Abstract
Background
Anaemia is a serious global public health problem that disproportionally affects children, adolescent girls, and women of reproductive age, especially pregnant women. Women of reproductive age are more vulnerable to anaemia, particularly severe and moderate anaemia leads to adverse outcomes among pregnant women. Despite continuous Government efforts, anaemia burden still poses a serious challenge in India. The objective of this study is to assess the trends in prevalence and determinants of severe and moderate anaemia among women of reproductive age between 15 and 49 years.
Method
We used three rounds of the large-scale National Family Health Survey (NFHS) India, conducted on a representative sample of households using a cross-sectional design across the country in 2005–06, 2015–16 and 2019–2021. We included all the women aged 15 to 49 years in our analysis. We used the same haemoglobin (Hb) cut-off values for all the three rounds of surveys to ensure comparability. Generalized linear regression analyses with log link were done. Survey weights were incorporated in the analysis.
Results
The prevalence of severe or moderate Anaemia (SMA) in non-pregnant women was 14.20%, 12.43% and 13.98%; it was 31.11%, 25.98% and 26.66% for pregnant women in 2006, 2016 and 2021 respectively. The decline in SMA prevalence was 1.54% in non-pregnant women, whereas it was 14.30% in pregnant women in 15 years. Women who were poor, and without any formal education had a higher risk for severe and moderate Anaemia.
Conclusion
Despite the intensive anaemia control program in India, SMA has not declined appreciably in non-pregnant women during the last two decades. Despite the decline, the prevalence of SMA was about 26% in pregnant women which calls for a comprehensive review of the existing anaemia control programmes and there must be targeted programmes for the most vulnerable and high-risk women such as rural, poor and illiterate women of reproductive age to reduce the burden of anaemia among them.
Introduction
Anaemia is one of the highly prevalent health conditions and a major risk factor contributing significantly to the global burden of disease [1]. According to the World Health Organization (WHO), Anaemia is defined as having haemoglobin (Hb) levels lower than 11.0, 12.0, and 13.0 g/dL in pregnant women, non-pregnant women and men, respectively. It disproportionally affects children, adolescent girls, and women of reproductive age, especially pregnant women [2]. Due to the persistent reduction in oxygen-carrying capacity, anaemia can significantly reduce the cognitive, physical and work capacities and is associated with reduced economic productivity, increased susceptibility to infections due to its effect on immunity, increased morbidity and mortality [3–5]. Among pregnant women, iron-deficient anaemia can lead to adverse pregnancy outcomes, including stillbirth, preterm delivery, low birth weight, and infant mortality [6–8]. Moreover, anaemia can be a risk or a prognostic factor for other diseases, such as tuberculosis and heart failure [9,10].
Globally, the anaemia prevalence in women of reproductive age was 29.9%; equivalent to over half a billion women aged 15–49 years in 2019. The prevalence was relatively higher in pregnant women with 36.5% compared to non-pregnant women (29.6%) [11]. The prevalence of anaemia among women of reproductive age in the South Asia region was 41%, it was 48% in pregnant women and 49% in non-pregnant women in 2019 [12]. According to National Family Health Survey (NFHS)–IV (2015–16), the prevalence of anemia among women aged 15 to 49 years was 53.1% it was the 5th highest among globally [13].
There have been consistent global efforts to address the burden of anaemia. For instance, the 65th World Health Assembly (WHA) in 2012 approved global targets for maternal, infant, and young child nutrition, with a commitment to halve anaemia prevalence in women of reproductive age (15–49 years) by 2025. Following this, WHO and UNICEF proposed extending this target to 2030 to align with the UN Sustainable Development Goals (SDGs) 2- End hunger, achieve food security and improved nutrition and promote sustainable agriculture. The Government of India has also been taking several efforts to address the burden of anaemia among women especially anaemia among pregnant women. The Anaemia Mukt Bharat (AMB) which was launched in 2018 as part of the Strengthened Nationwide Iron Plus Initiative Project aims to lower the prevalence of anaemia by 1 to 3 percentage points each year, targeting children and women of reproductive age group [14]. Despite the significant efforts, 2/3rd of all women of reproductive age in India are still having any form of anaemia (mild, moderate, and severe). Though, all types of anaemia must be given due importance, moderate and severe anaemia in non-pregnant women are to be treated with utmost care as significant health consequences are predominantly associated with moderate to severe anaemia [15]. In many cases, mild and asymptomatic anaemia require no management [16]. A recent study indicated that pregnant women with moderate and severe anaemia had higher risk for some adverse outcomes, including maternal shock, admission to the ICU, mortality, fetal growth restriction and stillbirth and increased risks were found among those with moderate or severe anaemia [17,18]. Severe anaemia is strongly correlated with maternal morbidity and mortality [19,20]. A study from central India highlighted similar risks from mild anaemia as well [21].
Besides, studies have widely reported the multiple risk factors associated with anaemia. For instance, rural residence; low socio-economic status such as eating <1 serving of meat/ week, farming and more number of children (>3 children); women with lower income level or wealth; lower education level; underweight women; women without toilet facilities or improved water facilities and women with more than one children had significantly higher risk for anaemia [22–27]. Though the exact link of BMI with anemia is controversial issue, several studies have highlighted that woman with higher BMI had greater likelihood of being anemic [28–30]. In addition, several clinical conditions, acute and chronic infections and diseases like Cancer, Chronic Kidney Disease, Malaria etc. reported to be associated with higher likelihood of anaemia [31–33].
Considering the high prevalence of anaemia among women in India, a focus on moderate to severe anaemia will be more appropriate to reduce the functional consequences and improve the overall health status of the women of reporductive health [15]. Therefore, our objective was to study the trends in prevalence and determinants of severe and moderate anaemia among women of reproductive age (15–49 years) using the three rounds of National Family Health Survey (NFHS-3, 4, and 5) which provides nationally representative cross-sectional data.
Methods
We used three rounds of the large scale NFHS, conducted on representative sample of households across the country in 2005–06 (NFHS-3), 2015–16 (NFHS-4) and 2019–2021 (NFHS-5). The data was abstracted from https://dhsprogram.com/data/dataset_admin. The cross-sectional surveys collected detailed information on population, health and nutrition.
