Quality of life at the dead sea region: the lower the better? an observational study

<p>Abstract</p> <p>Background</p> <p>The Dead Sea region, the lowest in the world at 410 meters below sea level, is considered a potent climatotherapy center for the treatment of different chronic diseases.</p> <p>Objective</p> <p>To assess the prevalence of chronic diseases and the quality of life of residents of the Dead Sea region compared with residents of the Ramat Negev region, which has a similar climate, but is situated 600 meters above sea level.</p> <p>Methods</p> <p>An observational study based on a self-administered questionnaire. Data were collected from kibbutz (communal settlement) members in both regions. Residents of the Dead Sea were the study group and of Ramat Negev were the control group. We compared demographic characteristics, the prevalence of different chronic diseases and health-related quality of life (HRQOL) using the SF-36 questionnaire.</p> <p>Results</p> <p>There was a higher prevalence of skin nevi and non-inflammatory rheumatic diseases (NIRD) among Dead Sea residents, but they had significantly higher HRQOL mean scores in general health (68.7 ± 21 vs. 64.4 ± 22, p = 0.023) and vitality (64.7 ± 17.9 vs. 59.6 ± 17.3, p = 0.001), as well as significantly higher summary scores: physical component score (80.7 ± 18.2 vs. 78 ± 18.6, p = 0.042), and mental component score (79 ± 16.4 vs. 77.2 ± 15, p = 0.02). These results did not change after adjusting for social-demographic characteristics, health-related habits, and chronic diseases.</p> <p>Conclusions</p> <p>No significant difference between the groups was found in the prevalence of most chronic diseases, except for higher rates of skin nevi and NIRD among Dead Sea residents. HRQOL was significantly higher among Dead Sea residents, both healthy or with chronic disease.</p

The Price of a Neglected Zoonosis: Case-Control Study to Estimate Healthcare Utilization Costs of Human Brucellosis.

Human brucellosis has reemerged as a serious public health threat to the Bedouin population of southern Israel in recent years. Little is known about its economic implications derived from elevated healthcare utilization (HCU). Our objective was to estimate the HCU costs associated with human brucellosis from the insurer perspective. A case-control retrospective study was conducted among Clalit Health Services (CHS) enrollees. Brucellosis cases were defined as individuals that were diagnosed with brucellosis at the Clinical Microbiology Laboratory of Soroka University Medical Center in the 2010-2012 period (n = 470). Control subjects were randomly selected and matched 1:3 by age, sex, clinic, and primary physician (n = 1,410). HCU data, demographic characteristics and comorbidities were obtained from CHS computerized database. Mean±SD age of the brucellosis cases was 26.6±17.6 years. 63% were male and 85% were Bedouins. No significant difference in Charlson comorbidity index was found between brucellosis cases and controls (0.41 vs. 0.45, respectively, P = 0.391). Before diagnosis (baseline), the average total annual HCU cost of brucellosis cases was slightly yet significantly higher than that of the control group ($439 vs.$382, P<0.05), however, no significant differences were found at baseline in the predominant components of HCU, i.e. hospitalizations, diagnostic procedures, and medications. At the year following diagnosis, the average total annual HCU costs of brucellosis cases was significantly higher than that of controls ($1,327 vs.$380, respectively, P<0.001). Most of the difference stems from 7.9 times higher hospitalization costs (p<0.001). Additional elevated costs were 3.6 times higher laboratory tests (P<0.001), 2.8 times higher emergency room visits (P<0.001), 1.8 times higher medication (P<0.001) and 1.3 times higher diagnostic procedures (P<0.001). We conclude that human brucellosis is associated with elevated HCU costs. Considering these results in cost-effective analyses may be crucial for both reducing health inequities and optimal allocation of health systems' scarce resources

Time-varying association of acute and post-acute COVID-19 with new-onset diabetes mellitus among hospitalized and non-hospitalized patients

Introduction Previous studies have shown disruption of glycometabolic control and new diabetes mellitus (DM) diagnosis among patients with COVID-19. It is still unclear how the association of COVID-19 and new-onset DM may be modified by disease severity or vary over time, during acute and post-acute phases.Research design and methods In this retrospective matched cohort study, 157 936 patients with COVID-19 (aged ≥25 years, diagnosis date between March 01, 2020 and August 31, 2021) were compared with individuals without COVID-19, separately for non-hospitalized, hospitalized, and severe hospitalized patients. Stratified Cox proportional hazards models, with changing baseline time (starting at the date of COVID-19 diagnosis, and at 1, 2, 3, and 4 months afterwards), were used to evaluate the occurrence of new DM in relation to COVID-19 infection in different time frames—from each landmark date until end of study.Results During mean follow-up time of 10.9 months, there were 1145 (0.72%) new diagnoses of DM compared with 1013 (0.64%) in the individuals without COVID-19 (p=0.004). Non-hospitalized patients with COVID-19 were not at higher risk of new DM neither during the acute phase nor afterward. Hospitalized patients with COVID-19 had a higher risk of developing DM, with the highest risk among severe hospitalized patients. This risk among hospitalized patients was highest in the acute phase (HR 2.47 (95% CI 1.86 to 3.29)), attenuated over time, but remained significant at 4-month landmark analysis (HR 1.60 (95% CI 1.12 to 2.29)).Conclusions Acute and post-acute COVID-19 were associated with new DM only among hospitalized patients, with the highest risk among those hospitalized with severe disease. Those patients should be followed and monitored post-discharge for new DM. Patients who were not hospitalized did not have higher risk of new-onset DM

Characteristics of study population.

<p>Characteristics of study population.</p

Annualized utilization of diagnostic procedures before and after diagnosis, by study group.

<p>Annualized utilization of diagnostic procedures before and after diagnosis, by study group.</p

Annualized healthcare utilization before and after diagnosis, by study group.

<p>Annualized healthcare utilization before and after diagnosis, by study group.</p