Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu

Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu Vigneswari Aravindalochanan, Sheela Sinharoy, Gurusamy Thangavel, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5280307/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Mar, 2026 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 11 You are reading this latest preprint version Abstract Background Anaemia during pregnancy is a major public health concern in India. Biomass fuel use is common in a few rural areas of Tamil Nadu state and there is growing evidence that its use is associated with anaemia. The aim of this study was to estimate the prevalence of anaemia and to identify the coexisting determinants of anaemia among exclusive biomass-using rural pregnant women. Methods We used baseline data collected from 799 rural pregnant women with a gestational age between 9 and 20 weeks from two districts of Tamil Nadu, India who were enrolled in a multi-country randomised controlled trial of a liquefied petroleum gas intervention. Haemoglobin (Hb) was measured in capillary blood using the Haemocue-201 point of case device. Hb was categorized as normal (Hb ≥ 11g/dL), mild anaemia (Hb 10.0-10.9g/dL), moderate anaemia (Hb 7.0-9.9g/dL), or severe anaemia (Hb < 7.0g/dL). Multinomial logistic regression was used to identify factors associated with anaemia. Results Overall prevalence of any anaemia was 66.7% (95% confidence interval: 63.4–70%) and that of mild anaemia was 31.9% (28.7–35.1%), moderate anaemia 33.7% (30.4–36.9%), and severe anaemia was 1.1% (0.6–2.2%) Wealth index, body mass index classified as underweight (< 18.5 kg/m 2 ), being a multigravida, and hand wash area not observed were associated with a significantly higher odds of anaemia. Conclusion This exclusive biomass using pregnant women had quite higher rates of anaemia and underweight than that of rural estimates from recent NFHS reports for Tamil Nadu state indicating the need to improve their overall anthropometric status and anaemia together emphasizing on healthy dietary habits. Anemia Pregnant women South India Rural Biomass users Figures Figure 1 Figure 2 Figure 3 Introduction Maternal and child malnutrition is a leading risk factor for disease burden, contributing to 11.6% of all annual global disability-adjusted life years [ 1 ]. Anaemia during pregnancy is a major public health concern due to its impact on maternal and child health such as increased risk of infection, preterm delivery, low birth weight, infant mortality, and poor growth and development of children [ 2 – 4 ]. Globally, 38% of pregnant women are anaemic [ 5 ] with South Asian countries carrying the largest burden. In India, home to one-fifth of the world’s population, the most recent National Family Health Survey (NFHS-5) conducted in 2019–2021 indicated that 52.2% of pregnant women were anaemic [ 6 ]. Most worrying is that the prevalence of anemia among pregnant women has increased, from 50.4% in 2015–2016, [ 7 ] despite widespread iron supplementation. These findings support the complex aetiology of maternal anaemia, with factors other than micronutrient intake playing a role. The study on subnational variations showed that several states in India are not on-track to meet National Nutrition Program (NNM) targets by 2022 or the UN Sustainable Development Goals by 2030 [ 8 ]. Similar to the national estimates, the NFHS-4 to NFHS- 5 reports show an increase in anaemia prevalence for pregnant women in Tamil Nadu state as well[ 6 , 7 ]. A main driver of anaemia among pregnant women is an increased physiological iron requirement during pregnancy, combined with inadequate iron intake and absorption, especially among those following vegetarian diets. Social drivers of anaemia in rural pregnant women include socio-economic conditions, maternal education, age at pregnancy. Environmental drivers of anaemia include poor sanitation conditions, including the practice of open defaecation [ 9 ], inadequate access to safe water [ 10 ] and use of biomass fuels for cooking [ 11 ]. These environmental conditions can lead to chronic systemic inflammation developed by long-term exposure to pathogens and pollutants [ 12 ]. Anaemia of inflammation, like iron deficiency anaemia, is among the most prevalent forms of anaemia worldwide [ 13 ]. Gaps remain in our knowledge of the relative contribution of these factors to the prevalence of anaemia among pregnant women in rural India. To date, studies of anaemia in rural India have focused on individual determinants of anaemia in isolation rather than looking at them jointly [ 14 – 23 ]. Evidence is also lacking specific to anaemia among pregnant women in rural Tamil Nadu state. Therefore, the objective of our study was to assess the prevalence of anaemia and to identify any coexisting determinants among exclusive biomass-using pregnant women in two rural sites in Tamil Nadu state using a conceptual framework for anaemia [ 24 , 25 ]. Methods We used baseline data from pregnant women who were recruited to participate in the India site of the Household Air Pollution Intervention Network (HAPIN) trial, a multi-centre randomised controlled trial implemented in four countries (Guatemala, India, Peru and Rwanda) to assess the effect of a liquefied petroleum gas stove intervention. Details of the trial protocol [ 26 ] and