1. Introduction
Vitamin D deficiency (VDD) has become an urgent micronutrient issue globally [1] because of its high prevalence [2], and it has become a potential cause of non-communicable [3,4] and infectious [5,6] diseases. Although VDD has been addressed as a global public health problem in all age groups, the population-representative data regarding vitamin D were limited to several risky groups [7]. Pregnant women are a vulnerable population affected by VDD [1], which can lead to adverse pregnancy outcomes [8,9]. Moreover, VDD may result in health disparities [10], which leads to the increment of stillbirths and pregnancy-related deaths [11]. Hence, improving vitamin D status is necessary to upgrade the reproductive health and well-being of mothers and their infants.
The major factors for VDD are sun exposure and dietary vitamin D intake [12]. However, obtaining vitamin D through sun exposure can be inefficient or unsafe because of the skin cancer risk from ultraviolet radiation [13]. Additionally, the dermal synthesis of vitamin D was suggested to be influenced in different climate zones using an in vitro model [14]. The adequate achievement of vitamin D intake from diet alone is hard [15]. Therefore, vitamin D supplementation is a crucial nutritional priority recommended by many physicians to achieve optimal serum concentration [16] that could prevent short and long-term maternal and infant health complications [17].
Vitamin D status has been explored in the literature. However, population-based research on pregnant women in East Asia is still limited. To our best knowledge, relevant information regarding the potential effect of the climatic zone has not been explored. Taiwan is an East Asian island characterized by two climatic zones [18]. Based on this unique advantage, Taiwan has the opportunity to assess whether sunlight-related factors and dietary vitamin intake contribute differently to vitamin D levels among people living in different parts of the country. Exploring the prevalence of VDD and its potential risk factors among pregnant women in Taiwan is an important task to address the research gap and for future policy planning. This study aimed to assess the determinants of VDD and to examine the contribution of sunlight-related factors and dietary vitamin D intake to vitamin D status in different regions of Taiwan using a nationally representative survey.
2. Materials and Methods
Study Population
A national cross-sectional nutritional survey of pregnant women was conducted from June 2017 to February 2019 across Taiwan. A multiple-stage cluster sampling approach was used, including (1) the selection of eight layers according to geographical location (northern, central, southern, and eastern Taiwan) and (2) the random selection of hospitals (large and small sizes) from the list based on the number of women availing pregnancy-related services per year and the probability proportional to size in each layer and (3) the whole selection of participants arriving in the selected hospitals for antenatal examination with the expectation of 150–300 women from one or two hospitals in each layer enrolled based on the potential number of annual outpatients in each hospital [19]. The distribution of eleven selected hospitals across Taiwan was in Figure 1.
We calculated a sample size of 1062 based on 200,000 deliveries by pregnant women during the study period, with a 3% margin of error and a 95% confidence interval (CI). We recruited participants aged ≥15 years who were legal residents of Taiwan and who underwent antenatal examinations at the selected hospitals. A satisfactory sample of 1502 pregnant women was included in the final analysis after the exclusion of nonsingleton pregnancies, participants unable to understand and speak Mandarin, and incomplete questionnaires. All participants provided written informed consent before taking the survey.
3. Data Collection
During study periods, all pregnant women making an antenatal visit were enrolled consecutively. At recruitment, collection of questionnaires, physical examination and blood sample were performed. Information was obtained from standardized face-to-face interviews by trained interviewers using the structured questionnaires. Variables regarding participants’ sociodemographic status, histories of diseases before and during pregnancy, pregnancy-related factors, and intake histories of prenatal and natal dietary supplements were collected by the self-reported baseline questionnaire. The dosage of supplements during pregnancy was asked and recorded in brand, exact dosage and frequency per week. Food frequency questionnaires was also used to record the intake frequency during past 3 months in 66 items of foods including egg, milk, meat, fish and vegetables. After interview of questionnaires, a 24 h dietary recall was recorded by trained dietitians. Food models were used to assist participants in recalling the food portion sizes and details of the dietary information. Then, we estimated participants’ energy intake and nutrient intake from foods. The intakes of several nutrients (e.g., vitamin D) were labeled the sources of foods or supplements respectively. We used the online software Cofit Pro (Cofit Health Care, Taipei, Taiwan) to analyze participants’ nutrient intake using the 2015 version of the Taiwan Food and Nutrient Database.
