Background and objective Post-infectious sequelae can increase burden on healthcare systems. We aimed to assess the long-term effect of COVID-19 on healthcare utilization across all levels of care.Methods In this register-based cohort study, we included all adult (>= 18 years) residents in Sweden's two largest counties with a registered COVID-19 index date between 31 January 2020 and 9 February 2022. Each exposed individual was matched 1:1 to a control without registered COVID-19 on index date based on gender, birth year, vaccination status and the change in number of healthcare contacts between 2018 and 2019. We counted the number of healthcare contacts across all levels of care during the pre-index (13-1 months) and post-index (4-15 months) full-year periods. A difference-in-difference (DID) analysis was used to assess changes in the number of healthcare contacts and specific diagnoses, between each individual's pre- and post-periods, as well as comparing individuals with and without COVID-19.Results The study included 753,905 matched pairs, comprising 1,415,432 unique individuals. Trends in healthcare contacts were parallel between the matched groups prior to the index date. The DID analysis revealed a mean increase of 0.33 (95%CI 0.30-0.36) healthcare contacts following COVID-19, mainly observed from a smaller proportion of the population (5%) and by contacts with primary healthcare. The largest diagnosis-specific difference was observed for reactions to severe stress (0.02, 0.01-0.03). The estimate varied across gender, acute COVID-19 severity, virus variant period and vaccination status.Conclusion This study demonstrates increased healthcare utilization after COVID-19 in a smaller proportion of the population.

BackgroundFall injuries on ice and snow are a major public health problem in cold climates, placing a substantial seasonal burden on healthcare systems and affected individuals. To prevent such injuries, many Swedish municipalities have implemented programs that distribute ice cleats, typically restricted to adults aged >= 65. Evidence suggests these programs increase cleat use, reduce injuries, and are cost-effective. However, it remains unclear whether restricting distribution to older adults is more effective than broader or universal strategies. This study is the first to formally evaluate this question. MethodsWe developed a cohort simulation model combining elements of the local average treatment effects framework and the health belief model to evaluate the cost-effectiveness of alternative ice cleat distribution strategies across age thresholds. The model incorporates age-specific injury risks, compliance, costs, and quality-adjusted life year losses, using input from behavioral surveys, register data on ice-related fall injuries, and published literature. It is calibrated to outcomes from real-world distribution programs. Cost-effectiveness was assessed from a societal perspective, defining optimal thresholds as those maximizing net monetary benefit and acceptable thresholds as those with > 50% probability of being cost-effective compared to no distribution in probabilistic sensitivity analyses. ResultsOur primary analysis identified >= 55 years as the optimal eligibility threshold (acceptable range: 42-72). Sensitivity analyses indicated that universal distribution may be acceptable if cleats are purchased restrictively and targeted to non-users, but it is unlikely to be optimal. When assuming short-lived behavior change (<= 2 years) or valuing costs from a healthcare perspective only, no distribution was preferable. ConclusionAge-targeted ice cleat distribution appears more cost-effective than universal provision and preferable to no distribution, but current programs limited to older adults may be suboptimal. Extending eligibility to middle-aged adults could further improve cost-effectiveness from a societal perspective.

ObjectivesIn contemporary urban environments, the intersection between Vulnerable Road Users, specifically unprotected pedestrians, and motor vehicles poses a persistent challenge to road safety. Reversing accidents, characterized by vehicles moving backward, often result in severe consequences for pedestrians due to limited visibility and inherent blind spots for the driver. This paper aims to provide a thorough examination of reversing accidents involving pedestrians in Sweden between 2000-2021; shedding light on the magnitude of the problem, contributing factors, consequences, and potential mitigation strategies.MethodUsing a national database, reversing accidents were compiled by classifying free text descriptions of traffic accidents reported to the Swedish Traffic Accident Data Acquisition. The database was then used to describe reversing accidents and their consequences for struck pedestrians using descriptive epidemiology and cluster analysis.ResultsThe results show that reversing accidents accounted for 12% of all pedestrians injured in a collision with a motor vehicle in Sweden during the studied period. In terms of personal characteristics, the struck pedestrians were more often female and of old or young age, whilst the drivers of the reversing vehicles were more often between 18-54 years and men. Most accidents were non-fatal, with only roughly 2% resulting in fatalities. Through a cluster analysis, four distinct accident types were identified. Two of these were identified as particularly important to prioritize in future preventative work: accidents that are characterized by occur during daylight hours in the urban environment (often low speeds) and often result in fatality or serious injury.ConclusionsDue to the low speeds, reversing accidents are rarely fatal. However, they account for a large proportion of accidents with pedestrians and can be grouped into clearly distinguishable accident types that can function as templates in road safety development in Sweden.

Bayesian disease-mapping models are widely used in small-area epidemiology to account for spatial correlation and stabilize estimates through spatial smoothing. In contrast, difference-in-differences (DID) methods - commonly used to estimate treatment effects from observational panel data - typically ignore spatial dependence. This paper integrates disease mapping models into an imputation-based DID framework to address spatially structured residual variation and improve precision in small-area evaluations. The approach builds on recent advances in causal panel data methods, including two-way Mundlak estimation, to enable causal identification equivalent to fixed effects DID while incorporating spatiotemporal random effects. We implement the method using Integrated Nested Laplace Approximation, which supports flexible spatial and temporal structures and efficient Bayesian computation. Simulations show that, when the spatiotemporal structure is correctly specified, the approach improves precision and interval coverage compared to standard DID methods. We illustrate the method by evaluating local ice cleat distribution programs in Swedish municipalities.

