Background and Objectives: Many studies have analyzed the impact of rurality on the incidence and consequences of COVID-19 infection. However, these studies have not considered the impact of different numbers of nursing homes in rural, semi-urban, or urban areas. Our objective was to analyze the effect of the factor of rurality on the incidence and mortality of COVID-19 while accounting for the impact of the variable of nursing home residency. In addition, we performed a comparative analysis of the infected population in semi-urban and rural areas. Methods: We first analyzed COVID-19 infection in all populations in the Balaguer Primary Health Care Area before examining the impact of rurality using Bayesian logistic regression analysis, specifically excluding the population living in nursing homes. We also performed an epidemiological and clinical analysis comparing rural and semi-urban areas. Results: We found higher incidence of and higher relative and absolute mortality from COVID-19 infection in semi-urban areas than in rural areas. After excluding nursing home residents from our sample, the Bayesian analysis indicated that rurality was not protective against COVID-19 infection or mortality. The incidence rates, specific mortality rates, and case fatality rates were similar in semi-urban and rural areas. All comorbidities, except chronic obstructive pulmonary disease, were associated with higher mortality, while no symptoms were associated with higher mortality. Conclusions: Excluding the population residing in nursing homes from the analysis, we found that rurality was not a protective factor against either infection or mortality during the first COVID-19 wave. Our Bayesian model analysis confirmed that rurality alone did not enhance survival among residents of rural areas.

The aim of this study is to assess the influence of living in nursing homes on COVID-19-related mortality, and to calculate the real specific mortality rate caused by COVID-19 among people older than 20 years of age in the Balaguer Primary Care Centre Health Area during the first wave of the pandemic. We conducted an observational study based on a database generated between March and May 2020, analysing COVID-19-related mortality as a dependent variable, and including different independent variables, such as living in a nursing home or in the community (outside nursing homes), age, sex, symptoms, pre-existing conditions, and hospital admission. To evaluate the associations between the independent variables and mortality, we calculated the absolute and relative frequencies, and performed a chi-square test. To avoid the impact of the age variable on mortality and to assess the influence of the «living in a nursing home» variable, we established comparisons between infected population groups over 69 years of age (in nursing homes and outside nursing homes). Living in a nursing home was associated with a higher incidence of COVID-19 infection, but not with higher mortality in patients over 69 years of age (p = 0.614). The real specific mortality rate caused by COVID-19 was 2.27(0)/(00). In the study of the entire sample, all the comorbidities studied were associated with higher mortality; however, the comorbidities were not associated with higher mortality in the infected nursing home patients group, nor in the infected community patients over 69 years of age group (except for neoplasm history in this last group). Finally, hospital admission was not associated with lower mortality in nursing home patients, nor in community patients over 69 years of age.

Simple Summary Since several studies have described a relationship between sleep disturbances and abnormal glucose metabolism, improving sleeping habits in people with type 2 diabetes should improve glucose metabolism. To prove this hypothesis, we conducted an educational intervention to ameliorate sleep hygiene through nine simple recommendations in patients with prediabetes and type 2 diabetes. We then evaluated if sleep quality, levels of blood glucose and glycated haemoglobin had improved. In the intervention group, we found a significant improvement in sleep quality and diabetes control compared with the control group. Education in sleep hygiene is an important tool for improving health in people with prediabetes and diabetes. Background: Evidence supports a causal relationship between circadian disturbance and impaired glucose homeostasis. Methods: To determine the effect of an educational intervention delivered by primary care nurses to improve sleep hygiene, a parallel, open-label clinical trial in subjects aged 18 and older with impaired fasting glucose (IFG) or type 2 diabetes mellitus (T2DM) was performed. Study variables were sex, age, fasting glucose, glycated haemoglobin A1c (HbA1c), Pittsburgh Sleep Quality Index (PSQI), sleep duration and efficiency, body mass index, antidiabetic treatment, diet and physical exercise. An individual informative educational intervention was carried out following a bidirectional feedback method. The intervention aimed to develop skills to improve sleep through nine simple tips. An analysis of covariance was performed on all the mean centred outcome variables controlling for the respective baseline scores. Results: In the intervention group, PSQI dropped, the duration and quality of sleep increased, and a decrease in fasting glucose and in HbA1c levels was observed. Conclusion: The proposed intervention is effective for improving sleep quality, length and efficiency, and for decreasing fasting glucose and HbA1c levels in only 3 months. These findings support the importance of sleep and circadian rhythm education focused on improving IFG and T2DM.

