Paper Example on Epidemiology: Study Design and Data Analysis

RQ: Is there an association between morbidity, and quality of life and the effectiveness of combined antiretroviral therapy (cART)

Dependent Variable: HIV (combined antiretroviral therapy (cART))Independent Variable(s): morbidity and quality of life

Null Hypothesis: There is no association between mortality and quality of life (QOL) and combined antiretroviral therapy.Alternate Hypothesis: There is an association between mortality and quality of life (QOL) and combined antiretroviral therapy.

Statistical Test: Cohort profiling, logistic regression, Kaplan-Meier curves, Poisson regression and Cox regression.

Study sample

The study population will consist of 280 patients diagnosed with HIV at Zeta Medical Center between 2010 and 2015. The inclusion of patients in the cohort will be done in two phases. The first phase aims at undertaking the events of interest that might have taken place before the patients, and in the second phase, all the patients that had been started on cART between January 2009 and December 2010 will be enrolled onto the study. During the first phase will be a retrospective cohort study and will include observation of the events of interest had occurred before the patients were enrolled to cART and the second phase will be mainly prospective that will entail observing of events as they occurred including follow-up of the routine clinical outpatient services.

The eligible patients will be first identified from the total number of patients that have been enrolled for a clinical follow-up in each of the sites, using the electronic cART dispensing system. All the patients have to be above the age of 18 to be enrolled (Grangeiro et al., 2014). In areas where electronic cART does not exist, non-probabilistic sampling will be applied, depending on the features that are registered at the cART dispensation. Possible discrepancies will be identified by comparing the characteristics of the non-probabilistic samples to those that are on site (DiMaggio, 2013).

Data Collection Methods

The data for the study will be obtained from the routine clinical care services that are provided in the health centers which are included in the study. The other data will be obtained from the clinical records, by trained information abstractors and converted into standardized forms. The clinical records have to be reviewed at the intervals that would not exceed six months to allow investigation of the events that are recorded during the routine clinical follow-up within each period. To ensure that the information is consistent, as well as its completeness, it has to be checked at cohort coordination levels, including cross-checking, against the existing information that exists on the clinical records (DiMaggio, 2013).

A standardized self-report questionnaire will be used to collect information regarding the quality of life, QOL, which will be administered at the beginning of every follow-up session, that is once every 12 months following the initiation of cART. The information that is related to treatment facilities, as well as the appropriateness and the quality of the participant enrollment can be obtained using electronic forms that are completed at the study sites. Some of the major outcomes of interest in the study would include the occurrence of diseases that are either related or unrelated to HIV, the suppression of the viral replication, treatment modification that is the initial or subsequent regiment, treatment failure that results from the clinical, immunological or virological failure, adverse effects and even death.

Clinical and epidemiological data will be collected during the entire study period including the socio-demographic data, category of HIV transmission, the alcohol and tobacco use, use of illicit drugs, the family history of metabolic disorders, cardiovascular diseases, hypertension, AID-related as well as non-AIDS related illnesses; the initial and subsequent cART regiments that are applied for treatment and prophylaxis, the adverse effects as a result of cART use, the prophylaxis for the opportunistic infections, and vaccines, the CD4, viral loads as well as genotyping results and the laboratory results for cART monitoring and mortality (Grangeiro et al., 2014).

Statistical Analyses to be Conducted

According to Woodward, (2013) Cohort profiling is a crucial method in cohort studies because it examines the characteristics of the participants including their socio-demographics, the levels of exposure to treatment, the mortality, quality of life, the number of patients that are lost to follow up, the retention rates, the risks of morbidity and mortality, and the effectiveness of the stated treatment on the patients based on the improvement of the quality of life. Profiling will also be useful in determining the clinical manifestation of the disease during treatment of the patient in the facility (Twisk, 2013). It will also help determine if the regiments that are administered are working for the patients. These values will be all dependent on the clinical tests that are performed during the follow-up.

Regression analysis is crucial in establishing the existing relationships between variables and in this case cART treatment and mortality and quality of life. The models compare the different outcomes among patients in response to cART medication based on their behaviors and background information such as the history of chronic illness (Woodward, 2013). The influence of such factors would only be determined fully through the employment of the subgroup analyses, and or through adjustment of the regression models. Cox regression and Poisson regression can be used in this study. Cox regression would, for example, analyze a given parameter until an event occurs for instance, death. In our case, Cox regression might be used to examine the ineffectiveness of cART medication to a patient until a disease or death occurs. Logical regression models can also be used to establish whether the observed effects are statistically significant. Measures of confidence give probabilities of events, and in this case, it can be used to make conclusions regarding a specific outcome (Woodward, 2013).

References

Grangeiro, A., Escuder, M. M., Cassanote, A. J. F., Souza, R. A., Kalichman, A. O., Veloso, V., ... & Lucena, N. O. (2014). The HIV-Brazil cohort study: design, methods and participant characteristics. PLoS One, 9(5), e95673.

DiMaggio, C. (2013). Introduction. In SAS for Epidemiologists(pp. 1-5). Springer New York.

Twisk, J. W. (2013). Applied longitudinal data analysis for epidemiology: a practical guide. Cambridge University Press.Woodward, M. (2013). Epidemiology: study design and data analysis. CRC press.