Socioeconomic Status And The Outcomes Of Aortic Dissection Surgery - Dissertation Example

Socioeconomic Status And The Outcomes Of Aortic Dissection Surgery Chapter 3: Methodology Introduction It is evident that effects of acute type A aortic dissection has escalated in the recent past based on financial constraints and in particular, amongst the lower SES cohort (quote*). Time is a key factor in care and treatment of aortic diseases. Conventionally, it is a fundamental requirement that diagnosis and subsequent dissection of this cardiovascular disease be executed at early stages so as to prevent further dissection and complications which can result in death (quote *). This research paper strives to identify associated issues concerned with the outcomes of aortic dissection surgery. Particularly, the paper looks into how socioeconomic status of patients affects their outcomes with aortic dissection surgery. A number of reasons necessitated the study including the alarming documented statistics with respect to the frequency with which type A aortic disease dissection has shot up at alarming rates. Such statistics clearly presents strong grounds for every healthcare provider to divert their attention to projects and actions solely aiming at designing proactive and counteractive strategies to immediately weed out this catastrophic disorder (Clark, DesMeules, Luo, Duncan & WIelgosz, 2009). Mortality and morbidity rates have shot at shocking rates thereby killing many energetic and skilled persons (Chiappini, Schepens, Tan, Dell’Amore, Morshuis, Dossche, &…DiBartolomeo, 2005). Besides, huge funds are being drawn in an attempt to diagnosis, dissect as well as to treat the ailments. This fund could have otherwise been diverted to promising income generating activities and hence GDP multipliers culminating into increased and better standards of living accompanied by fast growing economic growth rates. Research Design and Rationale The study was conducted as a descriptive correlational research design. This is a type of quantitative research design that seeks to establish the relationship between given number of variables (quote). The descriptive correlational design can be found to be directly connected to the research question as the research questions seek to draw a relationship between socioeconomic statuses of patients their chances with type A aortic dissection surgery. The methodological study design will focus on the reciprocal linkages between socioeconomic status and the outcomes of aortic dissecting surgery. The outcome of the aortic dissection surgery in this case, forms our dependent variable and the socioeconomic status of patients, the independent variable. With respect to socioeconomic status, middle and high level income persons and the hierarchical factors are analyzed. This will be achieved by the use of documented data from participants. As part of the descriptive correlational research design, the researcher conducted three major analysis which were regression, causality and correlation analysis to help identify the relationship between socioeconomic status and outcome of aortic dissection surgery. To effectively implement the correlations, there needed to be variables that would be tested for their relationships. The independent variable was the socioeconomic status of patients whiles the dependent variable was the surgical outcome with type A dissection. Some level of time constraints were faced as the researcher needed to have sufficient data gathered by both variables. Not much of resource constraints were faced because the facility used provided most forms of resources needed for data collection. The selected design which is descriptive correlation is consistent with the need to advance knowledge in the field of health science and medicine. This is because in most cases of the practice, unless the cause-effect of problems are known, it is difficult to come out with suitable solutions to curb them (quote*). Meanwhile, the descriptive correlational research design functions on a cause-effect basis whereby the cause of a problem is investigated as the independent variable and linked to an effect as the dependent variable (quote). For example in this study, the research design focuses on critical examination of patients treated via thoracic aortic repair for Type A aortic dissection. This descriptive correlation is preceded by evidence pointing out that socioeconomic status (SES) greatly impacts aortic dissection outcomes. The design in turn acts as a determinant to treatment and care allocation accessibility with respect to cardiovascular disease diagnosis and its consequential surgery (type A aortic dissection surgery). Methodology Population The population of the study was made up of patients of the Hospital of the University of Pennsylvania, where there is adequate data which is streamed through the Society of Thoracic Surgeons database. The hospital presently perform over 400 reconstructive procedures a year and manage over 2,500 patients a year in the Thoracic Aortic Disease Clinic. In effect, all 400 cases managed within 2,500 patients come under the population as the population represents all people with whom there is some possibility of the researcher interacting or collecting data from (quote). Sampling and sampling procedure Of the number of patients and reconstructive procedures