Determinants of Hemoglobin Status in Filipino Children and

Keywords: anemia, hemoglobin, iron intake, preschool-aged, school-aged, women of reproductive age

Determinants of Hemoglobin Status in Filipino Children and Women Population Based on a Nationwide Survey

*Corresponding Author: [email protected]

Department of Science and Technology – Food and Nutrition Research Institute Gen. Santos Ave., Bicutan 1631 Taguig City, Metro Manila, Philippines

This study aimed to determine anemia prevalence and assess the determinants of hemoglobin status of Filipino children and women using data from the National Nutrition Survey (NNS) 2013. The NNS 2013 covered 17 regions and 80 provinces by the Department of Science and Technology – Food and Nutrition Research Institute (DOST-FNRI) using a multistage sampling design. Data analysis was done in three population groups: preschool-aged children (PSAC) 6–71 mo (n = 3,089), school-aged children (SAC) 6–14 yr old (n = 7,561), and non-pregnant women (NPW) of reproductive age 15–49 yr old (n = 7,074). The prevalence of anemia was 13.9%, 9.9%, and 13.4% in PSAC, SAC, and NPW, respectively. Among PSAC, the older age group 3–5 yr old (β = 0.68, p < 0.001) was associated with a higher hemoglobin level than the younger age group 6 mo – 2 yr. Among SAC, being a female (β = –0.19, p < 0.001) and residing in rural areas (β = –0.11, p = 0.03) determined lower levels of hemoglobin. Adequate serum retinol level was associated with higher hemoglobin levels in PSAC (β = 0.01, p = 0.02), SAC (β = 0.01, p < 0.001), and NPW (β = 0.01, p < 0.001). Adequate serum zinc concentration was associated with higher hemoglobin level in SAC (β = 0.01, p < 0.001) and NPW (β = 0.00, p = 0.01). Stunting was negatively associated with hemoglobin levels in PSAC (β = –0.32, p = 0.01) and SAC (β = –0.16, p = 0.01). Observed associations of age, sex, place of residence, marital status, micronutrient status, and stunting with hemoglobin levels could support nutrition-specific interventions such as micronutrient supplementation, food fortification, and nutrition education aimed at anemia control and treatment. Future studies could also improve our understanding of the determinants of anemia in the Philippines.

Philippine Journal of Science149 (3-a): 731-746, October 2020ISSN 0031 - 7683Date Received: 27 Feb 2020

Eva A. Goyena*, Ma. Lynell V. Maniego, and Imelda Angeles-Agdeppa

INTRODUCTIONHemoglobin is a protein component in red blood cells that acts as the primary oxygen carrier in the human body. When the hemoglobin level falls below the cut-off necessary to sustain cellular respiration and other vital processes, the resulting condition is referred to as anemia (WHO 2015). Considered an important global public health problem affecting all age groups, anemia is particularly

damaging and difficult to reverse when it occurs during fetal development and early childhood – when there are higher nutritional requirements during pregnancy, lactation, and growth – thereby posing health consequences such as higher frequency of illness and impairment of physical or mental development (Borwankar et al. 2007). The World Health Organization (WHO) categorizes anemia prevalence in the context of its status as a public health concern into severe (anemia prevalence of more than 40%), moderate (between 20% and 39.9%), or mild public health problem (between

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5% and 19.9%) (WHO 2001). Globally, the WHO estimated that in 2015, approximately 800 million women and children were anemic – around 300 million of which reside in Southeast Asia. The causes and health determinants of anemia are varied and often co-existent. Half of the anemia cases are attributed primarily to iron deficiency (WHO 2014a), although infection, inadequate intake of micronutrients, micronutrient deficiencies, poor dietary diversity, household food insecurity, and hemoglobinopathy are also risk factors commonly associated with anemia (Rahman et al. 2016; Stevens et al. 2013).

In the Philippines, the prevalence of anemia based on the cut-off criteria for low hemoglobin level has been assessed across population age groups since 1993 through the NNS. While a notable downward trend in the prevalence of anemia in all age groups was observed in the past two decades, anemia prevalence remained high among infants 6–11 mo old, infants 12–23 mo old, and pregnant women – with prevalence reaching to 48.2%, 35.4%, and 26.1% in 2018, respectively (DOST-FNRI 2019). The decreasing overall anemia prevalence – from 29.0% in 1998 to 11.3% in 2018 – is consistent with global anemia trends that indicate some progress in anemia reduction, although this still falls short of the World Health Assembly’s global target of a 50.0% reduction in anemia occurrence among women of reproductive age by 2025 (WHO 2014b).

The high prevalence of anemia – alongside its irreversible consequences on human health, maternal and child mortality, low birth weight, and impaired cognitive and physical development – has made initiatives toward reducing anemia prevalence a public health priority in many countries amid difficulties in implementing programs and measures to control it. Therefore, it is important to define more specifically its attendant risk factors and to develop or design more effective supplementation, fortification, and promotion interventions in the country wherever necessary. Addressing maternal anemia is also essential in achieving the other four global nutrition targets (stunting, low birth weight, childhood overweight, and wasting) within the context of the First 1000 Days of Life approach (WHO 2014b).

This research aimed to determine the prevalence of anemia and identify the possible determinants of hemoglobin status among the three population groups: PSAC 6–71 mo, SAC 6–14 yr old, and NPW of reproductive age 15–49 yr old.

MATERIALS AND METHODS

Study Design and PopulationThis study analyzed secondary data from the 8th NNS conducted between June 2013 and 2014. The NNS is

a periodic, population-based, cross-sectional survey composed of anthropometric, biochemical, clinical, dietary, socio-economic characteristics, food security, infant and young child feeding, and maternal health components. This comprehensive survey is conducted every five years by DOST-FNRI, with support from other government and non-government agencies, covering all 17 regions and 80 provinces in the Philippines.

The 8th NNS adopted the 2003 Master Sample design developed by the Philippine Statistics Authority (PSA). Samples were selected through a stratified three-stage sampling design. The first stage involved selecting primary sampling units (PSUs) consisting of one “barangay” or a combination of contiguous barangays with at least 500 households. The second stage of the sampling process was the identification of enumeration areas, each having 150–200 households where 3,114 were identified from the PSUs. The last stage involved the selection of 35,825 households with a response rate of 91%. All subjects who participated in the NNS 2013 with complete information on hemoglobin level, serum retinol and zinc levels, weight, height, dietary intake, and sociodemographic and household socioeconomic characteristics were included in the analysis. Data of the 3,089 (97%) PSAC, 7,561 (99%) SAC, and 7,074 (96%) NPW were analyzed for this present study. Details of the 8th NNS sampling procedure and methodology were described elsewhere (DOST-FNRI 2015a).

Blood Collection and Determination of Hemoglobin, Serum Retinol, and Zinc LevelsBlood samples were collected by licensed and trained medical technologists between 06:00 AM and 12:00 PM through finger prick method for PSAC and venous blood collection for SAC and NPW, following the WHO guidelines (2010) on blood collection to ensure the quality of blood samples collected and safety of subjects. Thereafter, 20 µL of whole blood was pipetted directly into tubes each containing 5 mL of cyanmethemoglobin solution to measure hemoglobin at the field site. Hemoglobin was assessed using the cyanmethemoglobin method employing a portable spectrophotometer for absorbance measurements. In this study, anemia was defined as having hemoglobin level < 11g/dL for PSAC aged 6–71 mo and SAC 6.0–14 yr old, and hemoglobin level < 12g/dL for NPW of reproductive age (WHO 2001). Serum retinol and zinc levels were also assessed as part of the national survey. Serum retinol was measured based on isocratic elution high-performance liquid chromatography method using a Hitachi model (China) automated clinical chemistry analyzer. Serum zinc was analyzed using a flame atomic absorption spectrometer (Agilent 240 FS AA).

Philippine Journal of ScienceVol. 149 No. 3-a, October 2020

Goyena et al.: Determinants of Hemoglobin Status in Filipino Children and Women

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Data Collection and Analysis of the Predictor VariablesData on socio-economic status (SES), place of residence (urban/rural), household sanitation, household food insecurity, food intake, and nutritional status of participants were taken from the various survey components of the NNS 2013. Briefly, trained registered dietitians, nutritionists, and nurses carried out the face-to-face interviews using an electronic data collection system containing questionnaires to collect information on family SES, food security, water and sanitation, and weight and height of children and women in a centralized location such as a health center or barangay hall during the NNS 2013. Two-day, 24-hr food recalls using a structured questionnaire were conducted by nutritionists with the parent or caregiver of each PSAC, SAC, and NPW respondent during household visits. The interviewer asked about all foods and beverages that the participants consumed on the previous day. The amount of each food item or beverage was estimated using common household measurements, such as cups and tablespoons, by size or number of pieces. The information was then converted to grams and was further converted into weight in grams to assess consumption. A detailed description of the data collection and questionnaires used in the NNS 2013 were presented in other publications (DOST-FNRI 2015a, 2015b; 2015c).

SES was assessed by constructing the household wealth index through principal component analysis, taking into consideration the type of housing materials used on the floor, roof, and wall; household water access and toilet facility; and household ownership of selected assets such as appliances (TV, radio, refrigerator, etc.), vehicles (car, bicycle, etc.), and electricity connection (DOST-FNRI 2015a). The household wealth index was categorized into five quintiles (poorest, poor, middle, rich, and richest). Household food insecurity was assessed using the Household Food Insecurity Access Scale, which was modified, translated, and pre-tested for its applicability in the Philippine setting during the pilot study of the NNS conducted in 2013. Household source of drinking water was categorized as “improved sources” (protected well or spring; communal faucet system, stand post, or waterworks system; and bottled water) or “not improved sources” (unprotected dug well or spring, tanker truck/cart with drum, and surface water). Household type of toilet facility was classified as “no facility” (bush/field, wrap and throw), “water–sealed” (pour/flush to septic tank), or “not water-sealed” (without septic tank, pit latrine). Place of residence was categorized into two: “urban residents” were defined as respondents living in an urban area, and “rural residents” were those living in a rural area as specified by the national census. NPW age (15–25 yr, 26–49 yr), education (no schooling, elementary, high school, college and above), marital status (single, married,

live-in, widowed, and separated), and working status (not working/not earning cash, working/earning cash) were included. The nutritional status of study participants was assessed based on weight and height. Two measurements each for weight and height were collected, and the means of the two anthropometric measurements were calculated and used in the analysis. The WHO Child Growth Standard (CGS) (2006) and WHO growth reference (De Onis et al. 2007) were used to assess the nutritional status of PSAC 6–60 mo old and SAC 5.1–14 yr old, respectively based on the z-scores for weight-for-age (WAZ), height-/length-for age (H/LAZ), and Body Mass Index (BMI) for age (BAZ). Underweight among PSAC 6–71 mo old was defined as having WAZ below –2SD from the median distribution. Stunting, indicating chronic malnutrition among PSAC 6–71 mo and SAC 6–14 yr old, was defined as H/LAZ below –2SD from the median distribution. Wasting or thinness, indicating acute malnutrition among SAC 6–14 yr, was identified by BAZ below –2SD from the median distribution. Meanwhile, the BMI which is calculated as weight in kg divided by height in m2 (kg/m2) was used to assess the nutritional status of NPW of reproductive age. BMI was categorized chronic energy deficiency (CED) as < 18.5 kg/m2 and non-CED as ≥18.5 kg/m2. To estimate individual dietary intake, energy and nutrient values were calculated per 100 g of raw food consumed using the Philippine Food Composition Tables. The mean of the two-day energy and iron intakes was computed, and adequacy was based on the 2015 Philippine Dietary Reference Intakes. The proportion of children meeting energy and iron adequacy was evaluated by determining the number of individuals with adequacy equal to or above 100% of the estimated average requirement (EAR) for energy and iron.

Statistical AnalysisStatistical analysis was performed using the statistical software package STATA version 15 (Corp LLC, Texas, USA). Proportion estimates were calculated with a 95% confidence interval. Mean estimates were calculated with standard error. T-test of means and analysis of variance were used to compare means of two and more than two categories, respectively. Pearson’s chi-squared (χ2) test was used to compare proportions. Sampling weights were calculated as the product of base-weights, (unit) nonresponse adjustment, and post-stratification adjustment. Post-stratification adjustments were made to ensure that survey estimates conformed to well-known population age-sex distribution by province obtained from PSA. Thus, all estimates – such as proportions and regression analysis – were computed based on weighted data. In the regression analysis, both bivariate and multivariable linear regression analyses were done to identify the independent predictors of hemoglobin



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levels among the three population groups. All variables were analyzed in bivariate regression analysis, and only significant predictors were considered in multivariable regression analysis. The detection of multicollinearity between variables was done by checking the extremely high values of standard errors. Independent variables with multicollinearity were excluded in the analysis. All statistical tests were analyzed at a 5% level of significance.

Ethics Approval This study was based on the analysis of the available dataset collected from the 8th NNS. The 8th NNS was reviewed and approved by the FNRI Institutional Ethics Review Committee (FIERC) with protocol code number FIERC-2012-001. All 8th NNS questionnaires were cleared by the National Statistical Coordinating Board of the PSA in June 2013.

