The Prevalence of Parkinson Disease in Ukraine
Notice bibliographique
Résumé
Reported prevalence of Parkinson's disease (PD) varies significantly among countries.1 To date, little information has been gathered from Eastern Europe.2, 3 Crude prevalence rate (CPR) of PD (coded G20 in International Classification of Diseases-10) was collected in 2010 and 2017 from a national database published in Ukraine (Appendix S1: Part A).4 As Ukraine is divided into 25 regions with additional data for the cities of Kyiv and Sevastopol, there were 27 data points in 2010. The Autonomous Republic of Crimea (including Sevastopol city) was temporarily occupied in 2014 so in 2017, only 25 data points were collected. National information has not been available since 2018 because of reorganization of the healthcare system. However, adult prevalence data from 2019 to 2020 was obtained from the Kyiv region (excluding Kyiv city) (personal communication Dr. Anatoly Galusha, Chief Neurologist of Kyiv region). The overall CPR of PD in Ukraine was 59.6 per 100,000 in 2010, and rose to 67.5 per 100,000 by 2017. Significant geographic differences were seen (Fig. 1, Appendix S1: Table S1). The latest CPR in Ukraine remains lower than the rate reported by other eastern European countries collected over a similar time period, which range from 93.3 to 404/100,000.2, 3 We suspect that underdiagnoses and unequal access to healthcare remain determinants of the relatively low Ukrainian prevalence (Appendix S1: Part B). Recent data from the Kyiv region revealed the prevalence of PD in adults (age, 18–100 years-old) was 97/100,000 in 2019, and decreased to 77/100,000 by the end of 2020. Although the Kyiv region previously recorded a higher prevalence in 2017, rates were lower in 2019 and 2020. The reasons for this are still unclear, but deaths and lower accessibility to medical care related to the coronavirus disease 2019 (COVID-19) pandemic could have played a role. Recent studies show a growing prevalence of PD in the last decades; for instance, a systematic analysis in the Global Burden of Disease Study 2016 found an increase of 74.3% in CPR from 1990 to 2016, and upward trends in most countries, including Ukraine.1 Increasing life expectancy may account for much of the worldwide rising prevalence. In Ukraine, the mild increase in life expectancy estimated during our study period may partly explain increasing PD prevalence from 2010 to 2017.5 However, we feel a more significant factor is likely higher diagnostic precision by Ukrainian neurologists in recent years because of improved educational opportunities. Limitations from our study include lack of information on age and sex. Second, the diagnosis of PD was based on expert opinions of neurologists with heterogeneous levels of subspecialty training. Despite these limitations, our study adds to the current literature by providing recent data and contextualizing current prevalence trends. It is the first to provide data during the COVID-19 era. Epidemiological studies are crucial to help inform and plan appropriate management. (1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique. Y.T.: 1A, 1B, 1C, 3A, 3B L.M.O.: 1C, 3A, 3B N.S.: 1C, 3A, 3B O.S.: 1C, 3A, 3B Ethical Compliance Statement: The authors confirm that the approval of an institutional review board was not required for this work. Informed patient consent was not necessary for this work. Data was obtained with permission from the Ministry of Health of Ukraine. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. Funding Sources and Conflicts of Interest: No specific funding was received for this work. The authors declare that there are no conflicts of interest relevant to this work. Financial Disclosures for the Previous 12 Months: Y.T., L.M.O., and N.S. have no disclosures to report. O.S. serves on the advisory board of AbbVie and Sunovion Pharmaceuticals. O.S. receives royalties for UpToDate, Springer, and grants from WaveLife Sciences, Roche, and CHDI Foundation. Appendix S1. Supporting Information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».