Introduction
In health research and clinical practice, height and weight are fundamental measurements, serving as critical indicators of overall health and well-being. These seemingly simple metrics are essential for calculating Body Mass Index (BMI), a widely used tool to categorize weight status and assess the risk of obesity-related diseases. While standardized physical examinations offer the most accurate method for obtaining height and weight, numerous large-scale studies, particularly in public health, rely on self-reported data due to practical constraints such as cost and logistical feasibility. However, relying on individuals’ recollections introduces a potential source of bias. Research consistently shows a tendency for people to overstate their height and understate their weight, which can significantly skew BMI calculations and consequently, obesity prevalence estimates. This discrepancy between self-reported and measured anthropometric data raises critical questions about the reliability and comparability of health statistics derived from different data collection methods.
In the United States, three nationally representative surveys provide valuable data on adult height, weight, BMI, and obesity prevalence: the National Health and Nutrition Examination Survey (NHANES), the National Health Interview Survey (NHIS), and the Behavioral Risk Factor Surveillance System (BRFSS). NHANES, known for its rigorous methodology, includes both self-reported and directly measured height and weight data, offering a unique opportunity to evaluate the accuracy of self-reporting. NHIS and BRFSS, while invaluable for broad health surveillance, rely solely on self-reported measurements. All three surveys are meticulously designed to represent the civilian noninstitutionalized US population, yet the methodologies differ in data collection techniques – NHANES employs in-person interviews and physical examinations, NHIS uses household interviews, and BRFSS conducts telephone interviews. These methodological variations, coupled with the inherent biases in self-reporting, might lead to inconsistencies in the resulting health estimates across surveys. Previous studies have extensively explored the factors influencing misreporting in height and weight within NHANES, identifying demographics like age, sex, and health status as potential contributors.
This article delves into a detailed comparison of height, weight, BMI, and obesity prevalence across these three key national surveys over the period spanning 1999 to 2016. By leveraging both self-reported and measured data from NHANES alongside self-reported data from NHIS and BRFSS, we aim to illuminate the extent of discrepancies and understand the complexities of comparing height weight data from different sources. Our analysis goes beyond simply comparing mean values; we examine the distribution of BMI and obesity prevalence to provide a comprehensive understanding of how self-reporting biases and survey methodologies impact the accuracy and comparability of these critical health indicators. Understanding these nuances is crucial for researchers, public health professionals, and policymakers who rely on these national surveys to monitor population health trends, formulate effective interventions, and allocate resources appropriately.
Methods
Survey Descriptions
The National Health and Nutrition Examination Survey (NHANES) stands out due to its comprehensive approach, combining in-home interviews with thorough physical examinations conducted in mobile examination centers. Since 1999, NHANES has operated continuously, releasing data in two-year cycles. Each cycle employs a complex, stratified, multistage probability cluster sampling design to ensure a nationally representative sample of the US civilian noninstitutionalized population. The meticulous nature of NHANES is reflected in its response rates; for the 2015-2016 cycle, the interview and examination response rates were 61% and 59%, respectively. Detailed information regarding NHANES’ design and operation is publicly available.
The National Health Interview Survey (NHIS) is another cornerstone of national health data collection, gathering information on a wide spectrum of health topics through in-person household interviews. NHIS employs a household-based survey design where basic sociodemographic data is collected from a family respondent. Subsequently, a randomly selected adult and child from the household are chosen to answer more detailed health-related questions. While primarily in-person, follow-up interviews may be conducted via telephone when necessary. In 2016, the sample adult component response rate for NHIS was 54.3%. Since 2006, NHIS data releases indicate whether any portion of the interview was conducted by telephone, although not the specific sections. Analysis of NHIS data from 2006 to 2016 reveals that 22.5% of respondents had at least part of their interview conducted over the phone. NHIS content is periodically updated to remain relevant to evolving health concerns. The survey’s findings are instrumental in tracking health status, healthcare access, health insurance coverage, health-related risk factors, and progress towards national health objectives. Further details about NHIS are readily accessible online.
The Behavioral Risk Factor Surveillance System (BRFSS), initiated in 1984 with 15 states and becoming nationwide by 1993, is a state-based, ongoing surveillance system. BRFSS collects data via telephone interviews, focusing on residents’ health-related risk behaviors, chronic health conditions, healthcare access, and preventive service utilization. A significant methodological update in 2011 involved incorporating cell phone users and refining statistical weighting procedures to enhance representativeness. In 2016, BRFSS achieved landline and cell phone response rates of 48% and 46%, respectively. Comprehensive information on BRFSS design and methodology is publicly available.