rationale for the selection of study sites in India [ 27 ] are provided elsewhere. For the India site, study participants were identified from lists of pregnant women registered for routine antenatal care at health facilities in two districts in Tamil Nadu: Kallakurichi (formerly Villupuram) and Nagapattinam. A total of 799 pregnant women were enrolled between April 2018 and November 2019. Eligibility criteria included: aged 18–35 years, ultrasound-confirmed gestational age between 9 and 20 weeks and lived in a household that was using biomass as their primary cooking fuel. Sociodemographic information was self-reported through surveys and included mother’s education, occupation, father’s education, household assets, access to sanitation facility, access to safe drinking water, self-reported treatment of drinking water, observable hand washing area with soap, and women’s autonomy in decision making. The household wealth categories were calculated based on household asset ownership and classified them into five national wealth indices [ 28 ]. Parity, birth spacing and details of consumption of micronutrient supplements were captured from the medical records. Haemoglobin (Hb) was measured in capillary blood using point-of-care testing (HemoCue® 201 + ) using standard procedure [ 29 ]. WHO criteria for anaemia in pregnancy were used to categorize participants as normal (Hb ≥ 11g/dL), mild (Hb 10.0–10.9 g/dL), moderate (Hb 7.0–9.9 g/dL), and severe anemia (Hb < 7.0 g/dL) [ 30 ]. Anaemia was treated as a categorical variable in all analyses [ 30 ]. Height (Seca 213 stadiometer) and weight (Seca 876 digital scale) were measured by trained enumerators following standardised procedures. Two readings of height were recorded to 0.1 cm and a third measurement was taken if the difference between the first two readings exceeded 1 cm. Similarly, two weight measurements were taken and recorded to 0.1 kg. If the difference between two measurements exceeded 0.5 kg, a third measurement was performed. Averages of the height and weight measurements were used to calculate body mass index (BMI) and categorised as normal (18.5 to 24.9 kg/m 2 ), underweight (< 18.5 kg/m 2 ), overweight (25 to 29.9 kg/m 2 ), and obese (≥ 30 kg/m 2 ) as per WHO classification [ 31 ]. Household food insecurity and dietary diversity were measured using the food insecurity experience scale (FIES) and minimum dietary diversity score for women (MDD-W), respectively [ 32 ]. FIES consists of eight questions on access to adequate food in the participant’s household in the past 30 days. Scores for the eight questions were summed and households were classified as being food secure (0), moderately food insecure (1 to 3), or severely food insecure (> 3). Data collection for the MDD-W involves an open recall of foods consumed in the previous day or night. For HAPIN, the MDD-W questionnaire was adapted to cover the previous 30 days, to align with the FIES. In MDD-W, ten food groups are included in the score: grains, white roots and tubers, and plantains; pulses (beans, peas, and lentils); nuts and seeds; dairy; meat, poultry, and fish; eggs; dark green leafy vegetables; other vitamin A-rich fruits and vegetables; other vegetables; and other fruits. A score of “1” was entered for those who reported a daily intake of each food group and others were given with the scores of “0”. The total is the sum of the scores for the above ten food groups and that could take any value between minimum of 0 to maximum of 10. Further, the women with total scores ≥ 5 were categorised as achieving minimum dietary diversity and those with scores of < 5 were categorised as not achieving minimum dietary diversity. Physical activity of women was recorded using International Physical Activity Questionnaire –short form. The total metabolic equivalent of task (METs) per week was calculated. Other risk factors such as alcohol and tobacco use were self-reported. Statistical Analysis: All analyses were conducted using R software (R Foundation for Statistical Computing, Vienna, Austria). Bivariate logistic regression was used to identify variables that were associated with anaemia at a level of p < 0.2, which were subsequently included in multivariable analysis. Multinomial logistic regressions were used to examine associations between these variables and any anaemia in general, and with grades of anaemia, respectively. Adjusted odds ratios (OR) are reported with 95% confidence intervals (CI). Results Sociodemographic and obstetric details of pregnant women are shown in Table 1 . The mean ± SD participant’s age was 23.9 ± 3.8 years. Overall, 72% of the study participants were either in the first or second lowest categories of the National wealth index. Half of the pregnant women in Villupuram had neither any formal education nor completed primary school, whereas over 45% of the participants from Nagapattinam had completed secondary school and higher. Similar differences were also observed in father’s education. Agriculture (87%) was reported to be the major occupation of participants in Villupuram whereas almost all (97%) were homemakers in Nagapattinam. The mean gestational weeks at the time of assessment was 16 weeks. Around 50% were primi gravidae; 40% of non-primi had children < 3 years old. Table 1 Socio Demographic and obstetric details of the pregnant women living in rural areas