Dataset Information

Sources of Dietary Salt in North and South India Estimated from 24 Hour Dietary Recall.

ABSTRACT: Recent data on salt intake levels in India show consumption is around 11 g per day, higher than the World Health Organization's (WHO) recommended intake of 5 g per day. However, high-quality data on sources of salt in diets to inform a salt reduction strategy are mostly absent. A cross-sectional survey of 1283 participants was undertaken in rural, urban, and slum areas in North (n = 526) and South (n = 757) India using an age-, area-, and sex-stratified sampling strategy. Data from two 24-h dietary recall surveys were transcribed into a purpose-built nutrient database. Weighted salt intake was estimated from the average of the two recall surveys, and major contributors to salt intake were identified. Added salt contributed the most to total salt intake, with proportions of 87.7% in South India and 83.5% in North India (p < 0.001). The main food sources of salt in the south were from meat, poultry, and eggs (6.3%), followed by dairy and dairy products (2.6%), and fish and seafood (1.6%). In the north, the main sources were dairy and dairy products (6.4%), followed by bread and bakery products (3.3%), and fruits and vegetables (2.1%). Salt intake in India is high, and this research confirms it comes mainly from added salt. Urgent action is needed to implement a program to achieve the WHO salt reduction target of a 30% reduction by 2025. The data here suggest the focus needs to be on changing consumer behavior combined with low sodium, salt substitution.

SUBMITTER: Johnson C

PROVIDER: S-EPMC6412427 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sources of Dietary Salt in North and South India Estimated from 24 Hour Dietary Recall.

Johnson Claire C Santos Joseph Alvin JA Sparks Emalie E Raj Thout Sudhir TS Mohan Sailesh S Garg Vandana V Rogers Kris K Maulik Pallab K PK Prabhakaran Dorairaj D Neal Bruce B Webster Jacqui J

Nutrients 20190201 2

Recent data on salt intake levels in India show consumption is around 11 g per day, higher than the World Health Organization's (WHO) recommended intake of 5 g per day. However, high-quality data on sources of salt in diets to inform a salt reduction strategy are mostly absent. A cross-sectional survey of 1283 participants was undertaken in rural, urban, and slum areas in North (<i>n</i> = 526) and South (<i>n</i> = 757) India using an age-, area-, and sex-stratified sampling strategy. Data from ...[more]

PMID: 30717304

Similar Datasets

Project description:BackgroundMonitoring salt consumption in children is essential for informing and implementing public health interventions to reduce children's salt intake. However, collection of 24-hour urines, considered as the most reliable approach, can be especially challenging to school children. This study aimed to assess the agreement between 24-hour urine (24hrU) and 24-hour food recall (24hrFR) in: (1) estimating salt intake in children; (2) classifying salt intakes above the recommended upper level set for children, and; (3) estimating change in mean salt intake over time.MethodsThis study utilised data from two cross-sectional surveys of school children aged 8 to 12 years living in the state of Victoria, Australia. A single 24hrU and 24hrFR were collected from each participant. Suspected inaccurate urine collections and implausible energy intakes were excluded based on pre-defined criteria. The agreement between the two methods was assessed using Bland-Altman methodology, the intraclass correlation coefficient (ICC), and the kappa statistic. The difference between the measured change in salt intake over time using 24hrU and 24hrFR was derived using mixed effects linear regression analysis.ResultsA total of 588 participants provided a 24hrU and 24hrFR. Overall, there was no meaningful difference in mean estimated salt intake between the two methods (- 0.2 g/day, 95% CI - 0.5 to 0.1). The Bland-Altman plot showed wide 95% limits of agreement (- 7.2 to 6.8). The ICC between the two methods was 0.13 (95% CI 0.05 to 0.21). There was poor interrater reliability in terms of classifying salt intake above the recommended upper level for children, with an observed agreement of 63% and kappa statistic of 0.11. The change in mean salt intake over time was 0.2 g/day (- 0.4 to 0.7) based on 24hrU, and 0.5 g/day (- 0.0 to 1.1) based on 24hrFR, with a difference-in-differences of 0.4 g/day (- 0.3 to 1.1).Conclusions24hrFR appears to provide a reasonable estimate of mean salt intake as measured by 24hrU in Australian school children. However, similar to previous observations in adults, and of studies exploring other alternative methods for estimating salt intake, 24hrFR is a poor predictor of individual-level salt intake in children.

