Background. In-hospital mortality is a measure recognized by US Agency for Healthcare Quality to represent quality of care within hospitals, that accounts for mortality based on three indicators: 1. select medical conditions and procedures; 2. procedures linked with questions of use (misuse, over/under use); 3. high volume procedures traditionally associated with lower mortality rates. Understanding how different comorbidity models measure in-hospital mortality is essential not only for determining patient health status in the hospital setting, but also help to regulating mortality risk and mortality risk predictions. One of the most widely used discriminatory models is the Charlson model, which predicts the risk of mortality within one year of hospitalization of patients with various comorbidities using CCSR codes for ICD-10 diagnoses which is quantified by the c-statistics, represented by the area under the curve (AUC). Objectives. To adapt a comorbidity index model to the National Inpatient Sample (NIS) database of 2020 to predict 1-year mortality for patients admitted with select ICD-10 codes of diagnoses. Methods Our study analysis examined mortality with comorbidity using the Charlson model in a sample population of estimated 5,533,477 adult inpatients (individuals ≥18 years of age). A multivariate logistic regression model was constructed with in-hospital mortality as the outcome variable and identifying predictor variables as defined by the Clinical Classifications Software Refined Variables (CCSR) codes for selected ICD-10 diagnoses (Table 3). Descriptive statistics and the base logistic regression analyses were conducted using SAS statistical software version 9.4. To avoid overpowering and avoid variables attaining statistical significance while only marginally changing the outcome, a subsample (n=100,000) was randomly selected from the original data set. Ultimately, 20 CCSR variables with p-values <0.20 from the base simple logistic regression models were included in the subsequent backward stepwise logistic regression analysis. Results Table 1 shows the prevalence of the selected diagnoses for our analysis. Anemia (28.32%), pulmonary disease (asthma, COPD, pneumoconiosis;21.88%), and diabetes without complications (19.47%) were the three most prevalent conditions among hospitalized patients. Table 2 shows the results of the base logistic regression analysis conducted, which excluded connective tissue/rheumatologic disorders, peptic ulcer disease, anemia, diabetes with complications, and human immunodeficiency as predictors of inpatient mortality. Results of the backward stepwise regression analysis revealed that severe liver disease/hepatic failure ([adjusted odds ratio (aOR): 10.50, (CI: 10.40-10.59)], acute myocardial infarction ([2.85, (2.83-2.87)] and malnutrition ([2.15, (2.14-2.16)] were three most important risk factors and had the highest impact on inpatient mortality (p-value <0.0001). However, smoking history, obesity, and liver disease were negatively associated with inpatient mortality. The c-statistic or the area under the curve (AUC) for the final model was 0.752. Conclusion Our findings, based on Charlson modeling procedures, indicate that independent variables representative of comorbidity with the strongest 1-year risk of mortality were among patients with ICD-10 codes relating to: severe liver disease/hepatic failure, acute myocardial infarction, and malnutrition. Hence, relevant stakeholders (patients, family members, and healthcare providers) can utilize this knowledge to advance models of care and prevention strategies that limit disease progression and improve patient outcomes.

Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

According to the National Drug Policy one of the health services objectives is to ensure the availability and accessibility of essential drugs to all citizens. An economic objective of the same policy is to promote the cost-effective and rational use of drugs (Department of Health, 1996). Currently, there is no system to scientifically determine the usage of medicines in the public sector and whether the current usage is satisfactory enough (John, 2004:2). The World Health Organization states that "good drug supply management is an essential component of effective and affordable health care services globally (World Health Organization, 1998:1). In the South African context, even though the Essential Drug List helps health care professionals to treat diseases in the public sector, it does not prescribe the minimum guidelines for medicine supply systems (Department of Health, 2006a). The general objective of this study was to develop a therapeutic medicine budget model in a district hospital in the public sector of the North-West Province to control medicine usage. This can be done after analysing the medicine usage patterns and then developing a framework for therapeutic budgeting by evaluating appropriate systems, i.e. the International Code for Disease (ICD-10) classification system, with the therapeutic budget model framework. Retrospective drug utilisation of six months (January 2007 until June 2007) was documented. A random sample population of 25% was selected (n = 1 494). After the data collection period of 9 weeks, the actual study population was only 18.67% (only 1 166 of the 1 494 patients files had a medicine history). All the medicine items prescribed were classified in the therapeutic budget model. Patient confidentially was assured by using a unique pin number on the survey form, so that no names of patients or other biographical details were collected from the patient files onto the survey form, which is in line with the requirements of the Ethics Committee approval conditions for the North-West University. The total number of medicine items dispensed during the study period was 11 768. The average cost per medicine item was R19.36 ± 86.79 for inpatients. The total number of consultations was 3 220. The average number of medicine items per consultation was 3.66 ± 1.98. The total cost of medicine items during the study period was R244 677.11. The average medicine cost per consultation for inpatients was R70.80 ± 177.72. The top three budget groups according to frequency represented 68.11% of all medicine used according to budget groups. The top three pharmacological groups according to total cost represented 61.68% of the total cost of pharmacological groups. The top three therapeutic codes according to frequency represented 18.75% of all therapeutic codes. The top three ICD-10 codes based on total cost represented 59.35% of all medical conditions diagnosed. The total hospital budget for 2007 was predicted at R3 276 750.00. Of this budget, 75% was for pharmaceuticals (R2 457 562.50). The total pharmaceutical medicine cost (excluding surgicals) from the study was R224 677.11 (this was for 18.67% of the total patient visits for six months) which can be calculated at R2 406 824.96 for all patients visits in a full year. The correlation between the actual budget and the projected budget showed a R50 737.54 surplus in the budget of the hospital. A therapeutic budget model can also help in achieving the following: • Proper preparation and planning of budgetary policies in a phased manner based on scientific evidence; • Evaluation of budgetary compliance, cost-efficiency of therapy and standard treatment guidelines (STG) / Essential Drug List (EDL) / formulary compliance; • Better procurement strategies based on demand, expenditure and inventory control; and • Better delivery and maintenance of quality health care by evaluating operational and clinical policies. The therapeutic budget model is a more appropriate manner to use in the projections of budgets and medicine usage. The scope of a therapeutic budget model to be implemented in the hospitals in the public sector of the North-West Province seems to be promising. / Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