of two districts in Tamil Nadu, India Description Villupuram Nagapattinam Total p-value N = 400 N = 399 N = 799 Age (Years) [Mean ± SD] 23.0 ± 3.4 24.8 ± 4 23.9 ± 3.8 < 0.001 Education-self [n %] No formal education/ Primary school incomplete 207(51.7) 78(19.5) 285(35.7) 0.002 High school incomplete 100(25.0) 127(31.8) 227(28.4) High school complete and other higher education 93(23.3) 194(48.6) 287(35.9) Education -Spouse [n%] No formal education or Primary school incomplete 183(45.8) 121(30.3) 304(38) < 0.001 Primary school complete 66(16.5) 56(14) 122(15.3) Secondary school complete or Vocational 49(12.3) 77(19.3) 126(15.8) Secondary school incomplete 52(13.0) 83(20.8) 135(16.9) Some college or university 47(11.8) 61(15.3) 108(13.5) Occupation-self [n%] Agriculture 337(84.3) 1(0.2) 338(42.3) < 0.001 Household work 45(11.3) 387(97) 432(54.1) Other 16(4.5) 11(2.8) 29(3.6) National Wealth indices [n %] Lowest 93(23.3) 86(21.6) 179(22.4) < 0.001 Second Lowest 232(58) 169(42.4) 401(50.2) Middle 71(17.8) 105(26.3) 176(22.0) Second highest 4(1.0) 39(9.8) 43(5.4) Gestational Age at baseline (Weeks) [Mean ± SD] 16.6 ± 3.0 15.5 ± 3 16.1 ± 3 < 0.0001 On vitamin supplements [n %] 390(97.5) 394(98.7) 784(98.1) 0.15 Primi gravidae [n %] 175(43.8) 217(54.4) 392(49.1) 0.03 Among multi gravidae [n %] N = 225 N = 182 N = 407 P-value Has children (< 3-year-old) 124(55.1) 70(38.5) 194(47.7) 0.01 Had C-Section 24(10.7) 71(39) 95(23.4) < 0.001 Had Still born babies [n %] 7(3.1) 8(4.4) 15(3.8) 0.41 Had pre term babies [n %] 8(3.6) 4(2.2) 12(2.9) 0.39 Had abortion [n %] 32(14.2) 52(28.6) 84(20.6) 0.02 Chi-square or t-test were used appropriately as test of significance Table 2 shows anthropometry, diet and physical activity details of the study participants. The mean and standard deviation of BMI was 19.7 ± 3.2 kg/m 2 . Half of participants were in underweight category in Villupuram. Overall, 80% (Villupuram 72%; Nagapattinam 90%) were food secure. Majority of the participants met the pregnancy period physical activity recommendation of > 600METs/week. None of them reported any tobacco smoking or alcohol consumption. Overall, 89% of participants (Villupuram 99%, Nagapattinam 79%) did not achieve minimum dietary diversity, as shown in Fig. 1 . Table 2 Anthropometry, Diet and Physical activity details of the pregnant women living in rural areas of two districts in Tamil Nadu, India Villupuram Nagapattinam Total p-value Diet Diversity Not achieving minimum dietary diversity (< 5) 395(98.8) 317(79.1) 712(89.1) <0.001 Achieved minimum dietary diversity (≥ 5) 5(1.3) 82(20.6) 87(10.9) Household food insecurity scale assessment No food insecurity (0) 286(71.5) 359(90) 645(80.7) 3) 25(6.3) 11(2.8) 36(4.5) Physical activity Total METs per week Mean (SD) 1386(4851) 5832 (1680) 5040(5334) 600METs per week) 351(87.8) 396(99.2) 747(93.5) < 0.0001 Anthropometry BMI (kg/m 2) 18.8 ± 2.3 20.7 ± 3.6 19.7 ± 3.2 < 0.0001 BMI categories Underweight (< 18.5 kg/m 2 ) 199(49.8) 114(28.6) 313(39.2) Normal (18.5 to 24.9 kg/m 2 ) 194(48.5) 235(58.9) 429(53.7) Overweight (25 to 29.9 kg/m 2 ) 6(1.5) 44(11) 50(6.3) 30 kg/m 2 ) 1(0.3) 6(1.5) 7(0.9) BMI –Body Mass Index Chi-square or t-test were used appropriately as test of significance Around 75% had access to safe drinking water and 98% reported following some method to treat their drinking water in Villupuram. On the other hand, 98% had access to safe drinking water and only 13% reported treating their drinking water in Nagapattinam. Open defaecation was common (Villupuram 93%, Nagapattinam 53%). Hand wash facility was observed in most of the houses in Villupuram, in contrast it was noted only in 18% of the houses in Nagapattinam as shown in Table 3 . Table 3 Details on water, sanitation and hand hygiene practices among the pregnant women living in rural areas of two districts in Tamil Nadu, India Characteristics Villupuram Nagapattinam Total p-value Access to safe drinking water 303(75.8) 390(97.7) 693(86.7) < 0.001 Safety method to process the drinking water 393(98.3) 51(12.8) 444(55.6) < 00.01 Absence of hand wash area in /around the house 2(0.8) 287(71.9) 289(36.2) < 0.001 Toilet - No facility Bush/Fields 372(93) 211(52.9) 583(73) < 0.001 Involved in decision making in Major Purchase Daily Purchase Visiting the relative's and other places 191(47.8) 194(48.6) 180(45) 181(45.3) 181(45.3) 172(43.1) 372(46.6) 375(46.9) 352(44.1) 0.52 0.41 0.62 Chi-square or t-test were used appropriately as test of significance Figure 2 shows that the mean Hb levels (10.4 ± 1.3gm/dl; p-value- 0.06) were similar between the study sites. The overall prevalence of anaemia was 66.7% (95% CI: 63.3–70.0%), and the comparisons by site and severity are shown in Fig. 3 . In Villupuram, 27.5% of participants were mildly anaemic, 35.8% moderately anaemic and 0.8% severely anaemic. Similarly, from Nagapattinam, 36.3% had mild, 31.3% had moderate and 1.8% had severe anaemia (p = 0.03) Results from bivariate logistic regressions examining associations of each variable with total anaemia and three group comparisons (Normal, mild and moderate and severe anaemia combined together) are illustrated in supplementary tables ST1 to ST4. Results from logistic regression for total anemia unadjusted and adjusted for study sites are shown in Table 4 . Multi gravidae [OR (CI; p-value)], [1.6 (1.1 to 2.28; p = 0.03)] and underweight [1.5 (1.1 to 2.03); p = 0.02] had higher odds of anaemia. In contrast, being classified as overweight or obese was associated with lower odds of anaemia [0.5 (0.3 to 1.0; p = XXX]. Participants in the middle wealth index had lower prevalence in this population compared to all other national wealth indices (p = 0.02) when not adjusted for study sites. After adjusting for study site, two additional variables were significantly associated with anaemia: no observable hand wash facilities in the dwelling place [1.8 (1.1 to 2.9); p = 0.02] and having children > 3 years old [1.6 (1.0 to 2.4); p = 0.04]. Gestational age, self-reported treatment of drinking water, minimum dietary diversity score, food insecurity, and occupation were not associated with anaemia in either model. Table 4 Results of Regression analysis-normal versus any form of anaemia as dependent variable Variables Odds ratio with (95% CI) Model-1 Model-2 GA (weeks) at baseline 1.1(1.0 to 1.1) 1.1(1.0 to 1.1) Hand washing facility not observed ^ 1.5 (0.9to 2.4) 1.8 (1.1 to 3) No treatment of drinking water 1.2 (0.7 to 2) 1.8 (1 to 3.3) Household food insecurity scores 1(0.9 to 1.2) 1.1 (0.9 to 1.1) National Wealth indices Lowest (ref) Second Lowest 1.1 (0.7 to 1.6) 1.1 (0.7 to 1.5) Middle 0.8 (0.5 to 1.3) 0.8 (0.5 to 1.2) Second highest 2.0 (0.9 to 4.7) 2.2 (0.9 to 5.1) Minimum diet diversity score 1 (0.9 to 1.2) 1.1 (0.9 to 1.2) BMI –Normal (ref) Underweight* ^ 1.5 (1.1 to 2) 1.4 (1 to 2) Overweight & obesity* ^ 0.5 (0.3 to 0.9) 0.5 (0.3 to 1) Not a primigravida*^ 1.5 (1.1to 2.3) 1.6 (1.1 to 2.4) Occupation-Agriculture (ref) Household work 1.04 (0.6 to 1.7) 1.6 (0.9 to 3.1) Others 1.3 (0.6 to 3.1) 1.5 (0.6 to 3.6) Has no children less than 3 years ^ 1.5(1.0 to 2.3) 1.6 (1.0 to 2.4) GA- gestational age, BMI – Body Mass Index Model-1 is unadjusted for study sites Model-2 is adjusted for study sites * Represents the variables with statistical significance at 0.05 level for model-1 ^ Represents the variables with statistical significance at 0.05 level for model-2 Factors responsible for mild, moderate and severe (combined together) anaemia are shown in Table 5 . The participants in second highest wealth index was associated with mild anaemia [2.9 (1.2 to 7.2); p = 0.03)] and women who were in underweight category had [1.6 (1.1 to 2.4); p = 0.08)] higher odds of developing moderate and severe anaemia compared to those who had normal BMI. Hand wash area not observed [2.0 (1.1 to 3.6); p = 0.02] was significantly associated with moderate and severe anaemia after adjusting for study sites. Table 5 Multinomial regression analysis- normal vs mild, moderate and severe grades of anaemia Variables Odds ratio (95% confidence interval) Mild Anaemia Model − 1 Model-2 Age (years) 1(1 to 1.1) 1(1 to 1.1) Minimum dietary diversity 1.1 (1 to 1.3) 1.1(0.9 to 1.3) Education = Higher education (reference category) No formal education/Primary school incomplete 1(0.6 to 1.6) 1(0.6 to 1.5) Primary school complete or Secondary school incomplete 0.8 (0.5 to 1.3 0.8(0.5 to 1.3) Occupation – Agriculture (reference category) Household work 1.4 (0.5 to 4.4) 1.4 (0.4 to 4.3) Other 1.3 (0.5 to 3.9) 1.5 (0.5 to 4.6) BMI-Normal (reference category) Underweight 1.0 (0.7 to 1.5) 1.0 (0.7 to 1.5) Overweight & obese 0.7 (0.4 to 1.4) 0.7 (0.4 to 1.4) No safety methods followed to process the water 1.4(0.8 to 2.5) 1.6(0.82 to 3.2) Has no child < 3 years old 1.1 (0.7 to 1.6) 1.1(0.7 to 1.7) Hand wash area not observable 1.5 (0.9 to 2.5) 1.6(0.9 to 2.8) National Wealth indices – Lowest (reference category) 2nd higher*^ 2.9 (1.2 to 7.2) 2.9 (1.2 to 7.4) Middle 0.8 (0.5 to 1.4) 0.8 (0.5 to 1.4) 2nd lower 1.1 (0.7 to 1.7) 1.1 (0.7 to 1.7) Moderate /Severe anaemia Age (years) 1 (0.9 to 1) 0.1 (0.95 to 1.1) Minimum dietary diversity 1 (0.8 to 1.2) 1 (0.86 to 1.2) Education- Higher education (reference category) No formal education or Primary school incomplete] 1.2 (0.8 to 1.9) 1.2(0.7 to 1.8) Primary school complete or Secondary school incomplete] 1.6 (1 to 2.5) 1.5(1 to 2.4) Occupation – Agriculture (reference category) Household work 0.5 (0.2 to 1.2) 0.4 (0.2 to 1.1) Other 0.4 (0.2 to 1.4) 0.8 (0.3 to 2.1) BMI- Normal (reference category) Underweight*^ 1.6 (1.1 to 2.4) 1.6 (1.1 to 2.3) Overweight & obese category*^ 0.3 (0.2 to 0.8) 0.3 (0.2 to 0.8) No safety methods followed to process the water 1 (0.6 to 1.8) 1.6(0.8 to 3.4) Has no child < 3 years old 1.2 (0.8 to 1.8) 1.2(0.8 to 1.8) Hand wash area not observable^ 1.6 (0.9 to 2.7) 2.0(1.1 to 3.6) National Wealth indices – Lowest (reference category) Second highest 1.3(0.5 to 3.6) 1.4(0.5 to 3.9) Middle 0.8 (0.5 to 1.3) 0.8(0.5 to 1.3) Second lowest 1 (0.7 to 1.7) 1(0.7 to 1.6) BMI –Body Mass Index Model-1 is unadjusted for study sites Model-2 is adjusted for study sites * Represents the variables with statistical significance at 0.05 level for model-1 ^ represents the variables with statistical significance at 0.05 level for model-2 Discussion The current study showed that 66.7% of biomass stove using women in two rural districts of Tamil Nadu were anaemic in pregnancy (9–20 weeks). Our results indicate that lower socioeconomic status, low BMI, absence of a handwashing facility, being multigravida, and having children more than 3 years old were all significantly associated with anaemia in this population. Inadequate dietary diversity, limited