Advances in Human Biology

: 2019  |  Volume : 9  |  Issue : 3  |  Page : 210--215

Dietary intake of Vitamin D pattern and its sociodemographic determinants in the Southwest of Iran, Khuzestan: An application of marginalised two-part model

Mohadeseh Shojaei Shahrokhabadi1, Amir Abbasnezhad2, Anoshirvan Kazemnejad1, Azadeh Ghaheri3, Farid Zayeri4,  
1 Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
2 Department of Nutrition, Nutritional Health Research Center, Lorestan University of Medical Sciences, Khorramabad; Department of Nutrition, Nutrition and Metabolic Diseases Researcher Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
3 Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
4 Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Correspondence Address:
Anoshirvan Kazemnejad
Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran


Background: Dietary intake of Vitamin D is an effective public health strategy to improve the current low Vitamin D status of populations. This research aimed to identify subpopulations at greater risk for Vitamin D deficiency. We studied the sociodemographic factors that are potentially associated with dietary intake of Vitamin D in the Iranian population. To do so, we used a marginalised two-part (MTP) model to control for the substantial proportion of zero Vitamin D intake. Methods: Data from 180 cross-sectional random samples were recorded. Dietary intake of Vitamin D was assessed using a 168-item validated food frequency questionnaire. Sociodemographic factors associated with intake of Vitamin D were explored using MTP-Weibull. Results: Mean (standard deviation) daily dietary Vitamin D intake was 0.54 (0.70) μg/day which was below the standard recommended level for all participants. The 'fish, milk and eggs' food group contributed 50% of total dietary intake of Vitamin D. Higher intake was associated with higher levels of education (P = 0.003). Furthermore, the odds of Vitamin D intake was associated with smoking status (odds ratio = 0.613, confidence interval: 0.407–0.914); where smoking decreases the chance of having a non-zero Vitamin D intake by 38.7%. Conclusion: Dietary intake of Vitamin D is below the recommended levels in our study in Iran, a developing country. We identified that education level and smoking status are associated with low intake. This result could lead to the straightforward recommendation for smokers and less-educated individuals to increase their intake of Vitamin D-rich foods.

How to cite this article:
Shahrokhabadi MS, Abbasnezhad A, Kazemnejad A, Ghaheri A, Zayeri F. Dietary intake of Vitamin D pattern and its sociodemographic determinants in the Southwest of Iran, Khuzestan: An application of marginalised two-part model.Adv Hum Biol 2019;9:210-215

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Shahrokhabadi MS, Abbasnezhad A, Kazemnejad A, Ghaheri A, Zayeri F. Dietary intake of Vitamin D pattern and its sociodemographic determinants in the Southwest of Iran, Khuzestan: An application of marginalised two-part model. Adv Hum Biol [serial online] 2019 [cited 2020 Jan 29 ];9:210-215
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Vitamin D is a fundamental prohormone necessary for normal growth of bone as well as calcium absorption.[1] It is supplied either from the sunlight exposure as the major source or through dietary sources.[2] Based on the Dietary Reference Intakes report, an adequate intake (AI) for Vitamin D is 5, 10 and 15 g/day for those aged 1–50, 51–70 and older than 70 years, respectively.[3] Both prospective and retrospective epidemiologic studies indicate the protective effect of Vitamin D on metabolic bone disorders. Higher Vitamin D levels in the optimal range have been shown to be associated with decreased risk of schizophrenia, depression, autoimmune diseases and cardiovascular morbidity and high-mortality cancers such as colon, prostate and breast cancer.[2],[4] Thus, reaching and maintaining an optimal Vitamin D intake in all life stages are of major individual and public health importance.[5]

A majority of people in the world are not obtaining adequate levels of Vitamin D due to its limited natural sources. Therefore, Vitamin D deficiency has attracted considerable attention in the 21st century.[6] Vitamin D deficiency prevalence in the world population ranges between 30% and 50%.[7] In Iran, the prevalence is approximately 60% (95% confidence interval [CI]: 50–70) according to a systematic review of 48 studies, including 18,531 samples.[8]

Many studies have suggested that the prevalence of Vitamin D deficiency is significantly different for various sociodemographic classes, lifestyles and geographical regions, but only a few studies have assessed daily Vitamin D intake in terms of socidemographics, anthropometric and lifestyle factors.[5],[8],[9] On the other hand, distribution of food consumption data is generally right skewed and includes substantial number of zero values (zero inflated). These zero values represent the non-consumers, in our case, individuals who do not eat any of the foods containing Vitamin D.[9],[10],[11] A naïve approach is removing the zero values from the dataset and analysing the consumer population using e.g., the simple regression models. Nonetheless, ignoring the zero inflation leads to biased estimates of the effect and decreased efficiency. Previous studies of food consumption have used the Tobit model and double-hurdle model to address the problem of zero consumption. However, empirical results obtained from these models are not usually robust to distributional assumptions which will not hold for smaller sample sizes. In this study, we explore an alternative approach to addressing the zero observation issue in food consumption data analysis.

Marginalised two-part (MTP) models are known to properly handle the semi-continuous (zero inflated) feature of food consumption data. The MTP models parameterise the marginal mean among all zero and non-zero values directly from the regression coefficients and provide a direct interpretation of covariate effects on the marginal mean (e.g., the entire population of consumers and non-consumers). The flexibility of these models will allow two separate models; one for zero values and another for positive values. In general, the first part uses a probit or logit link function for modelling the probability of being a positive value and the second part uses a link function with positive support to model the non-zero values. MTP-Weibull approach is a good choice for a real data applications, especially in case of small sample sizes.[12],[13] We estimated mean daily dietary Vitamin D intake of Ahvazian individuals who participated in this study, to investigate the variation of Vitamin D intake among population subgroups. Moreover, we used MTP-Weibull to assess the potential impact of sociodemographic factors on Vitamin D intake using both consumers and non-consumers.


The study population consisted of 180 individuals, aged 20–60 years, who lived in Ahvaz, southwest of Iran, in 2015. The estimated sample size was 125 based on the N = Z2*SD2/d2 formula at a 95% confidence level. N is the sample size, standard deviation (SD) is the anticipated SD and d is the desired precision, which was set to 0.7. The initial value for SD was adopted from Jayaratne's dietary Vitamin D intake study.[14] Since we used cluster sampling, an additional 40% was added to compensate for the design effect. Therefore, the total sample size was set to 175, which was rounded off to 180. A 168-item validated food frequency questionnaire was used to assess the usual dietary intakes of selected nutrients and foods over the previous year. This questionnaire contains a list of foods with a standard serving size (Willett format) and was administered by trained dieticians asking the participants to report the frequency of their consumption per item during the past 12 months. To assess the total Vitamin D intake, the NUTRITIONIST III software (version 7.0; N-Squared Computing, Salem, OR, USA) was used. Fish, milk, yogurt and eggs were considered as the main sources of dietary Vitamin D.[15] To measure sociodemographics, a brief questionnaire was used including sex, age, education and smoking behaviour. Cigarette smokers were defined as individuals who smoked more than one cigarette daily and had smoked regularly for more than a year before the interview. Non-smokers were defined as participants who had never smoked or had smoked <100 cigarettes by the time of interview. Participants' weight was recorded to the nearest 100 g with minimal clothing and no shoes. Height was measured to the nearest 0.1 cm, while the participants stood normally without shoes. All participants were provided with adequate information about the objectives of the study, and the informed consent was obtained from all of them. Ethical clearance for the research was obtained from the Ethics Committee of Jundishapur University of Medical Sciences.

In this study, univariate statistics (i.e., Mann–Whitney U-test and Kruskal–Wallis test) were used to evaluate the association between the sociodemographics factors and the outcome of interest. To assess the simultaneous effect of sex, age, education, body mass index (BMI) and smoking behaviour on dietary intake of Vitamin D, a MTP-Weibull model was used. Data management and analysis were performed using SPSS 16 and PROC NLMIXED in SAS. For all tests, two-sided P < 0.05 was interpreted as statistically significant.

MTP models are common approaches for modelling semi-continuous data. When adjusting for covariates, these models typically include one set of parameters for the binary response and a second set for the continuous component. Therefore, these models capture the zero inflation and skewness of semi-continuous data and allow researchers to evaluate the covariate effects on the overall marginal mean. For an MTP model, the general form of the pdf[12] can be written as:


Where πi is the probability of being non-zero. Using a logit link, πi is modelled as:


Which leads to a marginal mean of the form:


Where yi denotes the dietary intake of Vitamin D of the i th participant, as a positive continuous outcome with a point mass at zero. f denotes the probability density function (pdf) corresponding to a continuous distribution defined on a positive domain such as Weibull.[13] α and β are the vectors of model coefficients corresponding to the binary part and continuous part of the MTP-Weibull model, respectively.