Independent variables
The demographic, socioeconomic, cultural and behavioural covariates included in the analysis were age, place of residence, education, wealth, occupation, obesity, zone and parity. Age was categorised into four groups such as 15–19, 20–29, 30–39, and 40–49 years. Parity, defined as the number of children ever born, was categorised as 0, 1, 2, 3or more. Obesity was categorized as binary variable with BMI≥30.0 kg/m2. For wealth index, poorest and poor were combined as a category and rich and richest were combined as another category, but middle remains same. Education was categorized as no education, primary, secondary and higher education. Occupation of the respondent was classified as employed and unemployed. The states were grouped as north, east, west, south and north east [34].
Dependent variables
The outcome variable haemoglobin adjusted for altitude and smoking was measured in g/dl and categorized as mild, moderate and severe anaemia based on predefined cut-off values as recommended by WHO. The cut-off values of mild, moderate, and severe anaemia for pregnant women were 10.00–10.90 g/dl, 7.00–9.90 g/dl, and <7.00 g/dl respectively in all the three rounds of NFHS. Among non-pregnant women, the cut-off values of mild, moderate and severe anaemia were 10.0–11.9 g/dl, 7.0–9.9 g/dl and < 7.0 g/dl in NFHS-3 and 4. In NFHS-5, the cut-off values for non-pregnant women were revised as 11.00–11.90 g/dl, 8.00–10.90 g/dl and <8.00 g/dl for mild, moderate and severe anaemia respectively [35]. As the cut-off levels have been revised in NFHS-5, we analysed the data using the same cut-off levels used in the previous rounds and presented the results for better comparison. As the severe and moderate anaemia require programmatic importance, they were combined for adjusted analysis.
Total number of women aged between 15 and 49 was 124,385, 699,686, and 724,115 in NFHS 3, 4, and 5 surveys respectively. In NFHS 3, Hb was measured among 112,714 (91%) women, it was not conducted in the state of Nagaland (3896, 3%) and in other states Hb value was not available for 7,775 (6%) women. In NFHS 4 and 5 surveys, 684,911 (98%) and 690,153 (95%) women were tested for Hb respectively. However, the Hb value was not available for 14,775 (2%) and 33962 (5%) women from NFHS 4 and 5 surveys due to various reasons.
Statistical methods. The variables were presented as frequency and percent for pregnant and non-pregnant women separately. Generalized linear model was used with log link as the prevalence was over 10%. The survey weights were incorporated in the analyses, which are provided in the NFHS data. The dependent variable anaemia was categorised as bivariate (Moderate and Severe vs. Mild and Normal). The model was repeated with the same covariate separately for pregnant and non-pregnant women. Negelkerke R2 and Hosmer and Lemeshow Goodness of Fit test was used to assess the model fit. Data was analysed using STATA software version 16.0. The survey (svy) command was used to weight the data in the regression analysis. The effect size is presented as risk ratio (RR) and 95% confidence intervals.
Ethical considerations
Informed consent was obtained from participants at the time of interview, and further consent was obtained prior to blood testing as per the NFHS protocol. All survey participants were provided an informational leaflet at the time of anaemia testing; women diagnosed with severe anaemia were asked if they could be referred to local health services. The analysis was approved by Institutional Review Board of Christian Medical College, Vellore, India.
Results
Table 1 presents the prevalence of different levels of anaemia by the year of NFHS and pregnancy status. Considering similar Hb cut-off level for all three rounds, the prevalence of severe anaemia (SA) was about 1.56%, 1.01% and 1.17% in 2006, 2016 and 2021 respectively. However, in non-pregnant women the reported prevalence of SA was 2.64% in 2021, which is a significant increase from the previous rounds due to the revised Hb cut-off level. The prevalence of moderate anaemia (MoA) was 13.43%, 12.04% and 13.31% in 2006, 2016 and 2021 respectively. Both, SA and MoA prevalence declined in 2016 and increased in 2021. Mild anaemia (MA) indicated a marginal increase from 2006 to 2016 and 2021.
Table 1. Anaemia among women of reproductive age group by pregnancy status in NFHS 3, 4, and 5.
| Variables | Anaemia | ||||
|---|---|---|---|---|---|
| Severe | Moderate | Mild | Not anaemic | Total | |
| Over all | |||||
| NFHS 3 (2006) | 1763 (1.56) | 15138 (13.43) | 41749 (37.04) | 54064 (47.97) | 112714 |
| NFHS 4 (2016) | 6950 (1.01) | 82490 (12.04) | 263132 (38.42) | 332339 (48.52) | 684911 |
| NFHS 5 (2021) | 8078 (1.17) | 91856 (13.31) | 287865 (41.71) | 302354 (43.81) | 690153 |
| NFHS 5- New definition | 18221 (2.64) | 195685 (28.35) | 173893 (25.20) | 302354 (43.81) | 690153 |
| Non Pregnant | |||||
| NFHS 3 (2006) | 1650 (1.54) | 13597 (12.66) | 40402 (37.62) | 51748 (48.18) | 107397 |
| NFHS 4 (2016) | 6489 (0.99) | 74679 (11.44) | 255598 (39.14) | 316297 (48.43) | 653063 |
| NFHS 5 (2021) | 7657 (1.16) | 84995 (12.82) | 281356 (42.45) | 288828 (43.57) | 662836 |
| NFHS 5 -New definition | 17800 (2.69) | 188824 (28.49) | 167384 (25.25) | 288828 (43.57) | 662836 |
| Pregnant | |||||
| NFHS 3 (2006) | 113 (2.13) | 1541 (28.98) | 1347 (25.33) | 2316 (43.56) | 5317 |
| NFHS 4 (2016) | 461 (1.45) | 7811 (24.53) | 7534 (23.66) | 16042 (50.37) | 31848 |
| NFHS 5 (2021) | 421 (1.54) | 6861 (25.12) | 6509 (23.83) | 13526 (49.51) | 27317 |
Note: The definition for Anaemia has been changed for non-pregnant women only.
Prevalence of anaemia among non-pregnant women
The prevalence of SA was 1.54%. 0.99% and 1.16% in 2006, 2016 and 2021 respectively. According to the new definition there was about 2 times increase in the prevalence of SA in 15 years. The prevalence of moderate and mild anaemia also showed the similar trends. The prevalence of MoA was about 12.66%, 11.44% and 12.82% in 2006, 2016 and 2021 surveys respectively.
Prevalence of anaemic among pregnant women
The prevalence of SA was 2.13%, 1.45% and 1.54% in 2006, 2016 and 2021 surveys respectively. The prevalence of MoA was about 28.98%, 24.53% and 25.12% in 2006, 2016 and 2021 surveys respectively.
