Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
ICES, Toronto, Ontario, Canada.
BMJ Open. 2023 Jan 13;13(1):e067689. doi: 10.1136/bmjopen-2022-067689.
Population-based chronic disease surveillance systems were likely disrupted by the COVID-19 pandemic. The objective of this study was to examine the immediate and ongoing impact of the COVID-19 pandemic on the claims-based incidence of dementia.
We conducted a population-based time series analysis from January 2015 to December 2021 in Ontario, Canada. We calculated the monthly claims-based incidence of dementia using a validated case ascertainment algorithm drawing from routinely collected health administrative data. We used autoregressive linear models to compare the claims-based incidence of dementia during the COVID-19 period (2020-2021) to the expected incidence had the pandemic not occurred, controlling for seasonality and secular trends. We examined incidence by source of ascertainment and across strata of sex, age, community size and number of health conditions.
The monthly claims-based incidence of dementia dropped from a 2019 average of 11.9 per 10 000 to 8.5 per 10 000 in April 2020 (32.6% lower than expected). The incidence returned to expected levels by late 2020. Across the COVID-19 period there were a cumulative 2990 (95% CI 2109 to 3704) fewer cases of dementia observed than expected, equivalent to 1.05 months of new cases. Despite the overall recovery, ascertainment rates continued to be lower than expected among individuals aged 65-74 years and in large urban areas. Ascertainment rates were higher than expected in hospital and among individuals with 11 or more health conditions.
The claims-based incidence of dementia recovered to expected levels by late 2020, suggesting minimal long-term changes to population-based dementia surveillance. Continued monitoring of claims-based incidence is necessary to determine whether the lower than expected incidence among individuals aged 65-74 and in large urban areas, and higher than expected incidence among individuals with 11 or more health conditions, is transitory.
基于人群的慢性病监测系统可能因 COVID-19 大流行而中断。本研究的目的是探讨 COVID-19 大流行对基于索赔的痴呆发病率的即时和持续影响。
我们在加拿大安大略省进行了一项基于人群的时间序列分析,时间范围为 2015 年 1 月至 2021 年 12 月。我们使用从常规收集的健康管理数据中提取的经过验证的病例确定算法,计算基于索赔的每月痴呆发病率。我们使用自回归线性模型来比较 COVID-19 期间(2020-2021 年)基于索赔的痴呆发病率与假设大流行未发生时的预期发病率,同时控制季节性和长期趋势。我们根据确定来源和性别、年龄、社区规模和健康状况数量的分层检查发病率。
基于索赔的每月痴呆发病率从 2019 年的平均 11.9 例/10000 人降至 2020 年 4 月的 8.5 例/10000 人(比预期低 32.6%)。发病率在 2020 年末恢复到预期水平。在整个 COVID-19 期间,观察到的痴呆病例比预期少 2990 例(95%CI 2109 至 3704),相当于新病例 1.05 个月。尽管总体上有所恢复,但在 65-74 岁人群和大城市地区,确定率仍低于预期。在医院和有 11 种或更多健康状况的人群中,确定率高于预期。
基于索赔的痴呆发病率在 2020 年末恢复到预期水平,表明人群为基础的痴呆监测系统的长期变化最小。需要继续监测基于索赔的发病率,以确定在 65-74 岁人群和大城市地区发病率低于预期以及有 11 种或更多健康状况的人群发病率高于预期是否是暂时的。