Future Hospital Admissions Research Articles

A case for use of the ratio of admissions per death to predict future hospital admissions

Rodney P Jones presents his perspective of hospital admission forecasting, explaining why deaths may be a more significant indicator than widely used forecasting methods allow.

Emergency Medicaid enrollment after traumatic injury predicts long-term health care utilization.

Injured patients have high rates of uninsurance, which is associated with worse outcomes. Insurance linkage programs that connect patients to Medicaid coverage can prevent catastrophic costs for patients. Less is known about the long-term impact of insurance enrollment. We examined health care utilization for previously uninsured patients, hypothesizing that newly insured patients postinjury would use health care more than those remaining uninsured. We linked institutional trauma registry data to the electronic medical record to identify injured patients aged 18 to 64 years from 2017 to 2021. Patients admitted without insurance and then retroactively insured (RI) during hospitalization were compared with patients with preestablished Medicaid (Medicaid insured [MI]) and those remaining uninsured. We compared demographic and injury characteristics and future health care utilization, including hospitalizations, emergency department (ED) visits, and clinic visits, among groups at 30 days and 12 months postinjury. Patient-reported outcomes (PROs) 6 months after injury were compared by group for a subset of patients participating in an ongoing study of long-term PROs. We compared 494 RI patients with 1,706 MI and 148 uninsured patients. Retroactively insured patients were younger, more likely to have penetrating injuries, and longer hospitalization than other groups. There was a significant increase in ED and clinic visits and hospital admissions at 30 days and 12 months between RI and uninsured patients (p < 0.001). Using multivariable logistic regression, RI was associated with higher future ED utilization, hospital admissions, and specialist visits at 30 days and 12 months compared with uninsured patients. Of the 265 patients with 6-month PROs, Medicaid coverage was not associated with any significant difference in physical function or anxiety. Patients enrolled in insurance postinjury are more likely to use health care in the future than patients without insurance, but the downstream effects are less clear. Health insurance is a necessary step but not independently sufficient to optimize care and improve health outcomes. Prognostic and Epidemiological; Level IV.

The Whys and Wherefores of Reducing Cannabis Use in Early Psychosis: A Qualitative Study of Service Users’ Experiences of Quitting and the Support

Objectives: Cannabis is the most widely used substance among people with recent onset psychosis, but it can have significant negative consequences for long term wellbeing in this cohort. We explored the perspectives of people with recent onset psychosis who used cannabis on their motivations for quitting, their experiences of trying to do so, and their views of the support they had received and the strategies that they had tried. Methods: Twenty one-to-one qualitative interviews were conducted with Early Intervention in Psychosis service users in England who had participated in the CIRCLE trial. Purposive sampling was used to recruit a mix of demographic, cannabis use status, and other characteristics. Results: Quitting cannabis is often very challenging for people and can require making substantial changes in their lives, including to their social relationships, living arrangements, or pathways through work or education. Participants reported wanting help, but often experienced support from mental health services as insubstantial and poorly tailored. Support from peers with relevant lived-experience, where available, was highly valued. Common reasons for quitting included its impact on key life goals or engaging with hobbies, finances, mental health, incompatibility with self-image, and negative use expectancies of intoxication. Concerns regarding mental health were primarily related to psychotic illness, including fear of exacerbating symptoms or experiencing future hospital admissions. Discussion: It is currently unclear how best to support people in this cohort. Interventions that provide support from people with relevant lived experience may be more valued and more clinically effective than current offerings.

Ophthalmic evaluation of a population simulated with AI: Epidemiology and future disease trends

The development of AI models for the diagnosis of diseases in Ophthalmology is accelerating rapidly. These models, however, mostly refer to a single or only a few diseases. In addition, only a few examination techniques are considered in a given model. For example, many current AI models only consider fundus images for their diagnosis. Thus, general clinical information that is also available about a patient is often neglected. This information includes, for example, age, sex and secondary diagnoses.In most cases, diseases have different frequencies depending on age and sex. This leads to a different a priori probability for a disease diagnosis depending on the patient's age. When considering several diseases in one model, this class imbalance must be considered in the model. Large longitudinal data sets of disease histories of individual persons are rare or under high data privacy requirements.With the aim of more holistic AI models that include multiple diagnoses, a novel AI model was developed that is able to simulate longitudinal disease histories of individual (synthetic) patients. This was made possible based on data from the German Federal Statistical Office and a newly developed AI algorithm. In this present study, the model is used to simulate different diagnoses of the German population that lead to inpatient hospital admissions. The data were generated as completely synthetic data and have no data privacy requirements. Information on epidemiology and disease incidence can be derived from this model. Furthermore, it is possible to generate forecasts about future hospital admissions and their underlying diagnoses by extrapolating the model into the future.