Prevalence of severe & moderate anaemia in non-pregnant and pregnant women by socio-economic, demographic variables
The prevalence of severe and moderate anaemia (SMA) is presented by covariates such as age, residence, education, wealth, obesity, zone, occupation and Parity. In Table 2, among non-pregnant women, the prevalence of SMA was nearly similar for all age categories in all three rounds of NFHS including the adolescent women aged 15 to19 years. Women without formal education, rural, economically poor, and women without any children reported higher prevalence of SMA compared to women with some formal education, urban and economically wealthy (middle or rich) women, and having at least one child in all the three rounds. Interestingly, employed women reported a higher prevalence of SMA compared to unemployed women. On the other hand, obese women indicated relatively less prevalence of SMA compared non-obese women. The North East region reported lower prevalence in 2016 and 2021as compared to other regions.
Table 2. Descriptive statistics for Anaemia among non-pregnant women.
|
Variable |
NFHS 3 (2006) | NFHS 4 (2016) | NFHS 5 (2021) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Severe & Moderate | Mild | Total | Severe & Moderate | Mild | Total | Severe & Moderate | Mild | Total | |
| n (%) | n (%) | n | n (%) | n (%) | n | n (%) | n (%) | N | |
| Age | |||||||||
| 15–19 | 2962 (14.32) | 7950 (38.44) | 20681 | 14250 (12.06) | 47586 (40.29) | 118123 | 16162 (14.32) | 50060 (44.36) | 112837 |
| 20–29 | 5042 (14.20) | 13545 (38.15) | 35509 | 25993 (12.39) | 83681 (39.88) | 209813 | 28289 (13.66) | 89426 (43.17) | 207149 |
| 30–39 | 4200 (13.85) | 11180 (36.87) | 30320 | 22126 (12.35) | 68519 (39.26) | 179094 | 25836 (13.89) | 77476 (41.65) | 185996 |
| 40–49 | 3043 (14.57) | 7727 (36.99) | 20887 | 18799 (12.87) | 55812 (38.22) | 146033 | 22365 (14.26) | 64394 (41.05) | 156854 |
| Type of Place of residence | |||||||||
| Urban | 6113 (12.77) | 17227 (36.00) | 47851 | 21821 (11.47) | 70785 (37.20) | 190289 | 19979 (12.34) | 64960 (40.13) | 161868 |
| Rural | 9134 (15.34) | 23175 (38.92) | 59546 | 59347 (12.83) | 184813 (39.94) | 462774 | 72673 (14.51) | 216396 (43.2) | 500968 |
| Highest educational level | |||||||||
| No education | 5952 (17.18) | 13941 (40.24) | 34645 | 26661 (14.39) | 76133 (41.11) | 185194 | 23882 (15.39) | 68152 (43.93) | 155149 |
| Primary | 2450 (15.76) | 5989 (38.51) | 15550 | 10726 (12.99) | 32473 (39.33) | 82573 | 11576 (14.74) | 33623 (42.83) | 78509 |
| Secondary | 5869 (12.63) | 16908 (36.40) | 46446 | 36724 (11.78) | 119937 (38.44) | 311982 | 46960 (13.89) | 143520 (42.43) | 338247 |
| Higher | 975 (9.07) | 3560 (33.13) | 10745 | 7057 (9.63) | 27055 (36.9) | 73314 | 10234 (11.25) | 36061 (39.66) | 90931 |
| Wealth | |||||||||
| Poor | 4993 (18.10) | 11764 (42.64) | 27589 | 35525 (13.49) | 110023 (41.79) | 263260 | 42437 (14.88) | 128411 (45.01) | 285295 |
| Middle | 3163 (15.37) | 7822 (38.01) | 20581 | 17411 (12.61) | 52242 (37.84) | 138063 | 19950 (14.26) | 57921 (41.41) | 139880 |
| Rich | 7091 (11.98) | 20816 (35.15) | 59227 | 28232 (11.21) | 93333 (37.08) | 251740 | 30265 (12.74) | 95024 (39.98) | 237661 |
| Obesity | |||||||||
| Non Obese | 14892 (14.40) | 39064 (37.78) | 103387 | 78304 (12.56) | 245336 (39.33) | 623766 | 88262 (14.10) | 266929 (42.64) | 625977 |
| Obese | 330 (8.50) | 1288 (33.20) | 3880 | 2729 (9.56) | 9992 (34.98) | 28567 | 4235 (11.76) | 14064 (39.05) | 36013 |
| Zone | |||||||||
| North | 4083 (13.30) | 10749 (35.02) | 30694 | 26300 (13.01) | 77384 (38.28) | 202137 | 30952 (14.22) | 88022 (40.44) | 217642 |
| East | 2498 (15.35) | 7750 (47.63) | 16272 | 15562 (13.24) | 55572 (47.28) | 117540 | 16376 (15.08) | 55721 (51.3) | 108609 |
| North East | 2173 (13.34) | 6226 (38.22) | 16288 | 7317 (7.96) | 28336 (30.84) | 91873 | 10203 (10.65) | 38263 (39.92) | 95841 |
| West | 3270 (13.73) | 8579 (36.01) | 23821 | 16382 (12.13) | 53775 (39.8) | 135120 | 19764 (14.47) | 60652 (44.42) | 136539 |
| South | 3223 (15.86) | 7098 (34.93) | 20322 | 11875 (13.77) | 31645 (36.7) | 86235 | 15357 (14.74) | 38698 (37.14) | 104205 |
| Occupation | |||||||||
| Unemployed | 8596 (13.61) | 23631 (37.43) | 63136 | 10487 (13.37) | 30391 (38.75) | 78431 | 9120 (13.61) | 27766 (41.43) | 67011 |
| Employed | 6638 (15.03) | 16733 (37.88) | 44170 | 4980 (14.68) | 13161 (38.79) | 33929 | 4804 (14.71) | 13290 (40.72) | 32639 |
| Parity | |||||||||
| No children | 241 (17.81) | 486 (35.92) | 1353 | 4334 (13.19) | 12549 (38.18) | 32866 | 4702 (14.93) | 13239 (42.03) | 31498 |
| Single Child | 1347 (12.35) | 4032 (36.96) | 10910 | 4562 (11.31) | 15597 (38.66) | 40345 | 5657 (13.53) | 17698 (42.33) | 41805 |
| Two Children | 8943 (14.02) | 23909 (37.49) | 63766 | 48259 (12.46) | 153599 (39.64) | 387507 | 59779 (14.23) | 180243 (42.9) | 420120 |
| 3+ Children | 4220 (14.89) | 10908 (38.49) | 28342 | 23367 (12.50) | 71829 (38.43) | 186917 | 21390 (13.19) | 67146 (41.42) | 162120 |
The prevalence of SMA among pregnant women by covariates is presented in Table 3. Among pregnant women who were aged 40–49 years indicated relatively higher SMA prevalence compared to other age groups especially in 2006, and 2016. Similar to non-pregnant women, rural pregnant women, women without formal education, economically poor had consistently higher prevalence of SMA compared to urban, women with some formal education, and wealthy. Besides, obese women indicated relatively lower prevalence of SMA compared to non-obese women.