For this study, we utilized NHANES data from nine 2-year cycles (1999–2000 through 2015–2016) and annual data from NHIS and BRFSS covering the same period. All data were derived from publicly accessible, anonymized data files, without any additional modifications for this analysis. The total sample sizes were substantial: 43,320 for NHANES, 510,620 for NHIS, and 6,200,791 for BRFSS. Year-specific sample sizes are detailed in Supporting Information Table S1.
Weight and Height Data Collection
Data collection methods for height and weight differ across the three surveys. In NHANES, both self-reported and measured data are collected. During an in-person household interview, participants are asked to self-report their height and weight. They are also informed that a subsequent physical examination at a mobile examination center will include standardized measurements of height and weight. This awareness of upcoming measurements might influence the accuracy of self-reported data in NHANES. In NHIS, weight and height are only queried through self-report during in-person household interviews, similar to the initial interview stage of NHANES. BRFSS, in contrast, collects self-reported height and weight data via telephone interviews. The specific wording of the questions regarding weight and height in each survey are provided in Supporting Information Table S2.
To ensure data quality, each survey employs data editing procedures to address implausible values. For NHANES, both measured and self-reported height and weight values falling above the 99th or below the 1st percentile for a given age or age-gender group are flagged for review during data processing. Values deemed unrealistic (fewer than 10 per survey cycle) are set to missing. Importantly, original body measurement data are not altered, and no imputed values are generated. We utilized both measured and self-reported height and weight data from the public use files to calculate BMI.
NHIS data release protocols address extreme values for confidentiality reasons. For the period 2006–2016, height or weight data were reported as “not available” in public use files for records falling within the lowest or highest 1.5% for either height or weight. Specific cutoffs were height less than 59 inches or 76 inches or greater, and weight less than 100 lb or 299 lb or greater. Slightly different criteria were applied for 1999–2005. However, NHIS calculates BMI for all individuals providing valid height and weight values, even if specific height and weight values are subsequently masked as “not available” in public use files for confidentiality. Extremely implausible values, such as weight less than 50 or greater than 500 lb, and height less than 24 inches or greater than 95 inches, are considered invalid.
BRFSS also addresses extreme values for data quality purposes. Public release data files have height and weight values set to missing if they fall outside predefined ranges. For 2011–2016, exclusions were applied for height less than 3 feet or 8 feet or greater, and weight less than 50 lb or 650 lb or greater. Pre-2011 exclusion criteria were slightly different. Values set to missing are not used in BMI calculations within the public use data files.
It’s important to note that in all surveys, BMI and obesity prevalence are derived variables, calculated from reported or measured height and weight. Obesity is consistently defined as a BMI of 30 or greater across all surveys. All analyses in this study are presented separately for men and women, recognizing established sex-based differences in height, weight, adiposity, and reporting biases.
Comparisons Across Surveys and Statistical Methods
Our comparative analysis focused on four key variables across the surveys: weight, height, BMI, and obesity prevalence. We used graphical representations to illustrate differences across surveys and survey years. Sex-specific graphical comparisons were created for measured NHANES data, self-reported NHANES data, NHIS data, and BRFSS data, for all survey years from 1999 to 2016. Solid lines connect data points in the graphs, acknowledging that not all surveys collected data in every year. NHANES data points are plotted at the midpoint of each 2-year interval. To compare sex differences in obesity prevalence across surveys, we calculated the ratio of age-adjusted obesity prevalence in men to that in women for each year. Statistical testing was not performed for comparisons of self-reported data across surveys, focusing instead on descriptive comparisons.
Obesity prevalence estimates were age-adjusted using the direct method to the 2000 US standard population, employing age groups 20-39 years, 40-59 years, and 60 years and older. Pregnant women were excluded from all analyses. Statistical analyses were performed using SAS version 9.4 and SUDAAN 11.0.0. Sampling weights provided with each survey were incorporated into all analyses to account for unequal probabilities of selection and adjustments for nonresponse and planned oversampling. While all three surveys are weighted to be nationally representative, subtle differences exist in their sample designs and weighting procedures.
