Publications by authors named "Marjolein Engelkes"

11 Publications

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Multinational cohort study of mortality in patients with asthma and severe asthma.

Respir Med 2020 Apr - May;165:105919. Epub 2020 Mar 2.

Medical Informatics, ErasmusMC, Rotterdam, the Netherlands. Electronic address:

Background: Data on the risk of death following an asthma exacerbation are scarce. With this multinational cohort study, we assessed all-cause mortality rates, mortality rates following an exacerbation, and patient characteristics associated with all-cause mortality in asthma.

Methods: Asthma patients aged ≥18 years and with ≥1 year of follow-up were identified in 5 European electronic databases from the Netherlands, Italy, UK, Denmark and Spain during the study period January 1, 2008-December 31, 2013. Patients with asthma-COPD overlap were excluded. Severe asthma was defined as use of high dose ICS + use of a second controller. Severe asthma exacerbations were defined as emergency department visits, hospitalizations or systemic corticosteroid use, all for reason of asthma.

Results: The cohort consisted of 586,436 asthma patients of which 42,611 patients (7.3%) had severe asthma. The age and sex standardized all-cause mortality rates ranged between databases from 5.2 to 9.5/1000 person-years (PY) in asthma, and between 11.3 and 14.8/1000 PY in severe asthma. The all-cause mortality rate in the first week following a severe asthma exacerbation ranged between 14.1 and 59.9/1000 PY. Mortality rates remained high in the first month following a severe asthma exacerbation and decreased thereafter. Higher age, male gender, comorbidity, smoking, and previous severe asthma exacerbations were associated with mortality.

Conclusion: All-cause mortality following a severe exacerbation is high, especially in the first month following the event. Smoking cessation, comorbidity-management and asthma-treatment focusing on the prevention of exacerbations might reduce associated mortality.
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January 2021

Incidence, risk factors and re-exacerbation rate of severe asthma exacerbations in a multinational, multidatabase pediatric cohort study.

Pediatr Allergy Immunol 2020 07 20;31(5):496-505. Epub 2020 Mar 20.

Medical Informatics, ErasmusMC, Rotterdam, The Netherlands.

Background: There are sparse real-world data on severe asthma exacerbations (SAE) in children. This multinational cohort study assessed the incidence of and risk factors for SAE and the incidence of asthma-related rehospitalization in children with asthma.

Methods: Asthma patients 5-17 years old with ≥1 year of follow-up were identified in six European electronic databases from the Netherlands, Italy, the UK, Denmark and Spain in 2008-2013. Asthma was defined as ≥1 asthma-specific disease code within 3 months of prescriptions/dispensing of asthma medication. Severe asthma was defined as high-dosed inhaled corticosteroids plus a second controller. SAE was defined by systemic corticosteroids, emergency department visit and/or hospitalization all for reason of asthma. Risk factors for SAE were estimated by Poisson regression analyses.

Results: The cohort consisted of 212 060 paediatric asthma patients contributing to 678 625 patient-years (PY). SAE rates ranged between 17 and 198/1000 PY and were higher in severe asthma and highest in severe asthma patients with a history of exacerbations. Prior SAE (incidence rate ratio 3-45) and younger age increased the SAE risk in all countries, whereas obesity, atopy and GERD were a risk factor in some but not all countries. Rehospitalization rates were up to 79% within 1 year.

Conclusions: In a real-world setting, SAE rates were highest in children with severe asthma with a history of exacerbations. Many severe asthma patients were rehospitalized within 1 year. Asthma management focusing on prevention of SAE is important to reduce the burden of asthma.
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July 2020

IL1RL1 gene variations are associated with asthma exacerbations in children and adolescents using inhaled corticosteroids.

Allergy 2020 04 17;75(4):984-989. Epub 2019 Dec 17.

Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

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April 2020

Hair cortisol and inhaled corticosteroid use in asthmatic children.

Pediatr Pulmonol 2020 02 25;55(2):316-321. Epub 2019 Oct 25.

