Questions related to Environmental Epidemiology
What kind of jobs a PhD in environmental epidemiology can do after Ph.D.? Data analysis or research associate etc etc? Please enlist some or share your experience?
I have to check the added effect of heatwave. I will add temperature variable along with heatwave variable in the model to get the added effect of heatwave. The RR of heatwave will depict the added heatwave effect or RR for temperature will show the added heatwave effect?
If RR of heatwave will show the added heatwave effect then what would the RR of temperature will depict?
I am using a distributed linear model (DLNM package in R) to study the effects of temperature lags on cardiovascular disease admissions.
So i did as following:
#Creating the cross basis: cb.temp <- crossbasis(workdata$templg0, lag=14, argvar=list(fun="lin"), arglag=list(fun="strata",breaks=3)) #Running the GLM model using natural splines modelA <- glm(cvd~ cb.temp+ ns(trend, 5*16)+holiday+influ_cat3+rhlg0+rhd1d3+bplg0+bpd1d3+catpol4lg0+pm25lg0+pm10lg0+no2lg0+luuo3h8lg0+timestr, data=workdata, family=poisson(link="log"), na=na.omit)
ns function has been used to define normal splines for trend variable ( to account for seasonality)
#prediction using crosspred: predglm<-crosspred(cb.temp, modelA, model.link="log", at = -25:8.5, bylag = 1, cumul= TRUE)
Since I am taking temperature as a linear term, so all the temperature range was included i.e. "at= -25:8.5"
#Using prediction to plot the graph: plot(predglm, "slices", var= -1, ci="bars", type="p", col=2, pch=19, ci.level=0.95, main="Lag-response a 1-unit increase above threshold (95CI)")
I used var= -1 because as I am looking for the winter season so I assumed this value will predict each 1 degree less than 0-degree Celsius (as reference temperature is by default = 0).
Please find the graphs (attached).
# to check RR predglm$allRRfit["-1"]
It gives me 1.00. Does this mean that there is no cumulative risk for 14 days lag?
I am conducting a study for association of cold season temperature with hospital admissions. I want that for a decreasing temperature and hospital admission if
RR > 1 it means less risk
RR < 1 means more risk...
Does RR works in reverse for decreasing temperature.
Let' say we have RR 0.87 for a GLM model of cold season temperature with total hospital admission.
Can we say that " with each 1 degree celsius decrease in temperature there is an increasing risk of 0.87 times? How does it workds? Can someone please explain about it more?
As the research field is really competitive, so what are the skills recommended for a Ph.D. student to learn while doing a Ph.D.? For example, I am doing a Ph.D. in environmental health epidemiology. What sort of skills should I learn during my Ph.D. so I don't have to struggle for a job after completion of a Ph.D.?
I am checking the effect of temperature on kidney disease hospital admissions with NO2(an air pollutant). In other words, how the effect of daily temperature on Kidney disease admissions modifies with changing levels of NO2 in air.
I got the significant result with as follows:
Estimate Std. Error z value Pr(>|z|)
templg0:no2lg0 -0.0004774 0.0001964 -2.431 0.015068
if I interpret it as:
An increase in level of No2 is significantly associated with a decrease in the effect of temperature on respiratory admissions.
The effect of temperature on hospital admissions decreases as NO2 concentration increases.
Is that the right interpretation or any changes required. Please give your suggestions.
Social environments are a well known determinant of health, as stated in the Ottawa Declaration on Health Promotion of 1986 and confirmed in the Sundvall Statement on Supportive Environments for Health in 1991.
Still it seems they are largely ignored in Environmental Epidemiology - exception made for work environments and occupational health.
Why do you consider that is?
I am running a GAM for temperature and Cardio admissions. After running the script i am getting the summary output as :
My script is
> model1<- gam(cvd ~ s(templg0), family=poisson)
Link function: log
cvd ~ s(templg0)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.195669 0.004877 655.2 <2e-16 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df Chi.sq p-value
s(templg0) 3.422 4.295 57.23 2.93e-11 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.0152 Deviance explained = 1.68%
UBRE = 1.016 Scale est. = 1 n = 1722
I am not able to understand what these whole bunch of things mean. Can someone please help me to understand what these parametric coefficients and other things in summary of GAM means? I have tried searching online a lot but found no help.
As many of you, we get involved all the time with issues of sample size calculation. However, besides the use of some software, it is difficult to find a good source of guidance on this critical topic. Are you aware of any good books on this in the literature? Maybe a statistics or epidemiology book that has good chapter on this? Or a book entirely devoted to it?
I am writing a book and in it, I mention the epidemiological triangle (sometimes called the public health or infectious disease triad). Where did the formulation come from? Who first described it? The references I have found are not informative but it is clearly a very old idea. I am aware that the idea as applied to animal health was formulated in 1974 by an eminent fisheries biologist, Stanislas F. Snieszko (1902 – 1984) to apply to fish diseases. However, I am sure that there must be earlier versions for human health. So far I have been unable to find them.