RESULTS A total of 3,089 PSAC aged 6–71 mo, 7,561 SAC aged 6–14 yr, and 7,074 NPW aged 15–49 yr were included in the study. At the national level, the prevalence of anemia was 13.9%; the prevalence in rural and urban was 14.0% and 13.7%, respectively (Appendix Table I). The anemia prevalence among in SAC was 9.9% (national), with the same prevalence in rural (10.0%) and urban (9.9%). Among NPW, anemia prevalence was 13.1% (national), 13.0% (rural), and 13.1% (urban), as shown in Appendix Table I.

The mean hemoglobin level among PSAC was 12.1 mg/dL (Appendix Table I). Mean hemoglobin level was significantly higher among PSAC in the 36–71 mo age group (12.4 g/dL), those born from mothers with above high school education (12.2 g/dL) and working mothers (12.2 g/dL), those from food secure (12.2 g/dL) and richest households (12.4 g/dL), with improved source of drinking water (12.1 g/dL) and toilet facility (12.1 g/dL), those who were not underweight (12.1 g/dL) and not stunted (12.1 g/dL), and those with no vitamin A (12.1 g/dL) and no zinc deficiencies (12.1 g/dL) – as compared with their PSAC counterpart subgroups. Anemia prevalence was significantly higher among younger PSAC aged 6–35 mo (23.6%), born from mothers without schooling (16.2%) and who were not working (16.0%), those who were stunted (18.5%), and those with vitamin A deficiency (18.1%) and zinc deficiency (17.1%) than among their PSAC counterpart subgroups. Moreover, anemia prevalence among PSAC was found to significantly decrease as households become progressively more food secure, from 16.9% among severely food-insecure to 10.4% among food-secure

households. Similarly, a significant downward trend was observed in the prevalence of anemia among PSAC as a household wealth index increases. The prevalence of anemia was significantly lower among PSAC with a water-sealed toilet facility (12.9%) than those without an improved facility.

Among SAC aged 6–14 yr, the mean hemoglobin level was 13.2 g/dL (Appendix Table I). Mean hemoglobin levels were significantly higher among the following SAC subgroups relative to their counterparts: male (13.3 g/dL), older SAC aged 12–14 yr (13.5 g/dL), born from mothers with above high school education (13.3 g/dL) and who were working (13.2 g/dL), those with improved source of drinking water (13.3 g/dL) and toilet facility (13.2 g/dL), those from food secure (13.3 g/dL) and richest households (13.4g/dL), those who were not stunted (13.2 g/dL), not thin/wasted (13.2%), and with no vitamin A deficiency (13.2g/dL) and no zinc deficiency (13.2g/dL) (Appendix Table I). Anemia prevalence was significantly higher among 6–11 yr old (23.6%), thin/wasted SAC 6–14 yr old (11.9%), and vitamin A deficient (13.4%) than among their peer counterparts. Moreover, an increasing trend in the prevalence of anemia among SAC was observed with increasing severity of household food insecurity, ranging from 8.7% among food-secure households to 13.0% among food-insecure households. Significantly more anemic SAC (12.3%) were noted in the poorest than in the richest households (7.2%), and those from households with water-sealed toilet facility (13.1%) than among their SAC counterparts.

Meanwhile, anemia prevalence did not significantly differ between urban and rural NPW (Appendix Table I). However, anemia was significantly higher among women aged 26–49 yr (14.0%) and those with no schooling (17.5%), who were separated (16.4%), among women with CED (14.9%), vitamin A deficiency (30.5%), and zinc deficiency (15.5%) than their counterpart subgroups. Anemia prevalence by wealth status followed an inverse pattern, appearing significantly highest in the poorest quintile and lowest in the richest quintile. Similarly, there was a decreasing trend in the anemia prevalence with increasing level of educational attainment among women.

Appendix Table II shows the mean dietary iron intake and the proportion of children and women meeting the iron adequacy based on the 24-hr food recall data. The national mean dietary iron intake was 5.5 mg, with one fourth (24.5%) of PSAC meeting the daily EAR for iron. Significantly higher proportions of PSAC males (26.9%), PSAC whose mothers obtained at least a high school level of education (39.7%) and were working (31.5%), and PSAC living in rural areas (29.4%) met the EAR for iron relative to their counterpart subgroups. Consistently, the adequacy of dietary iron intake among PSAC significantly



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increased as household food security improved, with 39.0% of children in food-secure households meeting the dietary iron adequacy, as against 15.0% among those in the severely food insecure households. Similarly, there was an increasing trend in the adequacy of dietary iron intake in PSAC as a household wealth index increased. A significantly higher proportion of PSAC meeting the dietary iron adequacy was noted among households with an improved source of drinking water (28.1%) and toilet facility (27.0%) than their PSAC counterparts. On the other hand, a significantly lower proportion of PSAC underweight (10.9%), stunted (18.1%), vitamin A deficient (16.8%), and zinc-deficient (16.2%) met the age-specific iron adequacy requirement compared with PSAC with normal growth Z-scores and micronutrient levels. The observations among PSAC were directly complemented by the values of mean dietary iron intake, which significantly increased as maternal education attainment increased, household food security, and wealth status improved. The same trend was observed in the proportion of SAC 6–14 yr old meeting iron adequacy, which increased with increasing household food security and wealth status. More SAC 6–14 yr old from food-secure households (25.7%), belonging to households with improved toilet facility (18.3%), born from mothers with at least high school level of education (27.8%), and who were working (18.3%) met the iron adequacy requirements than their counterpart subgroups. Similarly, 29.1% of SAC from the richest wealth quintile satisfied the requirement, as compared with 8.9% from the poorest households. The proportion of SAC meeting iron adequacy requirements was significantly lower among children who were stunted (10.5%), thin (11.8%), and zinc-deficient (12.1%) compared to those who were not stunted (18.9%), not thin (17.5%), and not zinc deficient (18.3%). A significantly higher proportion of SAC who were male (19.9%), aged 6–11 yr (21.8%), and residing in urban areas (21.4%) met iron requirements relative to their counterpart subgroups. A similar pattern was observed in the mean dietary iron by sex, age, place of residence, maternal education, working status, improved water and sanitation, and micronutrient status. Meanwhile, the overall mean dietary iron intake was 8.2 mg, with only 1.6% of NPW meeting the daily iron requirement. The proportion of NPW meeting the daily iron requirement ranged from 0.8–29.1% across subgroups. A significantly higher proportion of women in the younger age group 15–25 yr (2.8%), single women (2.7%), and from urban areas (21.4%) met dietary adequacy for iron compared to those in older age group 26–49 yr (0.9%), married/live-in/widow/separated (0.8%; 1.2%; 1.4%; 2.1%) and from rural areas (12.4%). A significant pattern was observed among NPW in terms of meeting iron adequacy by wealth status, with the lowest proportion meeting the EAR of iron noted among the poorest quintiles (8.9%), with the

highest noted among the richest quintile (29.1%). No significant trend was observed in the proportion of NPW meeting iron adequacy across food security status, source of water and toilet facility, BMI classification, and vitamin A and zinc levels.