Table 3. Descriptive statistics for Anaemia among pregnant women.
| Variables |
NFHS 3 (2006) | NFHS 4 (2016) | NFHS 5 (2021) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Severe & Moderate | Mild | Total | Severe & Moderate | Mild | Total | Severe & Moderate | Mild | Total | |
| n (%) | n (%) | n | n (%) | n (%) | n | n(%) | n(%) | n | |
| Age | |||||||||
| 15–19 | 283 (29.45) | 262 (27.26) | 961 | 932 (25.84) | 916 (25.40) | 3607 | 828 (28.76) | 755 (26.22) | 2879 |
| 20–29 | 1109 (30.93) | 893 (24.90) | 3586 | 5980 (25.83) | 5509 (23.80) | 23149 | 5255 (26.89) | 4687 (23.98) | 19546 |
| 30–39 | 243 (33.75) | 182 (25.28) | 720 | 1256 (26.32) | 1051 (22.02) | 4772 | 1129 (24.30) | 1015 (21.84) | 4647 |
| 40–49 | 19 (38.00) | 10 (20.00) | 50 | 104 (32.50) | 58 (18.13) | 320 | 70 (28.57) | 52 (21.22) | 245 |
| Type of place of residence | |||||||||
| Urban | 545 (27.78) | 504 (25.69) | 1962 | 1722 (22.73) | 1716 (22.65) | 7576 | 1158 (21.91) | 1153 (21.82) | 5285 |
| Rural | 1109 (33.06) | 843 (25.13) | 3355 | 6550 (26.99) | 5818 (23.97) | 24272 | 6124 (27.80) | 5356 (24.31) | 22032 |
| Highest educational level | |||||||||
| No education | 760 (38.58) | 496 (25.18) | 1970 | 2647 (32.95) | 1996 (24.85) | 8033 | 1529 (34.03) | 1127 (25.08) | 4493 |
| Primary | 246 (31.74) | 188 (24.26) | 775 | 1212 (28.67) | 972 (23) | 4227 | 903 (30.27) | 722 (24.2) | 2983 |
| Secondary | 571 (26.64) | 557 (25.99) | 2143 | 3732 (23.64) | 3781 (23.95) | 15790 | 4007 (25.94) | 3706 (23.99) | 15445 |
| Higher | 77 (17.95) | 106 (24.71) | 429 | 681 (17.93) | 785 (20.67) | 3798 | 843 (19.18) | 954 (21.7) | 4396 |
| Wealth | |||||||||
| Poor | 731 (39.07) | 482 (25.76) | 1871 | 4541 (30.04) | 3724 (24.64) | 15114 | 3975 (30.31) | 3314 (25.27) | 13114 |
| Middle | 345 (31.97) | 265 (24.56) | 1079 | 1590 (24.50) | 1575 (24.27) | 6490 | 1404 (25.33) | 1286 (23.2) | 5542 |
| Rich | 578 (24.42) | 600 (25.35) | 2367 | 2141 (20.90) | 2235 (21.82) | 10244 | 1903 (21.97) | 1909 (22.04) | 8661 |
| Obesity | |||||||||
| Non Obese | 1638 (31.34) | 1318 (25.22) | 5226 | 8076 (26.08) | 7352 (23.75) | 30962 | 7019 (26.80) | 6247 (23.85) | 26194 |
| Obese | 14 (16.09) | 28 (32.18) | 87 | 188 (21.68) | 176 (20.3) | 867 | 257 (23.53) | 254 (23.26) | 1092 |
| Zone | |||||||||
| North | 508 (30.46) | 386 (23.14) | 1668 | 2879 (27.47) | 2308 (22.02) | 10481 | 2302 (25.24) | 2025 (22.21) | 9119 |
| East | 313 (33.73) | 280 (30.17) | 928 | 1910 (29.70) | 1776 (27.62) | 6431 | 1734 (33.43) | 1483 (28.59) | 5187 |
| North East | 260 (30.99) | 197 (23.48) | 839 | 764 (17.06) | 903 (20.16) | 4479 | 937 (20.40) | 953 (20.74) | 4594 |
| West | 349 (31.30) | 282 (25.29) | 1115 | 1691 (27.02) | 1570 (25.08) | 6259 | 1517 (29.62) | 1257 (24.54) | 5122 |
| South | 224 (29.20) | 202 (26.34) | 767 | 652 (21.72) | 680 (22.65) | 3002 | 792 (24.04) | 791 (24.01) | 3295 |
| Occupation | |||||||||
| Unemployed | 1100 (30.27) | 918 (25.26) | 3634 | 1157 (27.19) | 1020 (23.97) | 4255 | 845 (27.25) | 706 (22.77) | 3101 |
| Employed | 553 (32.92) | 428 (25.48) | 1680 | 319 (29.70) | 255 (23.74) | 1074 | 222 (26.52) | 186 (22.22) | 837 |
| Parity | |||||||||
| No children | 10 (37.04) | 5 (18.52) | 27 | 177 (26.18) | 153 (22.63) | 676 | 146 (26.31) | 117 (21.08) | 555 |
| Single Child | 84 (24.00) | 92 (26.29) | 350 | 271 (21.93) | 259 (20.95) | 1236 | 261 (23.92) | 230 (21.08) | 1091 |
| Two Children | 938 (30.21) | 792 (25.51) | 3105 | 4906 (25.29) | 4606 (23.74) | 19402 | 4716 (26.90) | 4284 (24.44) | 17530 |
| 3+ Children | 578 (33.39) | 440 (25.42) | 1731 | 2869 (27.62) | 2488 (23.95) | 10387 | 2060 (26.21) | 1823 (23.19) | 7860 |
Factors associated with the severe and moderate anaemia among women of reproductive age
The results of multivariable analysis are presented in Table 4. In non-pregnant women, the risk of SMA in 2016 declined by 18% from 2006 and declined in 2021 by 7%. Wealth status, education, obesity and region were significantly associated with the prevalence of SMA. Poor and middle-class women had 16% (RR: 1.16; CI: 1.14–1.18) and 12% (RR: 1.12; CI: 1.10–1.14) higher risk of having SMA compared to rich women. Similarly, those who had no education (RR: 1.35; CI: 1.31–1.39) or primary (RR: 1.31; CI: 1.27–1.35) or secondary education (RR: 1.21; CI: 1.18–1.25) had higher risk for SMA compared to those women who had higher education. The non-obese women had 1.21 times (RR: 1.21; CI: 1.17–1.25) more risk of having SMA as compared to obese women. Compared to north region, North East women had less risk (RR: 0.92; CI: 0.89–0.95) of having SMA and women from East (RR: 1.07; CI: 1.05–1.10), West (RR: 1.06; CI: 1.03–1.08) and Southern region (RR: 1.21; CI: 1.19–1.24) had higher risk of having SMA.
Table 4. Multivariable analysis results (GLM with log link).
| Variables | Non Pregnant Women | Pregnant Women | ||
|---|---|---|---|---|
| Risk Ratio (95% CI) | P value | Risk Ratio (95% CI) | P value | |
| NFHS Survey | ||||
| NFHS 3 | Ref | Ref | ||
| NFHS 4 | 0.82 (0.80, 0.84) | <0.001 | 0.83 (0.79, 0.88) | <0.001 |
| NFHS 5 | 0.93 (0.90, 0.96) | <0.001 | 0.94 (0.88, 0.99) | 0.029 |
| Age | ||||
| 15–19 | 0.99 (0.97, 1.01) | 0.515 | 0.88 (0.74, 1.05) | 0.147 |
| 20–29 | 0.99 (0.97, 1.002) | 0.095 | 0.93 (0.78, 1.10) | 0.393 |
| 30–39 | 0.97 (0.96, 0.99) | 0.001 | 0.94 (0.79, 1.11) | 0.446 |
| 40–49 | Ref | Ref | ||
| Residence | ||||
| Rural | 1.01 (0.99, 1.03) | 0.219 | 1.02 (0.96, 1.08) | 0.531 |
| Urban | Ref | Ref | ||
| Wealth | ||||
| Poor | 1.16 (1.14, 1.18) | <0.001 | 1.28 (1.21, 1.35) | <0.001 |
| Middle | 1.12 (1.10, 1.14) | <0.001 | 1.17 (1.10, 1.24) | <0.001 |
| Rich | Ref | Ref | ||
| Education | ||||
| No education | 1.35 (1.31, 1.39) | <0.001 | 1.61 (1.48, 1.75) | <0.001 |
| Primary | 1.31 (1.27, 1.35) | <0.001 | 1.50 (1.37, 1.64) | <0.001 |
| Secondary | 1.21 (1.18, 1.25) | <0.001 | 1.33 (1.24, 1.44) | <0.001 |
| Higher | Ref | Ref | ||
| Obesity | ||||
| Non Obese | 1.21 (1.17, 1.25) | <0.001 | 1.01 (0.89, 1.13) | 0.912 |
| Obese | Ref | Ref | ||
| Zone | ||||
| North | Ref | Ref | ||
| East | 1.07 (1.05, 1.10) | <0.001 | 1.08 (1.03, 1.13) | 0.001 |
| North East | 0.92 (0.89, 0.95) | <0.001 | 0.93 (0.86, 0.99) | 0.028 |
| West | 1.06 (1.03, 1.08) | <0.001 | 1.10 (1.05, 1.16) | 0.000 |
| South | 1.21 (1.19, 1.24) | <0.001 | 0.98 (0.92, 1.04) | 0.451 |
Among pregnant women, the risk of SMA declined by 17% and 6% in 2016 and 2021 surveys respectively (p < .001). Like non-pregnant women, wealth status, education, and region except obesity were significantly associated with the prevalence of SMA. Poor and middle-class women had 28% (RR: 1.28; CI: 1.21–1.35) and 17% (RR: 1.17; CI: 1.10–1.24) higher risk of having SMA compared to rich women. Similarly, those who had no education (RR: 1.61; CI: 1.48–1.75) or primary (RR: 1.50; CI: 1.37–1.64) or secondary education (RR: 1.33; CI: 1.24–1.44) had higher risk for SMA compared to those women with higher education. Compared to north region, north east women had less risk (RR: 0.93; CI: 0.86–0.99) of having SMA and women from east (RR: 1.08; CI: 1.03–1.13), and west (RR: 1.10; CI: 1.05–1.16) had risk of having SMA.
Discussion
Anaemia is largely preventable and easily treatable if the determinants at the local and national level are identified, appropriate strategies are devised and implemented to combat anaemia recognising its multi factorial etiology [24]. The findings of the last three rounds of NFHS in India indicated that the prevalence of any anaemia which includes severe, moderate and mild anaemia among women of reproductive age increased significantly from 52% to 56% in 15 years, though there was a slight decline in 2016. According to WHO, the current situation falls under the severe category of public health significance (prevalence >40%) [36]. It is evident that there has been little or no progress in reducing anaemia among women over the past two decades. Especially, the prevalence of severe and moderate anaemia (SMA) remains almost similar during the last 15 years among non-pregnant women; however it declined significantly among pregnant women (14% decline in 15 years). At the same time, prevalence of SMA was still considerably high at 27% among pregnant women as compared to non-pregnant women which has enormous programmatic importance and implications in the country. These trends and patterns were almost similar across all socioeconomic groups. The increase in the prevalence of any anaemia and the consistent high prevalence of SMA over the last 15 years, despite the comprehensive anaemia policy framework, intensive programmatic efforts of the state and central governments, significant economic development and increase in the investment in health is a real concern. This could be due to the poor implementation and targeting that lead to poor coverage of potential beneficiaries of the National Anaemia Control Programme (NACP) and National Iron plus Initiative (NIPI) guidelines [37,38].
Importantly, almost half of the pregnant women in India had any anaemia and over a quarter of them (27%) had SMA as per the NFHS 2021, which is the highest prevalence of anaemia in pregnancy and the largest number of anaemia pregnant women worldwide [39]. Despite the current higher prevalence of SMA, the significant decline of it over the last 15 years among pregnant women could be due the focused anaemia control programmes among pregnant women in India. Moreover, there have been significant improvements in the nutrition and health of women, increasing utilisation of antenatal care and iron and folic acid supplementation, increasing use of contraception, as well as increased age at marriage and decreased total fertility rate over the years [37]. In specific, the previous rounds of NFHS have indicated improvement in coverage of iron-folic acid supplementation and ANC which could have had an effect in the reduction of SMA among pregnant women [40].
The study also revealed that anaemia especially SMA disproportionally affects the socio-economically vulnerable women of reproductive age group in the country. For instance, the higher prevalence of SMA among illiterate, rural, and economically poor among both pregnant and non-pregnant women indicates the persistence inequalities in the health status of women which could be due to the inequalities in coverage and access to anaemia control interventions among these groups. These findings corroborate with several other studies carried out in India and other less-developed and developing countries that indicate that anaemia disproportionately affect the rural, poor, less educated and other socially vulnerable population [41–44]. The NFHS indicated a higher prevalence of SMA among women without any children, however, it indicated an increasing trend with number of children. This pattern in agreement with several other studies that highlighted high parity as a risk factor for developing severity of iron deficiency anaemia in pregnancy [45–47].
Interestingly, employed pregnant and non-pregnant women reported a higher prevalence of SMA compared to unemployed women in all the three rounds of NFHS, except pregnant women in 2021. Though employment and socio-economic status of the women are correlated, the reasons for the higher rates of SMA among unemployed women while the prevalence of SMA was higher among illiterate and poor women are not clearly known and need to be studied further. On the other hand, obese women reported low prevalence of SMA compared to non-obese women which is in corroboration with numerous studies across the word [29,48]. However, several other studies have indicated either no difference or higher prevalence of anaemia among obese women [48]. A cross-sectional study conducted in Israel in 2003 showed a higher prevalence of iron deficiency in overweight and obese children and adolescents [49]. Few other studies reported an increase in the prevalence of iron deficiency in obese adults with significantly lower serum iron level and higher soluble transferrin receptor level than non-obese adults [50,51].
Limitations
Several studies have reported significant association between anaemia and many diseases, clinical conditions, and infections. However, we could not include them in our analysis as the focus of the paper was limited to the burden of severe and moderate anaemia and their socio-economic and demographic correlates. Being a cross sectional study, the cause-and-effect relationship could not be established. For instance, there is a strong association between socio-economic situation and anaemia, which could be bidirectional. Though systems are calibrated against standard tool, the upgraded model of analyser to measure Hb used in subsequent surveys could have affected the Hb measurements during the different surveys [27].
Conclusion
The analysis of three rounds of NFHS indicated that there has been little or no progress in the reduction of anaemia, despite the intensive programmatic efforts in the country. Especially, the consistent high prevalence of severe or moderate anaemia among women of reproductive age groups over the past two decades is a serious concern which would lead to several complications and consequences. The reduction of severe and moderate anaemia among pregnant women could be due to the programmatic efforts. However, SMA prevalence is unacceptably high among pregnant women compared to non-pregnant women which call for urgent targeted programmes among pregnant women to accelerate decline in anaemia in pregnancy. Universal testing, measures for reducing anaemia and early initiation of treatment in pregnant women are critical to combat the issue among pregnant women. Similarly, targeted efforts are required to address the consistent problem of SMA among non-pregnant women of reproductive age group. The analysis clearly indicated that women’s education and socio-economic improvement of women are the most important determinants of anaemia control among women of reproductive age group which must be addressed through appropriate structural interventions to improve and ensure universal coverage of anaemia control programmes in the country. The study also indicated regional variability in terms of severe and moderate anaemia which need to be studied further developing appropriate regional specific strategies. Considering the high proportion of mild anaemia, policies and programs aiming at reducing the severe and moderate anaemia will be more effective and relevant to improve the overall health and productivity of women in India.
Acknowledgments
We thank DHS and ICF macro for access to the data used in this analysis.
Abbreviations
- WHO
World Health Organization
- Hb
Haemoglobin
- NFHS
National Family Health Survey
- RR
Risk ratio
- SA
Severe Anaemia
- MoA
Moderate Anaemia
- MA
Mild Anaemia
- SMA
Severe and Moderate anaemia
Data Availability
All data files are available from the https://dhsprogram.com/data/dataset_admin database.
Funding Statement
The author(s) received no specific funding for this work.
References
- 1.Safiri S, Kolahi AA, Noori M, Nejadghaderi SA, Karamzad N, Bragazzi NL, et al. Burden of anemia and its underlying causes in 204 countries and territories, 1990–2019: results from the Global Burden of Disease Study 2019. J Hematol Oncol. 2021;14(1):185. Epub 2021/11/06. doi: 10.1186/s13045-021-01202-2 ; PubMed Central PMCID: PMC8567696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva: Vitamin and Mineral Nutrition Information System, World Health Organization, 2011. [Google Scholar]
