Concordance between COVID-19 mortality statistics derived from clinical audit and death certificates in Östergötland county, Sweden.

OBJECTIVES: Despite early notions that correct attribution of deaths caused by SARS-CoV-2 infection is critical to the understanding of the COVID-19 pandemic, three years later, the accuracy of COVID-19 death counts is still contested. We aimed to compare official death statistics with cause-of-death assessments made in a clinical audit routine by experienced physicians having access to the full medical record. STUDY DESIGN: Health service quality evaluation. METHODS: In Östergötland county (pop. 465,000), Sweden, a clinical audit team assessed from the start of the pandemic the cause of death in individuals having deceased after testing positive for SARS-CoV-2. We estimated the concordance between official data on COVID-19 deaths and data from the clinical audit using correlations (r) between the cause-of-death categories and discrepancies between the absolute numbers of categorised deaths. RESULTS: The concordance between the data sources was poor regarding whether COVID-19 was the underlying or a contributing cause of death. Grouping of the causes increased the correlations to acceptable strength. Also including deaths implicated by a positive SARS-CoV-2 test in the clinical categorisation of COVID-19 deaths reduced the difference in absolute number of deaths; with these modifications, the concordance was acceptable before the COVID-19 vaccination program was initiated (r = 0.97; symmetric mean absolute percentage error (SMAPE) = 19%), while a difference in the absolute numbers of deaths remained in the vaccination period (r = 0.94; SMAPE = 35%). CONCLUSIONS: This study highlights that carefulness is warranted when COVID-19 death statistics are used in health service planning and resonates a need for further research on cause-of-death recording methodologies.

Place-based social contact and mixing: a typology of generic meeting places of relevance for infectious disease transmission.

This study aims to develop a typology of generic meeting places based on social contact and mixing of relevance for infectious disease transmission. Data were collected by means of a contact diary survey conducted on a representative sample of the Swedish population. The typology is derived from a cluster analysis accounting for four dimensions associated with transmission risk: visit propensity and its characteristics in terms of duration, number of other persons present and likelihood of physical contact. In the analysis, we also study demographic, socio-economic and geographical differences in the propensity of visiting meeting places. The typology identifies the family venue, the fixed activity site, the family vehicle, the trading plaza and the social network hub as generic meeting places. The meeting place typology represents a spatially explicit account of social contact and mixing relevant to infectious disease modelling, where the social context of the outbreak can be highlighted in light of the actual infectious disease.

Influenza detection and prediction algorithms: comparative accuracy trial in Östergötland county, Sweden, 2008-2012.

Methods for the detection of influenza epidemics and prediction of their progress have seldom been comparatively evaluated using prospective designs. This study aimed to perform a prospective comparative trial of algorithms for the detection and prediction of increased local influenza activity. Data on clinical influenza diagnoses recorded by physicians and syndromic data from a telenursing service were used. Five detection and three prediction algorithms previously evaluated in public health settings were calibrated and then evaluated over 3 years. When applied on diagnostic data, only detection using the Serfling regression method and prediction using the non-adaptive log-linear regression method showed acceptable performances during winter influenza seasons. For the syndromic data, none of the detection algorithms displayed a satisfactory performance, while non-adaptive log-linear regression was the best performing prediction method. We conclude that evidence was found for that available algorithms for influenza detection and prediction display satisfactory performance when applied on local diagnostic data during winter influenza seasons. When applied on local syndromic data, the evaluated algorithms did not display consistent performance. Further evaluations and research on combination of methods of these types in public health information infrastructures for 'nowcasting' (integrated detection and prediction) of influenza activity are warranted.

Algorithms for detecting and predicting influenza outbreaks: metanarrative review of prospective evaluations.

OBJECTIVES: Reliable monitoring of influenza seasons and pandemic outbreaks is essential for response planning, but compilations of reports on detection and prediction algorithm performance in influenza control practice are largely missing. The aim of this study is to perform a metanarrative review of prospective evaluations of influenza outbreak detection and prediction algorithms restricted settings where authentic surveillance data have been used. DESIGN: The study was performed as a metanarrative review. An electronic literature search was performed, papers selected and qualitative and semiquantitative content analyses were conducted. For data extraction and interpretations, researcher triangulation was used for quality assurance. RESULTS: Eight prospective evaluations were found that used authentic surveillance data: three studies evaluating detection and five studies evaluating prediction. The methodological perspectives and experiences from the evaluations were found to have been reported in narrative formats representing biodefence informatics and health policy research, respectively. The biodefence informatics narrative having an emphasis on verification of technically and mathematically sound algorithms constituted a large part of the reporting. Four evaluations were reported as health policy research narratives, thus formulated in a manner that allows the results to qualify as policy evidence. CONCLUSIONS: Awareness of the narrative format in which results are reported is essential when interpreting algorithm evaluations from an infectious disease control practice perspective.

Relevance of workplace social mixing during influenza pandemics: an experimental modelling study of workplace cultures.

Workplaces are one of the most important regular meeting places in society. The aim of this study was to use simulation experiments to examine the impact of different workplace cultures on influenza dissemination during pandemics. The impact is investigated by experiments with defined social-mixing patterns at workplaces using semi-virtual models based on authentic sociodemographic and geographical data from a North European community (population 136 000). A simulated pandemic outbreak was found to affect 33% of the total population in the community with the reference academic-creative workplace culture; virus transmission at the workplace accounted for 10·6% of the cases. A model with a prevailing industrial-administrative workplace culture generated 11% lower incidence than the reference model, while the model with a self-employed workplace culture (also corresponding to a hypothetical scenario with all workplaces closed) produced 20% fewer cases. The model representing an academic-creative workplace culture with restricted workplace interaction generated 12% lower cumulative incidence compared to the reference model. The results display important theoretical associations between workplace social-mixing cultures and community-level incidence rates during influenza pandemics. Social interaction patterns at workplaces should be taken into consideration when analysing virus transmission patterns during influenza pandemics.

Predictive performance of telenursing complaints in influenza surveillance: a prospective cohort study in Sweden.

Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics.