Delayed healthcare seeking and prolonged illness in healthcare workers during the COVID-19 pandemic: a single-centre observational study

Impact of the COVID-19 Pandemic on Musculoskeletal Disorder-Related Absenteeism Among Pediatric Healthcare Workers

Background and Objectives: Musculoskeletal disorders (MSDs) are a leading cause of absenteeism among healthcare workers (HCWs), impacting healthcare delivery. Pediatric HCWs face specific physical demands such as lifting and awkward postures. While absenteeism rose during the COVID-19 pandemic, its effects on pediatric MSD-related leave remain unclear. This study examined MSD-related absenteeism trends among pediatric HCWs in a Romanian hospital across the pre-pandemic (2017-2019), pandemic (2020-2021), and post-pandemic (2022-2023) periods. Materials and Methods: We conducted a retrospective observational study using records from the hospital's occupational health database. We included all HCWs who took MSD-related leave during 2017-2023. Diagnoses included arthropathies, dorsopathies, other osteoarticular/connective tissue disorders, and acute trauma or fractures. We used chi-square tests, ANOVA, and regression models to identify trends and predictors. Results: A total of 3388 cases were analyzed. Post-pandemic absenteeism increased significantly (40.1%), especially among women (86.8%), nurses (46.7%), and workers aged ≥46 (62.7%). A seasonal shift was observed, with spring peaks (March 9.7% and May 9.9%) replacing the pre-pandemic autumn peaks (October 11.9% and November 12.8%). The regression models identified age, occupation, and diagnosis type as significant predictors of leave duration. Conclusions: MSD-related absenteeism rose post-pandemic and showed altered seasonal patterns. Occupational and demographic predictors identified through a multivariate analysis highlight the need for anticipatory, evidence-based strategies to support pediatric HCWs, enhance workforce resilience, and sustain healthcare performance.

Impact of the Covid-19 pandemic on odontogenic infections in maxillofacial surgery

BACKGROUND

This study investigates the impact of the COVID-19 pandemic on the distribution, characteristics, and treatment of odontogenic infections (OI) in a cranio-maxillofacial hospital, comparing data from pre-covid (PC) and intra-covid (IC) periods.

MATERIALS AND METHODS

A retrospective analysis was conducted on 194 patients treated for OIs from February 2019 to January 2021. Patients were categorized into two cohorts: PC (02/2019 - 01/2020) and IC (02/2020 - 01/2021). Data collected included demographics, infection types, symptoms, pre-existing conditions, treatments, length of hospital stay, and time from symptom onset to treatment.

RESULTS

The IC cohort exhibited a significant increase in submandibular (13.9% to 26.7%) and floor of mouth abscesses (6.5% to 18.6%). Patients in the IC period were more likely to present with fever (69.8% vs. 36.1%, p < 0.001) and dyspnea (48.8% vs. 33.3%, p = 0.029). Additionally, there was an increase in multi-space infections (7.4% to 22.1%) and higher inflammatory markers, with leukocyte counts rising from 12.51 × 10^9/l to 15.41 × 10^9/l (p < 0.001). The mean length of stay in the hospital also increased significantly from 3.24 days to 8.01 days (p < 0.001).

CONCLUSION

The COVID-19 pandemic has significantly altered the landscape of OIs in oral and maxillofacial surgery, leading to more severe presentations, prolonged treatment durations, and increased hospital admissions. These findings underscore the necessity for improved public health strategies to ensure timely access to dental care during health crises. Enhanced awareness and proactive management are essential to mitigate the long-term impact of the aforementioned disruptions on patient outcomes.

The effect of COVID-19 pandemic on healthcare seeking in an urban informal settlement in Nairobi and a rural setting in western Kenya

The COVID-19 pandemic caused widespread changes and disruptions to healthcare seeking behavior. There are limited studies on the effect of the COVID-19 pandemic on healthcare seeking patterns in low-and middle-income countries (LMICs), especially in settings with inequitable access to healthcare in rural and urban informal settlements. We investigated the effect of the COVID-19 pandemic on reported healthcare seeking at health facilities and chemists using morbidity data from participants in an ongoing population-based infectious disease surveillance platform in Asembo in Siaya County, a rural setting in western Kenya and Kibera, an urban informal settlement in Nairobi County. We described healthcare seeking patterns before (from 1st January 2016 to 12th March 2020) and during the pandemic (from 13th March 2020 to 31st August 2022) by gender and age for any reported illness and select clinical syndromes using frequencies and percentages. We used a generalized estimating equation with an exchangeable correlation structure to assess the effect of the pandemic on healthcare seeking adjusting for gender and age. Overall, there was a 19% (adjusted odds ratio, aOR: 0.81; 95% Confidence Interval, CI: 0.79-0.83) decline in odds of seeking healthcare at health facilities for any illness in Asembo during the pandemic, and a 30% (aOR: 0.70; 95% CI: 0.67-0.73) decline in Kibera. Similarly, there was a decline in seeking healthcare by clinical syndromes, e.g., for ARI, aOR: 0.76; 95% CI:0.73-0.79 in Asembo, and aOR: 0.68; 95% CI:0.64-0.72 in Kibera. The pandemic resulted in increased healthcare seeking at chemists (aOR: 1.23; 95% CI: 1.20-1.27 in Asembo, and aOR: 1.40; 95% CI: 1.35-1.46 in Kibera). This study highlights interruptions to healthcare seeking in resource-limited settings due to the COVID-19 pandemic. The pandemic resulted in a substantial decline in seeking care at health facilities, and an increase of the same at chemists.