identified in the population however, not all of them will be used. this is because the number is excessively high for the time frame within which the study must be completed. This creates the need to have a sample size in place. The sample size refers to that containable group of people from whom the researcher shall collect data from (quote). it will be emphasized however that the study is conducted through secondary data collection, which means that the researcher will not interact directly with the respondents (quote). in the light of this, the sample represents patients whose information shall be used as part of the correlational study. The sample will be made up of 100 cases of reconstructive type A dissection surgery at the hospital in the last one year. The sampling procedure to be used will be a random sampling procedure. The justification for the random sampling is because of the extent of fairness it carries and the ability for it to give the researcher group of respondents with as many different socioeconomic backgrounds as possible. In effect, the random sampling strategy is suitable because it avoids bias (quote). As part of the random sampling technique, an inclusion and exclusion criteria shall be developed that draws on the needed number of 100 cases. The procedure will be to write the letters of the alphabet in pieces of papers in a bowl. Then in a random manner, the researcher shall pick any letter. Names of all patients whose name begins with the selected letter shall be made automatic part of the study. If the first letter selected does not grant the needed number of 100 cases from the total of 400, another letter shall be drawn. The process shall be repeated until 100 patients and their cases come up to be studied. This procedure will mean that the researcher shall not require any power analysis in drawing the sample size. Procedures for Recruitment, Participation, and Data Collection The study does not involve any recruitment of participants as it is to be conducted through secondary data collection procedure. With this said, it is important to explain how data will be recruited into the study for further correlational analysis. The investigator undertakes secondary data collection from the Hospital of the University of Pennsylvania’s data, which is configured into a data collection base form from the Society of Thoracic Surgeons. In addition, distinctions with respect to baseline comorbidities and SES would be collected and subjected to thorough analysis to help establish the research findings. Clinical outcomes that will be measured from the data collected shall include mortality, where the in-house data verified by SSI death index including Kaplan-Meier analysis at one year shall be used. there shall also be major morbidity which will include stroke, renal dialysis, repeat operations, and discharge status. the social outcome on the other hand shall include preoperative an postoperative demographic variables. Some of these variables shall include educational status, annual household income, family size, number of dependents, and zip code. Integration and incorporation of hierarchical logistic regression modeling will play a pivotal role in the descriptive correlational design. This will enable the researcher in blending the effects of comorbidities with dissection outcomes based on the probability of conducting aortic Type A surgery driven by consideration to socioeconomic status determinants. It is estimated that the Hospital of the University of Pennsylvania operates on 65-70 patients annually, both acutely and electively. Adjustments for impacts of SES will be done with respect to aortic patients based on the probabilities of carrying out dissection and its associated aortic surgery amongst each assigned quartile patients with respect to lower SES through to those with higher SES. As far as data collection is concerned, it is important that participants who will be selected, whether dead or alive will have some level of consent. To do this, the most immediate family of dead participants and those alive shall be contacted with a consent form. The consent form basically outlines the aim of the study, what types of data shall be collected from the participants, and why it is important for the data to be collected for the study. Since the participants will not be directly involved in the study, there is no specific procedure for exiting the study. However, at any point during the study when patients whose information are used feel they no longer want their information to be used, they can contact the researcher or hospital administration and their data shall be excluded from the study. The secondary data to be collected shall not require any follow ups as the chances that data will change after they have been collected are very low. Using Archival Data One important feature of secondary data collection, which this study follows is the use of archival data. Based on the hypothesis that socioeconomic factors determines aortic dissection results, the decision to use secondary data and descriptive correlational study properly fits the research questions raised which will be revealed by regression, correlations and causality analysis. The research design selected will fully assist the researcher to understand the null hypothesis; patients with lower SES are attached to decline propensity to access