BACKGROUND: Understanding the relationship between serum ferritin levels and cardiovascular outcomes in type 2 diabetes is crucial for improving risk stratification and guiding therapeutic interventions aimed at preventing major adverse cardiovascular events (MACE). This study aimed to identify distinct clusters of individuals with type 2 diabetes who have varying risks of MACE using a data-driven clustering approach. METHODS: This retrospective cohort study analyzed data from 49,506 individuals within a multicenter, population-based primary care registry in Catalonia, Spain. Individuals diagnosed with type 2 diabetes at age 35 or older were recruited between January 2010 and December 2021 and followed for at least 10 years. Biomarkers associated with cardiovascular risk-including serum glucose, HbA1c, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, blood pressure, serum ferritin, leukocyte, and monocyte counts-were examined. Clustering analysis was applied to identify patient subgroups, and Cox proportional hazards models were used to assess associations with cerebrovascular events, coronary events, and composite MACE. RESULTS: Five distinct clusters were identified, characterized by differences in serum glucose, HbA1c, lipid profiles, blood pressure, and serum ferritin levels. Individuals with discordantly high serum ferritin levels relative to their body mass index (BMI) exhibited a lower risk of adverse cardiovascular outcomes. In men, hazard ratios (HR) were 0.68 (95% confidence interval [CI]: 0.53-0.87) for cerebrovascular events, 0.65 (95% CI 0.49-0.88) for coronary events, and 0.68 (95% CI 0.56-0.83) for MACE. In women, HRs were 0.81 (95% CI 0.67-0.92) for cerebrovascular events, 0.73 (95% CI 0.57-0.95) for coronary events, and 0.79 (95% CI 0.67-0.92) for MACE. CONCLUSIONS: Individuals with type 2 diabetes who exhibit higher-than-expected serum ferritin levels relative to their BMI may have a lower risk of cardiovascular events. These findings suggest that ferritin may play a more complex role in cardiovascular risk than previously assumed and highlight the potential for refined risk stratification strategies in type 2 diabetes management.

Objective: To examine the effectiveness of a 12-month MHBC intervention in the prevention of onset depression in primary health care (PHC).Methods: Twenty-two PHC centres took part in the cluster-randomized controlled trial. Patients were randomized to receive either usual care or an MHBC intervention. The endpoints were onset of major depression and reduction of depressive symptoms in participants without baseline depression at a 12-month follow-up. Results: 2531 patients agreed and were eligible to participate. At baseline, around 43% were smokers, 82% were non-adherent to the Mediterranean diet and 55% did not perform enough physical activity. The intervention group exhibited a greater positive change in two or more behaviours (OR 1.75 [95%CI: 1.17 to 2.62]; p = 0.006); any behaviour (OR 1.58 [95%CI: 1.13 to 2.20]; p = 0.007); and adherence to the Mediterranean diet (OR 1.94 [95%CI: 1.29 to 2.94]; p = 0.002), while this increase was not statistically significant for smoking and physical activity. The intervention was not effective in preventing major depression (OR 1.17; [95% CI 0.53 to 2.59)]; p = 0.690) or reducing depressive symptoms (Mean difference: 0.30; [95% CI-0.77 to 1.36]; p = 0.726) during follow-up.Conclusions: As compared to usual care, the MHBC intervention provided a non-significant reduction in the incidence of major depression.Trial registration: ClinicalTrials.gov, NCT03136211.