Supplemental NHANES Analyses
To further investigate differences between self-reported and measured data and assess potential changes over time, we conducted supplemental regression analyses using NHANES data. Within NHANES, we tabulated differences between self-reported and measured weight, height, BMI, and obesity prevalence (Supporting Information Tables S3-S6). Sex-specific linear regression models were used to examine the associations of survey cycle, age group (under 60 years and 60 years and older), and measured height with the difference between self-reported and measured height. Similarly, linear regression models assessed the associations of survey cycle, age group, and measured weight (categorized by obesity status: BMI < 30 and BMI ≥ 30) with the differences between self-reported and measured weight and BMI. Logistic regression models explored the associations of survey cycle and age group with differences in obesity prevalence estimates between self-reported and measured values. Results from all regression models are presented in Supporting Information Table S7 and discussed in the results section. All supplemental analyses utilized sampling weights and accounted for the complex sample design. Standard errors were estimated using Taylor series linearization in SUDAAN. Statistical significance was set at P < 0.05.
Results
Height Comparison
Figure 1 illustrates the mean height comparisons across surveys from 1999 to 2016. Consistently across all survey years, mean self-reported heights in all three surveys were greater than mean measured heights from NHANES for both men and women. The mean self-reported heights from BRFSS and NHIS were similar to, but slightly higher than, the mean self-reported heights in NHANES. For men, the overall mean differences in self-reported height relative to NHANES self-reported height were 0.1 cm in NHIS and 0.4 cm in BRFSS. For women, the corresponding values were 0.1 cm and 0.3 cm.
Figure 1.
Mean height by survey, 1999 through 2016.
Weight Comparison
Figure 2 displays the comparisons of self-reported to measured weights across surveys. The patterns differed somewhat between men and women. For women, in every survey cycle, mean self-reported weights in all three surveys were lower than mean measured weights from NHANES. Furthermore, mean self-reported weights in NHIS and BRFSS were lower than mean self-reported weights in NHANES. Specifically, for women, mean self-reported weights in NHIS were 2.8 kg lower, and in BRFSS were 1.9 kg lower, than mean self-reported weights in NHANES.
Figure 2.
Mean weight by survey, 1999 through 2016.
For men, the differences between self-reported and measured weights and between self-reported weights across surveys were less pronounced and less systematic compared to women. Overall, mean self-reported weights in NHIS and BRFSS tended to be lower than mean self-reported weights in NHANES across most survey cycles (Figure 2). Quantitatively, for men, mean self-reported weights in NHIS were 1.4 kg lower, and in BRFSS were 0.3 kg lower, than mean self-reported weights in NHANES (Figure 2).
BMI Comparison
Figures 3 and 4 present the mean BMI comparisons across surveys for men and women, respectively. For both sexes and across all survey cycles, mean BMI calculated from self-reported weights and heights in all three surveys was lower than mean BMI calculated from measured values in NHANES. For men, mean BMI values from BRFSS and NHIS were both 0.2 units lower on average compared to mean BMI values calculated from self-reported weights and heights from NHANES. For women, the corresponding differences were more substantial: 0.7 units lower from NHIS and 0.8 units lower from BRFSS.
Figure 3.
Mean BMI by survey for men, 1999 through 2016.
Figure 4.
Mean BMI by survey for women, 1999 through 2016.
Table 1 provides a detailed look at the distributions of BMI, weight, and height in the three surveys. Compared to measured BMI, BMI distributions from self-reported data were narrower, particularly evident at the higher percentiles. Median BMI values were lower for self-reported data than for measured data for both men and women, and further lower for self-reported data from NHIS and BRFSS compared to NHANES self-reported data. This resulted in a compressed BMI distribution for self-reported data from NHANES relative to measured data, and even more so for NHIS and BRFSS self-reported data. The interquartile range (IQR) for height was similar across different data types and slightly higher for self-reported than measured data. For weight, the distribution was slightly compressed for self-reported data. Notably, for women, the highest percentiles of weight were considerably lower for self-reported data compared to measured data.
TABLE 1.