Appendix Table III shows the results of the multiple linear regression analysis stating the different determinants of hemoglobin level among studied populations (PSAC, SAC, and NPW). Older PSAC (3–5 yr old; β = 0.68, p< 0.001) and SAC (12–14 yr old; β = 0.49, p < 0.001) had significantly higher hemoglobin levels than younger PSAC (6 mo – 2 yr old) and SAC (6–11 yr old), respectively. Older NPW in the 26–49-yr-old age group (β = –0.15, p < 0.001) and married women (β = –0.12, p = 0.031) had significantly lower hemoglobin level relative to their counterpart subgroups. Being a female SAC and living in rural areas (β = –0.11, p = 0.03) were negatively associated with hemoglobin. A significantly lower hemoglobin level was noted among SAC females and those who were living in rural areas as compared with their SAC males and those who were residing in urban areas. Maternal educational attainment and working status were not associated with the hemoglobin levels of children and NPW. Serum retinol was significantly associated with higher hemoglobin levels in PSAC (β = 0.01, p = 0.015), SAC (β = 0.01, p < 0.001) and NPW (β = 0.01, p < 0.001). Serum zinc was positively associated with hemoglobin levels among SAC (β = 0.01, p < 0.001) and NPW (β = 0.01, p < 0.001) but not among PSAC. Household wealth status and food insecurity were not associated with hemoglobin levels in all three groups in the final regression model, although these were negatively associated with hemoglobin in the bivariate analysis. Among nutritional indicators, stunting was found to be negatively associated with hemoglobin level among PSAC (β = –0.32, p = 0.005) and SAC (β = –0.16, p = 0.025).

DISCUSSIONProviding epidemiological evidence for the determinants of anemia as a foundation for future research in the context of children and women of reproductive age served as the overarching rationale in conducting this study. Study results could also guide policymakers and program planners in identifying and/or prioritizing strategies to prevent and control anemia in the country.

Research on anemia in the Philippines is limited and has focused mostly on PSAC 6–59 mo old (Tengco et al. 2008; Rohner et al. 2013), while studies among school-age children and women of reproductive age remain understudied. This study has drawn some parallel findings with the existing literature on anemia. In a



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nationally-representative study conducted in Haiti, it was observed that urban women and children are at higher risk of developing anemia compared to those residing in rural areas (Heidkamp et al. 2013). Meanwhile, a cross-sectional study conducted in the province of Cebu, Philippines found that 16.1% of preschool-age children were anemic (Tengco et al. 2008). Studies done in Nepal (Gautam et al. 2019) and Bangladesh (Rahman et al. 2016) showed that four out of 10 women of reproductive age belonging to the 15–49-yr-old age group were anemic. Prevalence and distribution of anemia in a population were found to vary by various contexts, and the causes often coexist with each other (Mesfin et al. 2015). The succeeding section discusses in detail the health determinants of anemia identified in this study.

SexBeing a female child was associated with lower hemoglobin levels among SAC. In the subgroup analysis between the 6–11- and 12–14-yr-old age groups (Appendix Table I), increased anemia prevalence was observed among older female SAC (9.1%). In a study conducted in India, girls aged 12–16 yr from urban and rural areas who have already attained menarche recorded higher anemia prevalence than those who have not (Mehta 2004). A local study in the Philippines found that among young women born from 1973–1977, the mean and median ages at menarche declined from 13.2–12.9 yr and from 12.6–12.3 yr, respectively (Tey et al. 2019). Thus, it is possible that some of the older girls were at greater risk of developing anemia because of the additional demand for iron imposed by iron loss during menstruation. In addition, the significantly lower dietary iron intake that is potentiating the synthesis of lower hemoglobin among SAC females (Table 3) could explain such a phenomenon. Similar findings have been noted in Bangladesh (Rahman et al. 2016) and the United States (Vacarro et al. 2013), where females were found to have lower consumption of dietary iron. This finding has an important policy significance in relation to iron deficiency anemia (IDA) reduction among adolescent girls, especially at another vulnerable time in their life cycle, which could complement ongoing efforts to address the problem during pregnancy and infancy.

Age and Marital StatusOlder age and being married were negatively associated with hemoglobin concentration among NPW (Table 3). In contrast, studies conducted in Africa and Asia showed that anemia prevalence tended to decrease with age (Msemo et al. 2018; Adamu et al. 2017). Lower hemoglobin concentrations among older NPW could be due to higher parity (number of births) or multiple pregnancies that could increase the likelihood of anemia and lower hemoglobin level (Rahman et al. 2016;

McLean et al. 2009). The trend observed in this study could also be attributed to the adverse effect of lower dietary iron intake and the additional requirements for iron due to iron loss during menstruation, pregnancy, and lactation among older NPW (McLean et al. 2009). Meanwhile, school-age children aged 12–14 yr registered significantly higher hemoglobin levels than the younger 6–11-yr-old age group, probably because older children eat more varied foods that contain more bioavailable iron. This suggests the need to target the younger SAC age group when it comes to interventions both within and outside the school system, including female SAC aged 12–14 yr. A similar finding was observed in Haiti, where increasing age corresponded to reduced odds of developing anemia among children (Iannotti et al. 2015).

Place of ResidenceAmong SAC, residence in rural areas is associated with lower hemoglobin level relative to those living in urban areas. This is consistent with the significantly higher mean dietary iron intake observed among urban SAC, with a larger proportion of urban SAC meeting the dietary iron intake than their rural counterparts (Appendix Table II). Families living in urban areas tend to have greater access to basic requirements – such as clean water, medical care, and sufficient food/nutritional requirements needed for proper children's health – in contrast to rural families. Such finding agrees with previous studies conducted in Bangladesh (Rahman et al. 2016), India (Mehta 2004), and Ethiopia (Kawo et al. 2018). However, rural areas did not predict lower levels of hemoglobin in PSAC and NPW. This finding is apparent despite the fact that the intake of dietary iron by PSAC and NPW, as well as the proportion of them meeting iron adequacy from rural areas, was significantly less than that of PSAC and NPW in urban areas. The underlying reason is unclear; however, it can be perhaps due to the larger sample of SAC from rural areas (n = 4,479; 51.0%) as opposed to PSAC (n = 1,708; 47%) and NPW living in rural areas (n = 3,740; 44.2%).

