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

Comparison of participants and non-participants to the ORISCAV-LUX population-based study on cardiovascular risk factors in Luxembourg

BACKGROUND: Poor response is a major concern in public health surveys. In a population-based ORISCAV-LUX study carried out in Grand-Duchy of Luxembourg to assess the cardiovascular risk factors, the non-response rate was not negligible. The aims of the present work were: 1) to investigate the representativeness of study sample to the general population, and 2) to compare the known demographic and cardiovascular health-related profiles of participants and non-participants. METHODS: For sample representativeness, the participants were compared to the source population according to stratification criteria (age, sex and district of residence). Based on complementary information from the "medical administrative database", further analysis was carried out to assess whether the health status affected the response rate. Several demographic and morbidity indicators were used in the univariate comparison between participants and non-participants. RESULTS: Among the 4452 potentially eligible subjects contacted for the study, there were finally 1432 (32.2%) participants. Compared to the source population, no differences were found for gender and district distribution. By contrast, the youngest age group was under-represented while adults and elderly were over-represented in the sample, for both genders. Globally, the investigated clinical profile of the non-participants was similar to that of participants. Hospital admission and cardiovascular health-related medical measures were comparable in both groups even after controlling for age. The participation rate was lower in Portuguese residents as compared to Luxembourgish (OR = 0.58, 95% CI: 0.48-0.69). It was also significantly associated with the professional status (P < 0.0001). Subjects from the working class were less receptive to the study than those from other professional categories. CONCLUSION: The 32.2% participation rate obtained in the ORISCAV-LUX survey represents the realistic achievable rate for this type of multiple-stage, nationwide, population-based surveys. It corresponds to the expected rate upon which the sample size was calculated. Given the absence of discriminating health profiles between participants and non-participants, it can be concluded that the response rate does not invalidate the results and allows generalizing the findings for the population

Investigating sources of variability in metabolomic data in the EPIC study: the Principal Component Partial R-square (PC-PR2) method

The key goal of metabolomic studies is to identify relevant individual biomarkers or composite metabolic patterns associated with particular disease status or patho-physiological conditions. There are currently very few approaches to evaluate the variability of metabolomic data in terms of characteristics of individuals or aspects pertaining to technical processing. To address this issue, a method was developed to identify and quantify the contribution of relevant sources of variation in metabolomic data prior to investigation of etiological hypotheses. The Principal Component Partial R-square (PC-PR2) method combines features of principal component and of multivariable linear regression analyses. Within the European Prospective Investigation into Cancer and nutrition (EPIC), metabolic profiles were determined by 1H NMR analysis on 807 serum samples originating from a nested liver cancer case-control study. PC-PR2 was used to quantify the variability of metabolomic profiles in terms of study subjects age, sex, body mass index, country of origin, smoking status, diabetes and fasting status, as well as factors related to sample processing. PC-PR2 enables the evaluation of important sources of variations in metabolomic studies within large-scale epidemiological investigations. © 2014 Springer Science+Business Media New York

Comparison of modified Borg scale and visual analog scale dyspnea scores in predicting re-intervention after drainage of malignant pleural effusion

Background: Dyspnea is the most common symptom in patients with malignant pleural effusion (MPE). Treatment decisions are primarily based on the perception of dyspnea severity. Aims: To study dyspnea perception following therapeutic thoracentesis using the visual analog scale (VAS) dyspnea score and modified Borg scale (MBS). To investigate whether patient reported outcome (PRO) measures can predict pleural re-interventions. Patients and methods: Consecutive patients presenting with symptomatic MPE and planned for therapeutic thoracentesis were asked to complete MBS and VAS dyspnea scores (both at rest and during exercise) daily for 14 consecutive days. Physicians, unaware of the results of these PRO measures, decided on the necessity of a re-intervention, according to routine care. PRO measures were analyzed and correlated with performed re-interventions and the volume of removed fluid. Results: Forty-nine out of 64 consecutive patients returned the diaries. Twenty-eight patients (57 %) had a re-intervention within 30 days. Patients who required a re-intervention reported significantly higher MBS than patients who did not. The extent of increase in MBS during exercise was related to the need for re-intervention. Regarding the MBS during exercise, median time to maximal relief was 2 days. Re-intervention was required sooner when larger volumes were drained. Conclusion: Patient reported outcomes are useful tools to assess treatment effect of therapeutic thoracentesis. Median time to maximal relief is 2 days. MBS rather than VAS dyspnea score appears to be more prognostic for repeat pleural drainage within 30 days.Rogier C. Boshuizen, Andrew D. Vincent, Michel M. van den Heuve

A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections

A-scab (Apple-scab) is a dynamic simulation model for Venturia inaequalis primary infections on apple. It simulates development of pseudothecia, ascospore maturation, discharge, deposition and infection during the season based on hourly data of air temperature, rainfall, relative humidity and leaf wetness. A-scab produces a risk index for each infection period and forecasts the probable periods of symptoms appearance. The model was validated under different epidemiological conditions: its outputs were successfully compared with daily spore counts and actual onset and severity of the disease under orchard conditions, and neither corrections nor calibrations have been necessary to adapt the model to different apple-growing areas. Compared to other existing models, A-scab: (i) combines information from literature and data acquired from specific experiments; (ii) is completely 'open' because both model structure and algorithms have been published and are easily accessible; (iii) is not written with a specific computer language but it works on simple-to-use electronic sheets. For these reasons the model can be easily implemented in the computerized systems used by warning services