First, you have never provided a link to the actual survey and the methodology of the study.

Well, I'm not hiring on as your research assistant, but here is the study which you apparently lack the search skills to discover on your own:

Link

Objectives. A 1993 study found a 25% higher risk of death among uninsured
compared with privately insured adults. We analyzed the relationship between
uninsurance and death with more recent data.
Methods. We conducted a survival analysis with data from the Third National
Health and Nutrition Examination Survey. We analyzed participants aged 17 to
64 years to determine whether uninsurance at the time of interview predicted
death.

Results. Among all participants, 3.1% (95% confidence interval [CI]=2.5%,
3.7%) died. The hazard ratio for mortality among the uninsured compared with
the insured, with adjustment for age and gender only, was 1.80 (95% CI=1.44,
2.26). After additional adjustment for race/ethnicity, income, education, self- and
physician-rated health status, body mass index, leisure exercise, smoking, and
regular alcohol use, the uninsured were more likely to die (hazard ratio=1.40;
95% CI=1.06, 1.84) than those with insurance.

Conclusions. Uninsurance is associated with mortality. The strength of that
association appears similar to that from a study that evaluated data from the
mid-1980s, despite changes in medical therapeutics and the demography of the
uninsured since that time.

So, there is the methodology you asked for.  You do understand and can talk about confidence intervals and statistical analysis, can't you?

Secondly you now leave out an important fact from the article:

    The researchers then extrapolated the results to census data from 2005 and calculated there were 44,789 deaths associated with lack of health insurance.

I quoted that in comment #60, PPJ, and I even offered an excerpt from the Wikipedia about extrapolation in comment #65, did you not notice them after posted them?

Here's from the study itself:

We analyzed the relation between insurance,demographics, baseline health status variables, and mortality by using c2 tests. We then used a Cox proportional hazards survival analysis controlling only for age and gender to determine if lack of health insurance predicted mortality.
We repeated the analysis of the relationship of
insurance to mortality after forcing all covariates in the model. In this Cox proportional hazards analysis, we controlled for gender, age, race/ethnicity (4 categories), income (poverty income ratio), education, current unemployment,
smoking status (3 categories), regular alcohol
use, self-rated health (4 categories), physician-rated health (4 categories), and BMI (4 categories).

We tested for significant interactions
between these variables and health insurance
status (i.e., P<.05). We handled tied failure
times by using the Efron method.
We performed multiple sensitivity analyses
to analyze the robustness of our results.

We developed a propensity score model and controlled for the variables in our previous models (with the exception of health insurance status), as well as marital status;
household size; census region; number of
overnight visits in hospital in past 12
months; number of visits to a physician in
past 12 months; limitations in work
or activities; job or housework changes or
job cessation because of a disability or
health problem; and number of self-reported
chronic diseases, including emphysema,
prior nonskin malignancy, stroke, congestive
heart failure, hypertension, diabetes, or
hypercholesterolemia. Next, we included
the propensity score in the multivariable
model with the indicator for insurance status.
In addition, we tested for the effect of
including those covered by Medicaid by
using our original Cox model and the propensity
score adjusted analysis. In a subsidiary
analysis, we excluded employment
and self- and physician-rated health, as
these covariates may be a result of limited
access to health care because of uninsurance.

To facilitate interpretation of our hazard
ratio, we first replicated the calculation in
the IOM report to estimate the number of US
adults who die annually because of
lack of health insurance. This approach applies the overall hazard ratio to 9-year
age strata and sums these figures to arrive at an annual number of deaths attributable to lack of health insurance. We then
recalculated this figure by using the slightly
different approach utilized by the Urban Institute, which does not age stratify when calculating total mortality. We believe this approach to be more accurate than that used to produce the IOM estimate, as it calculates mortality from the entire age range that the hazard ratio was calculated from, as opposed to calculating mortality over 10-year age strata.

Why is that? Should it be because a dictionary says:

extrapolate - to infer (an unknown) from something that is known; conjecture

conjecture - an opinion or theory so formed or expressed; guess; speculation.

Except that:

In mathematics, extrapolation is the process of constructing new data points outside a discrete set of known data points. It is similar to the process of interpolation, which constructs new points between known points, but the results of extrapolations are often less meaningful, and are subject to greater [ far beyond the known data.

And, gasp, this study involves extrapolation in the mathematical sense, which, PPJ, isn't to be conflated with mere conjecture as you've attempted to do with your puerile word games attempting to use a dictionary instead of any appeals to logical reasoning or,

gasp,

facts?

Link

lol

I certainly did, PPJ, thanks for knocking yourself out on this one, tiger.

Thanks for the helpful feedback you cheerfully provide here for me to make mincemeat out of in my posts.