The effects of COVID-19 stressors and family life on anxiety and depression one-year into the COVID-19 pandemic

The purpose of this study was to examine the effects of Coronavirus (COVID-19)-related stressors and family health on adult anxiety and depressive symptoms 1 year into the pandemic. The sample consisted of 442 adults living in the United States who were recruited via Amazon Mechanical Turk. Data were analyzed using multiple logistic regression. Results indicated that compared to a sample 1 month into the pandemic, participants in the current sample reported worse family health and increases in both positive and negative perceptions of the pandemic on family life and routines. COVID-19 stressors and perceived negative effects of the pandemic on family life increased the odds for moderate-to-severe depression and anxiety while having more family health resources decreased the odds for depression and anxiety symptoms. Participants reported lower odds for worse depression and anxiety since the beginning of the pandemic when they reported more positive family meaning due to the pandemic. The results suggest a need to consider the impact of family life on mental health in pandemics and other disasters.

Determinants of COVID-19 Outcome as Predictors of Delayed Healthcare Services among Adults ≥50 Years during the Pandemic: 2006-2020 Health and Retirement Study

BACKGROUND

The coronavirus disease 19 (COVID-19) was declared a global pandemic on 11 March 2020. To date, a limited number of studies have examined the impact of this pandemic on healthcare-seeking behaviors of older populations. This longitudinal study examined personal characteristics linked to COVID-19 outcomes as predictors of self-reported delayed healthcare services attributed to this pandemic, among U.S. adults, ≥50 years of age.

METHODS

Secondary analyses were performed using cross-sectional data (1413 participants) and longitudinal data (2881 participants) from Health and Retirement Study (HRS) (2006-2018) linked to the 2020 HRS COVID-19 Project (57% female, mean age: 68 years). Demographic, socioeconomic, lifestyle and health characteristics were evaluated in relation to delayed overall, surgical and non-surgical healthcare services ("Since March 2020, was there any time when you needed medical or dental care, but delayed getting it, or did not get it at all?" and "What type of care did you delay") using logistic regression and Ensemble machine learning for cross-sectional data as well as mixed-effects logistic modeling for longitudinal data.

RESULTS

Nearly 32.7% delayed healthcare services, 5.8% delayed surgical services and 31.4% delayed non-surgical services. Being female, having a college degree or higher and 1-unit increase in depression score were key predictors of delayed healthcare services. In fully adjusted logistic models, a history of 1 or 2 cardiovascular and/or metabolic conditions (vs. none) was associated with 60-70% greater odds of delays in non-surgical services, with distinct findings for histories of hypertension, cardiovascular disease, diabetes and stroke. Ensemble machine learning predicted surgical better than overall and non-surgical healthcare delays.

CONCLUSION

Among older adults, sex, education and depressive symptoms are key predictors of delayed healthcare services attributed to the COVID-19 pandemic. Delays in surgical and non-surgical healthcare services may have distinct predictors, with non-surgical delays more frequently observed among individuals with a history of 1 or 2 cardiovascular and/or metabolic conditions.

Temporal trends and forecasting of COVID-19 hospitalisations and deaths in Scotland using a national real-time patient-level data platform: a statistical modelling study

BACKGROUND

As the COVID-19 pandemic continues, national-level surveillance platforms with real-time individual person-level data are required to monitor and predict the epidemiological and clinical profile of COVID-19 and inform public health policy. We aimed to create a national dataset of patient-level data in Scotland to identify temporal trends and COVID-19 risk factors, and to develop a novel statistical prediction model to forecast COVID-19-related deaths and hospitalisations during the second wave.

METHODS

We established a surveillance platform to monitor COVID-19 temporal trends using person-level primary care data (including age, sex, socioeconomic status, urban or rural residence, care home residence, and clinical risk factors) linked to data on SARS-CoV-2 RT-PCR tests, hospitalisations, and deaths for all individuals resident in Scotland who were registered with a general practice on Feb 23, 2020. A Cox proportional hazards model was used to estimate the association between clinical risk groups and time to hospitalisation and death. A survival prediction model derived from data from March 1 to June 23, 2020, was created to forecast hospital admissions and deaths from October to December, 2020. We fitted a generalised additive spline model to daily SARS-CoV-2 cases over the previous 10 weeks and used this to create a 28-day forecast of the number of daily cases. The age and risk group pattern of cases in the previous 3 weeks was then used to select a stratified sample of individuals from our cohort who had not previously tested positive, with future cases in each group sampled from a multinomial distribution. We then used their patient characteristics (including age, sex, comorbidities, and socioeconomic status) to predict their probability of hospitalisation or death.

