Risk of death due to COVID-19 among current and former smokers in the Netherlands: a population-based quasi-cohort study.

BACKGROUND: Research on smoking as a risk factor for death due to COVID-19 remains inconclusive, with different studies demonstrating either an increased or decreased risk of COVID-19 death among smokers. To investigate this controversy, this study uses data from the Netherlands to assess the relationship between smoking and death due to COVID-19. METHODS: In this population-based quasi-cohort study, we linked pseudonymized individual data on smoking status from the 2016 and 2020 'Health Monitor Adults and Elderly' in the Netherlands (n = 914 494) to data from the cause-of-death registry (n = 2962). Death due to COVID-19 in 2020 or 2021 was taken as the main outcome. Poisson regression modelling was used to calculate relative risks (RRs) and 95% CIs of death due to COVID-19 for current and former smokers compared with never smokers while adjusting for relevant confounders (age, sex, educational level, body mass index and perceived health). RESULTS: Former smokers had a higher risk of death due to COVID-19 compared with never smokers across unadjusted (RR, 2.22; 95% CI, 2.04-2.42), age-sex-adjusted (RR, 1.38; 95% CI, 1.22-1.55) and fully adjusted (RR, 1.30; 95% CI, 1.16-1.45) models. Current smokers had a slightly higher risk of death due to COVID-19 compared with never smokers after adjusting for age and sex (RR, 1.21; 95% CI, 1.00-1.48) and after full adjustment (RR, 1.08; 95% CI, 0.90-1.29), although the results were statistically non-significant. CONCLUSIONS: People with a history of smoking appear to have a higher risk of death due to COVID-19. Further research is needed to investigate which underlying mechanisms may explain this.

Estimating incidence and prevalence rates of chronic diseases using disease modeling.

BACKGROUND: Morbidity estimates between different GP registration networks show large, unexplained variations. This research explores the potential of modeling differences between networks in distinguishing new (incident) cases from existing (prevalent) cases in obtaining more reliable estimates. METHODS: Data from five Dutch GP registration networks and data on four chronic diseases (chronic obstructive pulmonary disease [COPD], diabetes, heart failure, and osteoarthritis of the knee) were used. A joint model (DisMod model) was fitted using all information on morbidity (incidence and prevalence) and mortality in each network, including a factor for misclassification of prevalent cases as incident cases. RESULTS: The observed estimates vary considerably between networks. Using disease modeling including a misclassification term improved the consistency between prevalence and incidence rates, but did not systematically decrease the variation between networks. Osteoarthritis of the knee showed large modeled misclassifications, especially in episode of care-based registries. CONCLUSION: Registries that code episodes of care rather than disease generally provide lower estimates of the prevalence of chronic diseases requiring low levels of health care such as osteoarthritis. For other diseases, modeling misclassification rates does not systematically decrease the variation between registration networks. Using disease modeling provides insight in the reliability of estimates.

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty.

BACKGROUND: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. METHODS: Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. RESULTS: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. CONCLUSION: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

[Diabetes mellitus in the Netherlands: estimate of the current disease burden and prognosis for 2025]. / Diabetes mellitus in Nederland: schatting van de huidige ziektelast en prognose voor 2025.

OBJECTIVE: To estimate the number of people with diagnosed diabetes mellitus in the Netherlands in 2007 using a new method; to describe trends in the past; to predict the situation in 2025. DESIGN: Model calculations. METHODS: Based on five general practice records (Nijmegen Continuous Morbidity Registration [CMR], Netherlands Information Network of General Practice [LINH], Limburg Family Practice Registration Network [RNH-Limburg], Registration Network University Family Practices, Leiden and its environs [RNUH-LEO], and the transition project) the prevalence and incidence of diagnosed diabetes in the Netherlands in 2007 was estimated. Trends in the prevalence of diagnosed diabetes were estimated from the five records over the period 2000-2007. The prevalence of diagnosed diabetes in 2025 was estimated using the Dutch Chronic Diseases Model, which takes into account demographic developments and a further increase in obesity in the Netherlands in the future. RESULTS: In 2007, 740,000 persons (95% CI: 665,000-824,000) with diabetes were undergoing care. The incidence of new diabetes during 2007 was 71,000 (95% CI: 57,000-90,000). The prevalence of diagnosed diabetes increased by almost 80% in 2000-2007. The model projection resulted in an estimate of 1.3 million people with diagnosed diabetes in 2025, i.e. 8% of the Dutch population. There is a high level of uncertainty about these estimates. CONCLUSION: The increase in the number of diabetes patients in 2025 has consequences for care and will require measures to be taken in coming years in the areas of prevalence and care organisation.

Using registries in general practice to estimate countrywide morbidity in The Netherlands.

OBJECTIVE: Examining the possibility of using data from registries in general practice in order to present morbidity figures concerning a broad range of major diseases for the Dutch population. STUDY DESIGN: Qualitative and quantitative analysis of registered diagnoses. METHODS: Quantitative data from six registries were obtained. In addition, information about the registration process was obtained and discussed with representatives of the registries. Subjects for discussion were the general characteristics of the registries and disease-specific rules. RESULTS: Some important differences exist in the characteristics of the registries and the disease-specific coding rules for computing incidence and prevalence. However, for most diseases the rules of two or more registries corresponded with each other, so that a selection of registries that measured the occurrence of a particular disease in a similar way could be made. Nevertheless, for some age categories rather large differences between registries were observed. The best estimates for the whole country were calculated as the average incidence and prevalence of the selected registries. CONCLUSIONS: Data that were originally obtained during patient care can be made usable for public health policy purposes. To further improve the quality of data and to increase the usefulness of these data for public health policy purposes, more efforts are required.