Household Wealth and Food SecurityIn the final regression analysis, household wealth and food security status did not significantly predict lower levels of hemoglobin among PSAC, SAC, and NPW, although these factors were significantly associated with hemoglobin levels of these groups in the bivariate analysis. This is apparent despite the fact that dietary iron intake, as well as the number of children and women meeting the iron requirement, was significantly less among poor and food-insecure households. The insignificant differences between hemoglobin level and household wealth status are consistent with other studies conducted in Bangladesh (Kamruzzaman et al. 2015) and Nepal (Gautam et al. 2019).



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Serum RetinolSerum retinol was positively associated with a higher level of hemoglobin in PSAC, SAC, and NPW. Anemia prevalence among members of these groups having vitamin A deficiency was 18.1%, 13.4%, and 30.0%, respectively. These figures are significantly higher than their non-vitamin A deficient counterparts (p < 0.0011, p < 0.0035, and p < 0.000, respectively). This points to the important role of retinol in hemoglobin synthesis, highlighting a policy message on the crucial part of improving vitamin A intake in addressing not only vitamin A deficiency but also the anemia problem. The synergistic relationship between retinol and hemoglobin has been reported in epidemiological studies conducted in Bangladesh (Rahman et al. 2016) and India (Pasricha et al. 2010). Possible biological mechanisms by which adequate vitamin A intake can contribute to anemia reduction – include mobilization of iron stores from the liver, increased erythropoiesis, and modulation of immunity to infectious diseases (Semba and Bloem 2002).

Serum ZincSerum zinc is positively associated among SAC and NPW but insignificant among PSAC. Anemia prevalence was significantly higher among zinc-deficient SAC (17.1%; p < 0.0159) and zinc-deficient NPW (16.2%; p < 0.001) compared with their non-zinc deficient counterparts. The positive relationship between hemoglobin and zinc concentrations in study participants is in agreement with other studies (Rahman et al. 2016; Cole et al. 2010), suggesting that anemia may be associated with zinc through the co-existence of iron deficiency. This could also be explained by the common dietary sources of zinc and iron and the role of zinc in erythropoiesis. Furthermore, different mechanisms could account for the possible biochemical relationship between zinc and hemoglobin. Heme production includes a zinc-dependent step mediated enzyme called aminolevulinic acid dehydrase (Garnica 1981). Moreover, zinc – like vitamin A – has a potential role in immune functions, which suggests that childhood illnesses such as diarrhea and acute respiratory infection may increase due to deficient zinc intake, resulting in anemia (Shankar and Prasad 2010). Zinc deficiency did not predict lower hemoglobin levels in PSAC. These findings can be explained by the observation of non-significant differences in the mean zinc level of anemic (80.4 μg/L) and non-anemic PSAC (80.4 μg/L; data not shown) in the study.

StuntingStunting was negatively associated with hemoglobin levels among PSAC and SAC. The prevalence of anemia was significantly higher among stunted PSAC (18.5%; p < 0.0330) while not significantly different among

stunted SAC (11.4%; p < 0.1048). Mean hemoglobin levels were significantly lower among stunted PSAC (11.7 g/dL; p < 0.0000) and stunted SAC (13.1 g/dL; p < 0.0001) relative to children with normal height-for-age z scores. Low hemoglobin level has been reported to have a negative impact on linear growth (Roba et al. 2016); therefore, there is a higher risk of stunting among anemic children (Soliman et al. 2014; Stewart et al. 2013). A study conducted in Ethiopia found that stunting is negatively associated with hemoglobin levels (Mohammed and Esmaillzadeh 2017). One possible explanation for this is that hemoglobin improves tissue oxygenation, which subsequently enhances optimal cell proliferation and physical growth (Soliman et al. 2014). Synergistic mechanisms exist in the association of low hemoglobin and stunting, which may include micronutrient deficiency and infection morbidity. Stunting may result from chronic exposure to micronutrient deficiencies, including vitamin A and zinc, which are nutrients known to increase anemia risk through impaired erythropoiesis and oxidative stress pathway (Roba et al. 2016; Rahman et al. 2016; Cole et al. 2010). Poor zinc status may also compromise immunity and neurological functions, while iron and copper deficiency may result in anemia and affect the development of cognitive functions. Meanwhile, inadequate vitamin A intake may lead to increased susceptibility to infections. Thus, interference with this mechanism, alongside other micronutrient deficiencies, may cause growth delay or failure.

CEDAmong NPW, CED demonstrated a negative association with hemoglobin level, although this is not statistically significant. This is consistent with several studies reporting an insignificant association between BMI and hemoglobin level (Mendonça et al. 2014; Ghadiri-Anari et al. 2014). In the bivariate analysis, NPW with CED had significantly lower mean dietary iron intake (7.8 mg; p < 0.0278) and had a significantly higher prevalence of anemia (15.6%; p < 0.0391) compared to their non-CED counterparts. Parallel findings with this analysis were noted in the study conducted in Saudi Arabia (Hanafi et al. 2013) and Iran (Angraini et al. 2019), which reported that women of childbearing age with inadequate nutritional status and insufficient iron in the diet were more susceptible to anemia, specifically iron-deficiency anemia.

Strengths and LimitationsThe strength of this study lies in its use of a nationally representative survey and its focus on three physiologic groups (PSAC, SAC, and NPW of reproductive age). Moreover, a representative picture of the hemoglobin status of Filipino children and women was ensured by employing a comprehensive set of underlying correlates



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of hemoglobin level – such as SES, urban and rural residence, household food insecurity, and food intake. The high response rate among sample individuals suggests that results may be generalizable for the country as a whole and, possibly, for other developing countries in Asia.

Despite these strengths, study results should be considered in light of a number of limitations. First, results were generated from a cross-sectional study wherein only association was reported and causation cannot be inferred. Second, assessment of other biomarkers of IDA – particularly ferritin and serum transferrin, including c-reactive protein, as an indicator of infection – were not included but could have improved the detection of an association between anemia and IDA. Third, stool analysis for detection of parasitical infection among children – a potential risk factor of anemia – was not available, thereby restricting the study’s generalizability as regards other contributory factors of anemia. Fourth, the two non-consecutive, 24-hr food recall may have posed limitations that could have resulted in non-association of dietary intake and hemoglobin level in the regression analysis. Lastly, other conditions that might cause anemia among the population is genetic hemoglobin disorders. There is no nationally representative data available to report the magnitude of hemoglobinopathies; however, a study by Capanzana et al. (2018) reported that 27.8% anemic individuals in the Philippines’ National Capital Region had hemoglobinopathy. The link between anemia and congenital hemoglobin disorders has been reported in Bangladesh and in the neighboring countries of Vietnam and Cambodia (Merrill et al. 2012).

