that the lack of correlation was due to misclassification in the exposure assessment due to recall bias. We identified the cause of uncertainty to recognize the limitations of differences between exposure assessment and clinical outcomes assumed to be true value. Therefore, it was intended to check the availability of survey using questionnaires and required to reduce misclassification error/bias in exposure assessment. Methods HDs exposure assessment was conducted as a face-to-face interview, using a questionnaire. A total of 5245 applicants participated in the exposure assessment survey. The questionnaire included information on sociodemographic and exposure characteristics such as the period, frequency, and daily usage amount of HDs. Based on clinical diagnosis, a 4 × 4 cross-tabulation of exposure and clinical classification was constructed. When the values of the exposure rating minus the clinical class were ≥ 2 and ≤ − 2, we assigned the cases to the overestimation and underestimation groups, respectively. Results The sex ratio was similar in the overestimation and underestimation groups. In terms of age, in the overestimation group, 90 subjects (24.7%) were under the age of 10, followed by 52 subjects (14.2%) in their 50s. In the underestimation group, 195 subjects (56.7%) were under the age of 10, followed by 80 subjects (23.3%) in their 30s. The overestimation group may have already recovered and responded excessively due to psychological anxiety or to receive compensation. However, relatively high mortality rates and surrogate responses observed among those under 10 years of age may have resulted in inaccurate exposure in the underestimation group. Conclusions HDs exposure assessment using a questionnaire might not correlate with adverse health effects due to recall bias and various other causes such as recovery of injury and psychological anxiety. This study revealed exposure misclassification and characteristics affected by HDs and proposed a questionnaire-based exposure assessment methodology to overcome the limitations of past exposure assessment.

scholarly journals Impact of common tobacco products on human health and environmental pollution in Bangladesh

2021 ◽  
Vol 31 (3) ◽  
pp. 130-143
Author(s):  
MA Mondol ◽  
AMM Hosain ◽  
S Sultana ◽  
S Marzia ◽  
MA Islam ◽  
...  
Keyword(s):  
Environmental Pollution ◽  
Human Health ◽  
Smokeless Tobacco ◽  
Tobacco Product ◽  
Tobacco Products ◽  
Face To Face ◽  
Adverse Health Effects ◽  
Survey Results ◽  
Young Smokers ◽  
Structured Questionnaire

Worldwide, tobacco is one of the leading causes of disability and death. Over a million of pounds of toxic chemicals are released by tobacco products. This study aims to explore the effects of tobacco toxicants on human health and environmental pollution. Four districts (Dhaka, Kushtia, Chattogram and Rangpur) were selected where most of the tobacco product grown. Total 468 respondents were interviewed face-to-face using structured questionnaire to assess the knowledge about toxicant content in tobacco and health and environmental hazards of tobacco use. Survey results revealed that about 44.4% respondents used smoking tobacco products and 38.5% used smokeless tobacco, while only 17.1% used both. About two third (74.3%) of smoking tobacco users started smoking when they were between 15 to 24 years old and majority (61.6%) of smokeless tobacco users started tobacco when they were between 30 to 35 years above. Tobacco product has large impacts on health of young smokers. Smokers are suffering from various acute and chronic diseases. Among the respondents, 38% indicated that they were suffering from hypertension. The second most affected disease was COPD/Asthma (31.6%), while cancer was the least suffering disease (0.4%). Majority of the respondents were not aware about the presence of toxicants (i.e. nicotine, tar and metals) in tobacco products. However, 85.1% tobacco users had an idea about environmental pollution. There is a lack of knowledge among the survey respondents about toxicants in tobacco products that are linked to health hazards and environmental pollution. These results are important in strengthening existing policy considering adverse health effects of toxicants examined. Progressive Agriculture 31 (3): 130-143, 2020

scholarly journals Predictors of length of stay in psychiatric inpatient units: Does their effect vary across countries?

2018 ◽  
Vol 48 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Giorgia Dimitri ◽  
Domenico Giacco ◽  
Michael Bauer ◽  
Victoria Jane Bird ◽  
Lauren Greenberg ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Psychiatric Inpatient ◽  
Patient Characteristics ◽  
Clinical Severity ◽  
Face To Face ◽  
History Of ◽  
The Given ◽  
Inpatient Units ◽  
Previous Admission ◽  
Complex Influence

AbstractBackgroundPrevious studies in individual countries have identified inconsistent predictors of length of stay (LoS) in psychiatric inpatient units. This may reflect methodological inconsistencies across studies or true differences of predictors. In this study we assessed predictors of LoS in five European countries and explored whether their effect varies across countries.MethodsProspective cohort study. All patients admitted over 14 months to 57 psychiatric inpatient units in Belgium, Germany, Italy, Poland and United Kingdom were screened. Putative predictors were collected from medical records and in face-to-face interviews and tested for their association with LoS.ResultsAverage LoS varied from 17.9 days in Italy to 55.1 days in Belgium. In the overall sample being homeless, receiving benefits, social isolation, diagnosis of psychosis, greater symptom severity, substance use, history of previous admission and being involuntarily admitted predicted longer LoS. Several predictors showed significant interaction effects with countries in predicting LoS. One variable, homelessness, predicted a different LoS even in opposite directions, whilst for other predictors the direction of the association was the same, but the strength of the association with LoS varied across countries.ConclusionsThe same patient characteristics have a different impact on LoS in different contexts. Thus, although some predictor variables related to clinical severity and social dysfunction appear of generalisable relevance, national studies on LoS are required to understand the complex influence of different patient characteristics on clinical practice in the given contexts.

