For persons enduring a severe disability, daily life is a full time job.
Two bloggers share courageously with us their daily struggles to show the amount of supplementary efforts they have to produce just to save an appearance of fluidity (not to say normality).
One blogger compares disability with an iceberg whose greater part is not visible:
An other blogger compares disability with an handful of a limited number of spoons. All seems normal to the surrounding peoples who examine her life as long as she has a sufficient number of spoons left in her hand. But each daily life efforts along the day takes one spoon away from her and when there is only one left in her hand she must stop for the rest of the day and all the activities she has still to do must wait for the next day:
The body of work that economists have done on the field of relationship between happiness and disability shows that not only the disabled persons themselves are less happy but also are their spouses, although this must be tempered by the numerous adaptive strategies that the couple puts in place.
A resume of the scientific literature here:
Journal of Public Economics
June 2008, Vol.92(5):1061–1077, doi:10.1016/j.jpubeco.2008.01.002
Does happiness adapt? A longitudinal study of disability with implications for economists and judges
Andrew J. OswaldNattavudh Powdthavee
Social Science & Medicine
December 2009, Vol.69(12):1834–1844, doi:10.1016/j.socscimed.2009.09.023
Part Special Issue: New approaches to researching patient safety
What happens to people before and after disability? Focusing effects, lead effects, and adaptation in different areas of life
Social Science & Medicine
April 2014, Vol.107:68–77, doi:10.1016/j.socscimed.2014.02.009
Is shared misery double misery?
Merehau Cindy MervinPaul Frijters
We find that the events befalling a partner on average have an effect about 15% as large as the effect of own events.
Quoted from :
Journal of Economic Psychology
August 2009, Vol.30(4):675–689, doi:10.1016/j.joep.2009.06.005
I can’t smile without you: Spousal correlation in life satisfaction
Epidemiology and geography since long ago share common interests.
Epidemiologists have always searched the causes of contagious diseases by locating the very place where the outbreak began. Hence the necessity to develop sophisticated geographical statistical analysis methods in order to localize the point from where the disease originates and then spread across the country. But nowadays those methods are also implemented by searchers to highlight high concentrations of non epidemic, chronic, degenerative diseases in a given country. Here the causal agent is no more a bacteria nor a virus but indeed a spot of concentration of social inequality (or pollution, depending of the research question ). If a geographical concentration exist of lack of knowledge of what a healthy behavior is, or of low incomes restraining access to a healthy life, then the analysis should uncover a higher prevalence of the degenerative disease at less this is the hypothesis. Here below is a link toward a paper very accurate in demonstrating how different geographical statistical analysis methods can lead to a variation in the epidemiological results obtained. This point is crucial to consider because were it Ebola virus or social inequality or educational level context, causes of diseases will always have something to do with geography!
Frontiers in Massive Data Analysis, from the National Research Council, nails some of the challenges of big data. But the challenges for massive data go beyond
via Big data challenges.
Two studies, whose material encompassed the Independent workers health plan data base, analyze the consumption of tranquilizers among various categories of professionals. Lawyers ranked high and pharmacists too.
Lawyers are confronted to conflictual situations by the nature of their work itself and pharmacists are tempted, being surrounded by the product itself; these are the two explanations that I could find for these intriguing results.
Here below are the links to the two studies (I contributed to the second paper).
1-Oxford JournalsMedicine & Health Occupational Medicine Volume 64, Issue 3Pp. 166-171.
Mental health and substance use among self-employed lawyers and pharmacists
S. Leignel1,2,3, J.-P. Schuster1,2,3, N. Hoertel1,2,3, X. Poulain1,2,3 and F. Limosin1,2,3
2-Presse Med. 2011 Apr;40(4 Pt 1):e173-80. doi: 10.1016/j.lpm.2010.10.026. Epub 2011 Jan 11.
[Psychotropic medication use by French active self-employed workers].
[Article in French]
Ha-Vinh P1, Régnard P, Sauze L.
The French regulator ANSM is sued by a patient for not having withdrawn the Mediator from the market in the same timeline as the other developed countries.
Nevertheless the administrative court states that given that the French regulator ANSM speaks in the name of the French State the responsibility of the State is involved in this case should the causality between the product and the disease be shown.
The judgement of the administrative court (in French):
Propensity score gives the probability of a subject in a population to belong to a group of interest such as a treatment group.
Then comparing subjects with the same propensity scores across treatment and no-treatment groups enables the researcher to infer on the effect of the treatment regarding a given outcome even if he works on merely observational data.
But the researcher must beware of the unobserved differences between the group of interest and the comparison group created using the propensity score.
As always the relevance of the model depends on the nature of the covariates entered in it.
Garrido, M. M., Kelley, A. S., Paris, J., Roza, K., Meier, D. E., Morrison, R. S. and Aldridge, M. D. (2014), Methods for Constructing and Assessing Propensity Scores. Health Services Research. doi: 10.1111/1475-6773.12182
Unlike the case control studies the case base studies are well suited to the cross sectional extractions from the reimbursement data bases that we usually do.
The case base studies use the whole population of the database as a control group , including the subjects who are affected by the disease (ie the cases).
Thus, making no difference whether the subjects have the disease or not , the control group is far more easy to constitute.
Citation: Chui TT-T, Lee W-C (2013) A Regression-Based Method for Estimating Risks and Relative Risks in Case-Base Studies. PLoS ONE 8(12): e83275. doi:10.1371/journal.pone.0083275