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Effect of nutrition survey 'cleaning criteria' on estimates of malnutrition prevalence and disease burden: secondary data analysis.

ABSTRACT: Tackling childhood malnutrition is a global health priority. A key indicator is the estimated prevalence of malnutrition, measured by nutrition surveys. Most aspects of survey design are standardised, but data 'cleaning criteria' are not. These aim to exclude extreme values which may represent measurement or data-entry errors. The effect of different cleaning criteria on malnutrition prevalence estimates was unknown. We applied five commonly used data cleaning criteria (WHO 2006; EPI-Info; WHO 1995 fixed; WHO 1995 flexible; SMART) to 21 national Demographic and Health Survey datasets. These included a total of 163,228 children, aged 6-59 months. We focused on wasting (low weight-for-height), a key indicator for treatment programmes. Choice of cleaning criteria had a marked effect: SMART were least inclusive, resulting in the lowest reported malnutrition prevalence, while WHO 2006 were most inclusive, resulting in the highest. Across the 21 countries, the proportion of records excluded was 3 to 5 times greater when using SMART compared to WHO 2006 criteria, resulting in differences in the estimated prevalence of total wasting of between 0.5 and 3.8%, and differences in severe wasting of 0.4-3.9%. The magnitude of difference was associated with the standard deviation of the survey sample, a statistic that can reflect both population heterogeneity and data quality. Using these results to estimate case-loads for treatment programmes resulted in large differences for all countries. Wasting prevalence and caseload estimations are strongly influenced by choice of cleaning criterion. Because key policy and programming decisions depend on these statistics, variations in analytical practice could lead to inconsistent and potentially inappropriate implementation of malnutrition treatment programmes. We therefore call for mandatory reporting of cleaning criteria use so that results can be compared and interpreted appropriately. International consensus is urgently needed regarding choice of criteria to improve the comparability of nutrition survey data.

SUBMITTER: Crowe S

PROVIDER: S-EPMC4034601 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Effect of nutrition survey 'cleaning criteria' on estimates of malnutrition prevalence and disease burden: secondary data analysis.

Crowe Sonya S Seal Andrew A Grijalva-Eternod Carlos C Kerac Marko M

PeerJ 20140513

Tackling childhood malnutrition is a global health priority. A key indicator is the estimated prevalence of malnutrition, measured by nutrition surveys. Most aspects of survey design are standardised, but data 'cleaning criteria' are not. These aim to exclude extreme values which may represent measurement or data-entry errors. The effect of different cleaning criteria on malnutrition prevalence estimates was unknown. We applied five commonly used data cleaning criteria (WHO 2006; EPI-Info; WHO 1 ...[more]

PMID: 24883244

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Project description:IntroductionMalnutrition among adolescents is a persistent problem with a profound impact on different dimensions of health. The objective of this analysis is to assess the burden of malnutrition (Stunting, Thinness, Overweight, and Obesity) and their associated socio-demographic factors among Indian adolescents (10-19 years) from the Comprehensive National Nutritional Survey (CNNS 2016-18) data.MethodsWe used Individual-level data of 35,831 adolescents from the CNNS conducted in 2016-18 for this analysis. CNNS collected data on the nutritional status of adolescents along with socio-demographic variables from all states of India. Burden of stunting (Height for age Z score, HAZ < -2 SD), thinness (BMI for age Z score, BAZ < -2 SD), overweight (BAZ > 1 SD) and obesity (BAZ > 2 SD) were estimated for the entire country and individual states. A multivariable logistic regression analysis was used to assess the socio-demographic factors associated with stunting, thinness, and overweight.ResultsCNNS collected data from 35,831 adolescents, of which 31,941 with BAZ scores, and 32,045 with HAZ scores were included in the final analysis. The burden of stunting and thinness among Indian adolescents was 27.4% (95% CI 26.4, 28.4%) and 24.4% (23.5, 25.4%), respectively. The burden of overweight and obesity was 4.8% (4.5, 5.1%) and 1.1% (0.9, 1.3%), respectively. Adolescents in the age group of 15-19 years (AOR 1.23, 95% CI 1.11, 1.36) compared to 10-14 years, females (AOR 1.20; 1.08, 1.33) compared to males, were at increased odds of getting stunted. Adolescents from lowest wealth index families (AOR 1.66; 1.33, 2.07) were at increased odds of thinness compared to peers of higher wealth index families. Adolescents of 10-14 years (AOR 1.26, 95% CI 1.06, 1.49) compared to 15-19 years, urban residents (AOR 1.43, 95% CI 1.19, 1.71) compared to rural residents, were at increased odds of overweight.ConclusionIndian adolescents face the double burden of malnutrition that is undernutrition (stunting and thinness) alongside overnutrition (overweight and obesity) that are linked with socio-demographic factors. The National Nutritional Programs (POSHAN Abhiyan) should prioritize high-risk groups specifically older age group (15-19 years), females, and low wealth Index quintile families identified in this analysis.

