An Automatic Approach Designed for Inference of the Underlying Cause-of-Death of Citizens

Territorial gaps on quality of causes of death statistics over the last forty years in Spain

BACKGROUND

The quality of the statistics on causes of death (CoD) does not present consolidated indicators in literature further than the coding group of ill-defined conditions of the International Classification of Diseases. Our objective was to assess the territorial quality of CoD by reliability of the official mortality statistics in Spain over the years 1980-2019.

METHODS

A descriptive epidemiological design of four decades (1980-, 1990-, 2000-, and 2010-2019) by region (18) and sex was implemented. The CoD cases, age-adjusted rates and ratios (to all-cause) were assigned by reliability to unspecific and ill-defined quality categories. The regional mortality rates were contrasted to the Spanish median by decade and sex by the Comparative Mortality Ratio (CMR) in a Bayesian perspective. Statistical significance was considered when the CMR did not contain the value 1 in the 95% credible intervals.

RESULTS

Unspecific, ill-defined, and all-cause rates by region and sex decreased over 1980-2019, although they scored higher in men than in women. The ratio of ill-defined CoD decreased in both sexes over these decades, but was still prominent in 4 regions. CMR of ill-defined CoD in both sexes exceeded the Spanish median in 3 regions in all decades. In the last decade, women's CMR significantly exceeded in 5 regions for ill-defined and in 6 regions for unspecific CoD, while men's CMR exceeded in 4 and 2 of the 18 regions, respectively on quality categories.

CONCLUSIONS

The quality of mortality statistics of causes of death has increased over the 40 years in Spain in both sexes. Quality gaps still remain mostly in Southern regions. Authorities involved might consider to take action and upgrading regional and national death statistics, and developing a systematic medical post-grade training on death certification.

[Causes of death in the Moscow region according to medical death certificates]

OBJECTIVE

Determination of the leading causes of death based on data from primary medical death certificates (MDCs) depending on the place of death.

MATERIAL AND METHODS

From the electronic database of the Main Department of the Civil Registry Office of the Moscow Region (the USR registry office system) for 2021, all cases were selected in which diseases were indicated as the primary cause of death (PCD); all codes of external causes, injuries and poisonings were excluded. A total of 109.126 cases, 50.6% died in the hospital, 34% died at home, and 16.4% died elsewhere. Bureau of Forensic Medical Examination (BFME) issued 45.2% of MSS. Taking into account the frequency of use of ICD codes, the clinical similarity of individual codes, 20 groups were formed, which accounted for 90.1% of deaths from diseases.

RESULTS

The frequency of registration of individual groups of causes of death largely depends on the place of death. 5 leading groups of causes of death were established: 1) in general from COVID-19 23.55%, chronic ischemic heart disease (CIHD-1) without postinfarction cardiosclerosis, aneurysm and ischemic cardiomyopathy (CMP) 14.5%, from encephalopathy indefinite (EI) 11.4%, malignant neoplasms (MN) 11.3%, stroke 6.2%; 2) in a hospital from COVID-19 45%, stroke 10%, MN 8.3%; CIHD-1 7.1%, CIHD with a history of MI/ischemic CMP 2.7%; 3) at home from CIHD-1 21.8%, EI 21.5%, MN 15.5%, from diseases associated with alcohol 3.3% and brain cyst 3.3%; 4) elsewhere from CIHD-1 22.7%, EI 21.6%, MN 12%, from other forms of acute coronary artery disease 5.4%, alcohol-associated diseases 4.8%. Acute MI ranked 6^th among deaths in general - 2.7%. PCD is also associated with the place of issue of the MDCs - 90% of the MDC with the indication of EI and «other degenerative diseases of the nervous system» as the cause of death were issued by the BFME. Not a single MDC issued by the BFME contained such PCDs as "old age" or "brain cyst".

CONCLUSION

The nosological structure of the causes of death and the issuance of individual ICD codes in the MDC as a PCD varies significantly depending on the place of death and the issuance of the MDC. The reasons need to be further clarified. The use of codes that are not permitted for use has been registered.

An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens

It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms.