The Association Between Socioeconomic, Demographic, Serum Vitamin A, Serum Carotenoids and all-Cause Mortality of Adults
Vitamin A is a group of micronutrients that has important physiologic functions and has been found to be deficient in south pacific . Its deficiency has a significant effect on child mortality . Carotenoids have been found to be associated with lower adult mortality. Mechanistically, blood vitamins and carotenoids are thought to be mediated by lowering inflammatory molecules . In particular, carotenoids were found to interact with other micronutrients leading to their overall effects on mortality . The interaction between socio-economic factors and serum carotenoid levels on mortality has not been investigated fully. This study used NHAENS III and NHANES III mortality linked data files to study the association between socio- demographic factors, serum vitamin A, serum carotenoid concentrations and all-cause mortality. This study, was a part of a series screening for potential chemicals with beneficial health effects, took advantage of the vastness of the public use NHANES III (National Health and Nutrition Examination Survey) data to adjust for various socio-economic covariates in assessing the health effects of nutrition.Materials and MethodsNHANES and NHANES III
NHANES is a major program of National Center of Health Statistics (a part of Center of Disease Control (CDC) of United States of America) started in 1971. NHANES III is a national study based on a complex, multi-stage probability sampling design. For details of NHANES data and statistical guidance as well as their analysis examples see NHANES website . In brief, NHANES studies were approved by CDC internal institutional review boards. The public use data are made available to the public and researchers. The NHANES sample weights were calculated to represent non-institutionalized general US population to account for non-coverage and non-response. These patients were interviewed at home and examined in mobile examination centers (MEC). This eliminated the cofounding effects of sample persons being too frail, too young or old to go to the MEC for examinations. In this study, NHANES III (conducted between1988 – 1994) household adult data file was merged with NHANES III laboratory data and the NHANES III linked cancer mortality data.NHANES III linked mortality data
NHANES III participants were followed passively until December 31, 2006 for their mortality data. Detailed information about the data and analysis guidelines are available at their website . In brief, probability matching was used to link NHANES III with National Death Index for vital status and mortality, age 90 years old was censored because they contribute little in person years. NHANES used multiple sources including the use of death certificates and with the National Death Index to ascertain vital status and cause of death.
NHANES III employed a complex sampling strategy and analysis . Matlab programs (posted on Matlab File Exchange) were developed to convert SAS files provided by NAHNES to STATA programs to download NHANES III data files for further analysis. Specialized survey software is needed for NHANES complex data analysis . STATA 12 (College Station, TX) was among those recommended by CDC to analyze the complex NHANES data and was used in this study. The sampling weight used was WTPFEX6 because only the sample persons had examinations in the MEC were included in this study, SDPPSU6 was used for the probability sampling unit (PSU) and SDPSTRA6 was used to designate the strata for the STATA survey commands. STATA scripts were written for this analysis, and will be submitted for publication separately. Univariate and multivariate logistic regressions were used to study the relationship between serum retinyl ester (REPSI, umol/L), vitamin A (VAPSI, umol/L), alpha carotenoid (ACPSI, umol/L) and beta carotenoid (BCPSI, nmol/L) concentrations and all cause in adults (17 years or older). The status of mortality was coded as a binary outcome (1= death, 0 = otherwise). Linearized Taylor Standard Error estimation was used. The covariates and the corresponding NHANES III codes used were: MXPAXTMR (age at the MEC final examination in months), HSSEX (sex, _IHSSEX_ 1 = male, female as the reference group when applicable), HAM6S (weight in lbs without clothes), DMPMETRO (urban rural residence status), _IDMPMETRO_2 (rural residence, urban residence was used as the reference group), DMARETHN (race and ethnicity, _IDMARETHN_2 = non-Hispanic black, _IDMARETHN_3 = Mexican Americans, _IDMARETHN_4 = others, non-Hispanic white was used as the reference group), DMPPIR (poverty index ratio), HAN6JS (alcohol consumption, number of hard liquor drinks per month), and HAR4S (smoking, number cigarettes per day). For STATA analyses, only the patients without missing values for all of WTPFEX6, SDPPSU6, SDPSTRA6, REPSI , VAPSI, ACPSI, BCPSI, MXPAXTMR, HSSEX, DMPMETRO, DMARETHN, DMPPIR, HAR4S, and HAN6JS were included in this study. Further, these additional NHANES III codes considered not eligible: DMPPIR (888888), the numerator of DMPPIR was the midpoint of the observed family income category in the Family Questionnaire variable:HFF19R, and the denominator was the poverty threshold, the age of the family reference person, and the calender year in which the family was interviewed, HAR4S (666), HAR4S (777), HAR4S (888), HAR4S (999), HAN6JS (888), HAN6JS (999), not in BMI > 15 & BMI < 50, REPSI (8888), VAPSI ( 888), ACPSI ( 8888), BCPSI ( 88888), youth sample persons and incomplete mortality data. A total of 3400 sample persons were eligible for this study.
