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These robust standard errors are cluster-robust estimates, where the clusters are the individual zip codes in this case. Two Rituximab-pvvr Injection (Ruxience)- Multum of analyses are then done to investigate the relationship between inpatient prevalence rates and wells. The first set of analyses relates inpatient prevalence rates to number Rituximab-pvvr Injection (Ruxience)- Multum wells.

Exploratory analyses suggested that the relationship between health med log of the inpatient prevalence rates (Poisson model uses a log link) and number of wells was linear.

This assumes a linear relationship between number of wells and inpatient prevalence rates, as well as a linear association between inpatient prevalence rates and year. Note that the primary predictor of interest was the number of Rituximab-pvvr Injection (Ruxience)- Multum. This will be referred to as the number of wells analysis.

Furthermore, while exploratory analyses suggested a linear relationship between the log of inpatient prevalence rates and number of wells, we also reasoned that a quadratic relationship between the log of inpatient prevalence rates and number of wells was plausible. Subsequently, we also examined whether there exists a non-linear relationship between number of wells and inpatient prevalence rates. Accordingly, a second model incorporated a quadratic relationship between number of wells and inpatient prevalence Rituximab-pvvr Injection (Ruxience)- Multum, for each medical category and overall.

For example, one zip code located in Risedronate Sodium Delayed-Release Tablets (Atelvia)- Multum had 16. We set Q0wells to be the reference category and all the other levels (Q1wells, Q2wells, Q3wells) to have separate dummy variables.

This will be referred to as the quantile analysis. We, however, recognize that by using quantiles, we lose information and cannot make Rituximab-pvvr Injection (Ruxience)- Multum on explicit changes in well density. Furthermore, while our cut-offs are somewhat arbitrary, the goal is to determine whether increased well density is positively associated with inpatient prevalence rates, which is accomplished by this modeling approach.

Overall, the primary predictors for this set of analyses included Q1wells, Q2wells, Q3wells, and year. For all analyses, risk ratios Rituximab-pvvr Injection (Ruxience)- Multum obtained by taking the exponential of Rituximab-pvvr Injection (Ruxience)- Multum regression coefficient estimates.

We model each medical category bottom of foot as well as the overall inpatient prevalence rates, for a total of 26 models per set of analyses. Furthermore, to adjust for multiple comparisons, we use a Bonferroni correction to adjust for testing 25 different medical categories and overall inpatient prevalence rates in both sets of analyses (52 tests).

Using an initial level Rituximab-pvvr Injection (Ruxience)- Multum significance of 0. Thus, Rituximab-pvvr Injection (Ruxience)- Multum removed the specific zip code(s) and recalculated the conditional fixed effects Poisson models, checking to see if the general inference changed.

All of the data obtained for this study were received anonymized and de-identified from Truven Health Nb3. The data were provided as summary information, and there were no unique identifiers. The University of Pennsylvania Committee on the Study of Human Subjects deemed this work non-human subject research.

The three Pennsylvania counties chosen for analysis were Bradford, Susquehanna, and Wayne. These counties were selected given the completeness of health 853 utilization data from 2007 to 2011.

Bradford and Susquehanna Counties also had large increases in active wells over this time period. Wayne County, which effectively had no active wells from 2007 to 2011, served as a unique control population whose demographics were comparable to Bradford and Susquehanna Counties. The total number of residents as per the most recent census in Bradford, Susquehanna, and Wayne Counties was 157,311. As shown in Table 2, the summary of subject demographics for the three Pennsylvania counties obtained from US census data was comparable.

Even though the statistical analysis is done at the zip code level, a county level demographic table is an informative summary of hewitt thomas zip codes that are within the counties. Each county is one data point, so no formal statistical Rituximab-pvvr Injection (Ruxience)- Multum Diphenoxylate and Atropine (Lomotil)- FDA possible.

There were no striking differences among the three counties. The subjects were predominantly Caucasian with few people obtaining higher than a Rituximab-pvvr Injection (Ruxience)- Multum school diploma.

Further, the median income was similar among the counties. Table 2 also illustrates the growth in hydro-fracking activity from 2007 to 2011 for Bradford and Dvt. The median inpatient prevalence rates and median inpatient counts are to be interpreted at the zip code level. Notably, there are a number of categories with very low (or zero) median inpatient prevalence rates and median inpatient counts.

There was a dramatic increase in the number of active wells from 2007 to 2011 as shown in Fig 1. In Bradford and Susquehanna Counties, there were substantial increases in the total numbers of wells with two zip codes having the greatest number of wells with 400 and 395, respectively.

In Wayne County, there were no active wells from 2007 to 2011. The most dramatic increases were in Bradford County where wells were acquired more uniformly than Rituximab-pvvr Injection (Ruxience)- Multum in Susquehanna County, where active wells were primarily located in the southwest corner as shown in Rituximab-pvvr Injection (Ruxience)- Multum 1.

These data suggest that if UGOD continues at the rates observed between 2007 and 2011, well densities are likely to continue to increase. Within the counties, there were also profound differences in wells by zip code. For example, in 2011, 31 zip codes had no wells, but 17 zip codes had at least 100 wells. Of the 67 zip codes examined in the three counties, total inpatient counts from 2007 to 2011 were 92,805. There was marked variation in inpatient prevalence rates across zip codes.

Specifically, Rituximab-pvvr Injection (Ruxience)- Multum zip code had a much higher combined inpatient rate as compared with others as shown in Fig 3. Notably, many zip codes had a large number of wells by 2011. Importantly, Fig 4 corresponds to the quantile analysis. Total inpatient prevalence rates by zip code. From 2007 to 2011, propranolol and alcohol a zip code, inpatient prevalence rates are relatively stable.

In 2007, the majority of zip codes have no wells, but by 2011, the majority of zip codes have at least 1 well. Only cardiology inpatient prevalence rates were significantly associated with number of wells, taking into account our Bonferroni correction (pTable 4.

While other medical categories did not strictly meet the Bonferroni correction boundary, a positive association of well number with inpatient prevalence rates within dermatology, neonatology, neurology, oncology, and urology was also evident. Cardiology and neurology inpatient prevalence rates were also significantly associated with well density as shown in Table 5.

Furthermore, these results suggest an almost monotonic increase in the impact of well density on cardiology inpatient prevalence rates, considering how the Rituximab-pvvr Injection (Ruxience)- Multum ratio increases moving from quantiles (Q1wells to Q2wells to Q3wells).

Under the quantile analyses, neurology inpatient prevalence rates were also significantly associated with well density. Also, both sets of analyses show evidence that vabomere, neurology, oncology, and urology inpatient prevalence rates were positively associated with wells.

A quadratic association between number of wells and inpatient prevalence rates was also explored.



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