Department of Pediatrics, Division of Respiratory Medicine and Allergology, Erasmus MC-Sophia Children's Hospital, University Hospital Rotterdam, Rotterdam, The Netherlands.

Background: Adrenal suppression is a side effect of long-term use of inhaled corticosteroids (ICS). Hair cortisol concentration (HCC) measurement is a noninvasive tool for measuring adrenal function that may be useful for asthmatic patients who are on long-term ICS treatment. The aim of this study was to compare HCC between children with and without asthma and to explore the association between HCC and ICS dose in asthmatic children.

Methods: A cross-sectional observational study in subjects with or without asthma (n = 72 and 226, respectively, age 6-21 years). Hair samples were obtained from the posterior vertex for each subject and data on medication use were collected using questionnaires. HCC was analyzed by liquid chromatography-mass spectrometry in the most proximal 3 cm of hair.

Results: Median HCC was significantly lower in subjects with asthma than in subjects without asthma: 1.83 pg/mg and 2.39 pg/mg, respectively (P value after adjustment for age, sex, and body mass index: .036). Median HCC was 1.98 pg/mg in asthmatics using no ICS, 1.84 pg/mg in those using a low dose, 1.75 pg/mg in those on a medium dose, and 1.46 in those using a high ICS dose (P = .54).

Conclusion: We observed a significantly lower HCC in asthmatics than in healthy controls and a nonsignificant trend of lower HCC with increasing ICS dose. Whether HCC measurement may be used to detect individuals at risk for hypocortisolism and may be useful to monitor adrenal function in asthmatic children using ICS needs to be further investigated.
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February 2020

Brand and generic use of inhalation medication and frequency of switching in children and adults: A population-based cohort study.

J Asthma 2018 10 29;55(10):1086-1094. Epub 2017 Nov 29.

c Department of Pediatrics, Division of Respiratory Medicine and Allergology , Erasmus MC Sophia , Rotterdam , the Netherlands.

Background: The expiration of patents of brand inhalation medications and the ongoing pressure on healthcare budgets resulted in a growing market for generics.

Aim: To study the use of brand and generic inhalation medication and the frequency of switching between brand and generic and between devices. In addition, we investigated whether switching affected adherence.

Methods: From dispensing data from the Dutch PHARMO Database Network a cohort aged ≥ 5 years, using ≥ 1 year of inhalation medication between 2003 and 2012 was selected. Switching was defined as changing from brand to generic or vice versa. In addition, we studied change in aerosol delivery device type (e.g., DPI, pMDI, and nebulizers). Adherence was calculated using the medication possession ratio (MPR).

Results: The total cohort comprised 70,053 patients with 1,604,488 dispensations. Per calendar year, 5% switched between brand and generic inhalation medication and 5% switched between devices. Median MPRs over the first 12 months ranged between 33 and 55%. Median MPR over the total period was lower after switch from brand to generic and vice versa for formoterol (44.5 vs. 42.1 and 63.5 vs. 53.8) and beclomethasone (93.8 vs. 59.8 and 81.3 vs. 55.9).

Conclusion: Per year, switching between brand and generic inhalation medication was limited to 5% of the patients, switching between device types was observed in 5% as well. Adherence to both generic and brand inhalation medication was low. Effect of switching on adherence was contradictory; depending on time period, medication and type, and direction of switching. Further research on reasons for switching and potential impact on clinical outcomes is warranted.
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October 2018

Prescription patterns, adherence and characteristics of non-adherence in children with asthma in primary care.

Pediatr Allergy Immunol 2016 Mar 21;27(2):201-8. Epub 2015 Dec 21.

Department of Medical Informatics, Erasmus University, Rotterdam, The Netherlands.

Unlabelled: Adherence to treatment remains important for successful asthma management. Knowledge about asthma medication use and adherence in real-life offers opportunities to improve asthma treatment in children.

Objective: To describe prescription patterns, adherence and factors of adherence to drugs in children with asthma.