Correction of odds ratios in case-control studies for exposure misclassification with partial knowledge of the degree of agreement among experts who assessed exposures

2017 ◽  
Vol 75 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Igor Burstyn ◽  
Paul Gustafson ◽  
Javier Pintos ◽  
Jérôme Lavoué ◽  
Jack Siemiatycki
Keyword(s):  
Exposure Assessment ◽  
Sensitivity And Specificity ◽  
Interrater Reliability ◽  
Credible Interval ◽  
Case Control ◽  
Point Estimate ◽  
Exposure Misclassification ◽  
Odds Ratios ◽  
Case Control Studies ◽  
Distribution Centre

ObjectivesEstimates of association between exposures and diseases are often distorted by error in exposure classification. When the validity of exposure assessment is known, this can be used to adjust these estimates. When exposure is assessed by experts, even if validity is not known, we sometimes have information about interrater reliability. We present a Bayesian method for translating the knowledge of interrater reliability, which is often available, into knowledge about validity, which is often needed but not directly available, and applying this to correct odds ratios (OR).MethodsThe method allows for inclusion of observed potential confounders in the analysis, as is common in regression-based control for confounding. Our method uses a novel type of prior on sensitivity and specificity. The approach is illustrated with data from a case-control study of lung cancer risk and occupational exposure to diesel engine emissions, in which exposure assessment was made by detailed job history interviews with study subjects followed by expert judgement.ResultsUsing interrater agreement measured by kappas (κ), we estimate sensitivity and specificity of exposure assessment and derive misclassification-corrected confounder-adjusted OR. Misclassification-corrected and confounder-adjusted OR obtained with the most defensible prior had a posterior distribution centre of 1.6 with 95% credible interval (Crl) 1.1 to 2.6. This was on average greater in magnitude than frequentist point estimate of 1.3 (95% Crl 1.0 to 1.7).ConclusionsThe method yields insights into the degree of exposure misclassification and appears to reduce attenuation bias due to misclassification of exposure while the estimated uncertainty increased.

An algorithm to identify residential mobility from electronic health-record data

2021 ◽  
Author(s):  
Jessica R Meeker ◽  
Heather Burris ◽  
Mary Regina Boland
Keyword(s):  
Electronic Health Record ◽  
Exposure Assessment ◽  
Residential Mobility ◽  
Area Deprivation ◽  
Health Record ◽  
Exposure Misclassification ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
One Year ◽  
Zip Code

Abstract Background Environmental, social and economic exposures can be inferred from address information recorded in an electronic health record. However, these data often contain administrative errors and misspellings. These issues make it challenging to determine whether a patient has moved, which is integral for accurate exposure assessment. We aim to develop an algorithm to identify residential mobility events and avoid exposure misclassification. Methods At Penn Medicine, we obtained a cohort of 12 147 pregnant patients who delivered between 2013 and 2017. From this cohort, we identified 9959 pregnant patients with address information at both time of delivery and one year prior. We developed an algorithm entitled REMAP (Relocation Event Moving Algorithm for Patients) to identify residential mobility during pregnancy and compared it to using ZIP code differences alone. We assigned an area-deprivation exposure score to each address and assessed how residential mobility changed the deprivation scores. Results To assess the accuracy of our REMAP algorithm, we manually reviewed 3362 addresses and found that REMAP was 95.7% accurate. In this large urban cohort, 41% of patients moved during pregnancy. REMAP outperformed the comparison of ZIP codes alone (82.9%). If residential mobility had not been taken into account, absolute area deprivation would have misclassified 39% of the patients. When setting a threshold of one quartile for misclassification, 24.4% of patients would have been misclassified. Conclusions Our study tackles an important characterization problem for exposures that are assigned based upon residential addresses. We demonstrate that methods using ZIP code alone are not adequate. REMAP allows address information from electronic health records to be used for accurate exposure assessment and the determination of residential mobility, giving researchers and policy makers more reliable information.

scholarly journals Comparative Assessment of Particulate Air Pollution Exposure from Municipal Solid Waste Incinerator Emissions

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Danielle C. Ashworth ◽  
Gary W. Fuller ◽  
Mireille B. Toledano ◽  
Anna Font ◽  
Paul Elliott ◽  
...  
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Exposure Assessment ◽  
Dispersion Model ◽  
Atmospheric Dispersion ◽  
Comparative Assessment ◽  
Waste Incinerator ◽  
Exposure Misclassification ◽  
Ambient Exposure ◽  
Waste Incinerators

