Chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) is a rare but disabling disorder that often requires long-term immunomodulatory treatment. Background incidence rates and prevalence and risk factors for developing CIDP are still poorly defined. In the current study, we used a longitudinal population-based cohort study in The Netherlands to assess these rates and demographic factors and comorbidity associated with CIDP. We determined the incidence rate and prevalence of CIDP between 2008 and 2017 and the occurrence of potential risk factors in a retrospective Dutch cohort study using the Integrated Primary Care Information (IPCI) database. Cases were defined as CIDP if the diagnosis of CIDP was described in the electronic medical file. In a source population of 928 030 persons with a contributing follow-up of 3 525 686 person-years, we identified 65 patients diagnosed with CIDP. The overall incidence rate was 0.68 per 100 000 person-years (95% CI 0.45-0.99). The overall prevalence was 7.00 per 100 000 individuals (95% CI 5.41-8.93). The overall incidence rate was higher in men compared to woman (IRR 3.00, 95% CI 1.27-7.11), and higher in elderly of 50 years or older compared with people <50 years of age (IRR 17 95% CI 4-73). Twenty percent of CIDP cases had DM and 9% a co-existing other auto-immune disease. These background rates are important to monitor changes in the frequency of CIDP following infectious disease outbreaks, identify potential risk factors, and to estimate the social and economic burden of CIDP.
Community-based cohort studies have suggested that associations with vascular risk measurements change in strength and direction closer in time to the onset of dementia. It has not yet been clarified whether these are evident in data derived from routine care. Using health records databases resources available through the European Medical Information Framework (EMIF) we sought to investigate differences between dementia cases and matched controls in systolic/diastolic blood pressure (SBP/DBP), body mass index (BMI) and cholesterol levels recorded at different time periods prior to the diagnosis date. We analysed records from four primary care datasets from the Netherlands (IPCI), Italy (HSD), Spain (SIDIAP) and UK (THIN), and one hospital care dataset from Jutland, Denmark (AUH). A total of 286,711 cases of dementia aged 50+ at diagnosis were compared to 28,661,163 controls – matched on age and gender at the date of diagnosis (index date). Previously recorded SBP/DBP, BMI and total serum cholesterol levels were compared within 7 periods prior to this index date (0–2, 2–4, 4–6, 6–8, 8–10, 10–12 years). Mean differences between cases and controls were calculated for each site and then combined by random effects meta-analyses for each time period. Data availability was highest for blood pressure, followed by cholesterol, and BMI; availability diminished with increasing time prior to the index date (Figure 1), and did not differ substantially between cases and controls. Mean differences in outcomes are displayed in Figures 2–5. Because of the large sample size, most meta-analysis points differed from the null at statistical significance. While lower BMI in cases became progressively more exaggerated closer to the index date, lower SBP showed stability over time periods, only becoming exaggerated at the most recent, and both DBP and cholesterol levels were not consistently different between cases and controls over time. In a uniquely large and heterogeneous set of health records data, we observed differences in BMI and SBP and trends over time that are consistent with exaggerated weight loss and decline in blood pressure prior to the onset of dementia. These need to be considered if dementia risk is estimated from clinical data. Mean data availability (%) across contributing sites for the specified time periods prior to the index date (first dementia diagnosis). Mean differences in BMI (kg/m2) between cases and controls at different intervals prior to dementia diagnosis date. Mean differences in serum cholesterol (mmol/l) between cases and controls at different intervals prior to dementia diagnosis date. Mean differences in systolic blood pressure (mmHg) between cases and controls at different intervals prior to dementia diagnosis date. Mean differences in diastolic blood pressure (mmHg) between cases and controls at different intervals prior to dementia diagnosis date.