- 3.Chaparro CM, Suchdev PS. Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries. Ann N Y Acad Sci. 2019;1450(1):15–31. Epub 2019/04/23. doi: 10.1111/nyas.14092 ; PubMed Central PMCID: PMC6697587. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Kinyoki D, Osgood-Zimmerman AE, Bhattacharjee NV, Schaeffer LE, Lazzar-Atwood A, Lu D, et al. Anemia prevalence in women of reproductive age in low- and middle-income countries between 2000 and 2018. Nature Medicine. 2021;27(10):1761–82. doi: 10.1038/s41591-021-01498-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Teshale AB, Tesema GA, Worku MG, Yeshaw Y, Tessema ZT. Anemia and its associated factors among women of reproductive age in eastern Africa: A multilevel mixed-effects generalized linear model. PLOS ONE. 2020;15(9):e0238957. doi: 10.1371/journal.pone.0238957 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Suryanarayana R, Chandrappa M, Santhuram AN, Prathima S, Sheela SR. Prospective study on prevalence of anemia of pregnant women and its outcome: A community based study. J Family Med Prim Care. 2017;6(4):739–43. Epub 2018/03/23. doi: 10.4103/jfmpc.jfmpc_33_17 ; PubMed Central PMCID: PMC5848390. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Rahman MA, Khan MN, Rahman MM. Maternal anaemia and risk of adverse obstetric and neonatal outcomes in South Asian countries: A systematic review and meta-analysis. Public Health in Practice. 2020;1:100021. doi: 10.1016/j.puhip.2020.100021 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Zhang Q, Lu X-M, Zhang M, Yang C-Y, Lv S-Y, Li S-F, et al. Adverse effects of iron deficiency anemia on pregnancy outcome and offspring development and intervention of three iron supplements. Scientific Reports. 2021;11(1):1347. doi: 10.1038/s41598-020-79971-y [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Gelaw Y, Getaneh Z, Melku M. Anemia as a risk factor for tuberculosis: a systematic review and meta-analysis. Environmental Health and Preventive Medicine. 2021;26(1):13. doi: 10.1186/s12199-020-00931-z [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Anand IS. Anemia and Chronic Heart Failure: Implications and Treatment Options. Journal of the American College of Cardiology. 2008;52(7):501–11. doi: 10.1016/j.jacc.2008.04.044 [DOI] [PubMed] [Google Scholar]
- 11.WHO. Anaemia in women and children Geneva: World Health Organization; 2022. WHO Global Anaemia estimates, 2021 Edition:[Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children#:~:text=In%202019%2C%20global%20anaemia%20prevalence,39.1%25)%20in%20pregnant%20women. [Google Scholar]
- 12.WorldBank. Prevalence of anemia among non-pregnant women (% of women ages 15–49)—South Asia. 2022. [Google Scholar]
- 13.ICF IIfPSIa. National Family Health Survey (NFHS-4), 2015–16. Mumbai, India: 2017. [Google Scholar]
- 14.Bhatia PV, Sahoo D, Parida S. India steps ahead to curb anemia: Anemia Mukt Bharat. Indian Journal of Community Health. 2018;30:312–6. doi: 10.47203/IJCH.2018.v30i04.002 [DOI] [Google Scholar]
- 15.Rammohan A, Awofeso N, Robitaille M-C. Addressing Female Iron-Deficiency Anaemia in India: Is Vegetarianism the Major Obstacle? ISRN Public Health. 2012;2012:765476. doi: 10.5402/2012/765476 [DOI] [Google Scholar]
- 16.Turner J, Parsi M, Badireddy M. Anemia. StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.; 2022. [Google Scholar]
- 17.Shi H, Chen L, Wang Y, Sun M, Guo Y, Ma S, et al. Severity of Anemia During Pregnancy and Adverse Maternal and Fetal Outcomes. JAMA Network Open. 2022;5(2):e2147046–e. doi: 10.1001/jamanetworkopen.2021.47046 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.on behalf of the Child Health Epidemiology Reference G. Moderate to Severe, but Not Mild, Maternal Anemia Is Associated with Increased Risk of Small-for-Gestational-Age Outcomes. The Journal of Nutrition. 2012;142(2):358–62. doi: 10.3945/jn.111.149237 [DOI] [PubMed] [Google Scholar]
- 19.Daru J, Zamora J, Fernández-Félix BM, Vogel J, Oladapo OT, Morisaki N, et al. Risk of maternal mortality in women with severe anaemia during pregnancy and post partum: a multilevel analysis. The Lancet Global Health. 2018;6(5):e548–e54. doi: 10.1016/S2214-109X(18)30078-0 [DOI] [PubMed] [Google Scholar]
- 20.Sinha A, Adhikary M, Phukan JP, Kedia S, Sinha T. A study on anemia and its risk factors among pregnant women attending antenatal clinic of a rural medical college of West Bengal. Journal of Family Medicine and Primary Care. 2021;10(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Patel A, Prakash AA, Das PK, Gupta S, Pusdekar YV, Hibberd PL. Maternal anemia and underweight as determinants of pregnancy outcomes: cohort study in eastern rural Maharashtra, India. BMJ Open. 2018;8(8):e021623. doi: 10.1136/bmjopen-2018-021623 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sunuwar DR, Singh DR, Chaudhary NK, Pradhan PMS, Rai P, Tiwari K. Prevalence and factors associated with anemia among women of reproductive age in seven South and Southeast Asian countries: Evidence from nationally representative surveys. PLOS ONE. 2020;15(8):e0236449. doi: 10.1371/journal.pone.0236449 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Debnath A, Debbarma A, Debbarma SK, Bhattacharjya H. Proportion of anaemia and factors associated with it among the attendees of the antenatal clinic in a teaching institute of northeast India. J Family Med Prim Care. 2021;10(1):283–8. Epub 2021/05/22. doi: 10.4103/jfmpc.jfmpc_1499_20 ; PubMed Central PMCID: PMC8132755. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Osborn AJ, Muhammad GM, Ravishankar SL, Mathew AC. Prevalence and correlates of anemia among women in the reproductive age (15–49 years) in a rural area of Tamil Nadu: An exploratory study. J Educ Health Promot. 2021;10:355. Epub 2021/11/12. doi: 10.4103/jehp.jehp_1526_20 ; PubMed Central PMCID: PMC8552273. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Talukder A, Paul N, Khan ZI, Ahammed B, Haq I, Ali M. Risk factors associated with anemia among women of reproductive age (15–49) in Albania: A quantile regression analysis. Clinical Epidemiology and Global Health. 2022;13:100948. doi: 10.1016/j.cegh.2021.100948 [DOI] [Google Scholar]
- 26.Stephen G, Mgongo M, Hussein Hashim T, Katanga J, Stray-Pedersen B, Msuya SE. Anaemia in Pregnancy: Prevalence, Risk Factors, and Adverse Perinatal Outcomes in Northern Tanzania. Anemia. 2018;2018:1846280. Epub 2018/06/02. doi: 10.1155/2018/1846280 ; PubMed Central PMCID: PMC5954959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Balarajan YS, Fawzi WW, Subramanian SV. Changing patterns of social inequalities in anaemia among women in India: cross-sectional study using nationally representative data. BMJ Open. 2013;3(3). Epub 2013/03/22. doi: 10.1136/bmjopen-2012-002233 ; PubMed Central PMCID: PMC3612779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Kamruzzaman M. Is BMI associated with anemia and hemoglobin level of women and children in Bangladesh: A study with multiple statistical approaches. PLoS One. 2021;16(10):e0259116. Epub 2021/10/29. doi: 10.1371/journal.pone.0259116 ; PubMed Central PMCID: PMC8553127. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Qin Y, Melse-Boonstra A, Pan X, Yuan B, Dai Y, Zhao J, et al. Anemia in relation to body mass index and waist circumference among chinese women. Nutrition Journal. 2013;12(1):10. doi: 10.1186/1475-2891-12-10 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Ghose B, Yaya S, Tang S. Anemia Status in Relation to Body Mass Index Among Women of Childbearing Age in Bangladesh. Asia Pacific Journal of Public Health. 2016;28(7):611–9. doi: 10.1177/1010539516660374 [DOI] [PubMed] [Google Scholar]