Background.Research to date on health effects associated with incineration has found limited evidence of health risks, but many previous studies have been constrained by poor exposure assessment. This paper provides a comparative assessment of atmospheric dispersion modelling and distance from source (a commonly used proxy for exposure) as exposure assessment methods for pollutants released from incinerators.Methods.Distance from source and the atmospheric dispersion model ADMS-Urban were used to characterise ambient exposures to particulates from two municipal solid waste incinerators (MSWIs) in the UK. Additionally an exploration of the sensitivity of the dispersion model simulations to input parameters was performed.Results.The model output indicated extremely low ground level concentrations of PM10, with maximum concentrations of <0.01 μg/m3. Proximity and modelled PM10concentrations for both MSWIs at postcode level were highly correlated when using continuous measures (Spearman correlation coefficients ~ 0.7) but showed poor agreement for categorical measures (deciles or quintiles, Cohen’s kappa coefficients ≤ 0.5).Conclusion.To provide the most appropriate estimate of ambient exposure from MSWIs, it is essential that incinerator characteristics, magnitude of emissions, and surrounding meteorological and topographical conditions are considered. Reducing exposure misclassification is particularly important in environmental epidemiology to aid detection of low-level risks.

scholarly journals High Levels of Fine Particulate Matter (PM2.5) Concentrations from Burning Solid Fuels in Rural Households of Butajira, Ethiopia

2021 ◽  
Vol 18 (13) ◽  
pp. 6942
Author(s):  
Mulugeta Tamire ◽  
Abera Kumie ◽  
Adamu Addissie ◽  
Mulugeta Ayalew ◽  
Johan Boman ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Solid Fuel ◽  
Fine Particulate Matter ◽  
Primary Energy ◽  
Pollution Level ◽  
Face To Face ◽  
Adverse Health Effects ◽  
Fine Particulate ◽  
Cooking Practices ◽  
Mean Concentration

The use of solid fuel, known to emit pollutants which cause damage to human health, is the primary energy option in Ethiopia. Thus, the aim of this study was to measure the level of household air pollution by using the 24-h mean concentration of fine particulate matter (PM2.5) in 150 randomly recruited households in rural Butajira, Ethiopia. Data relating to household and cooking practices were obtained by conducting face-to-face interviews with the mothers. The 24-h mean (standard deviation) and median PM2.5 concentrations were 410 (220) and 340 µg/m3, respectively. Households using only traditional stoves and those who did not open the door or a window during cooking had a significantly higher mean concentration compared with their counterparts. There is a statistically significant correlation between the mean concentration of PM2.5 and the self-reported cooking duration. The pollution level was up to 16 times higher than the WHO 24-h guideline limit of 25 μg/m3, thus leaving the mothers and children who spend the most time at the domestic hearth at risk of the adverse health effects from solid fuel use in Ethiopia. Thus, effective short- and long-term interventions are urgently needed.

scholarly journals Obtaining prevalence estimates of COVID-19: A model to inform decision-making

2020 ◽  
Author(s):  
Ida Sahlu ◽  
Alexander B Whittaker
Keyword(s):  
Random Sampling ◽  
Geographic Area ◽  
Misclassification Error ◽  
Point Estimate ◽  
Prevalence Estimates ◽  
Error Bias ◽  
The Impact ◽  
Number Of Individuals ◽  
Case Data ◽  
Random Sampling Approach

Objectives: The primary aim was to evaluate whether randomly sampling and testing a set number of individuals for active or past COVID-19 while adjusting for misclassification error captures a simulated prevalence. The secondary aim was to quantify the impact of misclassification error bias on publicly reported case data in Maryland. Methods: Using a stratified random sampling approach, 50,000 individuals were selected from a simulated Maryland population to estimate the prevalence of active and past COVID-19. Data from the 2014-2018 and 2018 American Community Surveys were used. The simulated prevalence was 0.5% and 1.0% for active and past COVID-19, respectively. Bayesian models, informed by published validity estimates, were used to account for misclassification error when estimating the prevalence of active and past COVID-19. Results: Failure to account for misclassification error overestimated the simulated prevalence for active and past COVID-19. Adjustment for misclassification error decreased the point estimate for active and past COVID-19 prevalence by 55% and 29%, respectively. Adjustment for sampling method and misclassification error only captured the simulated past COVID-19 prevalence. The simulated active COVID-19 prevalence was only captured when set to 0.7% and above. Adjustment for misclassification error for publicly reported Maryland data increased the estimated average daily cases by 8%. Conclusions: Random sampling and testing of COVID-19 is needed but must be accompanied by adjustment for misclassification error to avoid over- or underestimating the prevalence. This approach bolsters disease control efforts. Implementing random testing for active COVID-19 may be best in a smaller geographic area with highly prevalent cases.

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