Wastewater surveillance provides 10-days forecasting of COVID-19 hospitalizations superior to cases and test positivity: A prediction study

BackgroundThe public health response to COVID-19 has shifted to reducing deaths and hospitalizations to prevent overwhelming health systems. The amount of SARS-CoV-2 RNA fragments in wastewater are known to correlate with clinical data including cases and hospital admissions for COVID-19. We developed and tested a predictive model for incident COVID-19 hospital admissions in New York State using wastewater data. MethodsUsing county-level COVID-19 hospital admissions and wastewater surveillance covering 13.8 million people across 56 counties, we fit a generalized linear mixed model predicting new hospital admissions from wastewater concentrations of SARS-CoV-2 RNA from April 29, 2020 to June 30, 2022. We included covariates such as COVID-19 vaccine coverage in the county, comorbidities, demographic variables, and holiday gatherings. FindingsWastewater concentrations of SARS-CoV-2 RNA correlated with new hospital admissions per 100,000 up to ten days prior to admission. Models that included wastewater had higher predictive power than models that included clinical cases only, increasing the accuracy of the model by 15%. Predicted hospital admissions correlated highly with observed admissions (r = 0.77) with an average difference of 0.013 hospitalizations per 100,000 (95% CI = [0.002, 0.025]) InterpretationUsing wastewater to predict future hospital admissions from COVID-19 is accurate and effective with superior results to using case data alone. The lead time of ten days could alert the public to take precautions and improve resource allocation for seasonal surges.

COVID-19 hospitalizations and deaths predicted by SARS-CoV-2 levels in Boise, Idaho wastewater

The viral load of COVID-19 in untreated wastewater from Idaho's capital city Boise, ID (Ada County) has been used to predict changes in hospital admissions (statewide in Idaho) and deaths (Ada County) using distributed fixed lag modeling and artificial neural networks (ANN). The wastewater viral counts were used to determine the lag time between peaks in wastewater viral counts and COVID-19 hospitalizations as well as deaths (14 and 23 days, respectively). Quantitative measurement of SARS-CoV-2 viral RNA counts in the untreated wastewater was determined three times a week using RT-qPCR over a span of 13 months. To mitigate the effects of PCR inhibitors in wastewater, a series of dilution tests were conducted, and the 1/4 dilution was used to generate the most successful model. Wastewater SARS-CoV-2 viral RNA counts and hospitalization from June 7, 2021 to December 29, 2021 were used as training data to predict hospitalizations; and wastewater SARS-CoV-2 viral RNA counts and deaths from June 7, 2021 to December 20, 2021 were used as training data to predict deaths. These training data were used to make predictive ANN models for future hospitalizations and deaths. To the best of our knowledge, this is the first report of prediction of deaths from COVID-19 based on wastewater SARS-CoV-2 viral RNA counts using machine learning-based multilayered ANN. The applied modeling demonstrates that wastewater surveillance data can be combined with hospitalizations and death data to generate machine learning-based ANN models that predict future COVID-19 hospital admissions and deaths, providing an early warning for medical response teams and healthcare policymakers.

How much disease risk is due to old age and established risk factors?

Improved healthcare is leading to older populations and increasing numbers of individuals experiencing multiple diseases, possibly concurrently (multimorbidity). This article asks whether the observed number of new diseases is more than expected based on age and established risk factors alone, assuming that disease risk is unchanged by prior or pre-existing disease. This is accomplished by designing a new epidemiological approach, where the expected number of disease types are estimated for individuals without prior disease, by combining individual risk predictions with a "Poisson-Binomial" model to estimate the expected number of new diseases and its confidence interval. For 123 diseases in men and 99 diseases in women, the expected number of new diseases based on age and established risk factors was approximately of that observed, with the observed number of new diseases approximately times that predicted. The differences could not be explained by natural statistical variation, and provide a rigorous statistical demonstration of lower disease risk for individuals without any previous disease. The multiple of was sufficiently consistent across different diseases to prevent its use for classification of disease types, but there were differences for subgroups such as smokers with high body mass index, and for some classes of disease (as defined by the International Classification of Diseases, version 10). The results suggest that empirical modeling might allow reliable predictions of future hospital admissions, and confirm the value of conventional epidemiological approaches that study disease risk in healthy individuals. The implications and future possibilities of this new approach are discussed.

