Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, The Netherlands.
Department of Infectious Diseases, Public Health Service Amsterdam (GGD Amsterdam), Amsterdam, The Netherlands.
Int J Equity Health. 2023 Jul 4;22(1):127. doi: 10.1186/s12939-023-01936-0.
Although risk factors for differences in SARS-CoV-2 infections between migrant and non-migrant populations in high income countries have been identified, their relative contributions to these SARS-CoV-2 infections, which could aid in the preparation for future viral pandemics, remain unknown. We investigated the relative contributions of pre-pandemic factors and intra-pandemic activities to differential SARS-CoV-2 infections in the Netherlands by migration background (Dutch, African Surinamese, South-Asian Surinamese, Ghanaians, Turkish, and Moroccan origin).
We utilized pre-pandemic (2011-2015) and intra-pandemic (2020-2021) data from the HELIUS cohort, linked to SARS-CoV-2 PCR test results from Public Health Service of Amsterdam (GGD Amsterdam). Pre-pandemic factors included socio-demographic, medical, and lifestyle factors. Intra-pandemic activities included COVID-19 risk aggravating and mitigating activities such as physical distancing, use of face masks, and other similar activities. We calculated prevalence ratios (PRs) in the HELIUS population that was merged with GGD Amsterdam PCR test data using robust Poisson regression (SARS-CoV-2 PCR test result as outcome, migration background as predictor). We then obtained the distribution of migrant and non-migrant populations in Amsterdam as of January 2021 from Statistics Netherlands. The migrant populations included people who have migrated themselves as well as their offspring. We used PRs and the population distributions to calculate population attributable fractions (PAFs) using the standard formula. We used age and sex adjusted models to introduce pre-pandemic factors and intra-pandemic activities, noting the relative changes in PAFs.
From 20,359 eligible HELIUS participants, 8,595 were linked to GGD Amsterdam PCR test data and included in the study. Pre-pandemic socio-demographic factors (especially education, occupation, and household size) resulted in the largest changes in PAFs when introduced in age and sex adjusted models (up to 45%), followed by pre-pandemic lifestyle factors (up to 23%, especially alcohol consumption). Intra-pandemic activities resulted in the least changes in PAFs when introduced in age and sex adjusted models (up to 16%).
Interventions that target pre-pandemic socio-economic status and other drivers of health inequalities between migrant and non-migrant populations are urgently needed at present to better prevent infection disparities in future viral pandemics.
尽管已经确定了高收入国家中移民和非移民人群之间 SARS-CoV-2 感染差异的风险因素，但这些因素对 SARS-CoV-2 感染的相对贡献仍不清楚，这有助于为未来的病毒大流行做准备。我们通过移民背景（荷兰人、非洲苏里南人、南亚苏里南人、加纳人、土耳其人和摩洛哥人）调查了荷兰 SARS-CoV-2 感染差异的与大流行前因素和大流行期间活动的相对贡献。
我们利用了 HELIUS 队列的大流行前（2011-2015 年）和大流行期间（2020-2021 年）的数据，并将其与阿姆斯特丹公共卫生服务局（GGD Amsterdam）的 SARS-CoV-2 PCR 检测结果相关联。大流行前因素包括社会人口统计学、医疗和生活方式因素。大流行期间的活动包括 COVID-19 风险加剧和缓解活动，如身体距离、使用口罩和其他类似活动。我们使用稳健泊松回归（SARS-CoV-2 PCR 检测结果为结果，移民背景为预测因子）计算了与 GGD Amsterdam PCR 检测数据合并的 HELIUS 人群中的患病率比（PR）。然后，我们从荷兰统计局获得了截至 2021 年 1 月阿姆斯特丹的移民和非移民人口分布。移民人口包括移民本人及其后代。我们使用 PR 和人口分布使用标准公式计算人群归因分数（PAF）。我们使用年龄和性别调整模型引入大流行前因素和大流行期间的活动，并注意 PAF 的相对变化。
从 20359 名符合条件的 HELIUS 参与者中，有 8595 名与 GGD Amsterdam PCR 检测数据相关联并纳入研究。大流行前的社会人口统计学因素（尤其是教育、职业和家庭规模）在年龄和性别调整模型中引入时导致 PAF 发生最大变化（高达 45%），其次是大流行前的生活方式因素（高达 23%，尤其是饮酒）。大流行期间的活动在年龄和性别调整模型中引入时导致 PAF 变化最小（高达 16%）。
目前迫切需要针对移民和非移民人群之间大流行前社会经济地位和其他健康不平等驱动因素的干预措施，以更好地预防未来病毒大流行中的感染差异。