aortic diagnosis, dissection, surgery and the associated treatment of a type A aortic dissection. However, before these data will be retrieved, an official permission will be written to the head of the Thoracic unit of the hospital. This will be done by the use of an introductory letter from the awarding university. Copy of the introductory letter has been provided at the appendix of the study. Despite this common knowledge, little has been done based on financial constraints that are required for surgery and treatment. The result is that aortic disease’s susceptibility to lower SES cohort escalates un-diagnosed exerting severe thoracic life impairments. Therefore, morbidity and mortality rates rise in such patients even if they finally afford dissections (Winkleby, Jatulis, Frank & Fortmann, 1992). Many patients expire shortly after the surgery or survivors are paralyzed. Based on these shocking revelations, the researcher intends to investigate the possible aortic dissection outcome impediments attached to socioeconomic status and design a model to help curb such severities. This will be unearthed by a close critical investigation of probability of interconnectedness between SES and dissection outcomes. This research outcome is intended to help design and develop a sound proactive framework and policies considered best to offer care and treatment against catastrophic cardiovascular aortic Type A disease. The research is embedded on a framework where documented weighted discharge records with respect to patients currently being diagnosed, undergoing aortic surgery and have surgeries already performed with respect to aortic Type A disease will be gathered and analyzed. Correlation, regression and causality analysis will be centerpiece analytical models upon which findings will be unearthed and critically evaluated. The target period will capture a median term goal of between January 1, 2008 and December 31, 2012 with the Hospital of the University of Pennsylvania being the area of study and the research sampling frame. Stratified sampling procedure is projected to aid in obtaining the research samples. Based on comorbid evaluations, Elixhauser Comorbid Disease category will take the center stage to weed out the burden of evaluating comorbid diseases. This will incorporate about 30 comorbid measures attributable to mortality rates, hospital charges, as well as increased patient life span. The superiority attached to Elixhauser measure over other comorbid options such as Charlson-Deyo based on its utilization with administrative databases particularly NIS informed the decision to settle on Elixhauser as the baseline for measuring comorbidity factors. Patient’s stratification will be conducted with respect to race as Hispanic, black, Asian, white, Native America with the remaining classified into other. With respect to Socioeconomic factors, categorization will be peddled by median household income-based Zone Improvement Plan NIS’s codes based on quartiles as: Code 1 representing individuals earning less than 25,000 dollars, Code 2, those falling between 25001-30000 dollars, Code 3 included individuals earning between 30001-35000 dollars with Code 4 characterized by individuals earning 35,000 dollars and above. Considerations will also be made with respect to procedure year, payer status, hospital procedure volume and elective status. Further, hospital procedures volume with respect to aortic dissection of Type A surgery as well as open aortic repair will be classified into quartiles for easier comparison with lowest being marked as 11, followed by 11-36 as medium while high marked as 37-92 as 92 and above classified as very high voluminous hospital procedures. Analysis to extract the interconnections between SES and aortic dissection outcomes will be spearheaded by hierarchical multivariable logistic regression modeling (Bashinskaya, Nahed, Walcott, Coumans & Onuma, 2012). With respect to variables of patients, determination will be based on surgery and confounding status, as well as clinical effects. This model will account for variations in particular patients’ features such as sex, Elixhauser comorbidities, age, and indication of thoracic aortic repair as well as payer status. In regard impacts of procedure year and hospital volume (total volume for aortic repair), regression analysis will be executed. Hierarchical regression modeling will aid in unearthing the influence of SES in determining the aortic dissection and the influence of SES on other independent variable as well as presenting avenues for regulating the impacts of other independent variables on undertaking aortic surgery and the associated outcomes. With respect to diagnosis and case clustering, it is projected that this will be left at hospital jurisdictions as well as individual degree as presented on admission diagnoses documents (Capingana, Magalhaes, Silva, Goncalves, Baldo, Ro, …Mill, 2013). American Hospital Association Identification number will help the model in curbing the random influence of individual hospitals. To gauge the relative strength of workability with respect to model variables and carrying out the aortic dissection, the researcher will undertake Wald x2 test. The probability of undertaking type A aortic dissection will be analyzed and evaluated based on the critical examination of the area enclosed by the receiver operating