Operational definitions of asthma in recent epidemiological studies are inconsistent.
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ABSTRACT: Objective
The best combination of questions to define asthma in epidemiological asthma studies is not known. We summarized the operational definitions of asthma used in prevalence studies and empirically assess how asthma prevalence estimates vary depending on the definition used.Methods
We searched the Thomson Reuters ISI Web of knowledge and included (1) cross-sectional studies (2) on asthma prevalence (3) conducted in the general population and (4) containing an explicit definition of asthma. The search was limited to the 100 most-cited papers or published since January 2010. For each paper, we recorded the asthma definition used and other variables. Then we applied the definitions to the data of the Portuguese National Asthma survey (INAsma) and of the 2005-2006 National Health and Nutrition Examination Survey (NHANES) computing asthma prevalence estimates for the different definitions.Results
Of 1738 papers retrieved, 117 were included for analysis. Lifetime asthma, diagnosed asthma and current asthma were defined in 8, 12 and 29 different ways, respectively. By applying definitions of current asthma on INAsma and NHANES data, the prevalence ranged between 5.3%-24.4% and 1.1%-17.2%, respectively.Conclusions
There is considerable heterogeneity in the definitions of asthma used in epidemiological studies leading to highly variable estimates of asthma prevalence. Studies to inform a standardized operational definition are needed. Meanwhile, we propose a set of questions to be reported when defining asthma in epidemiological studies.
SUBMITTER: Sa-Sousa A
PROVIDER: S-EPMC4136946 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundResearchers use different definitions to identify children with asthma in epidemiological surveys. We conducted a systematic review to describe the definitions used in epidemiologic studies for wheeze and asthma in the paediatric population, aimed to inform the development of a uniform definition of paediatric asthma for future epidemiological research.MethodsWe systematically searched terms to identify asthma and/or wheeze among children aged <13 years and published between 1995-2020 across seven databases (MEDLINE, EMBASE, PsycINFO, Global Health, AMED, LILACS and CINAHL). PRISMA guidelines were followed for this review.ResultsWe extracted a total of 11 886 records, where 190 met our eligibility criteria and included in the analysis. Among the included studies, 62.1% (n = 118/190) used the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaires, predominantly in developing countries (80%, n = 64/80). 'Wheeze' was reported in five categories, subdivided by 14 different definitions. "Current wheeze", defined as caregivers report of wheezing sounds from the chest of the child in the past 12 months and "Wheeze ever", defined as caregivers' report of wheezing or whistling in the chest of the child at any previous time, were the most common wheeze category reported in 129 and 95 studies, respectively. Asthma was reported in nine categories using 53 definitions. The most common asthma category was "Asthma ever", which was reported in 89 studies, based on caregiver statement that the child had asthma in the past.ConclusionDefinitions of wheeze and asthma for children used in surveys are primarily based on parent-reported clinical features. Studies from developing countries more frequently used the ISAAC definitions to report childhood asthma and wheeze compared to the studies from developed counties. The use of a uniform asthma definition will aid the interpretation of research findings globally.
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Project description:Youth metabolic equivalents (METy) are sometimes operationally defined as multiples of predicted basal metabolic rate (METyBMR) and other times as multiples of measured resting metabolic rate (METyRMR).PurposeThis study aimed to examine the comparability of METyBMR and METyRMR.MethodsIndirect calorimetry data (Cosmed K4b) were analyzed from two studies, with a total sample of 245 youth (125 male participants, 6-18 yr old, 37.4% overweight or obese). The Schofield equations were used to predict BMR, and K4b data from 30 min of supine rest were used to assess RMR. Participants performed structured physical activities (PA) of various intensities, and steady-state oxygen consumption was divided by predicted BMR and measured RMR to calculate METyBMR and METyRMR, respectively. Two-way (activity-METy calculation) analysis of variance was used to compare METyBMR and METyRMR (α = 0.05), with Bonferroni-corrected post hoc tests. Intensity classifications were also compared after encoding METyBMR and METyRMR as sedentary behavior (≤1.50 METy), light PA (1.51-2.99 METy), moderate PA (3.00-5.99 METy), or vigorous PA (≥6.00 METy).ResultsThere was a significant interaction (F(30) = 3.6, P < 0.001), and METyBMR was significantly higher than METyRMR for 28 of 31 activities (P < 0.04), by 15.6% (watching television) to 23.1% (basketball). Intensity classifications were the same for both METy calculations in 69.0% of cases.ConclusionsMETyBMR and METyRMR differ considerably. Greater consensus is needed regarding how metabolic equivalents should be operationally defined in youth, and in the meantime, careful distinction is necessary between METyBMR and METyRMR.
