include numbers from tables "Analysis of Maximum Likelihood Estimates and odd ratio estimate to the results and discussion " rephrase results below and discussion section INSTRUCTIONS : For this assignment you have to execute and interpret one regression model. The objective is to determine how much will be saved if 10% of complicated appendicitis cases were prevented, meaning they were instead "non-complicated." The dependent variable is defined as the total charge associated with the inpatient episode (TCHGS). The "treatment variable" of interest is complicated versus uncomplicated appendicitis. Pay special attention to it when discussing the results and provide an explanation for the outcomes between the models. Is it statistically significant? Why is it positive or negative and does this make sense? In addition to the main variable of interest, don't forget to discuss (very briefly) the other explanatory variables. In the model, in addition to the treatment variable, control for the influence of the following confounding variables (specifications are discussed in the short video): age, race, gender, ethnicity, insurance status, and severity. In your results section, provide both (a) the difference in total cost per case, e.g. complicated vs. non-complicated, and (b) the total savings is 10% of complicated cases were instead treated as non-complicated. To figure out how many cases are complicated versus non-complicated, you can use a frequency statement as shown below. 2. Which data set to use? Appendicitis.SAS7BDat. 3. Turn in a structured abstract (no more than 300 words, not including the title and section headers, so 307 in total) with the format shown below. a. A point will be deducted if you exceed the word limit. Adhering to the word limit is essential as it forces you to get to the point; at the same time you have to be careful not to omit anything of importance. i. Do not include tables or graphs in the abstract (that would go in the main text or presentation - if you were to do one)! Determine which values from your test(s) are most important to the reader and report those in the abstract. ii. Since the word limit applies to everyone equally, no exceptions will be made!!! 4. What is important or relevant information is by definition, at least partially, subjective. However, there are certain rules that have to be followed. i. For example, in the results section you should definitely discuss the significance of your test-statistic (at minimum you should provide the p-value associated with the treatment variable). If you do not, at least one point will be deducted (yes, this is a big oversight). Given the word limit, you may want to group the other variables and report as such. ii. In the methods section, provide a rationalization for using the specific method. 5. Special importance is attached to the discussion section of the abstract (see below). This is where you have to apply your creativity as a story teller. This is where you help make information out of data as it involves the reader. Try to convey why the hypothesis and the analysis are important (if you personally don't care, ask yourself why anyone would). Imagine how and what decisions could be affected by the analysis and findings and communicate this to your intended audience. Simple repetitive statements already expressed elsewhere in the abstract will result in deductions, so do not just repeat the conclusions! Abstract Objective: This contains a brief statement describing the general objective of the analysis. For example "The objective of this analysis is to examine the impact of ..." Side note: each independent variable included in the model is associated with a hypothesis. For example, a hypothesis (in alternative form) is that Hispanics have a lower percentage of charge associated with labs because (you fill in the blank). Given your word limit, you should just focus on the treatment variable. Data and methods: This contains a brief description of the particular dataset used to test the hypothesis. For example, concerning the dataset briefly mention the unit of measurement, whether the data is publicly available, and what types of variables it includes. You should also discuss what you are controlling for (e.g. age). Results: This contains a brief layman's description of the least squares results and whether or not each variable (or groups of were) was significant. Conclusions: A very brief statement indicating your conclusions pertaining to the hypothesis - did your results reject the null hypothesis or not? Discussion: This is perhaps the most important section. Here you tell the story of why you believe the analysis was important - why is it "information" and not just "data"? Why would anyone within related professional or policy circles find your analysis and results interesting? How could your analysis help such individuals in terms of decision making? You may want to select one of the listed independent variables and assume that it is your treatment variable in an experiment. This will help focus your abstract. As part of the discussion, indicate whether you would have liked to test the influence of additional variables if they had been available .

*************************Data output of SAS FOR LINEAR REGRESSION **************************

Dependent Variable is Operating room Total charges

The REG Procedure

Model: MODEL1

Dependent Variable: TCHGS TCHGS

Number of Observations Read	47460
Number of Observations Used	47460

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr>F
Model	8	1.842725E13	2.303406E12	2473.72	<.0001
Error	47451	4.418411E13	931152403
Corrected Total	47459	6.261136E13

Root MSE	30515	R-Square	0.2943
Dependent Mean	50896	Adj R-Sq	0.2942
Coeff Var	59.95464

