ABSTRACT
Background: Today, multi-morbidity (MM), the presence of more than one disease in the same person at the same time, has been prevalent. This is while the healthcare delivery systems are formed based on a single-disease-oriented approach. Hence, this study intended to address presenting a model for the management of patients with multi-morbidity in Iranian hospitals. Methods: This was a mixed-method study. The data was gathered from 54 semi-structured interviews with the participation of experts in inpatient care management who were purposefully selected. The qualitative data were analyzed using content analysis. The Interpretive Structural Modeling (ISM) via STATA and Excel software was exploited in the quantitative phase. Results: The factors affecting the management of patients with multi-morbidity were identified in 26 main themes and 142 sub-themes, and ultimately, a model for improving the management of patients with multi-morbidity in Iranian hospitals at six different levels was offered. The "Comprehensive Health Care Information System (CHIS) and Electronic Health Record (EHR)" had the greatest influence and the lowest dependency. "Efforts to remove patients' confusion" had the highest dependency and the lowest influence. The results of employing the Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis demonstrated that most of the variables are placed in the third group of linkage variables that have high driving power and dependence power. Conclusion: Concerning the sophisticated needs of patients with multi-morbidity for the management of their clinical conditions, the presented model could be provided to policymakers and health care managers as a beneficial performance guideline for improving the quality of care.
ABSTRACT
BACKGROUND: In May 2014, Iran launched the most far-reaching reform for the health sector, so-called Health Sector Evolution Plan (HSEP), since introduction of the primary health care network, with a systematic plan to bring about Universal Health Coverage. We aimed to analyze the time to first all-caused rehospitalization and all-caused 30-day readmission rate in the biggest referral hospital of Northwest of Iran before and after the reform. METHODS: We retrospectively analyzed discharge data for all hospitalization occurred in the six-year period of 2011-2017. The primary endpoints were readmission-free survival, and overall 30-day readmission rate. Using multivariate cox proportional hazards regression and logistic regression, we assessed between-period differences for readmission-free survival time and overall 30-day rehospitalization, respectively. RESULTS: Overall, 157969 admissions were included. After adjusting for available confounders including age; sex; ward of admission; length of stay; and admission in first/second half of year, the risk of being readmitted within 30 days after the reform was significantly higher (worse) compared to pre-reform hospitalization (odd ratio 1.22, P<0.001, 95% CI, 1.15-1.30). Adjusting for the same covariates, after-reform period also was slightly significantly associated with decreased (deteriorated) readmission-free time compared with pre-HSEP period (HR 1.06, P=0.005, 95% CI 1.01-1.11). CONCLUSION: HSEP seems insufficient to improve neither readmission rate, nor readmission-free time. It is advisable some complementary strategies to be incorporated in the HSEP, such as continuity of care promotion, self-care enhancement, effective information flow, and post-discharge follow up programs.
ABSTRACT
BACKGROUND: COVID-19 pandemic has spread all over the world. However, information regarding clinical characteristics and prognostic factors is scarce. The aim of this study was to explore the impact of preexistent chronic comorbid conditions and multimorbidity on risk of mortality in patients with COVID-19. MATERIALS AND METHODS: We designed a retrospective, cross-sectional, observational, single-center study. Data were analyzed from all consecutive patients diagnosed with COVID-19 who admitted in a pandemic hospital affiliated with Tabriz University of Medical Sciences, Tabriz, Iran, from February 20, 2020, to September 25, 2020. The independent effects of preexistent conditions were evaluated using multivariate logistic regression model. RESULTS: A total of 2597 hospitalized patients with COVID-19 were included. At least one preexistent condition was observed in 36.5% of study population. Multivariate logistic regression analysis showed that older age, male sex, diabetes, cardiovascular disease, hypertension, cancer, chronic kidney diseases, liver diseases, and chronic obstructive pulmonary disease were associated with increased risk of mortality. In addition, the number of comorbidities was significantly associated with increased odds of mortality compared to no-comorbidity. CONCLUSION: The results of this study suggest that patients with comorbidities have an increased risk of in-hospital mortality following COVID-19 infection.