SUMMARY AND CONCLUSIONThis study was primarily conducted to determine the different factors affecting hemoglobin levels in Filipino PSAC, SAC, and NPW. Older PSAC in the age group 3–5 yr old was associated with a higher hemoglobin level than the younger age group 6 mo – 2 yr old. Among SAC, being female and living in rural areas were associated with lower hemoglobin concentrations. Older SAC aged 12–14 yr had higher hemoglobin concentration than younger SAC 6–11 yr. Among NPW, older and married NPW exhibited lower hemoglobin levels than their counterpart subgroups. Serum retinol concentration was positively associated with hemoglobin levels among PSAC, SAC, and NPW. Serum zinc concentration is positively associated with hemoglobin levels among SAC and NPW but not among PSAC. Stunting was negatively associated with hemoglobin concentrations among PSAC and SAC. Maternal working status, marital status, and educational attainment – as well as household wealth,

sanitation, and sources of drinking – were found to have no effect on hemoglobin concentrations of the three physiological groups. Observed associations between these determinants of hemoglobin status could support nutrition-specific interventions such as micronutrient supplementation, food fortification, and nutrition education aimed at anemia control and treatment. Given that anemia prevalence remains a public health concern all over the country, it is necessary to scale-up health and nutrition interventions through effective mobilization of community health workers targeting specific groups and at-risk populations to address the burden of anemia. Future studies are also encouraged to improve our understanding of the determinants of anemia in the Philippines.

ACKNOWLEDGMENTThe authors acknowledge the contribution of Ms. Ma. Cristina A. Musa, RND in preparing tables and graphs and in conducting the review of literature during manuscript writing. The authors thank the DOST regional and provincial offices for the support during the pre-survey coordination meetings and field data collection; and the local government unit officials, including barangay officials and local survey aides, for their direct assistance during field data collection in their respective areas. Thanks are also extended to all DOST-FNRI technical, non-technical, and field-level staff for their contribution to the fruition of the 8th NNS 2013.

CONFLICT OF INTERESTThe authors declare that they have no competing interests.

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Appendix Table I. Hemoglobin level and anemia prevalence of PSAC, SAC, and NPW of reproductive age by household, child, and maternal characteristics: Philippines, 2013.

Characteristics

PSAC (6–71 mo old) SAC (6–14 yr old) NPW (15–49 yr old)

Hemoglobin1 Anemia2 Hemoglobin1 Anemia2 Hemoglobin1 Anemia2

Mean(g/dL) p-value* Prev.

% p-value* Mean(g/dL) p-value* Prev.


% p-value*

National 12.1 13.9 13.2 9.9 13.1 13.1

Sex

Male 12.1 0.6598 13.7 0.7429 13.3 < 0.001 9.3 0.1024 – – – –

Female 12.1 14.1 13.1 10.6 – – – –

Age

PSAC (in yr)

6–35 11.7 < 0.001 23.6 < 0.000 – – – – – – – –

36–71 12.4 6.3 – – – – – – – –

SAC (in yr)

6–11 – – – – 11.7 < 0.001 11.7 < 0.001 – – – –

12–14 – – – – 12.4 6.6 – – – –

NPW (in yr)

15–25 – – – – – – – – 13.1 0.0169 11.6 0.0073

26–49 – – – – – – – – 13.1 14.0Maternal education

No schooling 11.7 < 0.001 16.2 0.0310 12.8 < 0.000 14.0 0.1587 12.9 0.2991 17.5 0.0268

Elementary 12.0 13.5 13.2 11.2 13.1 15.3

High school 12.0 15.2 13.2 10.4 13.1 13.0Above high

school12.2 10.9 13.3 8.6 13.1 11.8

Maternal working status

No working 12.0 < 0.001 16.0 < 0.001 13.1 < 0.000 11.0 0.0652 13.1 0.2942 13.4 0.2086

Working 12.2 10.6 13.2 9.2 13.1 12.3

Marital status

Single – – – – 13.2 < 0.000 10.4 < 0.000

Married – – – – 13.0 14.2

Live-in – – – – 12.9 16.3

Widow – – – – 13.1 13.8

Separated – – – – 13.0 16.4Place of residence

Rural 12.1 0.7186 14.0 0.8426 13.2 0.4001 10.0 0.9197 13.1 0.2890 13.0 0.9486

Urban 12.1 13.7 13.2 9.9 13.1 13.1

Household food security**Severe food

insecurity 12.0 0.0424 16.9 0.0307 13.1 < 0.001 12.9 < 0.001 13.1 0.0680 13.9 0.3197

Moderate food insecurity 12.1 14.2 13.2 9.1 13.1 13.5

Mild food insecurity 12.1 12.0 13.2 8.8 13.0 13.4

Food secure 12.2 10.4 13.3 8.7 13.1 11.8

Wealth index

Poorest 12.0 < 0.001 16.5 0.0055 13.1 < 0.000 12.3 < 0.001 13.0 0.0748 14.9 < 0.001

Poor 12.0 15.9 13.2 9.0 13.1 13.2

Middle 12.1 14.4 13.2 11.9 13.1 14.2



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Characteristics

PSAC (6–71 mo old) SAC (6–14 yr old) NPW (15–49 yr old)

Hemoglobin1 Anemia2 Hemoglobin1 Anemia2 Hemoglobin1 Anemia2

Mean(g/dL) p-value* Prev.