About 67.8% of the 180 samples were female. The mean age was 38.2 ± 9.0 years. The age range of 161 participants (89.4%) was between 20 and 50 years, while only 10.6% was older than 50 years. Regarding the education level of the interviewees, 48 (26.7%) had primary education, 76 (42.2%) had secondary education and 56 (31.1%) had a university education. Mean BMI was 25.2 ± 3.0 kg/m2 and the prevalence of obesity (BMI ≥25 kg/m2) was 76.1%. A large proportion of the participants was non-smokers (72.2%) and the current non-smokers tended to be female, younger, slimmer and had studied beyond primary school. Further analyses are adjusted for these four factors.

Mean (SD) daily dietary Vitamin D intake in our study sample was 0.54 (0.70) μg/day, while 54.4% of participants did not take any dietary Vitamin D. Association of the mean daily Vitamin D intake from food, with each of the sociodemographic factors are shown in [Table 1]. The amount of food items consumed (mg/day) in different groups is presented in [Table 2] and [Figure 1].{Table 1}{Table 2}{Figure 1}

To assess the joint effect of independent variables on Vitamin D intake, we fitted MTP-Weibull. Results are presented in [Table 3]. The scale parameter σ suggests that the MTP-Weibull is a good choice for this data (P < 0.05). After adjusting for the effect other covariates, educational level was positively associated with Vitamin D intake in the continuous part. This indicates that having higher education is associated with higher Vitamin D intake. Moreover, smoking status was associated with the probability of having a non-zero outcome. The probability of having non-zero Vitamin D intake in non-smokers was higher compared to smokers. More specifically, the exponential of alpha coefficient corresponding to smoking status (odds ratio = 0.613, CI: 0.407–0.914) can be interpreted as: 'smoking decreases the chance of having a non-zero Vitamin D intake by 38.7%'. Finally, gender, age and BMI were not associated to Vitamin D intake (P > 0.05).{Table 3}


Mean daily dietary Vitamin D intake of the sample (0.54 μg/day) was below the recommended intake level, revealing a deficiency of Vitamin D intake in the study population. This finding was in accordance with the lack of Vitamin D intake in a majority of Americans, Europeans and Asians.[3],[10],[16],[17],[18] As oily fish, milk, eggs and fortified foods are considered as the limited dietary sources of Vitamin D, the low frequency of fish consumption in our study population could explain our finding.[5],[19] The current study found that Vitamin D level is higher among fish consumers in the study population. In addition, our study demonstrated that the people who received Vitamin D from food had a higher Vitamin D level than those who did not. This confirms that daily intake of Vitamin D can highly effect the serum Vitamin D level.

This low consumption could be due to the perception of fish as a very expensive product in cities with lower socioeconomic status.[20] Furthermore, people in the West of Iran tend to use more red meat in their diet due to animal husbandry and nomadic life.[21] The prevalence of Vitamin D deficiency in our study was a critical value of 49%. This finding is in agreement with the findings of the previous studies.[8],[22],[23] Our results indicated that the Vitamin D deficiency in Iranian adults is common. Given that Vitamin D deficiency is highly associated with several disease-related risk factors and even death, it is important that health professionals offer an effective regimen and other intervention strategies to correct the Vitamin D deficiency.[24]

Vitamin D deficiency is linked to a variety of factors including undesirable dietary habits, elderly age and/or lack of Vitamin D supplementation.[4],[25] Lack of sun exposure due to sociocultural issues[26],[27] may be compensated by a diet rich in oily fish or fish oil and also the use of Vitamin D supplements.[3],[26] In this study, the most extreme differences were observed in taking low-fat Yogurt and fish, respectively, between zero and non-zero Vitamin D intake groups. For the majority of the participants assessed in this study, the diet quality was unhealthy due to the small amount of vegetables, fruits, dairy and fish consumed. According to the previous studies, healthy eating reduces the risk of overall mortality.[28],[29],[30],[31]