access to safe drinking water and unimproved sanitation are highly prevalent in these communities but were not found to be associated with anaemia in our models. The participants in the middle wealth index showed a lower risk compared to the rest of the indices. The direction of the association of the wealth index is in par with other studies, except second highest category showing reverse association with mild anaemia in these biomass users. Our study findings are consistent with the predictors of anaemia identified from a larger set of data analyses of high burden countries of South East Asia: women of low socioeconomic status, lack of education, limited access to safe drinking water sources and poor sanitation and hygienic practices [ 33 ]. Similar studies among rural settings of Tamil Nadu have shown lower prevalence 17.5%[ 22 ] and 41.5% [ 14] The differences could be attributed to variability in sampling frame, the pregnancy period assessment, haemoglobin estimation methods used and also the mixed socioeconomic status in that settings. This study finding is similar (70%) with that of Salem study [ 34 ] because of comparable rates of almost universal biomass use and was even considered as a potential study site for the HAPIN study. The rural cohorts who were part of the air pollution (2010–2012) study had a much higher prevalence of mild (65% versus 31.9% in our study) and slightly lower moderate (30% versus 33.9% in our study) anaemia [ 35 ]. A modest decline in the anaemia prevalence in the state was observed till 2016 that could be attributed to the outreach and increased utilisation of national programme [ 36 ]. A comparative assessment between biomass stove users with that of clean fuel users in rural pregnant women in India showed a higher relative risk for mild (1.4) and moderate to severe (1.8) anaemia [ 11 ] among the biomass users indicating the possible role of inflammation in the aetiology of anaemia. Hence, the higher prevalence observed in the current study may be attributed to the additive effect of low socioeconomic condition and use of unclean fuel. Similar findings were reported in a recent study conducted in China [ 37 ]. Due to time bound high target rate for study enrollment of the pregnant women who met the eligible criteria, the expected time gap from antenatal registration to haemoglobin assessment would have rarely exceeded one month indicating the receipt of supplement is less likely to have influenced the estimated prevalence. This shows that the targeting adolescent girls and young women pre-conceptionally must be of top priority and more operational indicators and achievable targets are required to reduce anaemia prevalence prior to pregnancy. Underweight pregnant women with high rates of moderate anaemia indicate the poor dietary intake in terms of quality and quantity. The results are supported as majority of them have not achieved minimum diet diversity. Hence, in addition to fortification, multi sectoral coordination to improve the local agricultural yields, procurements and coordinating with the public distribution system to plan and establish a continuous supply of healthy dietary alternatives at an affordable cost can improve women’s nutritional status as measured by BMI in such rural communities. The relatively better economic condition, improved education, and underweight in the Nagapattinam have not spared them from higher rates of anaemia than the district average. A comparatively better diet diversity, lack of treatment of drinking water and lack of observed handwashing facilities were observed in this site. So the maternal and child care service providers while reiterating on awareness on the WASH components and dietary advices to the pregnant women should emphasize on region specific challenges. An important strength of our study is the contribution of data on anaemia prevalence among women in two rural districts of Tamil Nadu. The NFHS-5 estimate of anaemia prevalence among pregnant women in Nagapattinam district was 44.8%, but this was based on a very small, unweighted sample of between 25–49 women (exact N not specified). The NFHS-5 did not collect data from Villupuram. Therefore, our results represent a larger and more representative sample than is offered by NFHS. Our study also has several limitations. These include the lack of information on duration of consumption of iron supplementation, biomarkers to understand the other aetiologies of anemia (e.g., micronutrient deficiencies, infection, and genetic disorders) and on intake of enhancers and inhibitors of iron absorption. In addition, inadequate access to health services is considered to be an underlying cause of anaemia, but given that our study population was drawn from pregnant women who were registered for routine antenatal care at health facilities, we were not able to include this factor in our models. Similarly, an inclusion criterion for our study was the use of biomass as a primary cooking fuel, hence we were not able to assess associations between cooking fuel type and anaemia. The current study findings reveal that the existing burden of pregnancy anaemia in a few set of populations are quite higher than that of the NFHS estimate for state and the identified factors are amenable and offer a greater room for improvement with enhancement of socio economic components. Moreover, the