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Project description:AbstractThe objective of this paper is to compare post-2007 epidemiological results for plutonium workers to risk predicted by the software program NIOSH-IREP (IREP for short), which is used to determine the lowest dose for a US veteran to obtain cancer compensation. IREP output and methodology were used to predict excess relative risk per Gy (ERR Gy -1 ) for lung cancer at the 99 th credibility percentile, which is used for compensation decisions. Also estimated were relative biological effectiveness factors (RBE) predicted for workers using IREP methodology. IREP predictions were compared to results for Mayak and Sellafield plutonium workers, separately and pooled. Indications that IREP might underpredict 99 th -percentile lung cancer plutonium risk came from (1) comparison of worker RBEs and (2) from comparison of Sellafield results separately. When Sellafield and Mayak data were pooled, ERR Gy -1 comparisons at the 99 th percentile roughly matched epidemiological data with regression dose range restricted to < 0.05 Gy, the most relevant region to veterans, but overpredicted for the full dose range. When four plausible distributions for lung cancer risk, including both new and old data, were combined using illustrative weighting factors, compensation cutoff dose for lung cancer matched current IREP values unless regression results below 0.05 were chosen for Sellafield, producing a two-fold reduction. A 1997 claim of a dose threshold in lung cancer dose response was not confirmed in later literature. The benefit of the doubt is given to claimants when the science is unclear. The challenge for NIOSH-IREP custodians is dealing with the Sellafield results, which might best match US claimants.
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Project description:BackgroundDespite increasing research on the double burden of malnutrition (DBM; i.e., coexisting over- and undernutrition), there is no global consensus on DBM definitions.ObjectivesTo identify published operational DBM definitions, measure their frequency of use, and discuss implications for future assessment.MethodsFollowing a structured search of peer-reviewed articles with terms describing "overnutrition" [e.g., overweight/obesity (OW/OB)] and "undernutrition" (e.g., stunting, micronutrient deficiency), we screened 1920 abstracts, reviewed 500 full texts, and extracted 623 operational definitions from 239 eligible articles.ResultsWe organized three identified DBM dimensions (level of assessment, target population, and forms of malnutrition) into a framework for building operational DBM definitions. Frequently occurring definitions included coexisting: 1) OW/OB and thinness, wasting, or underweight (n = 289 occurrences); 2) OW/OB and stunting (n = 161); 3) OW/OB and anemia (n = 74); and 4) OW/OB and micronutrient deficiency (n = 73).ConclusionsExisting DBM definitions vary widely. Putting structure to possible definitions may facilitate selection of fit-for-purpose indicators to meet public health priorities.
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Project description:Background A subgroup of patients with asthma has been reported to have an increased risk for asthma-associated infectious and inflammatory multimorbidities (AIMs). To systematically investigate the association of asthma with AIMs using a large patient cohort, it is desired to leverage a broad range of electronic health record (EHR) data sources to automatically identify AIMs accurately and efficiently. Methods We established an expert consensus for an operational definition for each AIM from EHR through a modified Delphi technique. A series of questions about the operational definition of 19 AIMS (11 infectious diseases and 8 inflammatory diseases) was generated by a core team of experts who considered feasibility, balance between sensitivity and specificity, and generalizability. Eight internal and 5 external expert panelists were invited to individually complete a series of online questionnaires and provide judgement and feedback throughout three sequential internal rounds and two external rounds. Panelists’ responses were collected, descriptive statistics tabulated, and results reported back to the entire group. Following each round the core team of experts made iterative edits to the operational definitions until a moderate (≥ 60%) or strong (≥ 80%) level of consensus among the panel was achieved. Results Response rates for each Delphi round were 100% in all 5 rounds with the achievement of the following consensus levels: (1) Internal panel consensus: 100% for 8 definitions, 88% for 10 definitions, and 75% for 1 definition, (2) External panel consensus: 100% for 12 definitions and 80% for 7 definitions. Conclusions The final operational definitions of AIMs established through a modified Delphi technique can serve as a foundation for developing computational algorithms to automatically identify AIMs from EHRs to enable large scale research studies on patient’s multimorbidities associated with asthma. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01663-y.
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