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.

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.

During the coronavirus disease 2019 (COVID-19) pandemic, Sweden emphasized voluntary guidelines over mandates. We exploited a rapid change and reversal of the Public Health Agency of Sweden's COVID-19 testing guidelines for vaccinated and recently infected individuals as a quasi-experiment to examine sociodemographic differences in the response to changes in pandemic guidelines. We analyzed daily polymerase chain reaction tests from 1 October 2021 to 15 December 2021, for vaccinated or recently infected adults (>= 20 years; n = 1 596 321) from three Swedish regions (Stockholm, & Ouml;rebro, and Dalarna). Using interrupted time series analysis, we estimated abrupt changes in testing rates at the two dates when the guidelines were changed (1 November and 22 November). Stratified analysis and meta-regression were employed to explore sociodemographic differences in the strength of the response to the guideline changes. Testing rates declined substantially when guideline against testing of vaccinated and recently infected individuals came into effect on 1 November [testing rate ratio: 0.50 (95% confidence interval, CI 0.41, 0.61)], and increased again from these lowered levels by a similar amount upon its reversal on 22 November [testing rate ratio: 2.19 (95% CI: 1.69, 2.85)]. Being Sweden-born, having higher household income, or higher education, were all associated with a stronger adherent response to the guideline changes. Adjusting for stratum-specific baseline testing rates and test-positivity did not influence the results. Our findings suggest that the population was responsive to the rapid changes in testing guidelines, but with clear sociodemographic differences in the strength of the response.

AimTo investigates the financial consequences in the overall population spouses of persons with stroke in Sweden as well as for subgroups based on spouses age, sex and modified Rankin Scale (mRS) of the person with stroke.MethodsThe study population consists of spouses aged <= 60 during the year of their partner's stroke event. Each spouse was matched to four reference individuals. This longitudinal registry data covers spouses and a reference population between 2005 and 2016. We use difference-in-differences to estimate the impact on individual income from paid work, disposable individual income, and disposable family income.ResultsThe primary analysis shows a small and statistically insignificant decrease on spouses' individual income from paid work and disposable individual income. In the subgroup analysis based on mRS, the largest effect is seen in mRS 4-5, where spouses' individual income from paid work and disposable individual income increases after their partner's stroke. Further, younger female spouses' individual income from paid work decreases by 1 614 EUR (p = 0.008) on average.ConclusionThe financial consequences are small in the overall population of spouses. However, for some subgroups, younger women, and spouses of persons with stroke and mRS 4-5, the financial consequences are more prominent.

Background: Participants in epidemiological cohorts may not be representative of the full invited population, limiting the generalizability of prevalence and incidence estimates. We propose that this problem can be remedied by exploiting data on baseline participants who refused to participate in a re-examination, as such participants may be more similar to baseline non-participants than what baseline participants who agree to participate in the re-examination are. Methods: We compared background characteristics, mortality, and disease incidences across the full population invited to the Malmö Diet and Cancer (MDC) study, the baseline participants, the baseline non-participants, the baseline participants who participated in a re-examination, and the baseline participants who did not participate in the re-examination. We then considered two models for estimating characteristics and outcomes in the full population: one (“the substitution model”) assuming that the baseline non-participants were similar to the baseline participants who refused to participate in the re-examination, and one (“the extrapolation model”) assuming that differences between the full group of baseline participants and the baseline participants who participated in the re-examination could be extended to infer results in the full population. Finally, we compared prevalences of baseline risk factors including smoking, risky drinking, overweight, and obesity across baseline participants, baseline participants who participated in the re-examination, and baseline participants who did not participate in the re-examination, and used the above models to estimate the prevalences of these factors in the full invited population. Results: Compared to baseline non-participants, baseline participants were less likely to be immigrants, had higher socioeconomic status, and lower mortality and disease incidences. Baseline participants not participating in the re-examination generally resembled the full population. The extrapolation model often generated characteristics and incidences even more similar to the full population. The prevalences of risk factors, particularly smoking, were estimated to be substantially higher in the full population than among the baseline participants. Conclusions: Participants in epidemiological cohorts such as the MDC study are unlikely to be representative of the full invited population. Exploiting data on baseline participants who did not participate in a re-examination can be a simple and useful way to improve the generalizability of prevalence and incidence estimates. 

Background and ObjectiveCancer drug costs have increased considerably within healthcare systems, but many drugs lack quality-of-life (QoL) and overall survival (OS) data at the time of reimbursement approval. This study aimed to review the extent of subsequent literature documenting improvements in OS and QoL for cancer drug indications where no such evidence existed at the time of reimbursement approval.MethodsDrug indications with claims of added therapeutical value but a lack of evidence on OS and QoL that were reimbursed between 2010 and 2020 in Sweden were included for review. Searches were conducted in PubMed and ClinicalTrial.gov for randomized controlled trials examining OS and QoL.ResultsOf the 22 included drug indications, seven were found to have at least one trial with conclusive evidence of improvements in OS or QoL after a mean follow-up of 6.6 years. The remaining 15 drug indications either lacked subsequent randomized controlled trial data on OS or QoL (n = 6) or showed no statistically significant improvements (n = 9). Only one drug demonstrated evidence of improvement in both OS and QoL for its indication.ConclusionsA considerable share of reimbursed cancer drug indications continue to lack evidence of improvement in both OS and QoL. With limited healthcare resources and an increasing cancer burden, third-party payers have strong incentives to require additional post-reimbursement data to confirm any improvements in OS and QoL.

In studies recruited on a voluntary basis, lack of representativity may impair the ability to generalize findings to the target population. Previous studies, primarily based on surveys, have suggested that generalizability may be improved by exploiting data on individuals who agreed to participate only after receiving one or several reminders, as such individuals may be more similar to non-participants than what early participants are. Assessing this idea in the context of screenings, we compared sociodemographic characteristics and health across early, late, and non-participants in two large population-based screening studies in Sweden: STROKESTOP II (screening for atrial fibrillation; 6,867 participants) and SCREESCO (screening for colorectal cancer; 39,363 participants). We also explored the opportunities to reproduce the distributions of characteristics in the full invited populations, either by assuming that the non-participants were similar to the late participants, or by applying a linear extrapolation model based on both early and late participants. Findings showed that early and late participants exhibited similar characteristics along most dimensions, including civil status, education, income, and health examination results. Both these types of participants in turn differed from the non-participants, with fewer married, lower educational attainments, and lower incomes. Compared to early participants, late participants were more likely to be born outside of Sweden and to have comorbidities, with non-participants similar or even more so. The two empirical models improved representativity in some cases, but not always. Overall, we found mixed support that data on late participation may be useful for improving representativeness of screening studies.