Methods: Population-based cohort study in a Dutch primary care database (IPCI), containing medical records of 176,516 children, aged 5-18 years, between 2000 and 2012. From asthma medication prescriptions, age, gender, seasonal and calendar year rates were calculated. Adherence was calculated using medication possession ratio (MPR) and ratio of controller to total asthma drug (CTT). Characteristics of children with high-vs.-low adherence were compared.

Results: The total asthma cohort (n = 14,303; 35,181 person-years (PY) of follow-up) was mainly treated with short-acting β2-agonists (SABA; 40 users/100 PY) and inhaled corticosteroids (ICS; 32/100 PY). Median MPR for ICS was 56%. Children with good adherence (Q4 = MPR > 87%) were younger at start of ICS, more often visited specialists and had more exacerbations during follow-up compared to children with low adherence (Q1 = MPR < 37%).

Conclusion: In Dutch primary care children with asthma were mainly prescribed SABA, and ICS. Adherence to ICS was relatively low. Characteristics of children with good adherence were compatible with more severe asthma, suggesting that adherence is driven by treatment need or intensity of medical follow-up.
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March 2016

Time trends in the incidence, prevalence and age at diagnosis of asthma in children.

Pediatr Allergy Immunol 2015 Jun;26(4):367-74

Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands.

Background: Current knowledge on the prevalence of asthma is mainly based on cross-sectional questionnaire data. Current population-based data on the incidence of asthma in children are scarce.

Objective: To study the incidence, prevalence, and age at diagnosis of asthma in children in the Netherlands over the study period 2000-2012.

Methods: A population-based cohort study was conducted in the Integrated Primary Care Information database. The cohort consisted of 176,516 children (379,536 personyears (PY) of follow-up), aged 5-18 years between 2000 and 2012. All medical records of children with physician diagnosed asthma were validated. Incidence rates, annual percent change (APC), and prevalence for asthma were calculated. Influence of age and gender on incidence rates and change in age at diagnosis were studied.

Results: We identified an asthma cohort of 14,303 children with 35,118 PY. The overall incidence rate was 6.7/1000 PY (95% CI, 6.45-6.97). Until 2008, the incidence rate was significantly increasing (APC 5.79 (95% CI 1.43-10.34); from 2008 onwards, a non-significant decrease was observed (APC -12.16 (95% CI -23.07 to 0.28). Incidence for girls was lower than for boys, this difference decreased with increasing age. (p < 0.001) Overall, the age at diagnosis increased over calendar time and was lower for boys. (linear trend p < 0.001).

Conclusion: Our population-based cohort study observed an incidence rate of 6.7 per 1000 PY of physician-diagnosed asthma in children in the Netherlands over 2000-2012. The asthma incidence rate was increasing until 2008. Further studies are needed to confirm the decrease in asthma incidence rate from 2008 onwards.
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June 2015

Quick assessment of drug-related admissions over time (QUADRAT study).

Pharmacoepidemiol Drug Saf 2015 May 12;24(5):495-503. Epub 2015 Feb 12.

IQ Healthcare, Radboud University Medical Center (RUMC), Nijmegen, The Netherlands; Community Pharmacy Groesbeek, Groesbeek, The Netherlands.

Purpose: To develop a computerized prescreening procedure for the identification of possible/probably Hospital Admissions potential Related to Medications (HARMs).

Method: Pairs of drugs and reasons for hospitalization (generated automatically from the PHARMO record linkage database by using two data mining techniques) were assessed manually to determine whether they represented pharmacologically plausible adverse drug events (PP-ADEs). Two crude samples of these PP-ADEs (from 2005 and 2008) were examined manually to establish causality and preventability on the basis of hospital discharge letters plus medication dispensing data. The results were used to calculate the positive predictive value (PPV) of the crude causality PP-ADEs, the net percentage of possible/probably HARMs, and their potential preventability.