Table 1. Baseline demographic, socioeconomic and health status univariables. Indicator Death: 0=alive, 1=dead. Linearized Taylor Standard Error estimation was used. The NHANES III codes used were: BMI (body mass index), HSSEX (sex), MXPAXTMR (age at the MEC final examination), DMPMETRO (urbanicity), DMARETHN (race and ethnicity), DMPPIR (poverty index ratio), HAN6JS (alcohol consumption), and HAR4S (smoking) ), REPSI (serum retinyl ester concentration in S.I. units), VAPSI (serum Vitamin A concentration in S.I. units), ACPSI (serum carotene concentration in S.I. units), BCPSI (serum beta carotene concentration in S.I. units). n = 3400 samples.For univariate analysis, the significant univariates, odds ratios (95% confidence intervals) were: body mass index (BMI), 1.029 (1.0023 – 1.057); age (MXPAXTMR), 1.0086 (1.0076 – 1.0097); poverty income ratio (DMPPIR), 0 .918 (0 .855 – 0 .985); drinking hard liquors (HAN6JS), 1.017 (1.00043 – 1.033674); retiny ester (REPSI), 3.563 (1.1794 – 10.764); vitamin A (VAPSI), 1.450 (1.065 – 1.973); and beta carotene (BCPSI), 2.092 (1.308 – 3.346).For multivariate analysis, the significant variables (Table 2) were body mass index (BMI), 1.023 (0.993 – 1.053); age (MXPAXTMR) 1.0092 (1.0082 – 1.0101); poverty income ratio (DMPPIR), 0.824 (0.752 – 0.903); drinking hard liquors (HAN6JS), 1.011 (1.0014 – 1.020); and alpha carotene (ACPSI), 0.00045 (7.00e-06 – 0.02861).
Table 2. Multivariate analysis of covariates of all cause mortality. IndicatorDeath: 0=alive, 1=dead. Linearized Taylor Standard Error estimation was used. The NHANES III codes used were: BMI (body mass index), HSSEX (_IHSSEX2 = female, using male as the reference group), MXPAXTMR (age at the MEC final examination), DMPMETRO (urban rural residence status, _IDMPMETRO_2 = rural residence, urban residence used as the reference group), DMARETHN (race and ethnicity, _IDMARETHN_2 = non-Hispanic black, _IDMARETHN_3 = Mexicans, _IDMARETHN_4 = others, non-Hispanic white used as the reference group), DMPPIR (poverty index ratio), HAN6JS (alcohol consumption), and HAR4S (smoking) ), REPSI (serum retinyl ester concentration in S.I. units), VAPSI (serum Vitamin A concentration in S.I. units), ACPSI (serum carotene concentration in S.I. units), BCPSI (serum beta carotene concentration in S.I. units). n = 3400 samples.
This study analyzed the NHANES III data and HNAHES III linked mortality data that represented US non-institutionalized population as designed by NHANES. There were 3400 sample persons (Table 1) had complete data and were used in this analysis. The mean age was 39 years and 6 months old. The mean follow up was 14.1 years. They have a mean serum retinyl ester concentration (S.E.) (umol/L) of 0.176 (0.169- 0.183), a mean serum Vitamin A concentration (umol/L) of 1.993 (1.959-2.027), a mean serum alpha carotene concentration (nmol/L) of 0.055 (0.052-0.058), and a mean serum beta carotene concentration (nmol/L) of 0.244 (0.231-0.258). On average, their income was 2.7 times higher than the poverty level.
For univariate analysis, the significant univariates, odds ratios (95% confidence intervals) were: body mass index , age, poverty income ratio, drinking hard liquors, retiny ester, vitamin A and beta carotene, 2.092 (1.308-3.346). To ensure a more conservative analysis for not missing any potential covariates, all of the univariates were included in the multivariate analysis. For multivariate analysis, the significant variables (Table 2) were body mass index, age, poverty income ratio, drinking hard liquors, and alpha carotene. The effects of racial disparities  and the adverse effects of smoking, obesity and drinking  on mortality have been reported and were supported by this study.
After adjustment for these socio-demographic factors, only alpha carotenoids remained a significant predicator of adult mortality.
No conflict of interest.
2.Ford ES, Liu S, Mannino DM, Giles WH, Smith SJ. C-reactive protein concentration and concentrations of blood vitamins, carotenoids, and selenium among United States adults. Eur J Clin Nutr. 2003, 57(9): 1157-1163.
3.Shardell MD, Alley DE, Hicks GE, El-Kamary SS, Miller RR et al. Low-serum carotenoid concentrations and carotenoid interactions predict mortality in US adults: the Third National Health and Nutrition Examination Survey. Nutr Res. 2011, 31(3): 178-189.
8.Cheung R. Poor Treatment Outcome of Neuroblastoma and Other Peripheral Nerve Cell Tumors May be related to Under Usage of Radiotherapy and Socio-Economic Disparity: A US SEER Data Analysis. Asian Pac J Cancer Prev.2012, 13(9): 4587-4591.