Selected percentiles and IQR of BMI, weight, and height by sex and survey: NHANES, NHIS, and BRFSS, 1999–2016
| 5th | 10th | 25th | 50th | 75th | 90th | 95th | IQR | |
|---|---|---|---|---|---|---|---|---|
| BMI | ||||||||
| Male | ||||||||
| NHANES measured | 20.6 | 22.0 | 24.5 | 27.6 | 31.3 | 35.6 | 39.1 | 6.8 |
| NHANES self-report | 20.8 | 22.1 | 24.3 | 27.1 | 30.7 | 34.6 | 37.8 | 6.4 |
| NHIS | 20.9 | 22.2 | 24.3 | 27.0 | 30.4 | 34.5 | 37.6 | 6.1 |
| BRFSS | 20.9 | 22.2 | 24.3 | 27.0 | 30.2 | 34.2 | 37.2 | 5.9 |
| Female | ||||||||
| NHANES measured | 19.5 | 20.8 | 23.3 | 27.3 | 32.6 | 38.5 | 42.5 | 9.3 |
| NHANES self-report | 19.4 | 20.4 | 22.8 | 26.5 | 31.5 | 37.1 | 41.0 | 8.7 |
| NHIS | 19.2 | 20.2 | 22.4 | 25.7 | 30.3 | 35.7 | 39.8 | 7.9 |
| BRFSS | 19.3 | 20.3 | 22.4 | 25.7 | 30.0 | 35.2 | 39.1 | 7.6 |
| Weight (kg) | ||||||||
| Male | ||||||||
| NHANES measured | 61.6 | 66.2 | 74.8 | 85.3 | 98.6 | 112.9 | 124.0 | 23.8 |
| NHANES self-report | 63.1 | 67.6 | 74.8 | 85.0 | 98.1 | 112.1 | 122.3 | 23.3 |
| NHIS | 64.3 | 67.9 | 75.4 | 83.9 | 95.2 | 108.6 | 115.5 | 19.8 |
| BRFSS | 63.5 | 68.0 | 75.0 | 84.9 | 97.4 | 111.2 | 122.3 | 22.4 |
| Female | ||||||||
| NHANES measured | 50.2 | 53.8 | 61.0 | 71.4 | 85.7 | 102.5 | 114.4 | 24.7 |
| NHANES self-report | 49.8 | 53.4 | 59.9 | 69.9 | 82.8 | 99.6 | 111.0 | 22.9 |
| NHIS | 50.1 | 54.0 | 58.9 | 67.8 | 79.5 | 91.9 | 101.8 | 20.6 |
| BRFSS | 49.9 | 53.6 | 59.1 | 68.1 | 81.3 | 93.6 | 104.5 | 22.2 |
| Height (cm) | ||||||||
| Male | ||||||||
| NHANES measured | 163.3 | 166.2 | 170.8 | 176.0 | 181.0 | 185.5 | 188.2 | 10.2 |
| NHANES self-report | 162.9 | 165.9 | 170.7 | 176.4 | 181.6 | 186.2 | 188.8 | 10.9 |
| NHIS | 164.1 | 166.5 | 171.1 | 176.5 | 181.4 | 185.6 | 187.8 | 10.3 |
| BRFSS | 163.3 | 166.2 | 171.2 | 176.8 | 181.9 | 186.5 | 189.3 | 10.7 |
| Female | ||||||||
| NHANES measured | 150.4 | 153.0 | 157.2 | 162.0 | 166.6 | 170.7 | 173.3 | 9.4 |
| NHANES self-report | 150.2 | 152.4 | 156.6 | 161.5 | 166.5 | 170.8 | 173.6 | 9.9 |
| NHIS | 151.0 | 153.1 | 156.9 | 161.5 | 166.4 | 170.3 | 172.6 | 9.5 |
| BRFSS | 150.6 | 152.7 | 156.9 | 161.7 | 166.7 | 171.1 | 174.0 | 9.8 |
IQR calculated as difference between 75th percentile and 25th percentile.
IQR, interquartile range.
Obesity Prevalence Comparison
Obesity prevalence, defined as BMI ≥ 30, showed notable differences across surveys (Figures 5 and 6). For both men and women and across all survey cycles, obesity prevalence calculated from self-reported data in all three surveys was consistently lower than obesity prevalence derived from measured data in NHANES. For men, age-adjusted obesity prevalence using self-reported data from NHIS was 2.0 percentage points lower, and using BRFSS self-reported data was 2.7 percentage points lower, compared to NHANES self-reported data. The corresponding differences for women were larger, at 4.9 and 5.7 percentage points, respectively.
Figure 5.
Age-adjusted obesity prevalence by survey for men, 1999 through 2016.
Figure 6.
Age-adjusted obesity prevalence by survey for women, 1999 through 2016.
Interestingly, the direction of sex differences in obesity prevalence varied by survey. In both measured and self-reported NHANES data, obesity prevalence was generally lower among men than women, with a few exceptions (Table 2). The ratio of obesity prevalence in men to women across all data years was 0.91 for measured data and 0.93 for self-reported data in NHANES. However, in NHIS and BRFSS data, obesity prevalence was higher among men than women in almost every survey cycle, with a mean ratio across all data years of 1.03 for NHIS and 1.04 for BRFSS.