At the time of recruitment, pre-pregnancy body weight was self-reported by pregnant women, and their current body height and weight were measured. Blood samples were drawn, centrifuged, then froze (−80 °C) and analyzed in batches.
3.1. Sociodemographic and Pregnancy-Related Characteristics
Pregnant women were queried regarding their age (years); residential area; education level; household monthly income; religion; gravidity; parity; number of fetuses in the current pregnancy; gestational age; and body height (cm) and weight (kg) before pregnancy, which were used to calculate pre-pregnancy body mass index (BMI, kg/m2). Additional information related to pregnancy was extracted from the prenatal visit records of participants. The residence was categorized as living in Taiwan’s northern, central, southern, or eastern regions.
3.2. Dietary Characteristics
Pregnant women were asked whether they consumed sufficient amounts of the four groups of the following food items: (1) dairy products (e.g., fresh milk, yogurt, cheese, cream cheese, and powdered milk); (2) eggs; (3) red meat (e.g., pork, beef, and mutton); and (4) nut fruits (e.g., stone fruit, nuts, pistachios, and almonds). Women also reported their frequency of using vitamin D and/or calcium supplements during pregnancy as “never”, “less than 1 day per week”, “2–5 days/week”, and “almost daily”. Then, this factor was recoded into two categories of usage, “yes” or “no”, due to the small sample size. The 24 h dietary intake was recorded to assess the intake of total energy (kcal), raw protein (g), raw fat (g), total carbohydrates (g), and vitamin D content (mg) and the use of vitamin supplements. The percentages of calories from protein, fat, and carbohydrates were also calculated [19].
The dosages of supplements were calculated if participants provided the exact dosage. However, these parameters were frequently missing, as were the brands and models of vitamins. Therefore, in the present study, we only analyzed the usage frequency of vitamin D-only or D-based supplements.
3.3. Sunshine-Related Factors
Sun exposure was estimated using the question, “Were you exposed to outdoor sunlight last month?” and the answers were categorized as “no” if exposed to sunlight for less than 10 min per day and “yes” if exposed to sunlight for more than 10 min per day. The seasons of blood draw were categorized according to the month of blood sample collection, as follows: sunny months (June to November) and rainy months (December to May) established according to the rainfall report of the Central Weather Bureau, Taiwan. Participants also reported whether they had to stay indoors (e.g., bedridden) for any reason during their pregnancy (“yes” or “no” response) and the number of methods used for sun protection (e.g., sunscreen, parasols, hats and outerwear with UV-block) and how often they are used.
3.4. Vitamin D Deficiency Assessment
As 25-hydroxyvitamin D [25(OH)D] has the long half-life (15 days) and relative stability of concentration in the blood [20], the circulating 25(OH)D is the useful biomarker of vitamin D in the human body [21]. The plasma 25-hydroxyvitamin D [25(OH)D] concentration was measured using an electrochemiluminescence immunoassay, as described previously [19]. Although there is no consensus in the definition of the suboptimal vitamin D level, VDD was defined as a 25(OH)D level of <20 ng/mL, which is a common threshold for people in at-risk groups, including pregnant women [22,23,24]. The cutoff point of less than 20 ng/mL was also recommended for use for VDD by Institution of Medicine, Academy of Medicine and American Academy of Pediatrics.
4. Ethical Consideration
This study was funded by the Health Promotion Administration, Ministry of Health and Welfare in Taiwan (C1050912) and was approved by the institutional review board of the government and selected hospitals (IRB number: N201707039).
5. Statistical Analysis
First, descriptive analysis was performed to explore the distribution of independent variables. We performed chi-square tests (for categorical variables) and t tests or Mann–Whitney tests (for continuous variables) to compare the distribution of independent variables between pregnant women with and without VDD. Second, logistic regression analysis was used to determine the factors associated with VDD. Two models were constructed. Model 1 comprised variables associated with VDD that had p < 0.1 in bivariate analysis, including age, residential area, parity, gestational age, pre-pregnancy BMI, egg intake, red meat intake, fat, vitamin D content, vitamin supplements, sun exposure, remaining indoors during pregnancy, and the season of blood draw. Gravidity and carbohydrate intake were removed from model 1 because they were highly correlated with parity (rho = 0.82) and fat intake (rho = −0.89), respectively (Table S1). Model 2 comprised factors associated with VDD that had p < 0.1 in model 1, including age, residential area, gestational age, red meat intake, vitamin D content, vitamin supplements, sun exposure, remaining indoors during pregnancy, and the season of blood draw. Odds ratios (ORs) and 95% CIs were reported, and p < 0.05 was considered statistically significant.