Results showed that Vitamin D intake is significantly lower among smokers. Intriguingly, Vitamin D deficiency/insufficiency was approximately 83% for smokers compared to 64% for non-smokers. Reportedly, eating habits of smokers are different from the rest of population, smokers are generally less health conscious[32] and may have an altered taste due to smoking.[9],[32] A large difference in Vitamin D intake among smokers is attributable to lower intake of milk and fish. We also observed that smoking was inversely associated with Vitamin D intake. In addition, the results of the MTP-Weibull model showed that smoking decreases the chance of having a non-zero Vitamin D intake by 38.7%. The exact mechanisms by which smoking affects Vitamin D metabolism are still not clear. A possible explanation might be that most smokers have a less healthy lifestyle.[25] There is limited literature on the association between smoking status and daily intake of Vitamin D. Yet, our findings are consistent with studies that have reported a higher intake of Vitamin D for non-smokers.[2],[10],[32],[33],[34]

The results of this study also showed a statistically significant association between education level and Vitamin D intake. It seems that the less-educated population is either less health conscious or from a lower socioeconomic level. Both of these points to a lower knowledge of nutritional facts, influencing the quality of the diet and food choice.[35],[36] As highly educated individuals usually have indoor occupations, their higher intake of Vitamin D from food and supplements to compensate for low exposure to sunlight, could be another explanation for this result.[2] Education level can be associated to a healthy lifestyle and may increase one's access to knowledge about healthy dietary habits.[37] Furthermore, educated individuals may be more likely to read newspaper articles, to gain information and improve their lifestyles. We may speculate that highly educated individuals would also be better at understanding the sometimes complex information about diet–disease associations.[38] These results are consistent with those of Tønnesen et. al. and Alfawaz et. al.[35],[39]

The results of this study did not show any association between age and dietary Vitamin D intake. Although this contradicts some studies,[1],[2],[10],[40] it is consistent with the findings of some other studies.[41],[42] In our case, a possible explanation for is the relatively young age range. A large proportion of our study population was in the 20–50 age groups and has relatively similar dietary habits. Furthermore, we did not observe a significance difference in the total consumption of the main sources of Vitamin D among age groups. In contrast, previous studies have reported a significant difference in Vitamin D level and dietary Vitamin D intake between younger and elderly individuals.[10],[26],[33]

In the current study, gender and dietary Vitamin D intake were not associated. However, according to the previous research, women have lower levels of dietary Vitamin D intake than men.[42],[43] It is difficult to explain this result, but a potential reason is that most of our participants were woman. Regardless of gender, fish and dairy products are the major contributing food sources of Vitamin D. The amount of fish and dairy products consumed by both men and women was equal in our study. In terms of the main food sources of Vitamin D intake, our finding is in agreement with the current literature.[42]

Surprisingly, we did not find a significant association between BMI and dietary Vitamin D intake. This is in contrast with the previous research. Vitamin D intake has been shown to be inversely correlated with BMI and lower among overweight and obese individuals.[34],[41] Such variability may be caused by the natural variability in the Vitamin D content of each food consumed but may also be due to differences in analytical techniques employed.[14] However, with a small sample size, caution must be applied, as the findings might not be transferable to other populations.


These findings are important as they highlight factors associated with dietary intake of Vitamin D in the Southwest of Iran, Khuzestan. On average, Vitamin D intake of the participants did not meet the current AI levels. As Vitamin D deficiency has been associated with various diseases including metabolic bone disorders, cancer, schizophrenia, depression and autoimmune inflammatory states; a sufficient Vitamin D intake is usually recommended. Our findings could be a guide for dietary recommendations. This study has found that dietary Vitamin D intake is associated with educational level and smoking status in Ahvaz. This could lead to the straightforward recommendation for smokers and less-educated individuals to increase their intake of Vitamin D-rich foods. Future studies on the current topic are recommended, especially among different ethnic groups as the accuracy of Vitamin D estimates are limited by the data obtained from the self-reported questionnaire.


The authors gratefully acknowledge the Nutrition and Metabolic Diseases Research Centre, Jundishapur University of Medical Sciences for the data. This work is a part of PhD thesis of the first author in the Biostatistics Department of Tarbiat Modares University of Medical Sciences, Tehran, Iran. We would also like to thank Farzad Ebrahimzaheh and Mitra Ebrahimpoor for their technical contribution to this work.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


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