Aims/hypothesis: The aim of this study was to quantify the impact of childhood-onset type 1 diabetes on parental incomes in a Nordic welfare state. Methods: In this register-based quasi-experimental study, we included the parents of 13,358 children diagnosed with type 1 diabetes in Sweden from 1993 to 2014 together with 506,516 population-based matched control parents. A difference-in-differences approach was used to compare income trajectories between exposed parents and control parents. Work-related and pension-qualifying incomes (including parental benefits) were assessed during the first 7 years after diagnosis. The long-term incomes of parents of children diagnosed with type 1 diabetes in 1993–2004 were also investigated. Results: A sharp decline in work-related income was observed in both mothers and fathers of children diagnosed with type 1 diabetes. In the year after diagnosis, the mean yearly income difference (expressed in €100) was –15.4 for mothers (95% CI –17.2, –13.6) and –6.0 for fathers (95% CI –8.9, –3.2), representing a relative decrease of 6.6% and 1.6%, respectively. The effects on income were similar across sociodemographic groups and calendar periods. The pension-qualifying income of mothers increased in the first year after diagnosis by 28.7 (95% CI 27.1, 30.3), attributable to the parental care allowance, but gradually decreased during long-term follow-up (–10.9, 95% CI –16.6, –5.1, after 17 years). Conclusions/interpretation: This study highlights the enduring financial consequences for parents caring for a child with type 1 diabetes in Sweden. While parental benefits in Sweden mitigated the short-term loss of maternal income, the current welfare system does not adequately address long-term consequences. 

Timely detection and surveillance of disease community spread is a potent tool for implementing effective public health interventions. This study investigates the National Telehealth Service (1177 helpline) across 18 regions in Sweden in 2020 to identify early signals of community transmission of COVID-19 at the beginning of the pandemic. Focusing on calls related to key COVID-19 symptoms (cough, fever, and breathing difficulties in adults), we analyze their frequency and distribution across referral categories, comparing them to 2019 data. We employ an explainable time series anomaly detection algorithm using daily call data to identify the first collective anomalies across regions. The results show that anomalies in call data were correlated with, but preceded, the first confirmed case infected in Sweden by a median of 7 days (IQR: 2.5-10.5) and the first hospitalized case infected in Sweden by a median of 13 days (IQR: 7.25-16). They also preceded the estimated onset of community spread, indicated by the absolute confirmed cases (median: 24.5, IQR: 18.25-32.5), and severe outcomes defined by hospitalizations (median: 33, IQR: 27.25-44). These findings showcase how helpline call monitoring, using time series anomaly detection, can aid early outbreak detection.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method’s implementation, discussing its strengths, limitations, and suitable scenarios for application. 

Objectives: For drugs reimbursed with limited evidence of patient benefits, confirmatory evidence of overall survival (OS) and quality of life (QoL) benefits is important. For QoL data to serve as valuable input to patients and decision-makers, it must be measured and analyzed using appropriate methods. We aimed to assess the measurement and analyses of post-reimbursement QoL data for cancer drugs introduced in Swedish healthcare with limited evidence at the time of reimbursement. Methods: We reviewed any published post-reimbursement trial data on QoL for cancer drugs reimbursed in Sweden between 2010 and 2020 with limited evidence of improvement in QoL and OS benefits at the time of reimbursement. We extracted information on the instruments used, frequency of measurement, extent of missing data, statistical approaches, and the use of pre-registration and study protocols. Results: Out of 22 drugs satisfying our inclusion criteria, we identified published QoL data for 12 drugs in 22 studies covering multiple cancer types. The most frequently used QoL instruments were EORTC QLQ-C30 and EQ-5D-3/5L. We identified three areas needing improvement in QoL measurement and analysis: (i) motivation for the frequency of measurements, (ii) handling of the substantial missing data problem, and (iii) inclusion and adherence to QoL analyses in clinical trial pre-registration and study protocols. Conclusions: Our review shows that the measurements and analysis of QoL data in our sample of cancer trials covering drugs initially reimbursed without any confirmed QoL or OS evidence have significant room for improvement. The increasing use of QoL assessments must be accompanied by a stricter adherence to best-practice guidelines to provide valuable input to patients and decision-makers. 

BACKGROUND: Coronary atherosclerosis detected by imaging is a marker of elevated cardiovascular risk. However, imaging involves large resources and exposure to radiation. The aim was, therefore, to test whether nonimaging data, specifically data that can be self-reported, could be used to identify individuals with moderate to severe coronary atherosclerosis. METHODS AND RESULTS: We used data from the population-based SCAPIS (Swedish CardioPulmonary BioImage Study) in individuals with coronary computed tomography angiography (n=25 182) and coronary artery calcification score (n=28 701), aged 50 to 64 years without previous ischemic heart disease. We developed a risk prediction tool using variables that could be assessed from home (self-report tool). For comparison, we also developed a tool using variables from laboratory tests, physical examinations, and self-report (clinical tool) and evaluated both models using receiver operating characteristic curve analysis, external validation, and benchmarked against factors in the pooled cohort equation. The self-report tool (n=14 variables) and the clinical tool (n=23 variables) showed high-to-excellent discriminative ability to identify a segment involvement score ≥4 (area under the curve 0.79 and 0.80, respectively) and significantly better than the pooled cohort equation (area under the curve 0.76, P<0.001). The tools showed a larger net benefit in clinical decision-making at relevant threshold probabilities. The self-report tool identified 65% of all individuals with a segment involvement score ≥4 in the top 30% of the highest-risk individuals. Tools developed for coronary artery calcification score ≥100 performed similarly. CONCLUSIONS: We have developed a self-report tool that effectively identifies individuals with moderate to severe coronary atherosclerosis. The self-report tool may serve as prescreening tool toward a cost-effective computed tomography-based screening program for high-risk individuals.