Project description:BACKGROUND:Under-reporting because of the limitations of human memory is one of the key challenges in dietary assessment surveys that use the multiple-pass 24-hour recall. Research indicates that shortening a retention interval (ie, the time between the eating event and recall) reduces the burden on memory and may increase the accuracy of the assessment. OBJECTIVE:This study aimed to explore the accuracy and acceptability of Web-based dietary assessment surveys based on a progressive recall, where a respondent is asked to record multiple recalls throughout a 24-hour period using the multiple-pass protocol and portion size estimation methods of the 24-hour recall. METHODS:The experiment was conducted with a dietary assessment system, Intake24, that typically implements the multiple-pass 24-hour recall method where respondents record all meals they had for the previous day on a single occasion. We modified the system to allow respondents to add multiple recalls throughout the day using the multiple-pass protocol and portion size estimation methods of the 24-hour recall (progressive recall). We conducted a dietary assessment survey with 33 participants, where they were asked to record dietary intake using both 24-hour and progressive recall methods for weekdays only. We compared mean retention intervals (ie, the time between eating event and recall) for the 2 methods. To examine accuracy, we compared mean energy estimates and the mean number of reported foods. Of these participants, 23 were interviewed to examine the acceptability of the progressive recall. RESULTS:Retention intervals were found to be, on average, 15.2 hours (SD 7.8) shorter during progressive recalls than those during 24-hour recalls. We found that the mean number of foods reported for evening meals for progressive recalls (5.2 foods) was significantly higher (P=.001) than that for 24-hour recalls (4.2 foods). The number of foods and the amount of energy reported for other meals remained similar across the 2 methods. In interviews, 65% (15/23) of participants said that the 24-hour recall is more convenient in terms of fitting in with their daily lifestyles, and 65% (15/23) of respondents indicated that they remembered meal content and portion sizes better with the progressive recall. CONCLUSIONS:The analysis of interviews and data from our study indicate that progressive recalls provide minor improvements to the accuracy of dietary assessment in Intake24. Additional work is needed to improve the acceptability of progressive recalls in this system.

Project description:BACKGROUND:Undernutrition remains highly prevalent in African children, highlighting the need for accurately assessing dietary intake. In order to do so, the assessment method must be validated in the target population. A triple pass 24 hour dietary recall with volumetric portion size estimation has been described but not previously validated in African children. This study aimed to establish the relative validity of 24-hour dietary recalls of daily food consumption in healthy African children living in Mbale and Soroti, eastern Uganda compared to simultaneous weighed food records. METHODS:Quantitative assessment of daily food consumption by weighed food records followed by two independent assessments using triple pass 24-hour dietary recall on the following day. In conjunction with household measures and standard food sizes, volumes of liquid, dry rice, or play dough were used to aid portion size estimation. Inter-assessor agreement, and agreement with weighed food records was conducted primarily by Bland-Altman analysis and secondly by intraclass correlation coefficients and quartile cross-classification. RESULTS:19 healthy children aged 6 months to 12 years were included in the study. Bland-Altman analysis showed 24-hour recall only marginally under-estimated energy (mean difference of 149kJ or 2.8%; limits of agreement -1618 to 1321kJ), protein (2.9g or 9.4%; -12.6 to 6.7g), and iron (0.43mg or 8.3%; -3.1 to 2.3mg). Quartile cross-classification was correct in 79% of cases for energy intake, and 89% for both protein and iron. The intraclass correlation coefficient between the separate dietary recalls for energy was 0.801 (95% CI, 0.429-0.933), indicating acceptable inter-observer agreement. CONCLUSIONS:Dietary assessment using 24-hour dietary recall with volumetric portion size estimation resulted in similar and acceptable estimates of dietary intake compared with weighed food records and thus is considered a valid method for daily dietary intake assessment of children in communities with similar diets. The method will be utilised in a sub-study of a large randomised controlled trial addressing treatment in severe childhood anaemia. TRIAL REGISTRATION:This study was approved by the Mbale Research Ethics committee (Reference: 2013-050). Transfusion and Treatment of severe Anaemia in African Children: a randomized controlled Trial (TRACT) registration: ISRCTN84086586.

