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by Manuel Ribeiro, Leonardo Azevedo, André Peralta Santos, Pedro Pinto Leite, Maria João Pereira
During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic’s spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020–2021 period from those in the 2021–2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.To identify how family caregivers adapt to the caregiving role following a relative's COVID-19-related intensive care unit (ICU) hospitalisation.
Family caregiving is often associated with poor health amongst caregivers which may limit their capacity to effectively support patients. Though severe COVID-19 infection has necessitated increasing numbers of persons who require caregiver support, little is known about these caregivers, the persons they are caring for, or the strategies used to effectively adjust to the caregiving role.
A qualitative descriptive study design was adopted, and findings are reported using COREQ.
A secondary analysis of transcripts from semi-structured interviews conducted with recently discharged ICU patients who had COVID-19 (n = 16) and their family caregivers (n = 16) was completed using thematic analysis. MAXQDA 2020 and Miro were used to organise data and complete coding. Analysis involved a structured process of open and closed coding to identify and confirm themes that elucidated adaptation to family caregiving.
Six themes highlight how family caregivers adapt to the caregiving role following an ICU COVID-19-related hospitalisation including (1) engaging the support of family and friends, (2) increased responsibilities to accommodate caregiving, (3) managing emotions, (4) managing infection control, (5) addressing patient independence and (6) engaging support services. These themes were found to be congruent with the Roy adaptation model.
Family caregiving is a stressful transition following a patient's acute hospitalisation. Effective adaptation requires flexibility and sufficient support, beginning with the care team who can adequately prepare the family for the anticipated challenges of recovery.
Clinical teams may improve post-hospitalisation care outcomes of patients by preparing families to effectively adjust to the caregiver role—particularly in identifying sufficient support resources.
Participation of patients/caregivers in this study was limited to the data provided through participant interviews.
This study aimed to examine the association of midlife fitness and body mass index (BMI) with incident dementia later in life.
A cohort study of 6428 individuals (mean age 50.9±7.6 years) from the Cooper Center Longitudinal Study.
Cardiorespiratory fitness and BMI were assessed twice (1970–1999) during visits to the Cooper Clinic, a preventive medicine clinic in Dallas, Texas. These measures were examined as continuous and categorical variables. As continuous variables, fitness and BMI were examined at baseline (averaged of two examinations) and as absolute change between exams (mean time 2.1±1.8 years). Variables were categorised: unfit versus fit and normal versus overweight/obese. Medicare claims data were used to obtain all-cause dementia incidence (1999–2009). Mean follow-up between midlife examinations and Medicare surveillance was 15.7 ((SD=6.2) years. Multivariable models were used to assess the associations between fitness, BMI and dementia.
During 40 773 person years of Medicare surveillance, 632 cases of dementia were identified. After controlling for BMI and covariates, each 1-metabolic equivalent increment in fitness was associated with 5% lower (HR 0.95; 95% CI 0.90 to 0.99) dementia risk. In comparison, after controlling for fitness and covariates, each 1 kg/m2 increment in BMI was associated with a 3.0% (HR 1.03; 95% CI 1.00 to 1.07) higher risk for dementia, yet without significance (p=0.051). Similar findings were observed when the exposures were categorised. Changes in fitness and BMI between examinations were not related to dementia. Jointly, participants who were unfit and overweight/obese had the highest (HR 2.28 95% CI 1.57 to 3.32) dementia risk compared with their fit and normal weight counterparts.
Lower midlife fitness is a risk marker for dementia irrespective of weight status. Being unfit coupled with overweight/obese status might increase one’s risk for dementia even further.
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