Must cover lifetime history, and all potential sources of exposure. The outcome to be studied is mammographic density.
Exploring miRNA - environment relations. Is anyone interested by exploring associations between miRNA profiling in environmental epidemiology?
Asthma is a serious issue. Early warnings for asthmatic are very important. In many countries pollen count or its covering area, pollutants, dust are monitored, but there is a need for combined efforts to minimize the triggers.
In the dose–response assessment step of risk assessment process, for estimating the dose–response, either an upper bound estimate or a maximum likelihood estimate (MLE) is derived; What is the difference between them? Which of them can be used with small numbers of data points?
Methodology question: I am trying to figure out how to measure the degree to which a risk-related problem has become a public issue. The metric needs to be easily analyzed, accessible, quantitative, and stable over at least five years (from 2015 to 2020) and have high face validity. The measurement should be robust – it does not have to be particularly refined or capable of resolving small differences.
The application for this metric is that I am developing a project that has to do with factors (such as Peter Sandeman’s “outrage”) that determine which risk-related (environmental, health, sustainability) issues become public issues over time. I need a way to measure outcomes and compare them against predictions.
Social media is the most obvious approach. One metric would be Google hits, which is convenient, free, and cumulative, and which almost certainly will be around in five years in roughly the present form without too much bias from algorithm changes introduced over the period. On the other hand, I am concerned about Twitter because I’m not sure it will be as stable a platform over the time period and I’m not sure how much people tweet about issues as opposed to people and events. Newspaper inches in a journal of record (such as the New York Times), which used to be an old standby, might be completely obsolete by 2020.
I would be grateful for practical ideas.
Writing a book and one chapter is on how managing sustainability seems to be turning into a profession, with full-time managers doing this in business. What do professionals in sustainability need to know? Is there a distinct science underlying the field? If so, what does it involve? Are environmental sciences and studies programs providing adequate grounding in this science?
Please concentrate on the questions, not the definition of sustainability. There are lots of different definitions of sustainability (my book will offer another one), but for the purpose of this discussion, please assume that "sustainability" means doing business and managing enterprises in a way that works toward the goal that there is minimum impact on the environment, good prospects for the future, no degradation that would compromise the future, and that protects health and a decent life.
Recently EPA revoked the PM10 annual standard. California has not done so and actually they have a very strict standard. Many studies show long term effects of PM10 (usually when PM2.5 measurements) are not available. Chile wants has revoked it but I feel is not a wise decision. Any comments?
I am writing a proposal to conduct a fundamental research on housing and health in a country without the specified policy/guidelines. Can anybody help me? Many thanks.
The scientific literature of environmental and occupational health is old and during its long history has covered most models of scientific investigation, the problems they present, and the types of papers that communicate the work. It is also an illuminating case study for the evolution of the literature of so-called ,,Grenzgebiete", an old German term for scientific disciplines that cross disciplinary boundaries.
Our journal happens to be very old - it started in 1919 - and my colleagues and I have been interested in its history and how it shaped the field particularly in the early years.
We have arranged for a collection of articles on the literature of environmental and occupational health to be made available for free to our colleagues, especially for the benefit of new investigators and for teachers to make available to their students.
Starting in 2005, the journal ran a series of editorials by myself on the structure of the literature of our field, and a series of historical essays by our Deputy Editor, Derek Smith (University of Newcastle, Australia). We now want to make those articles available to readers who missed them when they first came out.
At our request, the publisher of our journal (Taylor & Francis) has pulled together 11 of the articles into a "special virtual issue" devoted to the literature of environmental and occupational health and has made them available at no cost on the internet for 90 days. (This is not a promotion for the journal.)
To access the articles, go to http://www.tandf.co.uk/journals/access/vaeh-virtual-issue.pdf
Tee L. Guidotti, MD, MPH, DABT
Archives of Environmental and Occupational Health
I am dealing with a problem about which I found no available literature: the comparison between results from observational epidemiological studies (e.g. excess risk of a pathology in a contaminated area) and modelled risk estimated through chemical fate-transport and dose-response toxicological models (e.g. excess lifetime cancer risk = 10^-5).
Suppose we have a model that predicts a certain excess cancer risk around an industrial plant and we have data from an observational cohort study on residents in the area. How to compare them?
I think it is generally not recommendable to directly compare risk models and observed data, at least until exposure duration, intensity, toxicological pathways, health endpoint considered etc. are really comparable.
Does anyone know any publication about this issue?
There is a significant number of software available to model particulate matter dispersion at regional and intraurban scale to map its concentration to analyse its health effect. However, all of them are too costly to be afforded by an individual researcher. Therefore, it is an appeal addressed to all researchers/ scholars involved in air pollution modelling.