FINDINGS

Our cohort included 5 384 819 people, representing 98·6% of the entire estimated population residing in Scotland during 2020. Hospitalisation and death among those testing positive for SARS-CoV-2 between March 1 and June 23, 2020, were associated with several patient characteristics, including male sex (hospitalisation hazard ratio [HR] 1·47, 95% CI 1·38-1·57; death HR 1·62, 1·49-1·76) and various comorbidities, with the highest hospitalisation HR found for transplantation (4·53, 1·87-10·98) and the highest death HR for myoneural disease (2·33, 1·46-3·71). For those testing positive, there were decreasing temporal trends in hospitalisation and death rates. The proportion of positive tests among older age groups (>40 years) and those with at-risk comorbidities increased during October, 2020. On Nov 10, 2020, the projected number of hospitalisations for Dec 8, 2020 (28 days later) was 90 per day (95% prediction interval 55-125) and the projected number of deaths was 21 per day (12-29).

INTERPRETATION

The estimated incidence of SARS-CoV-2 infection based on positive tests recorded in this unique data resource has provided forecasts of hospitalisation and death rates for the whole of Scotland. These findings were used by the Scottish Government to inform their response to reduce COVID-19-related morbidity and mortality.

FUNDING

Medical Research Council, National Institute for Health Research Health Technology Assessment Programme, UK Research and Innovation Industrial Strategy Challenge Fund, Health Data Research UK, Scottish Government Director General Health and Social Care.

Community risk perception and health-seeking behavior in the era of COVID-19 among adult residents of Harari regional state, eastern Ethiopia

BACKGROUND

COVID-19 brought significant challenges to public health. It changed the view of global health and safety, trust in the healthcare system, and clients' willingness to seek healthcare. To contain the course of the COVID-19 pandemic and its detrimental effects, understanding peoples' health behavior, especially healthcare-seeking, and determining the community risk perception is very important. Thus, this study aimed to determine the health-seeking behavior, community's risk perception to COVID-19 pandemics, and factors influencing the community risk perception in Harari regional state, Ethiopia.

METHODS

Community-based cross-sectional study was conducted from 5 to 30 February 2021. A total of 1320 adult (>18 years) participants were selected using systematic random sampling. The data were collected using an online kobo collect toolbox and analyzed using descriptive statistical tests. Chi-square test and multiple binary logistic regression were applied to examine the difference between variables. A p-value < 0.05 was considered to be of statistical significance.

RESULTS

The study included 1296 respondents >18 years old. The overall prevalence of willingness to seek healthcare in the study area was 35.6% (95% CI: 33%-38.3.0%). The mean cumulative score of risk perception was 30.5 (SD ± 7.25) with the minimum and maximum score of 13 and 63, respectively. A total of 656 (50.6%) of the participants had low-risk perceptions concerning COVID-19. The study found a statistically significant association between risk perception and sociodemographic characteristics (age, educational status, and income), and knowledge of the respondents.

CONCLUSION

The overall prevalence of willingness to seek healthcare was 35.6%. Healthcare intervention aimed to contain the COVID-19 pandemic should consider the factors associated with the study area. Similarly, the study found a low-risk perception among the community that needs critical action to manage the COVID-19 pandemic and to protect the community as a whole. Thus, it is necessary to improve community risk perception through health education.

Characteristics of work-related COVID-19 in Croatian healthcare workers: a preliminary report

Healthcare workers (HCWs) are considered to run a higher occupational risk of becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and develop coronavirus disease (COVID-19) than the rest of the population. The aim of this study was to describe and analyse the characteristics of work-related COVID-19 in Croatian HCWs. Study participants were HCWs who contacted their occupational physician between 1 May 2020 and 12 November 2020 with a request for the registration of COVID-19 as an occupational disease. All participants filled out our online Occupational COVID-19 in Healthcare Workers Questionnaire. The study included 59 HCWs (median age 45.0, interquartile range 36.0-56.0 years). Most (78 %) were nurses or laboratory technicians, and almost all (94.9 %) worked in hospitals. Hierarchical cluster analysis revealed three clusters of COVID-19-related symptoms: 1) elevated body temperature with general weakness and fatigue, 2) diarrhoea, and 3) headache, muscle and joint pain, anosmia, ageusia, and respiratory symptoms (nasal symptoms, burning throat, cough, dyspnoea, tachypnoea). Almost half (44.6 %) reported comorbidities. Only those with chronic pulmonary conditions were more often hospitalised than those without respiratory disorders (57.1 % vs. 2.5 %, respectively; P=0.001). Our findings suggest that work-related COVID-19 among Croatian HCWs is most common in hospital nurses/laboratory technicians and takes a mild form, with symptoms clustering around three clinical phenotypes: general symptoms of acute infection, specific symptoms including neurological (anosmia, ageusia) and respiratory symptoms, and diarrhoea as a separate symptom. They also support evidence from other studies that persons with chronic pulmonary conditions are at higher risk for developing severe forms of COVID-19.