% p-value*

Rich 12.1 11.8 13.3 7.8 13.1 13.1

Richest 12.4 7.9 13.4 7.2 13.1 10.7

Source of drinking waterUnimproved

source12.0 0.0167 12.5 0.1653 13.1 < 0.000 9.0 0.1254 13.1 0.2838 13.2 0.7388

Improved source

12.1 14.5 13.3 10.4 13.1 13.0

Toilet facility

No toilet 11.9 0.0002 18.4 0.0448 13.1 < 0.000 13.9 < 0.000 13.0 0.0879 14.6 0.3943Not water-

sealed11.9 17.0 13.0 13.8 13.0 14.4

Water-sealed 12.1 12.9 13.2 8.9 13.1 12.8

Height-for-age3

Stunted 11.7 < 0.001 18.5 0.0330 13.1 <0.001 11.4 0.1048 – – – –

Not stunted 12.1 13.3 13.2 9.0 – – – –

Weight-for-age4

Underweight 11.7 <0.001 15.9 0.4582 – – – – – – – –

Not underweight 12.1 13.3 – – – – – – – –

BMI-for-age5

Thin – – – – 13.1 0.0011 11.9 0.0412 – – – –

Not thin – – – – 13.2 9.6 – – – –

CED6

CED – – – – – – – – 13.0 0.0355 14.9 0.0840

Non-CED – – – – – – – – 13.2 12.2

Vitamin A level7

Deficient 11.9 < 0.001 18.1 < 0.001 13.0 < 0.000 13.4 0.0035 12.5 < 0.000 30.5 < 0.000

Not deficient 12.1 12.9 13.2 9.5 13.1 12.3

Zinc level8

Deficient 11.9 < 0.001 17.1 0.0158 13.1 < 0.000 10.8 0.2587 13.0 0.0021 15.5 0.0049

Not deficient 12.1 13.0 13.2 9.7 13.1 12.3Empty fields in the table indicate non-applicability1Hemoglobin level based on g/dL2Anemia defined as hemoglobin < 11 g/dL in PSAC, and < 12 g/dL in SAC and NPW3Stunted defined as height-for-age Z-score <– 2SD; not stunted defined as height-for-age Z score: –2SD to +2SD4Underweight defined as weight-for-age Z-score <– 2SD; not underweight defined as weight-for-age Z score –2SD to +2SD5Wasting or thinness defined as BMI-for-age <– 2SD among SAC 6–14 yr old6CED among NPW 20–49 yr old defined as BMI < 18.5kg/m2; non-CED defined as BMI ≥ 18.5 kg/m2 CED or thinness among 15–19 yr old defined as BMI-for-age <– 2SD7Deficient in serum retinol < 10 μg/dL for the three age groups8Deficient in serum zinc < 65 μg/dL in children < 10 yr old, < 66 μg/dL in females ≥ 10 yr old, and < 70 μg/dLin males ≥ 10 yr old*Significantly different across categorical variables at p < 0.05 using ANOVA (test of means); p < 0.05 using Pearson’s chi-squared test (test of proportion) **Assessed using the HFIAS

Appendix Table I continuation . . .



742

Characteristics

PSAC (6–71 mo) SAC (6–14 yr old) NPW (15–49 yr old)

Mean iron intake (mg)1 p-value*

% meeting iron

adequacy2p-value*

Mean iron intake (mg)1

p-value*% meeting

iron adequacy2

p-value*Mean iron

intake (mg)1

p-value*% meeting

iron adequacy2

p-value*

National 5.5 24.5 7.4 16.8 8.2 1.6

Sex

Male 5.8 0.033 26.9 0.0055 7.7 < 0.001 19.9 < 0.001 – – – –

Female 5.2 21.9 7.2 13.5 – – – –

Age

PSAC (in mo)

6–35 5.2 0.033 25.0 0.5818 – – – – – – – –

36–71 5.7 24.1 – – – – – – – –

SAC (in yr)

6–11 – – – – 7.0 < 0.001 21.8 < 0.001 – – – –

12–14 – – – – 8.3 7.5 – – – –

NPW (in yr)

15–25 – – – – – – – – 8.4 0.1055 2.8 < 0.000

26–49 – – – – – – – – 8.1 0.9

Maternal education

No schooling 2.8 < 0.000 5.2 < 0.000 5.6 < 0.000 7.0 < 0.000 7.4 < 0.000 1.0 0.0976

Elementary 3.9 11.8 6.2 9.2 6.8 0.7

High school 5.1 20.8 7.4 16.3 8.0 2.0

Above high school 7.2 39.7 9.1 27.8 9.0 1.6

Maternal working status

No working 5.1 < 0.000 20.7 < 0.000 7.1 < 0.000 15.3 0.0131 8.0 < 0.000 1.8 0.1052

Working 6.1 31.5 7.8 18.3 8.4 1.3

Marital status

Single – – – – 8.3 0.5540 2.7 < 0.000

Married – – – – 8.0 0.8

Live-in – – – – 8.2 1.2

Widow – – – – 7.9 1.4

Separated – – – – 7.9 2.1

Place of residence

Rural 4.8 0.033 19.0 < 0.001 6.7 < 0.001 12.4 < 0.001 7.4 <0.001 0.8 < 0.001

Urban 6.1 29.4 8.2 21.4 8.8 2.3

Household food security**

Severe food insecurity 4.4 < 0.001 15.0 < 0.001 6.5 < 0.001 12.2 < 0.001 7.3 < 0.000 1.6 0.4386

Moderate food insecurity 5.2 22.0 7.2 14.7 7.8 1.5

Mild food insecurity 5.9 29.1 7.9 18.7 7.9 1.1

Food secure 7.4 39.6 8.8 25.7 9.3 2.2

Wealth index

Poorest 4.0 < 0.001 11.8 < 0.001 5.9 < 0.000 8.9 < 0.000 6.98 < 0.000 1.1 < 0.006

Poor 4.6 16.4 7.0 13.6 7.31 0.8

Middle 5.2 23.1 7.6 16.4 7.89 0.8

Appendix Table II. Dietary iron intake and proportion of meeting iron adequacy among PSAC, SAC, and NPW of reproductive age by household, child, and maternal characteristics according to household, child, and maternal characteristics: Philippines, 2013.



743

Appendix Table II continuation . . .

Characteristics

PSAC (6–71 mo) SAC (6–14 yr old) NPW (15–49 yr old)

Mean iron intake (mg)1 p-value*

% meeting iron

adequacy2p-value*

Mean iron intake (mg)1

p-value*% meeting

iron adequacy2

p-value*Mean iron

intake (mg)1

p-value*% meeting

iron adequacy2

p-value*

Rich 6.5 32.9 8.5 22.7 9.02 2.9

Richest 8.8 51.0 9.4 29.1 9.15 2.2

Source of drinking water

Unimproved source 5.9 0.031 7 22.7 0.0154 7.6 0.0442 17.9 0.1589 8.5 < 0.001 2.0 0.2039