Parameter Estimates
Variable	Label	DF	Parameter Estimate	Standard Error	tValue	Pr>|t|
Intercept	Intercept	1	271956	2177.14211	124.91	<.0001
AGE	AGE	1	91.20939	7.26987	12.55	<.0001
black		1	5684.95352	470.91645	12.07	<.0001
otherNW		1	6813.95711	425.26911	16.02	<.0001
female		1	311.73308	284.31449	1.10	0.2729
hispanic		1	2457.95591	342.49490	7.18	<.0001
uninsured		1	1136.03849	366.21885	3.10	0.0019
complicated	complicated	1	11983	313.34904	38.24	<.0001
SeverityOverall	SeverityOverall	1	-237142	2110.40017	-112.37	<.0001

Logistic regression with Dependent variable =Emergency

The LOGISTIC Procedure

Model Information
Data Set	WORK.APPEND
Response Variable	postOperative	postOperative
Number of Response Levels	2
Model	binary logit
Optimization Technique	Fisher's scoring

Number of Observations Read	28736
Number of Observations Used	28736

Response Profile
Ordered Value	postOperative	Total Frequency
1	1	1848
2	0	26888

Probability modeled is postOperative='1'.

Model Convergence Status

Convergence criterion (GCONV=1E-8) satisfied.

Model Fit Statistics
Criterion	Intercept Only	Intercept and Covariates
AIC	13718.520	10969.309
SC	13726.786	11043.702
-2 Log L	13716.520	10951.309

Testing Global Null Hypothesis: BETA=0
Test	Chi-Square	DF	Pr>ChiSq
Likelihood Ratio	2765.2111	8	<.0001
Score	4864.3455	8	<.0001
Wald	1982.3999	8	<.0001

Analysis of Maximum Likelihood Estimates
Parameter	DF	Estimate	Standard Error	Wald Chi-Square	Pr>ChiSq
Intercept	1	7.5077	0.3847	380.8706	<.0001
AGE	1	0.0101	0.00227	19.9878	<.0001
black	1	0.1945	0.0808	5.7930	0.0161
otherNW	1	-0.1207	0.0913	1.7481	0.1861
female	1	-0.4945	0.0558	78.4029	<.0001
hispanic	1	-0.1497	0.0716	4.3635	0.0367
uninsured	1	-0.0698	0.0639	1.1918	0.2750
SeverityOverall	1	-11.3226	0.3626	975.2230	<.0001
complicated	1	1.1893	0.0551	466.5174	<.0001

Odds Ratio Estimates
Effect	Point Estimate	95% Wald Confidence Limits
AGE	1.010	1.006	1.015
black	1.215	1.037	1.423
otherNW	0.886	0.741	1.060
female	0.610	0.547	0.680
hispanic	0.861	0.748	0.991
uninsured	0.933	0.823	1.057
SeverityOverall	<0.001	<0.001	<0.001
complicated	3.285	2.949	3.659

Association of Predicted Probabilities and Observed Responses
Percent Concordant	85.6	Somers' D	0.712
Percent Discordant	14.4	Gamma	0.712
Percent Tied	0.0	Tau-a	0.086
Pairs	49689024	c	0.856

Frequency distribution of complicated versus uncomplicated Appendicitis

The FREQ Procedure

complicated
complicated	Frequency	Percent	Cumulative Frequency	Cumulative Percent
0	33068	69.68	33068	69.68
1	14392	30.32	47460	100.00

Results: suggestion that require rephrase

The regression model indicated that complicated appendicitis significantly increases total charges ( = 1.1893, p < 0.0001), with an odds ratio of 3.285.

Controlling for confounding variables, the model's R-square value is 0.2943, indicating that approximately 29.43% of the variability in total charges is explained by the model. Age, race (black and otherNW), ethnicity (Hispanic), insurance status (uninsured), and severity also significantly impact total charges. Preventing 10% of complicated cases, converting them to non-complicated, would result in a substantial cost reduction. Specifically, if 10% of the 14,392 complicated cases were non-complicated, the total savings would amount to approximately $17.23 million.

• Age: A small but significant positive effect on total charges ( = 0.0101, p < 0.0001) with a point estimate of 1.010.
• Race: Being Black significantly increases total charges ( = 0.1945, p = 0.0161) with a point estimate of 1.215. However, being from other non-White races did not significantly impact charges.
• Gender: Being female is associated with significantly lower charges ( = -0.4945, p < 0.0001) with a point estimate of 0.610.
• Ethnicity: Being Hispanic is associated with lower charges ( = -0.1497, p = 0.0367) with a point estimate of 0.861.
• Insurance Status: Uninsured status did not significantly impact charges ( = -0.0698, p = 0.2750).
• Severity Overall: A large negative estimate ( = -11.3226, p < 0.0001), suggesting that less severe cases (coded with higher numerical values) are associated with lower total charges.