study points out that there are regions similar to the current study sites even in states with high development index like Tamil Nadu, indicating the need of identification of such poor performing communities with respect to maternal and child malnutrition and its associated determinants. Declarations Ethics Declarations Ethics approval and consent to participate The HAPIN trial is registered with ClinicalTrials.Gov (Identifier: NCT02944682) dated 17th October 2016. Institutional Review Board approval was obtained from Emory University (Ref- no -00089799) and also from the Ethics committee of Sri Ramachandra Higher Institute of Education and Research (IECN1/16/JUL/54/49) and the Indian Council of Medical Research–Health Ministry Screening Committee (5/8/4–30/(Env)/Indo-US/2016-NCDI). Conflict of Interest Statement The authors have no conflicts of interest to declare. Funding Sources This study was funded by the U.S. National Institutes of Health [cooperative agreement 1UM1HL134590] in collaboration with the Bill & Melinda Gates Foundation [OPP1131279]. The funding agency has no role in design, data analysis and reports presented in this paper. Author Contribution VA conceptualised, designed, analysed, and initial drafting. SS, GT, had made substantial contribution to the design and analysis. KB, UR are the senior authors had made remarkable contributions to the conception of the work, analysis, and interpretation of data for the work; KR, RR, DB, PN, MSG, PK were part of design and data acquisition. NP, SJ, SGS, SSM, KM, critically reviewed the draft. LJ, TC, WC, JP revised the article critically for the content and approved the final version Acknowledgement We acknowledge the relentless service rendered by our field team. We are thankful to the study participants for their constant cooperation. Data Availability The raw data that support the findings of this study are secured in REDCap and available from the corresponding author upon request. References Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1223–49. Global WHA, Targets N. Box 1: What is anaemia? 2012; 1–7. Young MF, Ramakrishnan U. How to Feed the Fetus Maternal Undernutrition before and during Pregnancy and Offspring Health and Development. Metab [Internet]. 2020;76:41–53. Balarajan Y, Ramakrishnan U, Ozaltin E, Shankar A, Subramanian SV. Anaemia in low-income and middle-income countries. Lancet [Internet]. 2011;378:2123–35. 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. Lancet Glob Heal. 2018;6:e548–54. NFHS Fact sheet – Tamil Nadu state. National Family Health Survey – 5 Report [cited 2022 Oct 23]. http://rchiips.org/nfhs/factsheet_NFHS-5.shtml -. NFHS Fact sheet. National Family Health Survey – 4 State Fact Sheet Tamil Nadu. 2015 [cited 2022 Oct 23]. http://rchiips.org/nfhs/factsheet_NFHS-5.shtml -. Swaminathan S, Hemalatha R, Pandey A, Kassebaum NJ, Laxmaiah A, Longvah T, et al. The burden of child and maternal malnutrition and trends in its indicators in the states of India: the Global Burden of Disease Study 1990–2017. Lancet Child Adolesc Heal. 2019;3:855–70. Chakrabarti S, George N, Majumder M, Raykar N, Scott S. Identifying sociodemographic, programmatic and dietary drivers of anaemia reduction in pregnant Indian women over 10 years. Public Health Nutr [Internet]. 2018;21:2424–33. Kothari MT, Coile A, Huestis A, Pullum T, Garrett D, Engmann C. Exploring associations between water, sanitation, and anemia through 47 nationally representative demographic and health surveys. Ann N Y Acad Sci. 2019;1450:249–67. Page CM, Patel A, Hibberd PL. Does smoke from biomass fuel contribute to Anemia in pregnant women in Nagpur, India? A cross-sectional study. PLoS ONE. 2015;10:1–13. Andarge SD, Areba AS, Kabthymer RH, Legesse MT, Kanno GG. Is Indoor Air Pollution from Different Fuel Types Associated with the Anemia Status of Pregnant Women in Ethiopia? J Prim Care Community Heal 2021;12. Weiss G, Ganz T, Goodnough LT. Anemia of inflammation. Blood. 2019;133:40–50. Abiselvi A, Gopalakrishnan S, Umadevi R, Rama R. Anaemia among pregnant women in a rural area of Kancheepuram district, Tamil Nadu. Int J Community Med Public Heal. 2017;4:2400. Khan MS, Srivastav A, Dixit AK. The study of anaemia & its related socio-demographic factors amongst pregnant women in rural community of Uttar Pradesh. J Evol Med Dent Sci [Internet]. 2014;3:14–20. Mangla M, Singla D. Prevalence of anaemia among pregnant women in rural India: a longitudinal observational study. Int J Reprod Contracept Obstet Gynecol. 2016;5:3500–5. 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 [Internet]. 2018;8:21623. Ahmad N, Kalakoti P, Bano R, Aarif SMM. The prevalence of anaemia and associated factors in pregnant women in a rural Indian community. Australas Med J. 2010;1:276–80. Barney A, Abraham V, Danda S, Cherian A, Vanitha S. Prevalence of Vitamin B12 deficiency and its associated risk factors among pregnant women of rural South India: A community-based cross-sectional study. Indian J Community Med. 2020;45:399. Anand Rao C, Reddy S, Pambi S. Study on Prevalence of Anemia among Pregnant Women attending Antenatal Clinic at Rural Health Training Centre (RHTC) and. Int J Contemp Med Res [Internet]. 2016;343:2393–915. 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 [Internet]. 2020;15:e0236449. Gopalan VS. A study on prevalence of anemia in pregnancy in South India. Int J Reprod Contracept Obstet Gynecol. 2019;9:34. Sinha A, Adhikary M, Phukan JP, Kedia S. A study on anemia and its risk factors among pregnant women attending antenatal clinic of a rural medical college of West Bengal. J Fam Med Prim Care. 2021;10:1327–31. Onyeneho NG, Ozumba BC, Chalupowski M, Subramanian SV. Risk Factors of Childhood and Maternal Anemia in India. Int Q Community Health Educ [Internet]. 2020;40:209–17. USAID. Multisectoral Anemia Partners Meeting Conceptual Frameworks for Anemia, Washingt DC. 2013;(October):1–4. [cited 2022 Oct 23]. https://www.spring-nutrition.org/sites/default/files/events/multisectoral_anemia_meeting_diagrams.pdf Clasen T, Checkley W, Peel JL, Balakrishnan K, McCracken JP, Rosa G et al. Design and rationale of the HAPIN study: A multicountry randomized controlled trial to assess the effect of liquefied petroleum gas stove and continuous fuel distribution. Environ Health Perspect 2020; 128. Sambandam S, Mukhopadhyay K, Sendhil S, Ye W, Pillarisetti A, Thangavel G et al. Exposure contrasts associated with a liquefied petroleum gas (LPG) intervention at potential field sites for the multi-country household air pollution intervention network (HAPIN) trial in India: results from pilot phase activities in rural Tamil Nadu. BMC Public Health [Internet]. 2020;20(1). Ye W, Thangavel G, Pillarisetti A, Steenland K, Peel JL, Balakrishnan K et al. Association between personal exposure to household air pollution and gestational blood pressure among women using solid cooking fuels in rural Tamil Nadu, India. Environ Res [Internet] 2022; 208. Neufeld L, García-Guerra A, Sánchez-Francia D, Newton-Sánchez O, Ramírez-Villalobos MD, Rivera-Dommarco J. Hemoglobin measured by Hemocue and a reference method in venous and capillary blood: A validation study. Salud Publica Mex. 2002;44:219–27. Chan M. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva, Switz World Heal Organ [Internet]. 2011; 1–6. WHO Consultation on Obesity (‎1999. Geneva, Switzerland)‎ & World Health Organization. (‎2000)‎. Obesity: preventing and managing the global epidemic: report of a WHO consultation. World Health Organization. [cited 2022 Oct 23]. https://apps.who.int/iris/handle/10665/42330 Household food insecurity experience-based scale. [cited 2022 Oct 23]. https://www.fao.org/3/i5486e/i5486e.pdf ). 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 [Internet]. 2020;15:1–17. Rasappan M, Rangasamy S, Kumarasamy SL. Study of Prevalence of Anemia among Pregnant Women Attending Antenatal Clinic in a Tertiary. Int J Sci Res. 2021;10:2019–22. Balakrishnan K, Ghosh S, Thangavel G, Sambandam S, Mukhopadhyay K, Puttaswamy N, et al. Exposures to fine particulate matter (PM2.5) and birthweight in a rural-urban, mother-child cohort in Tamil Nadu. India. Environ Res. 2018;161:524–31. Rajpal S, Joe W, Subramanyam MA, Sankar R, Sharma S, Kumar A et al. Utilization of Integrated Child Development Services in India: Programmatic Insights from National Family Health Survey, 2016. Int J Environ Res Public Health. 2020; 3197. 10.3390/ijerph17093197 . P. He Y, Liu X, Zheng Y, Zhai Z, Wu X, Kang N, et al. Lower socioeconomic status strengthens the effect of cooking fuel use on anemia risk and anemia-related parameters: Findings from the Henan Rural Cohort. Sci Total Environ [Internet]. 2022;831:154958. Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.docx Cite Share Download PDF Status: Published Journal Publication published 27 Mar, 2026 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 30 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviews received at journal 21 Jan, 2025 Reviewers agreed at journal 15 Dec, 2024 Reviewers agreed at journal 07 Dec, 2024 Reviewers invited by journal 28 Nov, 2024 Editor invited by journal 24 Oct, 2024 Editor assigned by journal 21 Oct, 2024 Submission checks completed at journal 21 Oct, 2024 First submitted to journal 17 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5280307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":371572180,"identity":"1c64a249-e4b3-4006-8141-a0ffefd4f73d","order_by":0,"name":"Vigneswari Aravindalochanan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYDACZjiD+QCcfYBILWwJRGpBAB4DZsKKgMDgOPu1Bx/+HJY3b+f5+LiggkHOvH8B4+ECfFoO85Qbzmw7bDjnMO9m4xlnGIxlbjxgODwDjxbJZp40ad6G24wzmHm3SfO2MSTOkDjAcJiHkJY/f27bz2Dmef6b9x8RWviZ2Y9JM7DdTgRqYWPmbQBq4W8gpIWHTbK37X/yDGY2Y2meYxLGEhKMDXi1sPEffybx40+a7Qz+ww8/89TYyEnwHz78GZ8WUHQg8ySAKLEBrwYGBvYH6G49QEDHKBgFo2AUjDQAAKeYRYhCjZ3TAAAAAElFTkSuQmCC","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Vigneswari","middleName":"","lastName":"Aravindalochanan","suffix":""},{"id":371572183,"identity":"72ab8e78-4823-4633-890b-d776507e135c","order_by":1,"name":"Sheela Sinharoy","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sheela","middleName":"","lastName":"Sinharoy","suffix":""},{"id":371572184,"identity":"acc5ded8-6b33-4d22-9033-9e844bc71018","order_by":2,"name":"Gurusamy Thangavel","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gurusamy","middleName":"","lastName":"Thangavel","suffix":""},{"id":371572185,"identity":"3fa207e5-5485-4c49-ac95-6d0f178340b7","order_by":3,"name":"Naveen Puttaswamy","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Naveen","middleName":"","lastName":"Puttaswamy","suffix":""},{"id":371572186,"identity":"c17ebc20-90f2-4134-a861-193e5f0c8ffa","order_by":4,"name":"Sarada