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Abstract
Sweden has a long tradition of recording cause of death data. The Swedish cause of
death register is a high quality virtually complete register of all deaths in Sweden
since 1952. Although originally created for official statistics, it is a highly important
data source for medical research since it can be linked to many other national registers,
which contain data on social and health factors in the Swedish population. For the
appropriate use of this register, it is fundamental to understand its origins and
composition. In this paper we describe the origins and composition of the Swedish
cause of death register, set out the key strengths and weaknesses of the register,
and present the main causes of death across age groups and over time in Sweden. This
paper provides a guide and reference to individuals and organisations interested in
data from the Swedish cause of death register.
Electronic supplementary material
The online version of this article (doi:10.1007/s10654-017-0316-1) contains supplementary
material, which is available to authorized users.
Substantial discrepanies exist between clinical diagnoses and findings at autopsy. Autopsy may be used as a tool for quality management to analyze diagnostic discrepanies. To determine the rate at which autopsies detect important, clinically missed diagnoses, and the extent to which this rate has changed over time. A systematic literature search for English-language articles available on MEDLINE from 1966 to April 2002, using the search terms autopsy, postmortem changes, post-mortem, postmortem, necropsy, and posthumous, identified 45 studies reporting 53 distinct autopsy series meeting prospectively defined criteria. Reference lists were reviewed to identify additional studies, and the final bibliography was distributed to experts in the field to identify missing or unpublished studies. Included studies reported clinically missed diagnoses involving a primary cause of death (major errors), with the most serious being those likely to have affected patient outcome (class I errors). Logistic regression was performed using data from 53 distinct autopsy series over a 40-year period and adjusting for the effects of changes in autopsy rates, country, case mix (general autopsies; adult medical; adult intensive care; adult or pediatric surgery; general pediatrics or pediatric inpatients; neonatal or pediatric intensive care; and other autopsy), and important methodological features of the primary studies. Of 53 autopsy series identified, 42 reported major errors and 37 reported class I errors. Twenty-six autopsy series reported both major and class I error rates. The median error rate was 23.5% (range, 4.1%-49.8%) for major errors and 9.0% (range, 0%-20.7%) for class I errors. Analyses of diagnostic error rates adjusting for the effects of case mix, country, and autopsy rate yielded relative decreases per decade of 19.4% (95% confidence interval [CI], 1.8%-33.8%) for major errors and 33.4% (95% [CI], 8.4%-51.6%) for class I errors. Despite these decreases, we estimated that a contemporary US institution (based on autopsy rates ranging from 100% [the extrapolated extreme at which clinical selection is eliminated] to 5% [roughly the national average]), could observe a major error rate from 8.4% to 24.4% and a class I error rate from 4.1% to 6.7%. The possibility that a given autopsy will reveal important unsuspected diagnoses has decreased over time, but remains sufficiently high that encouraging ongoing use of the autopsy appears warranted.
Mortality statistics are important for epidemiological research. We examine if discrepancies between death certificate (DC) and hospital discharge condition (HDC) indicate certification errors. From 39,872 hospital deaths in Sweden in 1995, we randomly selected 600 "cases," where DC and HDC were incompatible, and 600 compatible "controls," matched on sex, age, and underlying cause of death. We obtained case summaries for 1,094 (91%) of these. Using a structured protocol, we assessed the accuracy of DCs. Regression analysis indicated diagnostic group and "case" or "control" as the variables that most affected the accuracy. Malignant neoplasm "controls" had the highest accuracy (92%), and benign and unspecified tumor "cases," the lowest (20%). For all diagnostic groups except one, compatible "controls" had better accuracy than incompatible "cases." The exception, chronic obstructive lung disease, had low accuracy for both "cases" (54%) and "controls" (52%). Incompatibility between DC and HDC indicates a greater risk of certification errors. For some diagnostic groups, however, DCs are often inaccurate even when DC and HDC are compatible. By requesting additional information on incompatible cases and all deaths in high-risk diagnostic groups, producers of mortality statistics could improve the accuracy of the statistics.
To evaluate the reliability of cause-of-death diagnoses among prostate cancer patients. Information from death certificates obtained from the Swedish Death Register was compared with systematically reviewed medical records from the population-based Swedish Regional Prostate Cancer Register, South-East Region. In total, 5675 patients were included who had been diagnosed with prostate cancer between 1987 and 1999 and who had died before 1 January 2003. The proportion of prostate cancer cases classified as having died from prostate cancer was 3% higher in the official death certificates than in the reviewed records [0.03, 95% confidence interval (CI) 0.02 to 0.04]. Overall agreement between the official cause of death and the reviewed data was 86% (95% CI 85 to 87%). A higher accuracy was observed among men with localized disease (88%, 95% CI 87 to 89%), aged 60 years or younger at death (96%, 95% CI 93 to 100%), or who had undergone curative treatment (91%, 95% CI 88 to 95%). This study indicates a relatively high reliability of official cause-of-death statistics of prostate cancer patients in Sweden. Mortality data obtained from death certificates may be useful in the evaluation of large-scale prostate cancer intervention programmes, especially among younger patients with localized disease.
[1
]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Unit of Epidemiology, Institute of Environmental Medicine, , Karolinska Institutet, ; PO Box 210, 171 77 Stockholm, Sweden
[2
]ISNI 0000 0004 0511 9852, GRID grid.416537.2, National Board of Health and Welfare, ; Stockholm, Sweden
[3
]ISNI 0000 0004 1936 9457, GRID grid.8993.b, Department of Public Health and Caring Sciences, , Uppsala University, ; Uppsala, Sweden
[4
]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Medical Epidemiology and Biostatistics, , Karolinska Institutet, ; Stockholm, Sweden
[5
]ISNI 0000 0001 0123 6208, GRID grid.412367.5, Department of Paediatrics, , Örebro University Hospital, ; Örebro, Sweden
[6
]ISNI 0000 0004 1936 8868, GRID grid.4563.4, Division of Epidemiology and Public Health, School of Medicine, , University of Nottingham, ; Nottingham, UK
[7
]ISNI 0000000419368729, GRID grid.21729.3f, Department of Medicine, , Columbia University College of Physicians and Surgeons, ; New York, NY USA
[8
]ISNI 0000 0004 1937 0626, GRID grid.4714.6, Department of Oncology and Pathology, , Karolinska Institutet, ; Stockholm, Sweden
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History
Date
received
: 22
March
2017
Date
accepted
: 21
September
2017
Funding
Funded by: FundRef http://dx.doi.org/10.13039/501100006636, Forskningsrådet om Hälsa, Arbetsliv och Välfärd;
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