Improved source 5.3 28.1 7.3 16.2 8.0 1.4

Toilet facility

No toilet 4.5 < 0.000 15.2 < 0.000 6.5 < 0.000 13.1 < 0.000 7.6 < 0.000 1.7 0.4039Not water-

sealed 4.3 14.4 6.3 9.6 7.6 0.9

Water-sealed 5.8 27.0 7.7 18.3 8.3 1.7

Height-for-age3

Stunted 4.7 < 0.001 18.1 0.0163 6.2 < 0.000 10.5 < 0.001 – – – –

Not stunted 5.8 26.8 7.8 18.9 – – – –

Weight-for-age4

Underweight 4.3 < 0.001 10.9 0.0016 – – – – – – – –

Not underweight 5.7 26.5 – – – – – – – –

BMI-for-age5

Thin – – – – 7.0 0.002 11.8 0.0005 – – – –

Not thin – – – – 7.5 17.5 – – – –

CED6

CED – – – – – – – – 7.8 0.1088 1.4 0.6046

Non-CED – – – – – – – – 8.2 1.7

Vitamin A level7

Deficient 4.6 < 0.000 16.8 < 0.001 6.6 < 0.000 16.0 0.5728 8.0 0.3777 0.7 0.4040

Not deficient 5.8 26.6 7.5 17.0 8.2 1.7

Zinc level8

Deficient 4.5 < 0.000 16.2 < 0.000 6.8 < 0.000 12.1 < 0.000 8.2 0.7169 1.8 0.6348

Not deficient 5.7 26.3 7.6 18.3 8.2 1.6

Empty fields in the table indicate non-applicability1Hemoglobin level based on g/dL2Anemia defined as hemoglobin < 11 g/dL in PSAC, and < 12 g/dL in SAC and NPW3Stunted defined as height-for-age Z-score <– 2SD; not stunted defined as height-for-age Z score –2SD to +2SD4Underweight defined as weight-for-age Z-score <– 2SD; not underweight defined as weight-for-age Z score –2SD to +2SD5Wasting or thinness defined as BMI-for-age <– 2SD among SAC 6–14 yr old6CED among NPW 20–49 yr old defined as BMI < 18.5kg/m2; non-CED defined as BMI ≥ 18.5 kg/m2 CED or thinness among 15–19 yr old defined as BMI-for-age <– 2SD7Deficient in serum retinol < 10 μg/dL for the three age groups8Deficient in serum zinc < 65 μg/dL in children < 10 yr old, < 66 μg/dL in females ≥ 10 yr old, and < 70 μg/dLin males ≥ 10 yr old*Significantly different across categorical variables at p < 0.05 using ANOVA (test of means); p < 0.05 using Pearson’s chi-squared test (test of proportion) **Assessed using the HFIAS



744

Appendix Table III. Multiple linear regression analyses assessing determinants of hemoglobin level in PSAC (6–59 mo old), SAC (6–14 yr old), and NPW of reproductive age (15–49 yr old): Philippines, 2013.

Covariates of hemoglobin

PSAC SAC NPW

Unstandardized β p-value 95% CI Unstandardized β p-value 95% CI Unstandardized β p-value 95% CI

Sex

Female (ref.: male) 0.03 0.707 -0.09; 0.13 -0.19 <0.001 -0.25; -0.12 - - -

Age

3–5 yr old (ref.: 6 mo – 2 yr old)

0.68 <0.001 0.58; 0.78 - - - - - -

12–14 yr old (ref.: 6 – 11 yr old)

- - - 0.49 <0.001 0.41; 0.56 - - -

26–49 yr old (ref.:15–25 yr old)

- - - - - - -0.15 <0.001 -0.23; -0.07

Maternal education1

(ref: no schooling)

Elementary -0.27 0.178 -0.67; 0.12 0.04 0.326 -0.08; 0.62 0.12 0.318 -0.16; 0.40

High school -0.19 0.329 -0.58; 0.20 0.25 0.164 -0.11; 0.60 0.07 0.618 -0.20; 0.34

Above high school -0.12 -0.539 -0.51; 0.27 0.26 0.162 -0.11; 0.62 0.08 0.636 -0.19; 0.35

Working status of mothers (ref.: not working)

Working 0.12 0.052 -0.00; 0.25 0.04 0.302 -0.04; 0.11 -0.01 0.969 -0.07; 0.06

Marital status (ref.: single)

Married - - - - - - -0.12 0.031 -0.20; -0.04

Live-in - - - - - - -0.12 0.171 -0.25; 0.01

Widow/widower - - - - - - -0.15 0.359 -0.40; 0.10

Separated/annulled/ divorced

- - - - - - -0.08 0.586 -0.28; 0.11

Micronutrient status

Serum retinol 0.01 0.015 0.00; 0.02 0.01 <0.001 0.01; 0.01 0.01 <0.001 0.01; 0.02

Serum zinc 0.00 0.659 -0.00;0.00 0.01 <0.001 0.00; 0.01 0.00 0.037 0.00; 0.01

Place of residence (ref.; urban)

Rural -0.01 0.921 -0.14; 0.13 -0.11 0.033 -0.20; -0.01 -0.03 0.548 -0.13; 0.07

Wealth quintile (ref.: poorest)

Poor -0.05 0.624 -0.27; 0.16 0.03 0.639 -0.09; 0.14 0.03 0.689 -0.09; 0.14

Middle 0.02 0.874 -0.19; 0.22 0.00 0.969 -0.13; 0.13 0.00 0.962 -0.13; 0.14

Rich -0.03 0.813 -0.25; 0.20 0.04 0.56 -0.10; 0.18 0.00 0.948 -0.12; 0.13

Richest 0.21 0.092 -0.03; 0.46 0.06 0.446 -0.09; 0.21 0.02 0.740 -0.14; 0.17

Source of drinking water (ref.: improved source)

Unimproved source 0.02 0.779 -0.10; 0.14 0.04 0.334 -0.05; 0.13 -0.02 0.329 -0.10; 0.05

Toilet facility (ref.: no toilet)

Water-sealed 0.06 0.589 -0.16; 0.29 0.07 0.375 -0.09; 0.23 0.04 0.760 -0.10; 0.18

Not water-sealed 0.00 0.972 -0.28; 0.27 -0.01 0.925 -0.18; 0.16 -0.08 0.440 -0.25; 0.08



745

Nutritional status

Underweight (ref: not underweight)

0.04 0.789 -0.25; 0.33 - - - - - -

Stunted (ref: not stunted)

-0.32 0.005 -0.55; -0.10 -0.15 0.025 -0.22; -0.00 - - -

Thin (ref: not thin) - - - -0.11 0.060 - - - -

CED (ref: no CED) - - - - - - -0.02 0.920 -0.12; 0.07

1Education refers to the level attained by mothers of PSAC and SAC; for women, it is the level attained by NPWRef. is reference categoryEmpty fields in the table indicate either non-applicability Significant at p < 0.05

Appendix Table III continuation . . .



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Documents

Determinants of Hemoglobin Status in Filipino Children and