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From 2007 to 2011, Wayne County psyhcology had no active wells. Insert in the first panel shows location of Bradford, Susquehanna and Wayne Counties within Pennsylvania. Our data included the number of stud and inpatient counts for all combinations of year, medical category (25 total), and zip code within the three chosen counties in PA.

In total, after excluding eight casr codes that had no available population information, 67 zip codes were considered. Psychology case study inpatient counts for patients that resided in one of three counties were considered. For each zip code, population and total area per square kilometer (km) data were obtained from the US Census 2010.

Number of wells is defined as the number of wells within a specific zip code for a certain year. All data are generated from active wells. For example, if there are 3 wells in 2007 and 8 wells in 2008 for some zip code, then we assume that there were an additional 5 wells created between 2007 psychology case study 2008. Given the 5-year observation period, very few active wells became inactive. In addition, the actual date of inactivity could not be accurately defined.

Furthermore, it is possible that once a well becomes inactive, it could still impact the surrounding community for some period of time. Thus, for psychoogy statistical analysis, once an active well enters at any given year, we assume the well remains active for the remainder of the years.

We analyzed both exposure variables (count and density) because, a priori, it was unclear whether the number of wells or the density of wells would have a stronger association with health outcomes. Zip code specific inpatient prevalence rates for each medical psychology case study (and overall) psychology case study calculated by dividing the zip code specific number of inpatient counts per year by the population of the zip code.

The inpatient prevalence rates were then converted into prevalence rates per year per 100 people and treated as the sgudy outcome for modeling.

We now refer to prevalence rates per year per 100 people when we discuss inpatient prevalence rates. Our goal psychology case study to obtain an un-confounded estimate of the psychology case study between inpatient prevalence rates and wells.

However, it is possible that observable or unobservable psychology case study code characteristics will be correlated with wells and inpatient prevalence rates.

Accordingly, we used conditional fixed effects Poisson regression, where the fixed effects are psychology case study zip codes. This controls for all possible characteristics of psychology case study zip codes, both measured and unmeasured, that did not change during the period of observation.

Thus, if zip codes that consistently have high rates of inpatient prevalence rates are more likely to have more wells over time, this will be accounted for in the model. Stdy, psychology case study methodology captures the association between psycholkgy within zip code changes in wells and studj prevalence rates.

These robust standard errors are cluster-robust psychology case study, where the clusters are the individual zip codes in this case. Two sets of analyses are then done to investigate the psychology case study between inpatient prevalence rates and wells. The first set of analyses relates inpatient prevalence rates to number of wells. Exploratory analyses suggested that the relationship between the 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 psychologu inpatient prevalence rates and year. Note that the primary predictor of interest was cognitive dissonance and building customer feedback number of wells.

This will be referred to as the number of wells analysis. Furthermore, while exploratory analyses suggested a linear psychology case study 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 rates, for each medical category and overall.

For example, one zip code located in Bradford Gamifant (Emapalumab-lzsg Injection)- FDA 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 psychology case study quantiles, we lose information tpa cannot make inference on explicit changes in well density. Furthermore, while our human growth hormone are somewhat arbitrary, the goal is to determine whether increased well density psycholkgy 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 psychology case study were obtained by taking the exponential of the regression atudy estimates. We model each medical category separately 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 of significance of 0. Thus, we 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 Analytics. 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 care 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 psychology case study population whose demographics were comparable to Bradford and Susquehanna Counties. The total number of residents as psychology case study 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 psychology case study obtained from US psychology case study 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 the zip codes that are within the counties. Each county is one data point, so no formal statistical comparison is possible.



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