Impact of COVID-19 and lockdown on COPD admissions to Christchurch Hospital, New Zealand 2020-2021: a combined quantitative and qualitative study.

During the year following New Zealand's first COVID-19 lockdown, a 33% reduction in chronic obstructive pulmonary disease (COPD)-related admissions occurred and persisted beyond this period at Christchurch Hospital. To identify contributing factors which may have resulted in a persistent decrease in COPD hospitalisation rates at Christchurch Hospital following the 2020 COVID-19 lockdown. Using an explanatory sequential mixed-methods research design, we (i) retrospectively analysed hospital admissions and primary healthcare access by people with COPD (n = 1358) in Canterbury before, during and after COVID lockdown (24 March 2019 to 2021) and (ii) undertook individual interviews from a sample of patients (n = 14). Patients who were not re-admitted following the COVID-19 lockdown had fewer general practice encounters, acute primary care access, antibiotic and prednisone prescriptions. Proportionally fewer Māori and more Pacific patients were admitted with COPD following lockdown. Positive contributing factors at a primary care level included improvements in primary care interactions and medication management. At a patient and community level, there were improvements in lifestyle, self-management practices, social support and contact precautions. However, a subgroup of patients described negative effects such as social isolation. A combination of patient, primary care and community-level factors led to an overall persistent decrease in COPD admissions following the COVID-19 lockdown. Future targeted and individualised measures focusing on these modifiable factors may decrease future COPD-related hospital admissions. The study design facilitated further explanation about factors that contributed to the persistent decrease in hospital admissions among people living with COPD and has underscored the importance of social support, patient empowerment and reduction in barriers in accessing care in admission reduction.

Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters. This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims' health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome. After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053-1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295-1·476]; p<0·0001), injury at the scene (1·484 [1·368-1·609]; p<0·0001), assessed high risk (1·600 [1·444-1·773]; p<0·0001), referral to other agencies (1·518 [1·358-1·697]; p<0·0001), history of violence (1·229 [1·134-1·333]; p<0·0001), attempted strangulation (1·311 [1·148-1·497]; p<0·0001), and pregnancy (1·372 [1·142-1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident. The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals. National Institute for Health Research.

#4199 PSYCHOSOCIAL ASSESSMENTS AND HOSPITAL ADMISSION RATES IN ELEVATED OR HIGH ACUITY HEMODIALYSIS PATIENTS

Abstract Background and Aims Using data routinely captured in electronic medical records of a large dialysis organization (LDO) in close collaboration with social workers (MSW), we developed a weekly patient acuity score to identify the level of need for psychosocial support in hemodialysis patients to help social workers optimize visits to deliver timely personalized care. We evaluated how interventions by a MSW affected future hospital admission rate in in-center hemodialysis (ICHD) patients with Elevated or High acuity in an LDO. Method Using data from prevalent (vintage &amp;gt;90 days) ICHD patients from Mar-2022 through Dec-2022, a weekly acuity score was computed using 95 routinely captured variables considered critical (e.g., psychosocial, cognitive, clinical, treatment, hospitalization). Each variable was assigned points based on a priori criteria which were summed and categorized based on population distribution for each week. Patient acuity was assigned as Low, Moderate, Elevated, or High based upon percentile distribution. We defined MSW interventions as the number of assessments completed (0, &amp;lt;1, 1, or &amp;gt;1 per month) during the baseline period (BL, Mar-2022 through May 2022). Next, we compared hospital admission rate in the BL period to that in a follow-up period (FU, June 2022 through Dec 2022) for patients whose acuity at start of BL was Elevated or High (E/H), stratified by monthly MSW assessment exposure. This analysis focuses on prevalent ICHD patients in the Elevated and High acuity categories at baseline (BL, March 2022 through May 2022) as they were expected to have been prioritized for MSW assessment. Results We identified 29,665 prevalent ICHD patients whose acuity was E/H at the start of BL; 8.2%, 24.8%, 20.9%, and 46.2% had 0, &amp;lt;1, 1, and &amp;gt;1 MSW assessments per month during BL, respectively. As expected, hospital admission rate was generally high for patients with E/H acuity. Likewise, for patients with E/H acuity, hospital admission rate increases as the number of monthly interventions in BL increases (Figure 1). For patients with E/H acuity, in each category of BL MSW assessment exposure, the hospital admission rate in FU is lower than in the BL period and the magnitude of the decrease is larger when there are more MSW assessments in the BL period; for 0, &amp;lt;1, 1, and &amp;gt;1 respective MSW assessments, the respective rate ratios were 0.99, 0.99, 0.98, and 0.90, and rate ratio was significantly lower for patients with &amp;gt;1 MSW assessment in BL. Conclusion Our results suggest that acuity is helping MSW to identify patients with requiring more psychosocial and other needs as 46.2% of patients with E/H acuity had &amp;gt;1 MSW assessment. Our results also suggest that &amp;gt;1 MSW assessments in E/H acuity patients results in a larger magnitude in the decrease of hospital admission rates compared to other levels of MSW assessment exposure. Future analyses are needed to confirm these results.