Further sensitivity analysis was performed and stratified by residential area (north vs. south and other regions) to examine the contribution of modifiable factors to vitamin D status. Two models were constructed for each layer, including one model adjusted for sunlight-related factors (season of blood draw and sun exposure) and one model adjusted for dietary vitamin D intake (red meat and supplements). The area under the receiver operating characteristic (AUROC) curve was computed to compare the models. It is favored due to the characteristics of invariant and independent from the prevalence of the condition. All analyses were performed using R software (version 4.1.3; R Foundation for Statistical Computing, Vienna, Austria).
6. Results
6.1. Characteristics of Study Participants
The data contained several missing values, but the distribution of variables before and after removing the missing information was the same. Therefore, the entire data of the 1502 pregnant women were used for analysis. Overall, the mean 25(OH)D concentration was 25.5 ± 8.9 ng/mL, and the prevalence of VDD was 30.1% (weighted). Compared with women without VDD, those with VDD were younger (p = 0.017); lived in the north (p < 0.001); had uniparity (p = 0.01); were in the first trimester of gestation (p < 0.001); consumed high quantities of carbohydrates (p = 0.013) but insufficient eggs (p = 0.034), red meat (p < 0.001), fat (p = 0.023), and vitamin D and/or calcium supplements (p < 0.001); had little sun exposure (p = 0.001); remained indoors during pregnancy (p = 0.018); and had blood drawn during the rainy months (p = 0.004). These data are displayed in (Table 1).
6.2. Associated Factors of Vitamin D Deficiency
As displayed in Table 2, the likelihood of VDD was significantly lower in pregnant women who were older (OR: 0.95, p < 0.001); lived in central (OR: 0.66, p = 0.010), southern, or eastern Taiwan (OR: 0.20, p < 0.001) or in the eastern and outlying islands (OR: 0.33, p < 0.001); were in the second trimester (OR: 0.72, p = 0.046) or the third trimester (OR: 0.60, p = 0.002); consumed sufficient red meat (OR: 0.50, p = 0.002); took vitamin D and/or calcium supplements (OR: 0.51, p < 0.001); received sun exposure (OR: 0.75, p = 0.034); and had blood drawn during the sunny months (OR: 0.59, p < 0.001).
In the sensitivity analysis, among participants living in northern Taiwan, dietary vitamin D intake (AUROC: 0.580, 95% CI: 0.528–0.633) had a greater influence on vitamin D status than did sunlight-related factors (AUROC: 0.536, 95% CI: 0.508–0.589). By contrast, among participants living in the south and other parts of Taiwan, sunlight-related factors (AUROC: 0.659, 95% CI: 0.618–0.700) were more influential than dietary vitamin D intake (AUROC: 0.617, 95% CI: 0.575–0.660). The differences in regional models were significant, with z value = 51.98, p < 0.001 for northern Taiwan and z value = 54.02, p < 0.001 for the remaining regions. These results are visualized in Figure 2.
7. Discussion
In the present study, the prevalence of 25(OH)D level < 20 ng/mL among pregnant women in Taiwan was 30.1% (weighted). The determinants of VDD included age, gestational age, red meat intake, vitamin D and/or calcium supplements, residential area, sun exposure, and the season of blood draw.