Project description:ObjectivesLatent Tuberculosis infection (LTBI) is marked by dynamic host-pathogen interactions with persistent low-grade inflammation and is associated with increased risk of cardiovascular diseases (CVD) including acute coronary syndrome, myocardial infarction, and stroke. However, few studies assess the relationship between LTBI and hypertension, an intermediate of CVD. We sought to determine the association between LTBI and hypertension using data representative of the adult US population.MethodsWe performed cross-sectional analyses using data from the 2011-2012 US National Health and Nutrition Examination Survey (NHANES). Eligible participants included adults with valid QuantiFERON-TB Gold In-Tube (QFT-GIT) test results who also had blood pressure measures and no history of TB disease. LTBI was defined by a positive QFT-GIT. We defined hypertension by either elevated measured blood pressure levels (i.e., systolic ≥130mmHg or diastolic ≥80mmHg) or known hypertension indications (i.e., self-reported previous diagnosis or use of antihypertensive medications). Analyses were performed using robust quasi-Poisson regressions and accounted for the stratified probability sampling design of NHANES.ResultsThe overall prevalence of LTBI was 5.7% (95%CI 4.7-6.7) and hypertension was present among 48.9% (95%CI 45.2-52.7) of participants. The prevalence of hypertension was higher among those with LTBI (58.5%, 95%CI 52.4-64.5) than those without LTBI (48.3%, 95%CI 44.5-52.1) (prevalence ratio [PR]=1.2, 95%CI 1.1-1.3). However, after adjusting for confounders, the prevalence of hypertension was similar for those with and without LTBI (adjusted PR=1.0, 95%CI 0.9 -1.1). Among individuals without CVD risk factors of elevated BMI (PRnormal BMI=1.6, 95%CI 1.2-2.0), hyperglycemia (PReuglycemia=1.3, 95%CI 1.1-1.5), or cigarette smoking (PRnon-smokers=1.2, 95%CI 1.1-1.4), the unadjusted prevalence of hypertension was higher among those with LTBI vs. no LTBI.ConclusionsMore than half of adults with LTBI in the US had hypertension. Importantly, we observed a relationship between LTBI and hypertension among those without established CVD risk factors.

Project description:BackgroundReexamining the prevalence of persons infected with tuberculosis (TB) is important to determine trends over time. In 2011-2012 a TB component was included in the National Health and Nutrition Examination Survey (NHANES) to estimate the reservoir of persons infected with TB.MethodsCivilian, noninstitutionalized U.S. population survey participants aged 6 years and older were interviewed regarding their TB history and eligibility for the tuberculin skin test (TST) and interferon gamma release assay (IGRA) blood test. Once eligibility was confirmed, both tests were conducted. Prevalence and numbers of TST positive (10 mm or greater), IGRA positive, and both TST and IGRA positive were calculated by adjusting for the complex survey design after applying corrections for item nonresponse and digit preference in TST induration measurements. To examine TST positivity over time, data from NHANES 1999-2000 were reanalyzed using the same statistical methods. The TST was performed using Tubersol, a commercially available purified protein derivative (PPD), rather than PPD-S, which was the antigen used in NHANES 1999-2000. Prior patient history of TB vaccination was not collected in this study nor were patients examined for the presence of a Bacillus of Calmette and Guerin (BCG) vaccine scar.ResultsFor NHANES 2011-2012, TST and IGRA results were available for 6,128 (78.4%) and 7,107 (90.9%) eligible participants, respectively. There was no significant difference between the percentage of the U.S. population that was TST positive in 2011-2012 (4.7% [95% CI 3.4-6.3]; 13,276,000 persons) compared with 1999-2000 (4.3%; 3.5-5.3). In 2011-2012 the percentage that was IGRA positive was 5.0% (4.2-5.8) and double TST and IGRA positivity was 2.1% (1.5-2.8). The point estimate of IGRA positivity prevalence in foreign-born persons (15.9%; 13.5-18.7) was lower than for TST (20.5%; 16.1-25.8) in 2011-2012. The point estimate of IGRA positivity prevalence in U.S.-born persons (2.8%; 2.0-3.8) was higher than for TST (1.5%; 0.9-2.6).ConclusionsNo statistically significant decline in the overall estimated prevalence of TST positivity was detected from 1999-2000 to 2011-2012. The prevalence of TB infection, whether measured by TST or IGRA, remains lower among persons born in the United States compared with foreign-born persons.

Project description:BackgroundAccurate data on hypertension is essential to inform decision-making. Hypertension prevalence may be underestimated by population-based surveys due to misclassification of health status by participants. Therefore, adjustment for misclassification bias is required when relying on self-reports. This study aims to quantify misclassification bias in self-reported hypertension prevalence and prevalence ratios in the Portuguese component of the European Health Interview Survey (INS2014), and illustrate application of multiple imputation (MIME) for bias correction using measured high blood pressure data from the first Portuguese health examination survey (INSEF).MethodsWe assumed that objectively measured hypertension status was missing for INS2014 participants (n = 13,937) and imputed it using INSEF (n = 4910) as auxiliary data. Self-reported, objectively measured and MIME-corrected hypertension prevalence and prevalence ratios (PR) by sex, age group and education were estimated. Bias in self-reported and MIME-corrected estimates were computed using objectively measured INSEF data as a gold-standard.ResultsSelf-reported INS2014 data underestimated hypertension prevalence in all population subgroups, with misclassification bias ranging from 5.2 to 18.6 percentage points (pp). After MIME-correction, prevalence estimates increased and became closer to objectively measured ones, with bias reduction to 0 pp - 5.7 pp. Compared to objectively measured INSEF, self-reported INS2014 data considerably underestimated prevalence ratio by sex (PR = 0.8, 95CI = [0.7, 0.9] vs. PR = 1.2, 95CI = [1.1, 1.4]). MIME successfully corrected direction of association with sex in bivariate (PR = 1.1, 95CI = [1.0, 1.3]) and multivariate analyses (PR = 1.2, 95CI = [1.0, 1.3]). Misclassification bias in hypertension prevalence ratios by education and age group were less pronounced and did not require correction in multivariate analyses.ConclusionsOur results highlight the importance of misclassification bias analysis in self-reported hypertension. Multiple imputation is a feasible approach to adjust for misclassification bias in prevalence estimates and exposure-outcomes associations in survey data.

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Effect of nutrition survey 'cleaning criteria' on estimates of malnutrition prevalence and disease burden: secondary data analysis.

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Effect of nutrition survey 'cleaning criteria' on estimates of malnutrition prevalence and disease burden: secondary data analysis.

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