characteristic curvature. A 1.0 figure would indicate perfect discrimination while 0.5 thumping model equity in terms of desired outcome projections. Multivariate logistic regression analysis will aid in accounting for patient’s comorbidities and other hospital-attached factors (Bavaria, Brinster, Gorman, Woo, Gleason & Pochettino, 2009,). The research will primarily showcase the effects of socioeconomic status to treatment allocation and dissecting aortic disease that is highly rated as the major cardiovascular disorder. Such analysis will further aid in unearthing the independent association between lower incomes and escalating propensity to access endovascular treatment with respect to thoracic aortic disease. Comparison will be done with respect to aortic dissection surgery based on its odds along the quartiles already been sub-sectioned in order to justify the hypothesis that odds should be more in lower-incomes quartiles as compared to upper quartiles. This will aid in making necessary conclusion on the effect of SES to outcomes of aortic dissection culminating into drawing policies aimed at proactively controlling this catastrophic cardiovascular aortic disease. This model will showcase that type A aortic dissection outcomes are in line with the hospital strata indicated by low-volume centers possessing massive similarities with respect to outcomes to high-volume healthcare practices (Chiappini, Schepens, Tan, Dell’Amore, Morshuis, Dossche &…DiBartolomeo, 2005). Aortic dissection is informed by the existence of a large pool of hospital and patient variables with close interconnectedness looming pending justification between socioeconomic status and aortic dissection. The model thus is highly attached to aneurysm morphology, a factor found to be paramount in aortic dissection paradigm. This model is significantly found appropriate based on the research questions as it has appreciated and subsequently incorporated avenues to aid in the analysis of other factors attributable to decisions people make in regard to aortic dissection and particularly decisions that are focused on age, comorbidities, payer status as well as hospital volume. The methodology further points out those lower socioeconomic cohorts have higher probability of being dissected in lower-volume hospitals with regard complexity of particular surgery requirements (Bavaria, Brinster, Gorman, Woo, Gleason & Pochettino, 2009). This methodological design further presents a platform to proactively curb complex and daunting challenges attached to open aortic repair by incorporating avenues that aid lower-volume center’s surgeons to the frequency of type A aortic dissections. The fact that the design presents avenues for analysis based on correlation between escalated aortic dissection and SES in which it is expected that this might not be the direct causal interconnection but attached to surrogate factors indicating multiple patent-specific variables culminating into raised probability of aortic dissection (Dominguez-Rodriguez, 2012). This further explains the need to critically examine the exact cause of this disorder so as to weed out it increased severe effects. The model further incorporate such specific factors as nature of aortic disease, patient belief, access to care as well as education levels in determination of socioeconomic factors that affects aortic dissection outcomes. Ethical standards will be upheld within the model through surgeon and patient surveys to spearhead upholding higher degree of non-individual bias and beliefs with respect to allocations of aortic dissection surgery of Type A thus, incorporating avenues to counteract the physician distrust variations based on socioeconomic status (Winkleby, Jatulis, Frank, & Fortmann, 1992). Patients’ socioeconomic status are analyzed driven by ethical practice as specific legal requirements are proactively obtained and data usage is also restricted to particular research and hence this prevents unscrupulous researchers from obtaining and using such patients information for their own unfair dealings. In summary, this methodological approach in curbing this catastrophic cardiovascular aortic disease stands the best based on its recognition, appreciation and incorporation of necessary socioeconomic status data as well as comorbidities data with an aim to design a proactive strategy to curb the disease severity (Agabiti, Cesaroni, Picciotto, Bisanti, Caranci & Perucci, 2008). The choice of hierarchical regression, correlation and causality analysis with respect to effects of socioeconomic status on aortic dissection outcomes is in line with the research hypothesis as such statistics and parameters computed will strictly help answer the questions posed. Based on the acquisition of strictly necessary and credible data obtained from a large pool of University of PA and NIS data, research problem will thus be curbed (Alter, Austin, Naylor & Tu, 2006). The data analysis will thus help the researcher to communicate his findings to relevant authorities and agencies to help design a long term policy statement and model that will ensure that the impacts and effects of aortic dissection outcomes resulting from socioeconomic factors discussed are brought underfoot creating enabling environment for patients and as well economic development based on the reduced amount of fund constraints. Read More