Hypothesis
Surgical and pneumonia process of care quality measures in minority-serving hospitals is lower than the process of care in the racially integrated, resulting from the underperformance on quality indicators in the minority-serving hospitals. This disparity in the quality of care could be due to the inadequacy of resources, the mediocrity of the care facilities or neglect by both the state and federal the governments.
Quantitative Research Design
The quantitative research method used in the study is casual-comparative/ quasi-experimental research method. In this research design, the researcher identifies an independent variable/ indicator but does not, however, manipulate the variable. The effects of the variable can be quantitatively expressed. These quantified variables are can then be compared with other variables within the study parameters. This is well depicted in this study. The independent variables were the surgical and pneumonia care quality indicators. These indicators were identified in the hospital data and compared for the minority-serving and racially integrated hospitals. Also, the researcher did not assign groups to the data but only used the pre-existing groups available in the data. This research design was selected because the study required a comparison of the indicators so as to form a conclusion about the study hypothesis (Gaskin et al. 2016).
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Variables Tested
The study used hospital characteristics variables obtained from the 2011 American Hospital Association (AHA) survey. There were 12 variables for surgical indicators and six variables for pneumonia indicators. The variables tested for surgical care indicators were: the percentage of surgery patients that were under antibiotic medication at the right time, the percentage of patients that had been under preventive antibiotic medication and was stopped at the right time, the percentage of patients prescribed with the right preventive antibiotics, patients who were put under treatment for blood clots after their surgeries, percentage of patients whose surgery was preceded by removal of hair from the parts to be operated on, the percentage of outpatients prescribed with the preventive antibiotics at the right time, the percentage of outpatients prescribed with the right antibiotics, the proportion of patients whose body temperature was normal following the surgery, the patients whose urinary catheters were taken out the day after the performing the surgery and the percentage of patients that were under cardiac drugs (Gaskin et al. 2016).
The indicators for pneumonia were the following: the percentage of patients that had received pneumococcal vaccination, the percentage that had taken initial antibiotics within six hours, those patients that had been advised against smoking, those patients whose emergency room blood samples were cultured after their first antibiotic dose, the percentage of patients prescribed with the right initial antibiotics and those patients that had been previously vaccinated against influenza.
The controlled variables were: hospital bed size, hospital teaching status, the hospital ownership status (i.e. whether non-profit, for profit, or public), urban-rural locations, the community service index, the patient’s income in terms of full time equivalent, the patient characteristics (i.e. Medicare and Medicaid patients discharge percentage), and the tertiary and highly technological services index. The hospital teaching status was determined using the interns and residents to bed ratio. An IRB greater than zero but less than0.25 meant that the hospital was minor teaching, while hospitals with IRB greater than 0.25 were labeled as major teaching hospitals. Those with IRB less than zero were designated as non-teaching hospitals.
Also, market/area-level variables were included in the study. These were the hospital competition, the median household income of the county as per the 2010 census, the urban-rural county designations.
Some hospital records had insufficient or missing data on the required variables. It was found that 14% of the values for Hospital Community indexes and Hi-Tech services were missing. Also, 17.7% of the values for case mix indexes were missing. This inadequacy of the variables data was catered for by using mean values and dummies in their place. Only hospitals with valid data for the required variables were included in the study (Gaskin et al. 2016).
Data Analysis
The study data analysis included descriptive bivariate, multivariate and sensitivity data analysis methods. The comparison of each variable pair between the majority-white and the minority-serving hospitals and the majority-white and racially integrated hospitals was made using the t -test. The Bonferroni correction was applied to evaluate the statistical variations in the data averages between the racially integrated and minority-serving hospitals . The descriptive data analysis identified the total number of general hospitals included in the study across the 11 states and the outcomes of their pneumonia and surgery care indicators. The multivariate data analysis used the unconditional quantile coefficient (UQF) for all the variables of quality indicators evaluated. Sensitivity analysis was used to perform specific percentile comparisons of the various minority serving and racially integrated facilities included in the study. The comparison in performance indicators for the racially integrated hospitals and the minority-serving hospitals showed that the statistical scores for the minority-serving hospitals were lower for both the surgical and pneumonia indicators. Stratified analysis based on the hospitals’ characteristics, i.e. size, location and teaching capacity was conducted to determine the implications of the hospital characteristics to the quality performance (Gaskin et al. 2016).
Results
1198 acute care general hospitals were included in the study sample. 80 to 95 percent of the hospitals had pneumonia and surgery quality care indicators. There were 149 minority-serving hospitals and 224 racially integrated hospitals. The analysis was restricted to only the hospitals whose data contained 30 or more outcomes for each indicator analyzed.
It was found that the percentage of Medicare patients was lower while that of Medicaid patients was higher in minority-serving hospitals. Also, the research yielded that most minority serving and racially integrated hospitals were located in urban areas where there were a higher hospital competition and median household income of the population (Gaskin et al. 2016).
For the unconditional quantile coefficient (UQC), there were significant differences between the minority-serving hospitals with the worst performance and the worst performing racially integrated hospitals. The difference increases with the transition the high performing racially integrated hospitals and the worst performing minority-serving hospitals. However, the study findings point out that there existed a similarity in the quality of surgical and pneumonia care in both the best performing minority serving and racially integrated hospitals.
In the sensitivity analysis data, the approximated associations of percentages of the discharges from minority-serving hospitals were negative and significant for the 7 out of the 12 quality indicators for surgical care and 2 out of the six indicators for pneumonia in the 10 th quantile. However, there was a difference in results in the 90 th quantile as only two of the 12 surgical indicators had statistically significant negative associations and there were no negative associations in all the six pneumonia indicators among the minority-serving hospitals.
The stratified analysis based on the minority-serving and racially integrated hospital characteristics depicted that the hospital size, location, and teaching categories contributed to the quality indicator disparity among the worst performing hospitals (Gaskin et al. 2016).
Conclusion
The study found that the best performing minority-serving hospitals performed equally with the best performing racially integrated hospitals. However, there were discrepancies in the performance indicators between the low performing minority serving and the low performing racially integrated hospitals. On this level, the minority performing hospitals performed worse than the racially integrated hospitals on pneumonia and surgical care quality indicators.
References
Gaskin, D. J., Zare, H., Haider, A. H., & LaVeist, T. A. (2016). The Quality of Surgical and Pneumonia Care in Minority ‐ Serving and Racially Integrated Hospitals. Health services research , 51 (3), 910-936.