Project description:BackgroundAssessment of food intake is a cornerstone of nutritional research. However, the use of minimally validated dietary assessment methods is common and can generate misleading results. Thus, there is a need for valid, precise and cost-effective dietary assessment tools to be used in large cohort studies.The objective is to validate a newly developed automated self-administered web-based 24-h dietary recall (R24W), within a population of adults taking part in fully controlled feeding studies.MethodsSixty two adults completed the R24W twice while being fed by our research team. Actual intakes were precisely known, thereby allowing the analysis of the proportion of adequately self-reported items. Association between offered and reported portion sizes was assessed with correlation coefficients and agreement with the kappa score while systematics biases were illustrated with Bland-Altman Plot.ResultsParticipants received an average of 16 food items per testing day. They reported 89.3% of the items they received. The more frequently omitted food categories were vegetables included in recipes (40.0%) as well as side vegetables (20.0%) and represented less than 5% of the actual daily energy intake. Offered and self-reported portion sizes were significantly correlated (r = 0.80 P < 0.001) and demonstrated a strong agreement as assessed by the kappa score of 0.62. Reported portion sizes for individual food items were on average 3.2 g over the offered portion sizes. Portions of 100 g and above were on average underestimated by 2.4% (r = 0.68 P < 0.01; kappa score = 0.50) while small portions (less than 100 g) were overestimated by 17.1% (r = 0.46 P < 0.01; kappa score = 0.43). A nonsignificant underestimation (-13.9 kcal ± 646.3 kcal; P = 0.83) of energy intake was noted.ConclusionR24W performed well as participants were able to report the great majority of items they ate and selected portion size strongly related to the one they received. This suggests that food items are easily to find within the R24W and images of portion sizes used in this dietary assessment tool are adequate and can provide valid food intake evaluation.

Project description:BackgroundInformation on long-term dietary intake is often required for research or program planning, but surveys routinely use short-term assessments such as 24-h recalls (24HRs). Methods to reduce the impact of within-person variation in 24HRs, such as the National Cancer Institute (NCI) method, typically require extensive training and skill.ObjectivesWe introduce the Simulating Intake of Micronutrients for Policy Learning and Engagement (SIMPLE) macro, a new tool to increase the accessibility of 24HR analysis. We explain the underlying theory behind the tool and provide examples of potential applications.MethodsThe SIMPLE macro connects the core NCI statistical code to estimate usual intake distributions and includes additional code to enable advanced analyses such as predictive modeling. The related SIMPLE-Iron macro applies the full probability method to estimate inadequate iron intake, and the SIMPLE-1D macro is used for descriptive or modeling analyses of data with a single 24HR per person. The macros and associated documentations are freely available. We analyzed data from the US National Health and Nutrition Examination Survey (NHANES) and the Cameroon National Micronutrient Survey to compare the SIMPLE macro to 1) the core NCI code using the Estimated Average Requirement cut point method, and 2) the IMAPP software for iron only, and to demonstrate the applications of the SIMPLE macro for estimating usual intake and predictive modeling.ResultsThe SIMPLE macro generates identical results to the core NCI code. The SIMPLE-Iron macro also produces estimates of inadequate iron intake comparable to the IMAPP software. The examples demonstrate application of the SIMPLE macro to 1) descriptive analyses of nutrient intake from food and supplements (NHANES), and 2) analyses accounting for breast-milk nutrient intake and modeling fortification and supplementation programs (Cameroon).ConclusionsThe SIMPLE macros may facilitate the analysis and modeling of dietary data to inform nutrition research, programs, and policy.

Dataset Information

Sources of Dietary Salt in North and South India Estimated from 24 Hour Dietary Recall.

Publications

Sources of Dietary Salt in North and South India Estimated from 24 Hour Dietary Recall.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets