الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 105 |  |  |
| | | from | 105 | | |
Abstract
Heart failure, among other cardiac disorders, has a substantial impact on patients’ social, emotional, and psychological well-being, as well as their perception of their own health, in addition to its physical effects. The fact that HF patients live longer, the substantial effects the disease has on each person’s or family’s social life, and the disease’s irreversible, chronic, and progressive characteristics are some of the most significant factors contributing to the decline in QoL of HF patients. Due to the necessity of numerous nonpharmacological and pharmacological interventions, QoL may deteriorate as HF progresses. Clinical stability for HF patients in this situation involves more than just adherence to medication; it also involves improvements in compliance, which can be improved by promoting health literacy.
Aim of the work:
The aim of this study was to explore the importance of health literacy for the quality of life of patients with heart failure using logistic versus linear regression models.
Subjects and methods:
This was a cross sectional study, that was conducted in two hospitals in Alexandria Governorate: one governmental hospital and one private hospital. A sample of 200 heart failure patients had been taken. Data collected through interviewing the patients and reviewing the medical records.
Quantitative data were described using Mean (SD). Qualitative data were described using number and percent. PCS-12 and MCS-12 dimensions of SF-12 were used as quantitative variables in the linear regression modelling. Dichotomization by median split, dividing groups into lower than median versus higher than median for PCS-12 and MCS-12, respectively, was done for the logistic regression modelling. Linear and logistic regression models were used to explore the relation between health literacy and the QoL then comparing between the two models.
The most important findings:
Patients with heart failure showed mean score of 34.16 ± 10.43 for the physical dimension, while the mental dimension displayed mean scores of 41.87 ± 14.34.
Out of the 200 patients, it was found that 46.5% of them had sufficient HL (n=93) and 53.5% (n=107) had insufficient HL.
Among the linear regression, univariate analysis was done then the independent variables that were statistically significant with the dependent variable were considered in the multiple linear regression model. Regarding PCS-12, results of the multiple linear regression analysis indicated that the model explained 28.2 % of the variation in PCS-12 dimension of SF-12 and it was statistically significant (F (20,179) = 4.905, P = 0.000). As for MCS-12, the model explained 21.9 % of the variation in MCS-12 component of SF-12 and it was statistically significant (F (16,183) = 4.485, P = 0.000).
Regarding the logistic regression, the same as in the linear regression, univariate analysis was done then the independent variables that were statistically significant with the dependent variable were considered in the multiple logistic regression model. Considering PCS-12, the Cox & Snell R Square value was 24.6% and the Nagelkerke R Square was 32.8%, these values imply a good fit of the model. As for MCS-12, the Cox & Snell R Square value was 20.4% and the Nagelkerke R Square was 27.2%, these values imply a good fit of the model.
For the linear regression regarding PCS-12, HL was statistically significant and an independent predictor of PCS-12 in patients with HF, both before and after adjustment for the personal and clinical characteristics. Concerning MCS-12, we found that HL was statistically significant and an independent predictor of MCS-12 only before adjustment. While in logistic regression, cconsidering PCS-12, analysis revealed that HL was not statistically significant predictor of the median-split dichotomized PCS-12 in both before and after adjustments for covariates. Regarding MCS-12, it was found that HL was statistically significant and an independent predictor of the median-split dichotomized MCS-12 only before adjustment for personal and clinical characteristics.
Adjusted R square % was higher in the logistic model than the linear model in both PCS-12 and MCS-12. The correct prediction percent in the logistic model of PCS-12 dimension (70%) was higher than the linear model (53.5%). Similarly, the correct prediction percent in the logistic model of MCS-12 dimension (68.5%) was higher than the linear model (57.5%).
Comparing the odds ratios and 95 percent confidence intervals of the two models revealed that the linear regression model produced lower confidence endpoint ratios than the binary logistic model. The logistic regression model’s inferences are only valid for comparisons across single cut points, but the inferences from the linear regression model are applicable throughout the entire range of outcomes.
6.2. Conclusions
The following is what we can conclude from this study:
• More than half of the heart failure patients admitted to one private and one governmental hospital in Alexandria, Egypt had insufficient health literacy.
• Our patients showed low mean score for both the physical and mental dimensions of SF-12 quality of life questionnaire.
• It has been explained that HL is a statistically significant independent predictor of only the PCS-12 of SF-12 in the linear regression model but not for MCS-12, while it was not statistically significant predictor in the logistic models for the two dimensions of SF-12.
• Each of the two models, linear regression model and logistic regression model, has its advantages and disadvantages.
6.3. Recommendations
• Heart failure patients’ quality of life is mediated by health literacy, and those with higher health literacy have better quality of life. Healthcare practitioners should acknowledge the prevalence of low health literacy among HF patients and begin implementing methods that may lessen its effects when engaging with them.
• An excellent place to start improving HL could be using communication techniques like those described in the Agency for Healthcare Research and Quality’s HL Universal Toolkit. Among the strategies mentioned in the toolkit include the use of the teach-back approach, videos, literacy sensitive teaching materials and easy to read signs.
• We highly recommend researchers who want to apply the findings of this work to conduct simulation and/or sensitivity analysis on their own data to ensure that the results are comparable.