The Alcohol Liaison Service: an update

A previous article published in this journal discussed an outpatients clinic in north London to illustrate the way it can treat patients effectively to keep them engaged and benefit from treatment. That article showed that the service can avoid future hospital admissions. This article provides a 6-month update and further evidences the positive outcomes that can be achieved with an outpatients clinic, treating patients who are admitted to hospital with alcohol dependency.

Deep Learning for Cross-Diagnostic Prediction of Mental Disorder Diagnosis and Prognosis Using Danish Nationwide Register and Genetic Data

Diagnoses and treatment of mental disorders are hampered by the current lack of objective markers needed to provide a more precise diagnosis and treatment strategy. To develop deep learning models to predict mental disorder diagnosis and severity spanning multiple diagnoses using nationwide register data, family and patient-specific diagnostic history, birth-related measurement, and genetics. This study was conducted from May 1, 1981, to December 31, 2016. For the analysis, which used a Danish population-based case-cohort sample of individuals born between 1981 and 2005, genotype data and matched longitudinal health register data were taken from the longitudinal Danish population-based Integrative Psychiatric Research Consortium 2012 case-cohort study. Included were individuals with mental disorders (attention-deficit/hyperactivity disorder [ADHD]), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), schizophrenia spectrum disorders (SCZ), and population controls. Data were analyzed from February 1, 2021, to January 24, 2022. At least 1 hospital contact with diagnosis of ADHD, ASD, MDD, BD, or SCZ. The predictability of (1) mental disorder diagnosis and (2) severity trajectories (measured by future outpatient hospital contacts, admissions, and suicide attempts) were investigated using both a cross-diagnostic and single-disorder setup. Predictive power was measured by AUC, accuracy, and Matthews correlation coefficient (MCC), including an estimate of feature importance. A total of 63 535 individuals (mean [SD] age, 23 [7] years; 34 944 male [55%]; 28 591 female [45%]) were included in the model. Based on data prior to diagnosis, the specific diagnosis was predicted in a multidiagnostic prediction model including the background population with an overall area under the curve (AUC) of 0.81 and MCC of 0.28, whereas the single-disorder models gave AUCs/MCCs of 0.84/0.54 for SCZ, 0.79/0.41 for BD, 0.77/0.39 for ASD, 0.74/0.38, for ADHD, and 0.74/0.38 for MDD. The most important data sets for multidiagnostic prediction were previous mental disorders and age (11%-23% reduction in prediction accuracy when removed) followed by family diagnoses, birth-related measurements, and genetic data (3%-5% reduction in prediction accuracy when removed). Furthermore, when predicting subsequent disease trajectories of the disorder, the most severe cases were the most easily predictable, with an AUC of 0.72. Results of this diagnostic study suggest the possibility of combining genetics and registry data to predict both mental disorder diagnosis and disorder progression in a clinically relevant, cross-diagnostic setting prior to clinical assessment.

Impacts of past occupational injury and long-duration compensated work disability on future hospital admissions.

