Death trends for 2010 - 2022 for members of a large private medical scheme in South Africa
Notice bibliographique
Résumé
BACKGROUND: In the absence of more recent national data on underlying causes of death in South Africa (SA), we examined mortality trends from 2010 to 2022 among members of a large private medical scheme. This analysis sheds light on the health profile of this specific demographic. OBJECTIVE: To investigate trends in Discovery Health Medical Scheme (DHMS) members' death rates and underlying cause of death patterns between 2010 and 2022. METHODS: All-cause deaths were compared across years accounting for demographic changes, by analysing age- and sex-standardised rates using 2019 age and sex population weightings. We used underlying cause-of-death data from death notifications. RESULTS: The 2019 age- and sex-standardised death rate was lower than the 2010 rate by 10%, with a steady decline experienced between 2010 and 2019. We have seen reduced age- and sex-standardised death rates from HIV/AIDS during this period, and despite the high prevalence, reduced age- and sex-standardised death rates from non-communicable diseases. Malignant neoplasms and cardiovascular disease have been and remained the two leading causes of death for Discovery Health Medical Scheme (DHMS) clients between 2012 and 2022. Age- and sex- standardised death rates, however, reached historic high levels during the first 2 years of the COVID-19 pandemic in SA. In 2020, overall age- and sex-standardised death rates for DHMS members increased to 542 deaths per 100 000 life years, which was higher than pre-pandemic levels. Age- and sex-standardised death rates went on to reach their highest level in the history of the scheme in 2021, at 767 deaths per 100 000 life years. Age- and sex-standardised death rates, however, had returned to near 2019 (pre-pandemic) levels by 2022, at 477 deaths per 100 000 life years. Males experienced a higher increase in age-standardised death rates during 2020 and remained at an increased risk of death in 2022 compared with pre-pandemic levels. When COVID-19 -related deaths are excluded, the age-standardised rates for both females and males in 2022 was lower than observed in the pre-pandemic years. While the low mortality experience could be related to competing causes and mortality displacement, further analysis over a longer period is needed to confirm this. CONCLUSION: DHMS experienced the highest level of age- and sex-standardised death rates during 2020 and 2021, the initial 2 years of the COVID-19 pandemic. Most of this increase was explained by COVID-19 deaths.
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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 | 0,005 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».