China
Africa
Explore chapters and articles related to this topic
Personalizing Environmental Health for the Military—Striving for Precision
Published in Kirk A. Phillips, Dirk P. Yamamoto, LeeAnn Racz, Total Exposure Health, 2020
The second way to build actionable information for exposures is to develop quantitative risk measurements. In genomic medicine and exposure science, there are some basic statistical tools that can be used to calculate risk. Odds ratios are used to measure the effectiveness of a particular diagnostic test (e.g., a genomic assay or a serum biomarker test). It is the probability of a test being positive if a patient has a disease, versus the odds of a test being positive if a patient does not have a disease. Absolute risk is the probability of a health effect occurring under specific conditions, while relative risk is the likelihood of a health effect occurring in a group of people compared to a separate group of people with different backgrounds or in different environments. The GBD group utilizes relative risk as the primary method for calculation of global disease burden. The quantitative measurement utilized is the summary exposure value (SEV), which represents the continuous relative risk accumulated over time.
Dose–Response Assessment — Quantitative Methods for the Investigation of Dose–Response Relationships
Published in Elizabeth L. Anderson, Roy E. Albert, RISK ASSESSMENT and INDOOR AIR QUALITY, 2019
where z1 through zn are the covariates of interest and the β’s are parameters to be estimated from the data. Note that the additive model posits that the relative risk is a linear function of the exposures of interest and that the effect of joint exposures is additive. The multiplicative model posits that the logarithm of relative risk is a linear function of the exposures and that the effect of joint exposures is multiplicative. Quite often the relative risk cannot be adequately described by either a multiplicative or an additive model. For example, the relative risk associated with joint exposure to radon and cigarette smoke is greater than additive but less than multiplicative (BEIR IV 1988). Various mixture models have been proposed (Thomas 1981; Breslow and Storer 1985; Guerrero and Johnson 1982) to address such situations. The use of these models presents special statistical problems (Moolgavkar and Venzon 1987; Venzon and Moolgavkar 1988).
Epidemiology
Published in Samuel C. Morris, Cancer Risk Assessment, 2020
Relative risk is calculated as a direct comparison between the exposed cohort and the control or comparison cohort. It is obvious that differences between the two, other than the exposure of concern, can affect the derived dose-response function. A common study design examines cancer mortality in an occupational population and compares the result to cancer in the general population on an age-, sex-, and race-specific basis. For cancer studies, smoking can be an important variable but, while detailed cancer mortality statistics are available for the general population, it is not broken down by smoking status. Sometimes smoking information is available for the exposed cohort, sometimes not. Lloyd (1983) shows the effect of comparing smoking-specific lung cancer rates in an occupational cohort with general population lung cancer rates in which smokers and nonsmokers are mixed. Assuming an occupational exposure that increases the lung cancer rate something less than smoking does, the rate among nonsmokers in the industrial cohort appears low, and among smokers high, in comparison to the rate in the mixed general population. The erroneous conclusion is that the effect seen is due to smoking rather than to occupational exposure. If the occupational exposure produces a lung cancer risk much higher than smoking, the effect will be apparent despite the mixed comparison population, but the quantitative dose-response function derived will be in error. There are localized differences in cancer rates; some are understood; some are not. This raises the question of which general population should be used as a comparison population. Cancer mortality data are usually available on the national, state, and county level, but the more localized the data, the fewer the number of cases the rates are based on, thus increasing the error involved. Consideration also must be given to the degree to which the occupational population is representative of the local population. Certain industries may attract skilled workers from a regional or even national pool, and the local county or even state rates may be inappropriate. In the case of standard, general population rates, data are easily available and, when there is a question, separate comparisons should be made with data from different levels so differences among the results can be taken into account in any inferences drawn from the study.
Energy affordability and trends of mortality in Cyprus
Published in International Journal of Sustainable Energy, 2022
Ioanna Kyprianou, Salvatore Carlucci, Despina Serghides
The relative risk is a measure to indicate the risk of the outcome of an exposed group relative to an unexposed group. In this case, the RR is modified to represent the risk of mortality due to EP-related causes of death relative to non-EP mortality. Equations 6 and 7 are used to estimate RR in urban and rural areas, respectively, using data from Eurostat to estimate the percentage of populations exposed to EP conditions (cardiovascular and respiratory) in each area (Eurostat, Persons reporting a chronic disease, by disease, sex, age and degree of urbanisation 2014). A similar approach is followed to estimate the relative risk of mortality of populations exposed to heatwaves in Cyprus (Pyrgou and Santamouris 2018).
Assessing short-term effects of ambient air pollution on respiratory diseases in Guwahati, India with the application of the generalized additive model
Published in Human and Ecological Risk Assessment: An International Journal, 2021
Abhishek Dutta, Wanida Jinsart
In epidemiology, relative risk (RR) is expressed as the ratio of the risk of the outcome in one group compared with another group. In this study, finally, to evaluate the potential health impact in terms of respiratory diseases in the concerned city against our estimates, we calculated relative risks (RRs) and 95% confidence intervals (95% CIs) by using the exposure-response coefficient of pollutants obtained from the GAM model. The relative risks (RR) and its 95% confidence intervals (95% CIs) for the predictor was computed as follows: where Δ is an increment in the pollutant concentration and SE is the standard error. RR is a measure of how much a particular risk factor (say, an increase in particulates level) influences the risk of a specified outcome (say, an increase in the number of hospital visits). To make the result of the study, i.e., the effect estimates of daily hospital visits due to air pollution more expressive, we calculated the percentage change (PC, %,) at 95% CI in the following way.
Association of air quality with respiratory and cardiovascular morbidity rate in Delhi, India
Published in International Journal of Environmental Health Research, 2018
Sanjoy Maji, Santu Ghosh, Sirajuddin Ahmed
There were consistent and statistically significant associations between hospital admission due to respiratory illness and increase in concentration levels of PM10 and CO and O3. Table 5 highlights the relative risk of morbidity due to incremental load in 75th percentile of daily pollution level for a month. Relative risk expresses how many times more (or less) likely an exposed person develops an outcome relative to an unexposed person. For hospital admission, largest effect estimate was observed for O3 whereas lowest effect estimate was observed for CO. Secondary pollutant O3 (above 65 μg/m3) was observed to be associated with 3.41% (95% CI 0.02–6.83) increase in hospital admission rate with respiratory disease for every 10 μg/m3 increase in 75th percentile of daily average O3 concentration for a month. Though significant health effects of SO2 and NO2 have been documented in a few of literature (Katsouyanni et al. 1997; Atkinson et al. 1999; WHO 2002) the present data didn’t not show significant effects of SO2 and NO2 either on respiratory or cardiovascular morbidity rate. The risk estimates of hospital admission with respiratory problem was estimated to be 0.47% (95% CI 0.03–0.91) per 10 μg/m3 increase in 75th percentile of daily average PM10 concentration for a month. Though effect of CO was generally weaker but was observed to be statistically significant. CO was observed to be significantly associated with 0.06% (95% CI 0.02–0.09) increase in hospital admission with respiratory diseases per 10 μg/m3 increase of 75th percentile of daily average CO exposure for a month. The present data also didn’t show significant association of air quality with OPD visit rate—either with respiratory or cardiovascular diseases.