In addition to the primary variable of interest, several other explanatory variables significantly affect hospital charges. Age has a positive relationship with total charges, indicating that older patients tend to incur higher costs. Race also plays a significant role; for instance, being black or belonging to the other non-white category is associated with higher charges. Hispanic ethnicity is also associated with increased charges. Moreover, being uninsured is linked to higher total charges, while the severity of the condition inversely affects the charges, which might seem counterintuitive at first glance. This negative relationship suggests that, once other factors are controlled for, the severity rating might capture aspects of care efficiency or coding practices that reduce charges.

The findings of this study reject the null hypothesis, confirming that the complication status significantly affects hospital charges. This analysis underscores the importance of preventive measures and timely interventions in managing appendicitis. By reducing the incidence of complicated appendicitis, healthcare providers can achieve substantial cost savings and improve patient outcomes. Preventive strategies might include better diagnostic practices, timely surgical interventions, and comprehensive postoperative care to prevent complications.

From a policy perspective, these results suggest that investments in early intervention and preventive care for appendicitis could be economically beneficial. Healthcare systems should consider allocating resources towards improving the early detection and treatment of appendicitis to minimize the occurrence of complications. Future research could enhance these findings by incorporating additional variables such as hospital characteristics, patient comorbidities, and healthcare provider practices. Such studies would provide a more comprehensive understanding of the factors influencing hospital charges and identify further opportunities for cost savings and quality improvement in healthcare delivery.

Discussion:

This analysis highlights the financial burden of complicated appendicitis on the healthcare system. Key findings include:

• Complicated Cases: The significant increase in total charges for complicated appendicitis emphasizes the importance of addressing these cases. With an odds ratio of 3.285, the likelihood of higher charges is markedly increased for complicated cases.
• Demographic Factors:
• Age: Older age slightly increases total charges, which could be due to more intensive care requirements.
• Race and Ethnicity: The findings that Black individuals have higher charges and Hispanics have lower charges may reflect disparities in healthcare access and outcomes, necessitating targeted interventions.
• Gender: Lower charges associated with being female might indicate differences in the treatment approach or underlying health conditions between genders.
• Insurance: While uninsured status did not have a significant impact on total charges, ensuring better coverage could potentially improve overall healthcare outcomes.
• Severity: The large negative estimate for "SeverityOverall" implies that less severe cases, which are coded with higher values, incur significantly lower costs. This underscores the potential financial savings from preventing severe cases of appendicitis.

By identifying and implementing preventive measures to reduce the incidence of complicated cases, healthcare providers and policymakers can achieve substantial cost savings. This information is crucial for decision-making in hospital management and policy formulation, emphasizing the need for targeted interventions and resource allocation to manage appendicitis more effectively.

Through improved preventive strategies and equitable healthcare practices, we can reduce the incidence and financial burden of complicated appendicitis, ultimately leading to better patient outcomes and more efficient use of healthcare resources.

Discussion: This analysis highlights the financial burden of complicated appendicitis on the healthcare system. Key findings include: Complicated Cases: The significant increase in total charges for complicated appendicitis emphasizes the importance of addressing these cases. With an odds ratio of 3.285, the likelihood of higher charges is markedly increased for complicated cases.

Demographic Factors: Age: Older age slightly increases total charges, which could be due to more intensive care requirements. Race and Ethnicity: The findings that Black individuals have higher charges and Hispanics have lower charges may reflect disparities in healthcare access and outcomes, necessitating targeted interventions. Gender: Lower charges associated with being female might indicate differences in the treatment approach or underlying health conditions between genders. Insurance: While uninsured status did not have a significant impact on total charges, ensuring better coverage could potentially improve overall healthcare outcomes. Severity: The large negative estimate for "SeverityOverall" implies that less severe cases, which are coded with higher values, incur significantly lower costs. This underscores the potential financial savings from preventing severe cases of appendicitis. By identifying and implementing preventive measures to reduce the incidence of complicated cases, healthcare providers and policymakers can achieve substantial cost savings. This information is crucial for decision-making in hospital management and policy formulation, emphasizing the need for targeted interventions and resource allocation to manage appendicitis more effectively. Through improved preventive strategies and equitable healthcare practices, we can reduce the incidence and financial burden of complicated appendicitis, ultimately leading to better patient outcomes and more efficient use of healthcare resources.