TABLE 2.
Ratio of age-adjusted obesity prevalence among men to age-adjusted obesity prevalence among women by year and survey: NHANES, NHIS, and BRFSS, 1999–2016
| Survey year | NHANES measured | NHANES self-report | NHIS | BRFSS |
|---|---|---|---|---|
| 1999 | 1.06 | 1.04 | ||
| 2000 | 0.82 | 0.88 | 1.00 | 1.04 |
| 2001 | 1.03 | 1.02 | ||
| 2002 | 0.83 | 0.88 | 1.06 | 1.04 |
| 2003 | 1.01 | 1.04 | ||
| 2004 | 0.94 | 0.97 | 1.03 | 1.04 |
| 2005 | 1.05 | 1.03 | ||
| 2006 | 0.94 | 1.00 | 1.02 | 1.06 |
| 2007 | 1.07 | 1.07 | ||
| 2008 | 0.91 | 0.94 | 0.98 | 1.08 |
| 2009 | 1.05 | 1.06 | ||
| 2010 | 0.99 | 0.98 | 1.04 | 1.06 |
| 2011 | 1.06 | 1.03 | ||
| 2012 | 0.93 | 0.93 | 1.02 | 1.01 |
| 2013 | 1.06 | 1.01 | ||
| 2014 | 0.87 | 0.89 | 1.00 | 1.01 |
| 2015 | 1.05 | 1.02 | ||
| 2016 | 0.92 | 0.92 | 1.01 | 1.01 |
| Total | 0.91 | 0.93 | 1.03 | 1.04 |
Supplemental NHANES Analysis Findings
Supplemental NHANES analyses revealed further insights into reporting biases. On average, both men and women overreported their height in every NHANES survey cycle (Supporting Information Table S3). The overall mean difference (self-reported minus measured height) was 1.36 cm for men and 0.87 cm for women. Regression models identified age group, measured height, and survey cycle as significant predictors of this height difference for both sexes.
Regarding weight, women, on average, underreported their weight in every NHANES survey cycle (Supporting Information Table S4). The overall mean difference (self-reported minus measured weight) was -1.37 kg for women. Measured BMI category emerged as a significant predictor of this difference in regression models, while age group and survey cycle were not significant. Men, on average, slightly underreported weight by 0.08 kg. Regression models showed that age group, measured BMI category, and survey cycle were all significant predictors of the difference between self-reported and measured weights for men. Men with measured BMI ≥ 30 tended to underreport weight, whereas those with BMI < 30 tended to overreport weight.
BMI calculated from self-reported data was consistently lower than BMI from measured data for both men and women in NHANES (Supporting Information Table S5). Regression models indicated that age group, measured BMI category, and survey cycle were significant predictors of this BMI difference for men, while age group and measured BMI category were significant predictors for women.
Obesity prevalence calculated from self-reported data was lower than prevalence based on measured data for both sexes in NHANES (Supporting Information Table S6). In logistic regression models, age group was the only significant predictor of the difference in obesity prevalence estimates for women. Survey cycle was not a significant predictor for either men or women. Age-adjusted obesity prevalence using self-reported NHANES data was 3.11 percentage points lower for men and 4.2 percentage points lower for women compared to prevalence using measured data.
Discussion
Our study provides a comprehensive comparison of height, weight, BMI, and obesity prevalence derived from three major US national surveys: NHANES, NHIS, and BRFSS, spanning from 1999 to 2016. Our findings regarding height and weight reporting align with prior research, confirming the general tendency for individuals to overreport height and underreport weight. Across all NHANES surveys during the study period, both men and women consistently overreported their height compared to measured values. Self-reported heights were generally similar across NHANES, NHIS, and BRFSS. For weight, underreporting was more pronounced in women than men, and self-reported weights were lower in NHIS and BRFSS compared to NHANES.
The relationship between reporting errors in height and weight and their impact on BMI and obesity prevalence is complex. BMI, being a ratio of weight to height squared, is influenced by the combined errors in both measurements. These errors can either amplify or mitigate the overall error in BMI. For instance, overreporting height, which is in the denominator of the BMI formula, tends to decrease BMI, while overreporting weight tends to increase it. The net effect on BMI depends on the magnitude and direction of reporting errors for both height and weight. Supporting Information Table S8 provides illustrative examples of these effects.