Results: Data mining by Gamma Poisson Shrinkage and trend analysis produced 1330 and 2941 significant drug-event pairs, respectively. After manual assessment, 307 different PP-ADEs remained. The annual prevalence of these PP-ADEs was stable at approximately 8% throughout 2000-2009. Manual assessment of two samples of crude PP-ADEs showed that their causality PPV was 53.7% (95%CI: 52.7%-54.7%) in 2005 and 47.9% (95%CI: 46.9%-49.0%) in 2008. The net contribution of possible/probably HARMs to all acute admissions was 4.6% (95%CI: 4.5%-4.8%) in 2005 and 3.9% (95%CI: 3.8%-4.0%) in 2008. The potential preventability of all possible/probably HARMs in the two samples was 19.3% (95%CI: 18.5-20.1).

Conclusion: Automated pre-selection of PP-ADEs is an efficient way to monitor crude trends. Further validation and manual assessment of the automatically selected hospitalizations is necessary to get a more detailed and precise picture.
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May 2015

Medication adherence and the risk of severe asthma exacerbations: a systematic review.

Eur Respir J 2015 Feb 16;45(2):396-407. Epub 2014 Oct 16.

Dept of Medical Informatics, Erasmus University, Rotterdam, The Netherlands.

The benefits of drug therapy for asthma have been well established, but adherence to treatment is poor, and this might be associated with an increased risk of asthma exacerbations. The aim of this study was to review the literature on the association between adherence to asthma controller treatment and risk of severe asthma exacerbations in children and adults. A systematic literature search was performed in PubMed, Embase and Web of Science, from inception until January 2014. Studies were included if data on the association between medication adherence and severe asthma exacerbations were presented. Quality was assessed using a modified version of the Newcastle-Ottawa Scale. The search yielded 2319 unique publications, of which 23 met the inclusion criteria and underwent data extraction and quality scoring. High levels of heterogeneity across studies with regard to adherence and exacerbation measurements, designs and analysis precluded a formal meta-analysis. Although effect measures varied widely, good adherence was associated with fewer severe asthma exacerbations in high-quality studies. Good adherence tended to be associated with lower risk of severe asthma exacerbations. Future studies should use standardised methodology to assess adherence and exacerbations, and should consider inhaler competence.
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February 2015

Automatic generation of case-detection algorithms to identify children with asthma from large electronic health record databases.

Pharmacoepidemiol Drug Saf 2013 Aug 17;22(8):826-33. Epub 2013 Apr 17.

Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands.

Purpose: Most electronic health record databases contain unstructured free-text narratives, which cannot be easily analyzed. Case-detection algorithms are usually created manually and often rely only on using coded information such as International Classification of Diseases version 9 codes. We applied a machine-learning approach to generate and evaluate an automated case-detection algorithm that uses both free-text and coded information to identify asthma cases.

Methods: The Integrated Primary Care Information (IPCI) database was searched for potential asthma patients aged 5-18 years using a broad query on asthma-related codes, drugs, and free text. A training set of 5032 patients was created by manually annotating the potential patients as definite, probable, or doubtful asthma cases or non-asthma cases. The rule-learning program RIPPER was then used to generate algorithms to distinguish cases from non-cases. An over-sampling method was used to balance the performance of the automated algorithm to meet our study requirements. Performance of the automated algorithm was evaluated against the manually annotated set.

Results: The selected algorithm yielded a positive predictive value (PPV) of 0.66, sensitivity of 0.98, and specificity of 0.95 when identifying only definite asthma cases; a PPV of 0.82, sensitivity of 0.96, and specificity of 0.90 when identifying both definite and probable asthma cases; and a PPV of 0.57, sensitivity of 0.95, and specificity of 0.67 for the scenario identifying definite, probable, and doubtful asthma cases.

Conclusions: The automated algorithm shows good performance in detecting cases of asthma utilizing both free-text and coded data. This algorithm will facilitate large-scale studies of asthma in the IPCI database.
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August 2013