ObjectivesTo investigate changes in the prevalence and nature of hospital admissions towards the end of long-duration workers’ compensation claims (&gt;2 years), and afterwards. To examine differences in hospitalisation when workers’ compensation claims end due to either a 260-week duration limit, or otherwise, and comparisons with hospitalisations of a community comparator. ApproachA retrospective cohort study examined 2475 workers, termed the s39 group, whose workers’ compensation ceased due to a 260-week limit in 2017/2018 under s39(1) of the Workers’ Compensation Act New South Wales 2012 legislative amendments (Australia). Comparator groups were injured workers with long-duration claims whose compensation ceased independently of s39 (termed the injured control group, N=3626) and a community group (N=8485). Workers’ compensation records were linked to national social security payments, and hospital admissions. Outcomes describe the prevalence and diagnostic categories of hospital admissions 12 months before, and after, the cessation of workers’ compensation stopped payments. ResultsMusculoskeletal health conditions were common diagnoses in overnight hospitalisations for injured workers. Single-day hospital care for mental health disorders were more common for injured workers (17% of same-day admissions) than for members of a community control (3% of same-day admissions) across two years. Exiting the workers’ compensation scheme is associated with significantly fewer annual hospital admissions for the injured control group (OR 0.76), but not for the s39 group (OR 1.01). Injured workers with long-duration compensated work disability were admitted to hospital more often than the community comparator group during the year after workers’ compensation stops (s39 group: OR 1.55, injured control group: OR 1.30). Across all study groups, hospital admission was more common for people receiving disability social security benefits and older age groups. ConclusionPolicy change in the New South Wales workers’ compensation system introduced a 260-week limit on compensation, leaving workers with an elevated need for hospital care after their compensation ended. Welfare policies that disrupt key determinants of health require dedicated inter-agency provisions to support the elevated health needs of those affected.

Time series forecasting for number of hospitalizations caused due to COVID-19 in the United Kingdom

Pandemics and epidemics have plagued humanity throughout history. The modern world faced one such devastating disease in 2019 called Coronavirus. As the world is still trying to recover from Coronavirus, an epidemic that might not see its end anytime soon. This study focuses on analyzing and predicting the future hospital admissions that arise due to Covid-19 .For this study, the choice of country is the United Kingdom. The data has been procured from reliable internet sources to carry out all necessary experiments. The data set contains daily hospitalisations due to Covid-19 in the United Kingdom. To carry out the time-series forecasting, the predictive model is built using a special Recurrent Neural Network, also known as Long Short-Term Memory (LSTM) . The final model was built using a Stacked LSTM to predict the number of hospitalisations due to Covid-19 that may arise in the United Kingdom for the next twenty days from the last day of the dataset used. The results of this study show a clear indication that a spike in the number of hospitalisations may arise in the upcoming days.

Developing and validating a predictive model for future emergency hospital admissions.

Although many emergency hospital admissions may be unavoidable, a proportion of these admissions represent a failure of the care system. The adverse consequences of avoidable emergency hospital admissions affect patients, carers, care systems and substantially increase care costs. The aim of this study was to develop and validate a risk prediction model to estimate the individual probability of emergency admission in the next 12 months within a regional population. We deterministically linked routinely collected data from secondary care with population level data, resulting in a comprehensive research dataset of 190,466 individuals. The resulting risk prediction tool is based on a logistic regression model with five independent variables. The model indicated a discrimination of area under the receiver operating characteristic curve of 0.9384 (95% CI 0.9325-0.9443). We also experimented with different probability cut-off points for identifying high risk patients and found the model's overall prediction accuracy to be over 95% throughout. In summary, the internally validated model we developed can predict with high accuracy the individual risk of emergency admission to hospital within the next year. Its relative simplicity makes it easily implementable within a decision support tool to assist with the management of individual patients in the community.

