Overdispersed poisson regression models for studies of air pollution and human health

1994 ◽  
Vol 22 (4) ◽  
pp. 421-440 ◽  
Author(s):  
Brad McNeney ◽  
John Petkau
Keyword(s):  
Air Pollution ◽  
Human Health ◽  
Poisson Regression ◽  
Regression Models

Overdispersed Regression Models for Air Pollution and Human Health

2002 ◽  
pp. 429-438
Author(s):  
Silvia L. P. Ferrari ◽  
Jacqueline S. E. David ◽  
Paulo A. André ◽  
Luiz A. A. Pereira
Keyword(s):  
Air Pollution ◽  
Human Health ◽  
Regression Models

scholarly journals Effect of air pollution on household insurance purchases. Evidence from China household finance survey data

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242282
Author(s):  
Wenxia Zhao
Keyword(s):  
Air Pollution ◽  
Health Insurance ◽  
Life Insurance ◽  
Poisson Regression ◽  
Regression Models ◽  
Household Finance ◽  
Economic Effects ◽  
Using Data ◽  
The Impact ◽  
Degree Of Heterogeneity

In recent years, the health and economic effects of air pollution have attracted considerable attention, and health and insurance services have been closely related to residents’ welfare. However, there are few studies on the influence of pollution on household purchases of insurance. Using data from the 2013 and 2015 China Household Finance Surveys, this study investigates the effect of air pollution on insurance purchases using Logit and Poisson regression models. It is found that air pollution significantly increases the probability of household insurance purchases and the level of premium expenditure, although the impact of air pollution on insurance purchases shows a degree of heterogeneity. Health insurance is more sensitive to air pollution than life insurance and other types of insurance. In areas where NO2 and O3 are the main types of pollutants, air pollution has a greater impact on household insurance purchases.

scholarly journals Anticipated help-seeking for cancer symptoms before and after the coronavirus pandemic: results from the Onco-barometer population survey in Spain

2021 ◽  
Author(s):  
Dafina Petrova ◽  
Marina Pollán ◽  
Miguel Rodriguez-Barranco ◽  
Dunia Garrido ◽  
Josep M. Borrás ◽  
...  
Keyword(s):  
Poisson Regression ◽  
Help Seeking ◽  
Regression Models ◽  
Population Survey ◽  
Waiting Times ◽  
Perceived Barriers ◽  
Older Individuals ◽  
Cancer Symptoms ◽  
Before And After ◽  
Diagnostic Delays

Abstract Background The patient interval—the time patients wait before consulting their physician after noticing cancer symptoms—contributes to diagnostic delays. We compared anticipated help-seeking times for cancer symptoms and perceived barriers to help-seeking before and after the coronavirus pandemic. Methods Two waves (pre-Coronavirus: February 2020, N = 3269; and post-Coronavirus: August 2020, N = 1500) of the Spanish Onco-barometer population survey were compared. The international ABC instrument was administered. Pre–post comparisons were performed using multiple logistic and Poisson regression models. Results There was a consistent and significant increase in anticipated times to help-seeking for 12 of 13 cancer symptoms, with the largest increases for breast changes (OR = 1.54, 95% CI 1.22–1–96) and unexplained bleeding (OR = 1.50, 1.26–1.79). Respondents were more likely to report barriers to help-seeking in the post wave, most notably worry about what the doctor may find (OR = 1.58, 1.35–1.84) and worry about wasting the doctor’s time (OR = 1.48, 1.25–1.74). Women and older individuals were the most affected. Conclusions Participants reported longer waiting times to help-seeking for cancer symptoms after the pandemic. There is an urgent need for public interventions encouraging people to consult their physicians with symptoms suggestive of cancer and counteracting the main barriers perceived during the pandemic situation.

Air pollution, human health and the benefits of trees: a biomolecular and physiologic perspective

2021 ◽  
pp. 1-22
Author(s):  
Patrick Mei ◽  
Vaishali Malik ◽  
Richard W. Harper ◽  
Juan M. Jiménez
Keyword(s):  
Air Pollution ◽  
Human Health

Associations Between Ambient Particulate Air Pollution and Cognitive Function in Indonesian Children Living in Forest Fire–Prone Provinces

2021 ◽  
pp. 101053952110317
Author(s):  
Bin Jalaludin ◽  
Frances L. Garden ◽  
Agata Chrzanowska ◽  
Budi Haryanto ◽  
Christine T. Cowie ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cognitive Function ◽  
Forest Fire ◽  
Forest Fires ◽  
Regression Models ◽  
Individual Factors ◽  
Aerodynamic Diameter ◽  
Particulate Air Pollution ◽  
Multivariable Models ◽  
Particulate Matter 2.5

Smoke from forest fires can reach hazardous levels for extended periods of time. We aimed to determine if there is an association between particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) and living in a forest fire–prone province and cognitive function. We used data from the Indonesian Family and Life Survey. Cognitive function was assessed by the Ravens Colored Progressive Matrices (RCPM). We used regression models to estimate associations between PM2.5 and living in a forest fire–prone province and cognitive function. In multivariable models, we found very small positive relationships between PM2.5 levels and RCPM scores (PM2.5 level at year of survey: β = 0.1%; 95% confidence interval [CI] = 0.01% to 0.19%). There were no differences in RCPM scores for children living in forest fire–prone provinces compared with children living in non-forest fire–prone provinces (mean difference = −1.16%, 95% CI = −2.53% to 0.21%). RCPM scores were lower for children who had lived in a forest fire–prone province all their lives compared with children who lived in a non-forest fire–prone province all their life (β = −1.50%; 95% CI = −2.94% to −0.07%). Living in a forest fire–prone province for a prolonged period of time negatively affected cognitive scores after adjusting for individual factors.

Application of the deletion/substitution/addition algorithm to selecting land use regression models for interpolating air pollution measurements in California

2013 ◽  
Vol 77 ◽  
pp. 172-177 ◽  
Author(s):  
Bernardo S. Beckerman ◽  
Michael Jerrett ◽  
Randall V. Martin ◽  
Aaron van Donkelaar ◽  
Zev Ross ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Models ◽  
Land Use Regression
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