- 31.Madu AJ, Ughasoro MD. Anaemia of Chronic Disease: An In-Depth Review. Medical Principles and Practice. 2017;26(1):1–9. doi: 10.1159/000452104 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Sumbele IUN, Kimbi HK, Ndamukong-Nyanga JL, Nweboh M, Anchang-Kimbi JK, Lum E, et al. Malarial Anaemia and Anaemia Severity in Apparently Healthy Primary School Children in Urban and Rural Settings in the Mount Cameroon Area: Cross Sectional Survey. PLOS ONE. 2015;10(4):e0123549. doi: 10.1371/journal.pone.0123549 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Chhabra S, Kashyap A, Bhagat M, Mahajan R, Sethi S. Anemia and Nutritional Status in Tuberculosis Patients. International Journal of Applied and Basic Medical Research. 2021;11(4). doi: 10.4103/ijabmr.ijabmr_76_21 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Mammen JJ, Asirvatham ES, Lakshmanan J, Sarman CJ, Pandey A, Ranjan V, et al. The clinical demand and supply of blood in India: A National level estimation study. PLOS ONE. 2022;17(4):e0265951. doi: 10.1371/journal.pone.0265951 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.ICF IIfPSIa. National Family Health Survey (NFHS-5), 2019–21: India. Mumbai: IIPS, 2021. [Google Scholar]
- 36.WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Vitamin and Mineral Nutrition Information System. Geneva: World Health Organization, 2011. Contract No.: WHO/NMH/NHD/MNM/11.1. [Google Scholar]
- 37.Kalaivani K, Ramachandran P. Time trends in prevalence of anaemia in pregnancy. Indian J Med Res. 2018;147(3):268–77. Epub 2018/06/21. doi: 10.4103/ijmr.IJMR_1730_16 ; PubMed Central PMCID: PMC6022385. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Kapil U, Kapil R, Gupta A. National Iron Plus Initiative: Current status & future strategy. Indian J Med Res. 2019;150(3):239–47. Epub 2019/11/14. doi: 10.4103/ijmr.IJMR_1782_18 ; PubMed Central PMCID: PMC6886130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Ramachandran P preventation& management of anaemia in pregnancy: Multi-pronged integrated interventions may pay rich dividends. Indian J Med Res. 2021;154(1):12–5. Epub 2021/11/17. doi: 10.4103/ijmr.IJMR_994_20 ; PubMed Central PMCID: PMC8715698. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Joe W, Patel N, Alambusha R, Kulkarni B, Yadav K, Sethi V. Coverage of iron and folic acid supplementation in India: progress under the Anemia Mukt Bharat strategy 2017–20. Health Policy Plan. 2022;37(5):597–606. Epub 2022/03/09. doi: 10.1093/heapol/czac015 ; PubMed Central PMCID: PMC9113188. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Hoque M, Hoque E, Kader SB. Risk factors for anaemia in pregnancy in rural KwaZulu-Natal, South Africa: Implication for health education and health promotion. South African Family Practice. 2009;51(1):68–72. doi: 10.1080/20786204.2009.10873811 [DOI] [Google Scholar]
- 42.Adamu AL, Crampin A, Kayuni N, Amberbir A, Koole O, Phiri A, et al. Prevalence and risk factors for anemia severity and type in Malawian men and women: urban and rural differences. Population Health Metrics. 2017;15(1):12. doi: 10.1186/s12963-017-0128-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Wirth JP, Woodruff BA, Engle-Stone R, Namaste SM, Temple VJ, Petry N, et al. Predictors of anemia in women of reproductive age: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2017;106(Suppl 1):416s–27s. Epub 2017/06/16. doi: 10.3945/ajcn.116.143073 ; PubMed Central PMCID: PMC5490645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Imai K. Parity-based assessment of anemia and iron deficiency in pregnant women. Taiwanese Journal of Obstetrics and Gynecology. 2020;59(6):838–41. doi: 10.1016/j.tjog.2020.09.010 [DOI] [PubMed] [Google Scholar]
- 45.Al-Farsi YM, Brooks DR, Werler MM, Cabral HJ, Al-Shafei MA, Wallenburg HC. Effect of high parity on occurrence of anemia in pregnancy: a cohort study. BMC Pregnancy and Childbirth. 2011;11(1):7. doi: 10.1186/1471-2393-11-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Imai K. Parity-based assessment of anemia and iron deficiency in pregnant women. Taiwan J Obstet Gynecol. 2020;59(6):838–41. Epub 2020/11/22. doi: 10.1016/j.tjog.2020.09.010 . [DOI] [PubMed] [Google Scholar]
- 47.Siddiqui MZ, Goli S, Reja T, Doshi R, Chakravorty S, Tiwari C, et al. Prevalence of Anemia and Its Determinants Among Pregnant, Lactating, and Nonpregnant Nonlactating Women in India. SAGE Open. 2017;7(3):2158244017725555. doi: 10.1177/2158244017725555 [DOI] [Google Scholar]
- 48.Alshwaiyat NM, Ahmad A, Wan Hassan WMR, Al-Jamal HAN. Association between obesity and iron deficiency (Review). Exp Ther Med. 2021;22(5):1268. Epub 2021/10/02. doi: 10.3892/etm.2021.10703 ; PubMed Central PMCID: PMC8456489. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Pinhas-Hamiel O, Newfield RS, Koren I, Agmon A, Lilos P, Phillip M. Greater prevalence of iron deficiency in overweight and obese children and adolescents. Int J Obes Relat Metab Disord. 2003;27(3):416–8. Epub 2003/03/12. doi: 10.1038/sj.ijo.0802224 . [DOI] [PubMed] [Google Scholar]
- 50.Yanoff LB, Menzie CM, Denkinger B, Sebring NG, McHugh T, Remaley AT, et al. Inflammation and iron deficiency in the hypoferremia of obesity. Int J Obes (Lond). 2007;31(9):1412–9. Epub 2007/04/18. doi: 10.1038/sj.ijo.0803625 ; PubMed Central PMCID: PMC2266872. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Menzie CM, Yanoff LB, Denkinger BI, McHugh T, Sebring NG, Calis KA, et al. Obesity-related hypoferremia is not explained by differences in reported intake of heme and nonheme iron or intake of dietary factors that can affect iron absorption. J Am Diet Assoc. 2008;108(1):145–8. Epub 2007/12/25. doi: 10.1016/j.jada.2007.10.034 ; PubMed Central PMCID: PMC2267256. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data files are available from the https://dhsprogram.com/data/dataset_admin database.