How Do Case Mangers Determine the Types of Services Provided to Users in the Intensive Case Management? A Longitudinal Study

Introduction Intensive case management is an essential component of community mental health care system for people with severe mental illness. We examined the association of service types with previous and future functioning and hospital admissions using a longitudinal data, to clarify the case managers’ activity. Methods The study included baseline assessment, recording and analysis of services, and follow-up assessment. At the baseline and follow-up assessments, we examined functioning (ie, Personal and Social Performance Scale: PSP) and hospital admission of users for past periods. For recording and analysis of services, we recorded services provided by case managers over 2 months, and five core services were used for the analyses: regular face-to-face contact, outreach services, hospital-based (outpatient) services, and easy contacts and coordination. Results Outreach services were significantly associated with hospital admissions at baseline and follow-up assessments. Hospital-based services were significantly associated with lower PSP scores and hospital admission at follow-up assessment. Easy contacts and coordination were associated with lower PSP scores at follow-up assessment. Conclusion In Japan, most services are provided in the outpatient department of hospital, and outreach services are relatively time consuming for case managers; thus, the indications for providing outreach services need to be carefully considered. Therefore, we suggest that case managers provide flexible services to users according to the circumstances in Japan.

Bariatric surgery decreases the number of future hospital admissions for diastolic heart failure in subjects with severe obesity: a retrospective analysis of the US National Inpatient Sample database.

Considerable evidence documents the effectiveness and efficacy of bariatric surgery (BaS) in reducing the prevalence and severity of obesity-related co-morbidities. Diastolic heart failure (DHF) is a condition with considerable morbidity and mortality, yet recalcitrant to medical therapy. Our objectives were to assess whether BaS is associated with a decrease in hospital admissions for DHF and determine its impact upon DHF hospital admissions among patients with hypertension (HTN) and coronary artery disease (CAD). Academic institution. Data on 296 041 BaS cases and 2 004 804 controls with severe obesity were extracted from the US National Inpatient Sample database for the years 2010 to 2015 and compared. Univariate and multivariable analysis were performed to assess the impact of pre-2010 BaS on the rate of hospital admissions for DHF, adjusting for demographics, co-morbidities, and other risk factors associated with cardiovascular disease (CVD). Relative to controls, all baseline CVD risk factors were less common among BaS cases. Nonetheless, even after adjusting for all CVD risk factors, controls exhibited marked increases in the odds of DHF overall (odds ratio = 2.80; 95% confidence interval = 2.52-3.10). Controls with HTN and CAD demonstrated an almost 3-fold increase in odds of DHF admissions. Similarly, controls with no HTN demonstrated a 5-fold increase in odds of admissions for DHF when compared to the surgical group. In this retrospective, case control study of a large, representative national sample of patients with severely obesity, BaS was found to be associated with significantly reduced hospitalizations for DHF when adjusted for baseline CVD risk factors. It also reduced DHF incidence in high-risk patients with HTN and CAD.

Predicting regional COVID-19 hospital admissions in Sweden using mobility data

The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.

Maternal risk factors for paediatric inguinal hernia.

Risk factors for paediatric inguinal hernia are poorly understood. This longitudinal cohort study assessed whether children with a maternal history of inguinal hernia or connective tissue disorders have a higher risk of developing inguinal hernias before 13 years of age. The study included children followed up between birth and 13 years of age in Quebec, Canada, 2006-2019. Newborns whose mothers had inguinal hernias or connective tissue disorders were followed over time to identify future hospital admissions for inguinal hernia. Cox proportional hazards regression adjusted for patient characteristics was used to estimate hazard ratios (HRs) and 95 per cent confidence intervals for the association between maternal hernia or connective tissue disorders and future childhood hernias. Associations in girls and boys were examined separately. The study included 786 322 children with 6 186 448 person-years of follow-up. There were 6861 children with inguinal hernias, corresponding to an incidence of 11.1 per 10 000 person-years. Children with a maternal history of inguinal hernia had 2.92 (95 per cent c.i. 2.39 to 3.58) times the risk of having inguinal hernias relative to children whose mothers had no such history. Children with a maternal history of connective tissue disorders had 1.30 (1.00 to 1.68) times the risk. Maternal hernias were strongly associated with risk of inguinal hernias in girls (HR 5.34, 3.82 to 7.47), whereas maternal connective tissue disorders were associated with inguinal hernias in boys (HR 1.35, 1.02 to 1.79). Paediatric inguinal hernias may be associated with maternal inguinal hernias and connective tissue disorders, but the underlying reason for this relationship requires further investigation.

Impact of an anticipatory care planning intervention on unscheduled acute hospital care using difference-in-difference analysis

Unscheduled admissions to hospital place great demands on the use of limited healthcare resources in health systems worldwide. A range of approaches exist to manage demand; however, interventions within hospitals...