The occurrence of VDD [25(OH)D < 20 ng/mL] is common in pregnant women, although the rates vary in different Asian countries, ranging from 7% to 40.7% [25,26]. The present study found that VDD occurred more frequently in pregnant women living in northern Taiwan than in those living in southern Taiwan. A nationwide report on VDD among older adults (a risk group of VDD) had similar findings, reporting that VDD occurrence was higher in the north than in the south [27]. This phenomenon has several possible explanations. First, northern Taiwan has a higher latitude than other regions [28], and vitamin D status decrease with increasing latitudes [29]. Second, northern Taiwan has a humid subtropical climate, and sunlight may be of lower intensity than that in southern Taiwan and other regions characterized by a tropical monsoon climate. The association between age and VDD was found in the previous studies with the controversial findings. The former authors showed that age over thirty was the risk factor for VDD among pregnant women [26]. However, the current study indicated that younger age was a contributing factor for VDD, which was in line with other studies [30,31]. Our findings could be due to the habits of avoiding sunlight among almost youngers that they were likely to apply sun protection (e.g., using sunscreen, wearing long-sleeved clothes, preferring indoor activities). Thus, our findings indicate that it is worth planning VDD prevention, such as educating health literacy related to VDD and lifestyle changes in younger women, and such methods should be promoted integrating with efficient intervention strategies.
Regarding the impact of gestational age on maternal VDD, the findings are inconsistent across studies. Although several studies have reported that vitamin D status decreased during advanced gestation [32], our results are in line with those of studies reporting that the likelihood of VDD was reduced during the second and third trimesters. For example, Perreault et al. indicated that serum 25(OH)D concentrations were significantly greater in the last trimester compared to the first trimester [33]. Similarly, Savard et al. found that serum 25(OH)D levels significantly increased across trimesters [34]. In addition, Shen et al. noted a positive relationship between the increased vitamin D concentration and later gestational week [35].
It has been well established that sunlight is the main source of vitamin D. Hence, sun exposure and the summer season are the most important contributing factors to the vitamin D concentration. Nevertheless, if sun exposure cannot provide sufficient vitamin D because of factors such as sunlight intensity, time of exposure, and application of sun protection, the vitamin D status in the human body can be adjusted through nutrition and dietary intake. In the literature, the natural vitamin D content in foodstuffs is usually limited to vitamin D3 from animal products [36]. Our findings indicated that the consumption of red meat was associated with lower VDD rates. Moreover, the present study demonstrated that vitamin D and/or calcium supplements could reduce the likelihood of VDD.
In our sensitivity analysis, the effects of sunlight-related factors and dietary vitamin D intake on 25(OH)D levels varied by region. In northern Taiwan, dietary vitamin D intake was more important than sunlight-related factors for improving maternal vitamin D status; however, sunlight-related factors were the main sources of vitamin D for pregnant women living in the south and other parts of Taiwan, and vitamin D intake played a minor role. These variations in effectiveness corresponded to the variations in climate across Taiwan. These findings can assist health policymakers in designing regional strategies for the prevention of prenatal VDD.
To date, suboptimal vitamin D levels is mostly indicated for bone health but remain controversial across populations and countries. For some investigators, deficiency was defined as specific to bone; however, insufficiency was defined relating to other health outcomes. For others, deficiency covered diseased population and insufficiency covered at-risk population. One of the most commonly used definitions comes from the Endocrine Society Clinical Practice Guidelines [24]; vitamin D deficiency was defined as 25(OH)D values below 20 ng/mL (50 nmol/L), and vitamin D insufficiency was defined as 25(OH)D of 21–29 ng/mL (52.5–72.5 nmol/L). This guideline was accepted and used widely by the International Osteoporosis Foundation, American Association for Clinical Endocrinologists, Institute of Medicine, American Academy of Pediatrics, and government of Australia, New Zealand, Germany, Austria and Switzerland as well as in Taiwan. In any case, cut point is very important when looking at the results in 25(OH)D level.
Particularly in older adults, having a higher BMI or body fat percentage are significant subject-specific characteristics that negatively affect vitamin D metabolism [37]. Normal-weight women reached the higher 25(OH)d level after vitamin D supplementation faster than women with obesity [38]. However, in pregnant women, the association between BMI and VDD was not consistent across the studies. While several studies showed that high BMI was associated with VDD, others showed that BMI was not statistically significantly associated with VDD [39,40]. Obesity is strongly associated with insufficient dietary vitamin D intake and low sun exposure. Pre-pregnancy obesity predicts poor vitamin D status in mothers [41]. In our study, pre-pregnancy BMI (as a continuous variable) was significantly different in two groups of VDD and non-VDD, but in logistic regression, after adjusting for confounders, pre-pregnancy BMI was not significantly associated with VDD. The findings for BMI (as a categorical variable) were also insignificant in multiple logistic regression. Obesity is not associated with 25(OH)D levels in our study.
The present study is the first national report on vitamin D status among pregnant women in Taiwan. Our findings demonstrated specific differences in the effects of sunlight-related factors and vitamin D intake on vitamin D concentrations in distinct regions of Taiwan. However, several limitations should be considered. First, because this was a cross-sectional study, we can only note associations; we cannot determine the causal relationship. Second, several factors influencing vitamin D status were not assessed in our study, such as occupation and the brand and dose of supplements. Third, we used a self-report questionnaire, which may introduce assessment bias because of subjective responses. Fourth, although the present study highlights the critical role of dietary vitamin D intake, the data on nutrient quantitation per serving are unavailable.
8. Conclusions
VDD was prevalent in pregnant women in Taiwan. On the basis of our findings, we recommend the promotion of a robust health policy regarding safe sunlight exposure and effective dietary vitamin D intake, with adjustments according to the characteristics of various climate zones. In doing so, clinicians can enhance maternal vitamin D status, reduce the VDD-induced burden, and improve health and well-being.
Y.-L.H., T.T.M.P., Y.-C.C., J.-S.C., J.C.-J.C. and C.-H.B.: conceptualization, methodology, validation, investigation, data curation, and writing review and editing draft. Y.-L.H., T.T.M.P. and C.-H.B.: formal analysis and writing—original draft. Y.-L.H., J.C.-J.C. and C.-H.B.: project administration. C.-H.B.: supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the government and selected hospitals (IRB number: N201707039).
Informed consent was obtained from all subjects involved in the study.
Not applicable.
The authors acknowledge all the participants and institutions for their contributions to the Nutritional Survey of Pregnant women in Taiwan.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
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Enlarge this image.Figure 2. Contribution of sunlight-related factors and dietary vitamin D intake to vitamin D status in different regions of Taiwan.
Characteristics of study participants according to vitamin D status (n = 1502).
| Variables | Total | Non-VDD |
VDD |
p |
|---|---|---|---|---|
| n (%) | n (%) | n (%) | ||
| Maternal age (years) (mean ± SD) | 32.5 ± 4.8 | 32.7 ± 4.8 | 32.1 ± 4.8 | 0.017 |
| Residential area | <0.001 | |||
| North | 501 (33.4) | 312 (28.5) | 189 (46.4) | |
| Central | 371 (24.7) | 260 (23.7) | 111 (27.3) | |
| South and east | 291 (19.4) | 254 (23.2) | 37 (9.1) | |
| Eastern and outlying islands | 339 (22.6) | 269 (24.6) | 70 (17.2) | |
| Education level * | 0.291 | |||
| High school and below | 237 (15.9) | 182 (16.8) | 55 (13.5) | |
| College, university | 1025 (68.7) | 740 (68.1) | 285 (70.0) | |
| Postgraduate studies | 231 (15.5) | 164 (15.1) | 67 (16.5) | |
| Household monthly income | 0.465 | |||
| Less than NT$30,000 | 212 (14.4) | 162 (15.1) | 50 (12.5) | |