Objective To validate use of the Manchester triage system in paediatric emergency care. Design Prospective observational study. Setting Emergency departments of a university hospital and a teaching hospital in the Netherlands, 2006-7. Participants 17 600 children (aged <16) visiting an emergency department over 13 months (university hospital) and seven months (teaching hospital). Intervention Nurses triaged 16 735/17 600 patients (95%) using a computerised Manchester triage system, which calculated urgency levels from the selection of discriminators embedded in flowcharts for presenting problems. Nurses over-ruled the urgency level in 1714 (10%) children, who were excluded from analysis. Complete data for the reference standard were unavailable in 1467 (9%) children leaving 13 554 patients for analysis. Main outcome measures Urgency according to the Manchester triage system compared with a predefined and independently assessed reference standard for five urgency levels. This reference standard was based on a combination of vital signs at presentation, potentially life threatening conditions, diagnostic resources, therapeutic interventions, and follow-up. Sensitivity, specificity, and likelihood ratios for high urgency (immediate and very urgent) and 95% confidence intervals for subgroups based on age, use of flowcharts, and discriminators. Results The Manchester urgency level agreed with the reference standard in 4582 of 13 554 (34%) children; 7311 (54%) were over-triaged and 1661 (12%) under-triaged. The likelihood ratio was 3.0 (95% confidence interval 2.8 to 3.2) for high urgency and 0.5 (0.4 to 0.5) for low urgency; though the likelihood ratios were lower for those presenting with a medical problem (2.3 (2.2 to 2.5) v 12.0 (7.8 to 18.0) for trauma) and in younger children (2.4 (1.9 to 2.9) at 0-3 months v 5.4 (4.5 to 6.5) at 8-16 years). Conclusions The Manchester triage system has moderate validity in paediatric emergency care. It errs on the safe side, with much more over-triage than under-triage compared with an independent reference standard for urgency. Triage of patients with a medical problem or in younger children is particularly difficult.
Electronic healthcare record (EHR)-based surveillance systems are increasingly being developed to support early detection of safety signals. It is unknown what the power of such a system is for surveillance among children and adolescents. In this paper we provide estimates of the number and classes of drugs, and incidence rates (IRs) of events, that can be monitored in children and adolescents (0-18 years).Data were obtained from seven population-based EHR databases in Denmark, Italy, and the Netherlands during the period 1996-2010. We estimated the number of drugs for which specific adverse events can be monitored as a function of actual drug use, minimally detectable relative risk (RR) and IRs for 10 events.The population comprised 4 838 146 individuals (25 575 132 person years (PYs)), who were prescribed 2170 drugs (1 610 631 PYs drug-exposure). Half of the total drug-exposure in PYs was covered by only 18 drugs (0.8%). For a relatively frequent event like upper gastrointestinal bleeding there were 39 drugs for which an association with a RR ≥4, if present, could be investigated. The corresponding number of drugs was eight for a rare event like anaphylactic shock.Drug use in children is rare and shows little variation. The number of drugs with enough exposure to detect rare adverse events in children and adolescents within an EHR-based surveillance system such as EU-ADR is limited. Use of additional sources of paediatric drug exposure information and global collaboration are imperative in order to optimize EHR data for paediatric safety surveillance.
Osteoarthritis (OA) is among the leading chronic diseases to cause pain and disability. Knowledge of common co-existing diseases in OA can contribute to early diagnosis, or even prevention of disease.
Objective:
In this study we aimed to estimate the prevalence of comorbidity in adults with incident OA in primary care, compared to matched controls without OA.
Study design:
A case-control study.
Setting:
Data was used from the Integrated Primary Care Information (IPCI) database, an electronic health record database that comprises the medical records of over 2.5 million patients from general practices throughout the Netherlands. It contains longitudinal data on patient characteristics, symptoms, diagnoses, test results, drug prescriptions, referral to specialists and hospitalization.
Population studied:
We defined OA cases as adults diagnosed with incident OA between January 2006 and December 2019. Diagnosis of OA was based on one of the following ICPC codes: L89 (hip OA), L90 (knee OA) and L91 (other OA). The first registration of an OA code within the study period was defined as the index date. Each case was matched with 1-4 controls without OA according to age, GP practice and sex, using incidence density sampling.
Outcome measures:
We selected 58 non-acute conditions as comorbidities of interest and analyzed them individually. The prevalence of each comorbidity at the index date was estimated and presented as odds ratio (OR) between cases and controls, using a p-value < 0.001 for significance to account for multiple testing.
Results:
We identified 80,099 incident OA cases of whom 79,937 (99.8%) were successfully matched with a total of 195,660 controls. Patients with incident OA had a significantly higher prevalence for 34 of the 58 studied conditions. In 10 conditions we found no significant difference in prevalence and a lower prevalence was found in 14 comorbidities. The associations (ORs (95% CI)) ranged from 1.67 (1.63-1.71) for fibromyalgia to 0.67 (0.63-0.71) for multiple sclerosis.
Conclusions:
The prevalence of most comorbidities differed significantly in individuals with newly diagnosed OA compared to their matched controls at the index date. The majority of the comorbidities showed a higher prevalence in incident OA cases compared to controls. A possible explanation for the low ORs in short-term fatal diseases can be found in the fact that OA may be subordinated to severe diseases, and as a result be registered less frequently.
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