Our analysis revealed that underreporting of obesity prevalence was more pronounced for women than men. Interestingly, while NHANES data (both measured and self-reported) showed lower obesity prevalence in men compared to women, NHIS and BRFSS data indicated the opposite – higher obesity prevalence in men. These gender-based discrepancies across surveys highlight the potential limitations of using self-reported data for subgroup comparisons. Yun et al. also reported similar findings of differences in obesity prevalence between NHANES and BRFSS across demographic and social categories, further emphasizing this point. Thus, caution is warranted when using self-reported data to compare obesity prevalence across subgroups.
Secondary analysis of NHANES data revealed a trend of increasing height overreporting over time in both men and women. Weight underreporting increased in men but not women, and this increase in men was primarily observed in those with a BMI of 30 or higher. Despite these temporal changes in reporting biases, the difference between self-reported and measured obesity prevalence remained relatively stable over time (survey cycle) for both sexes.
Relying solely on mean values of weight, height, and BMI to compare self-reported and measured data may not fully capture the nuances relevant to outcomes like obesity prevalence, which are sensitive to the entire distribution of values, not just the mean. Although mean BMI values in NHANES might suggest better agreement between measured and reported BMI for men compared to women, obesity prevalence estimates still exhibit significant differences between measured and reported data in both sexes. Systematic errors inherent in self-reported weight and height data can lead to a compression of the BMI distribution. This compression, even with small mean differences, can result in substantial misclassification of individuals into BMI categories, potentially biasing hazard ratios in epidemiological studies. Furthermore, the narrower distribution of BMI derived from self-reported data compared to measured BMI can artificially inflate hazard ratios, making associations appear stronger than they truly are.
Several studies have attempted to develop correction equations using NHANES self-reported and measured weight data to adjust self-reported data from BRFSS, NHIS, and other datasets. However, the observed differences between self-reported data in NHANES and that in NHIS and BRFSS in our study suggest that such corrections may not be entirely accurate. For example, the male-female ratios of age-adjusted obesity prevalence differed significantly across surveys. Moreover, the systematic nature of errors in weight and height data makes it challenging to correct these errors reliably using prediction equations. Our findings underscore that conclusions drawn from comparisons of measured and self-reported data within NHANES cannot be directly generalized to characterize the properties of self-reported data from BRFSS and NHIS.
Prior studies comparing self-reported weight and height data across NHANES, NHIS, and BRFSS have yielded mixed results. Li et al. found similar obesity prevalence estimates across the three surveys based on self-reported data alone but did not consider obesity prevalence based on measured data from NHANES. Fahimi et al.’s comparison of 2004 self-reported data found no significant overall height differences but reported significant differences for men and not for women. Conversely, for weight, they found significant overall differences and significant differences for women but not for men.
Methodological variations across the three surveys, including survey design, question wording, data editing procedures, and mode of administration, may contribute to the observed differences. In NHANES, the combination of household interviews and subsequent physical examinations, where participants are aware of upcoming measurements, might influence reporting accuracy. Evidence suggests that self-reported data might be more accurate when participants anticipate subsequent measurements and when data are collected through face-to-face interviews (as in NHANES and NHIS) compared to telephone interviews (as in BRFSS). However, our findings suggest that this is not always consistently the case, highlighting the complexity of reporting biases and survey methodologies.
Conclusion
This study provides a detailed examination of how self-reported and measured anthropometric data in NHANES compare with self-reported anthropometric data from NHIS and BRFSS for the same time period and target population, focusing on height, weight, BMI, and obesity prevalence. Patterns of height and weight reporting vary by age, sex, measured weight or height, and survey type. The overall impact of misreporting on mean BMI is determined by the magnitude and direction of misreporting for both height and weight, which can differ. Secondary measures like BMI and obesity prevalence, calculated from self-reported data, can exhibit complex and sometimes unpredictable biases. The similarity in mean BMI across surveys does not guarantee comparable obesity prevalence estimates. The net effect of misreporting on obesity prevalence can vary depending on the population’s age and sex composition and other factors related to misreporting. Ultimately, the patterns of misreporting of height and weight and their subsequent effects on BMI and obesity prevalence are intricate and require careful consideration when interpreting and comparing data from different national surveys.
Supplementary Material
Supplemental Tables
NIHMS1590171-supplement-Supplemental_Tables.pdf
Footnotes
Disclosure: The authors declared no conflict of interest. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the National Center for Health Statistics, Centers for Disease Control and Prevention.
Additional Supporting Information may be found in the online version of this article.
References
[References from the original article should be listed here]
Associated Data
Supplementary Materials
Supplemental Tables