| NT$30,000–59,999 | 634 (43.0) | 464 (43.2) | 170 (42.4) | |
| NT$60,000–99,999 | 443 (30.1) | 318 (29.6) | 125 (31.2) | |
| More than NT$100,000 | 185 (12.6) | 129 (12.0) | 56 (14.0) | |
| Religion | 0.242 | |||
| None | 689 (45.9) | 488 (44.6) | 201 (49.4) | |
| Buddhism | 281 (18.7) | 205 (18.7) | 76 (18.7) | |
| Taoism | 345 (23.0) | 265 (24.2) | 80 (19.7) | |
| Other (Yiguandao, Christian, Catholic, Muslim) | 187 (12.5) | 137 (12.5) | 50 (12.3) | |
| Gravidity * | 0.061 | |||
| 1 | 694 (46.3) | 487 (44.6) | 207 (51.0) | |
| 2 | 498 (33.2) | 366 (33.6) | 132 (32.5) | |
| 3 | 199 (13.3) | 158 (14.5) | 41 (10.1) | |
| ≥4 | 107 (7.1) | 81 (7.4) | 26 (6.4) | |
| The ordinal of current pregnancy (parity) * | 0.010 | |||
| 1st child | 824 (55.0) | 577 (52.9) | 247 (60.7) | |
| 2nd child | 527 (35.2) | 395 (36.2) | 132 (32.4) | |
| ≥3rd child | 146 (9.8) | 118 (10.8) | 28 (6.9) | |
| Number of fetuses in this pregnancy | 0.972 | |||
| ≥2 | 33 (2.2) | 24 (2.2) | 9 (2.2) | |
| Gestational age | <0.001 | |||
| 1st trimester (less than 17 weeks) | 375 (25.0) | 235 (21.5) | 140 (34.4) | |
| 2nd trimester (17 weeks to less than 29 weeks) | 485 (32.3) | 357 (32.6) | 128 (31.4) | |
| 3rd trimester (more than 29 weeks) | 642 (42.7) | 503 (45.9) | 139 (34.2) | |
| Pre-pregnancy BMI * | 0.098 | |||
| Normal (18.5 ≤ BMI < 25.0) | 141 (9.4) | 99 (9.1) | 42 (10.3) | |
| Underweight (<18.5) | 1018 (68.1) | 730 (67.0) | 288 (70.9) | |
| Overweight/obese (≥25.0) | 336 (22.5) | 260 (23.9) | 76 (18.7) | |
| Dairy products * | 0.546 | |||
| Enough | 1213 (81.2) | 888 (81.6) | 325 (80.2) | |
| Egg * | 0.034 | |||
| Enough | 1397 (93.6) | 1027 (94.4) | 370 (91.4) | |
| Red meat * | <0.001 | |||
| Enough | 1390 (93.1) | 1029 (94.6) | 361 (89.1) | |
| Nut fruits * | 0.514 | |||
| Enough | 875 (58.6) | 643 (59.2) | 232 (57.3) | |
| Fat (%) (mean ± SD) | 35.8 ± 9.0 | 36.1 ± 9.1 | 34.9 ± 8.9 | 0.023 |
| Protein (%) (mean ± SD) | 15.3 ± 3.7 | 15.3 ± 3.7 | 15.0 ± 3.7 | 0.147 |
| Carbohydrate (%) (mean ± SD) | 49.8 ± 9.8 | 49.4 ± 9.9 | 50.8 ± 9.5 | 0.013 |
| Vitamin D content (g) (median, IQR) | 2.8 (7.7) | 2.8 (9.6) | 2.5 (4.8) | 0.031 |
| Vitamin supplements * | <0.001 | |||
| Vitamin D and/or Calcium | 698 (47.7) | 560 (52.3) | 138 (35.0) | |
| Sun exposure | 0.001 | |||
| Yes | 1046 (69.6) | 789 (72.1) | 257 (63.1) | |
| Protective methods for sunshine (mean ± SD) | 1.6 ± 1.3 | 1.6 ± 1.3 | 1.6 ± 1.3 | 0.504 |
| Remained indoors during pregnancy | 0.018 | |||
| Yes | 228 (15.3) | 152 (14.0) | 76 (19.0) | |
| Season of blood draw | 0.004 | |||
| Sunny months | 927 (61.7) | 700 (63.9) | 227 (55.8) |
Abbreviations: BMI, body mass index; IQR, interquartile range; NT$, New Taiwan dollar; SD, standard deviation; VDD, vitamin D deficiency. * Variables containing missingness of ≤0.6%, with the exception of remaining indoors during pregnancy, number of fetuses in this pregnancy, household monthly income, and vitamin supplements, which have 0.9%, 1.1%, 1.9%, and 2.5% missingness, respectively.
Factors associated with vitamin D deficiency via multiple logistic regression analysis models (n= 1502).