According to the National Drug Policy one of the health services objectives is to ensure the availability and accessibility of essential drugs to all citizens. An economic objective of the same policy is to promote the cost-effective and rational use of drugs (Department of Health, 1996). Currently, there is no system to scientifically determine the usage of medicines in the public sector and whether the current usage is satisfactory enough (John, 2004:2). The World Health Organization states that "good drug supply management is an essential component of effective and affordable health care services globally (World Health Organization, 1998:1). In the South African context, even though the Essential Drug List helps health care professionals to treat diseases in the public sector, it does not prescribe the minimum guidelines for medicine supply systems (Department of Health, 2006a). The general objective of this study was to develop a therapeutic medicine budget model in a district hospital in the public sector of the North-West Province to control medicine usage. This can be done after analysing the medicine usage patterns and then developing a framework for therapeutic budgeting by evaluating appropriate systems, i.e. the International Code for Disease (ICD-10) classification system, with the therapeutic budget model framework. Retrospective drug utilisation of six months (January 2007 until June 2007) was documented. A random sample population of 25% was selected (n = 1 494). After the data collection period of 9 weeks, the actual study population was only 18.67% (only 1 166 of the 1 494 patients files had a medicine history). All the medicine items prescribed were classified in the therapeutic budget model. Patient confidentially was assured by using a unique pin number on the survey form, so that no names of patients or other biographical details were collected from the patient files onto the survey form, which is in line with the requirements of the Ethics Committee approval conditions for the North-West University. The total number of medicine items dispensed during the study period was 11 768. The average cost per medicine item was R19.36 ± 86.79 for inpatients. The total number of consultations was 3 220. The average number of medicine items per consultation was 3.66 ± 1.98. The total cost of medicine items during the study period was R244 677.11. The average medicine cost per consultation for inpatients was R70.80 ± 177.72. The top three budget groups according to frequency represented 68.11% of all medicine used according to budget groups. The top three pharmacological groups according to total cost represented 61.68% of the total cost of pharmacological groups. The top three therapeutic codes according to frequency represented 18.75% of all therapeutic codes. The top three ICD-10 codes based on total cost represented 59.35% of all medical conditions diagnosed. The total hospital budget for 2007 was predicted at R3 276 750.00. Of this budget, 75% was for pharmaceuticals (R2 457 562.50). The total pharmaceutical medicine cost (excluding surgicals) from the study was R224 677.11 (this was for 18.67% of the total patient visits for six months) which can be calculated at R2 406 824.96 for all patients visits in a full year. The correlation between the actual budget and the projected budget showed a R50 737.54 surplus in the budget of the hospital. A therapeutic budget model can also help in achieving the following: • Proper preparation and planning of budgetary policies in a phased manner based on scientific evidence; • Evaluation of budgetary compliance, cost-efficiency of therapy and standard treatment guidelines (STG) / Essential Drug List (EDL) / formulary compliance; • Better procurement strategies based on demand, expenditure and inventory control; and • Better delivery and maintenance of quality health care by evaluating operational and clinical policies. The therapeutic budget model is a more appropriate manner to use in the projections of budgets and medicine usage. The scope of a therapeutic budget model to be implemented in the hospitals in the public sector of the North-West Province seems to be promising. / Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.