| Variables | Model 1 | Model 2 | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | p | OR | 95% CI | p | |
| Age | 0.96 | 0.93–0.98 | 0.005 | 0.95 | 0.93–0.98 | <0.001 |
| Residential area | ||||||
| North | 1.00 | |||||
| Central | 0.68 | 0.50–0.94 | 0.021 | 0.66 | 0.48–0.90 | 0.010 |
| South and east | 0.22 | 0.14–0.33 | <0.001 | 0.20 | 0.13–0.31 | <0.001 |
| Eastern and outlying Islands | 0.36 | 0.25–0.52 | <0.001 | 0.33 | 0.23–0.47 | <0.001 |
| The ordinal of current pregnancy (parity) | ||||||
| 1st child | 1.00 | |||||
| 2nd child | 0.83 | 0.62–1.10 | 0.203 | |||
| ≥3rd child | 0.69 | 0.42–1.12 | 0.141 | |||
| Gestational age | ||||||
| 1st trimester (less than 17 weeks) | 1.00 | 1.00 | ||||
| 2nd trimester (17 weeks to less than 29 weeks) | 0.73 | 0.52–1.01 | 0.054 | 0.72 | 0.52–0.99 | 0.046 |
| 3rd trimester (more than 29 weeks) | 0.61 | 0.44–0.84 | 0.002 | 0.60 | 0.44–0.83 | 0.002 |
| Pre-pregnancy BMI | ||||||
| Normal (18.5 ≤ BMI < 25.0) | 1.00 | |||||
| Underweight (<18.5) | 1.04 | 0.67–1.59 | 0.850 | |||
| Overweight/obese (≥25.0) | 0.87 | 0.63–1.20 | 0.397 | |||
| Egg intake | ||||||
| Not enough | 1.00 | |||||
| Enough | 0.72 | 0.43–1.23 | 0.236 | |||
| Red meat intake | ||||||
| Not enough | 1.00 | 1.00 | ||||
| Enough | 0.54 | 0.34–0.86 | 0.010 | 0.50 | 0.32–0.78 | 0.002 |
| Fat (%) | 0.99 | 0.98–1.01 | 0.711 | |||
| Vitamin D content | ||||||
| ≤median | 1.00 | 1.00 | ||||
| >median | 0.80 | 0.62–1.03 | 0.091 | 0.78 | 0.60–1.00 | 0.057 |
| Vitamin supplements | ||||||
| No relevant supplements | 1.00 | 1.00 | ||||
| Vitamin D and/or calcium | 0.47 | 0.36–0.62 | <0.001 | 0.51 | 0.39–0.66 | <0.001 |
| Sun exposure | ||||||
| No | 1.00 | 1.00 | ||||
| Yes | 0.77 | 0.59–1.01 | 0.064 | 0.75 | 0.57–0.98 | 0.034 |
| Remained indoors during pregnancy | ||||||
| No | 1.00 | 1.00 | ||||
| Yes | 1.33 | 0.95–1.87 | 0.089 | 1.35 | 0.97–1.88 | 0.071 |
| Season of blood draw | ||||||
| Rainy months | 1.00 | |||||
| Sunny months | 0.57 | 0.44–0.75 | <0.001 | 0.59 | 0.46–0.77 | <0.001 |
Abbreviations: BMI, body mass index; CI, confidence interval; NT$, New Taiwan dollar; OR, odds ratio.
Supplementary Materials
The following supporting information can be downloaded at:
References
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Details
; Yi-Chun, Chen 3
; Jung-Su, Chang 4
; Chao, Jane C-J 5
; Chyi-Huey Bai 6
1 Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110-31, Taiwan; School of Public Health, College of Public Health, Taipei Medical University, Taipei 110-31, Taiwan
2 School of Public Health, College of Public Health, Taipei Medical University, Taipei 110-31, Taiwan; Faculty of Public Health, Hai Phong University of Medicine and Pharmacy, Hai Phong 042-12, Vietnam
3 School of Nutrition and Health Sciences, Taipei Medical University, Taipei 110-31, Taiwan
4 School of Nutrition and Health Sciences, Taipei Medical University, Taipei 110-31, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei 110-31, Taiwan; Graduate Institute of Metabolism and Obesity Sciences, College of Nutrition, Taipei Medical University, Taipei 110-31, Taiwan; Chinese Taipei Society for the Study of Obesity, Taipei 110-31, Taiwan
5 School of Nutrition and Health Sciences, Taipei Medical University, Taipei 110-31, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei 110-31, Taiwan; Master Program in Global Health and Development, College of Public Health, Taipei Medical University, Taipei 110-31, Taiwan
6 Department of Public Health, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110-31, Taiwan; School of Public Health, College of Public Health, Taipei Medical University, Taipei 110-31, Taiwan; Nutrition Research Center, Taipei Medical University Hospital, Taipei 110-31, Taiwan