Garg","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sarada","middleName":"","lastName":"Garg","suffix":""},{"id":371572187,"identity":"c9c36bbe-93f1-4450-b7fd-67fa1159c47f","order_by":5,"name":"Sankar Sambandam","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sankar","middleName":"","lastName":"Sambandam","suffix":""},{"id":371572188,"identity":"c43f2f6b-6513-44db-8ea2-b2450a1de6dd","order_by":6,"name":"Krishnendu Mukhopadhyay","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Krishnendu","middleName":"","lastName":"Mukhopadhyay","suffix":""},{"id":371572189,"identity":"8f3c1bee-fcf6-432a-8791-87d23cfffecb","order_by":7,"name":"Durairaj Natesan","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Durairaj","middleName":"","lastName":"Natesan","suffix":""},{"id":371572190,"identity":"13a98919-3ed5-4bd8-ba55-73772dd905ca","order_by":8,"name":"Rengaraj Ramasamy","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rengaraj","middleName":"","lastName":"Ramasamy","suffix":""},{"id":371572191,"identity":"7ac61fb5-8147-4644-bdc3-276c1aa57de7","order_by":9,"name":"Karthikeyan Rajamani","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karthikeyan","middleName":"","lastName":"Rajamani","suffix":""},{"id":371572192,"identity":"a00a3334-ebca-44bc-89b1-a4cffaa81100","order_by":10,"name":"Priyakumar Natarajan","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Priyakumar","middleName":"","lastName":"Natarajan","suffix":""},{"id":371572193,"identity":"c1519994-e00a-40ff-9075-811273d1d7a8","order_by":11,"name":"Meenakshi Sundarem Gopalkrishna","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Meenakshi","middleName":"Sundarem","lastName":"Gopalkrishna","suffix":""},{"id":371572194,"identity":"9e168d06-9da1-42ed-aa74-cd17c6bd3d04","order_by":12,"name":"Lindsay M. Jaacks","email":"","orcid":"","institution":"The University of Edinburgh","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lindsay","middleName":"M.","lastName":"Jaacks","suffix":""},{"id":371572196,"identity":"5e5c47ca-7976-47a4-bd94-9265ebc0d2f3","order_by":13,"name":"Kalpana Balakrishnan","email":"","orcid":"","institution":"Sri Ramachandra Institute for Higher Education and Research (Deemed University)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kalpana","middleName":"","lastName":"Balakrishnan","suffix":""},{"id":371572197,"identity":"5c896c61-e34b-4621-bb34-b6e7d9ce4700","order_by":14,"name":"Usha Ramakrishnan","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Usha","middleName":"","lastName":"Ramakrishnan","suffix":""},{"id":371572199,"identity":"f8578b89-cb4a-4fd5-9154-f98a764cc031","order_by":15,"name":"Shirin Jabbarzadeh","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"","lastName":"Jabbarzadeh","suffix":""},{"id":371572200,"identity":"1abacc5f-9e5a-427e-88a3-c0f807080ce0","order_by":16,"name":"Barr DB","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Barr","middleName":"","lastName":"DB","suffix":""},{"id":371572204,"identity":"e2764f44-7f73-4046-a4e5-d3b548db5b11","order_by":17,"name":"Thomas Clasen","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Clasen","suffix":""},{"id":371572206,"identity":"77e55148-a7e6-4359-9274-d7db384a54e5","order_by":18,"name":"William Checkley","email":"","orcid":"","institution":"Johns Hopkins University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Checkley","suffix":""},{"id":371572210,"identity":"504cf055-24dc-4b66-b296-81624eb6ddee","order_by":19,"name":"Jennifer L. Peel","email":"","orcid":"","institution":"Colorado State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"L.","lastName":"Peel","suffix":""}],"badges":[],"createdAt":"2024-10-17 06:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5280307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5280307/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12884-026-08999-1","type":"published","date":"2026-03-27T16:10:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68540993,"identity":"fbbd948b-15c0-477f-88dd-c24333226ed8","added_by":"auto","created_at":"2024-11-08 10:50:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89116,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of daily intake of essential food groups between study sites\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5280307/v1/a46902f06ec861ba7b6b2d4b.jpeg"},{"id":68542357,"identity":"ba6698ce-69f4-4671-94c7-f34f622882ed","added_by":"auto","created_at":"2024-11-08 10:58:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":30161,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Haemoglobin level in pregnant women by study site\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5280307/v1/6a49ab335286f92c78141683.jpeg"},{"id":68540994,"identity":"c1753715-38ac-4d3a-bddc-020e1027a080","added_by":"auto","created_at":"2024-11-08 10:50:16","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30396,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of different grades of anemia in pregnant women across the study sites\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5280307/v1/10f39249724f2c4f80382d97.jpeg"},{"id":105755928,"identity":"b043aad1-5702-4a29-8fd9-3d5533cdba2a","added_by":"auto","created_at":"2026-03-30 16:33:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1184730,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5280307/v1/f165de4b-b7a3-44cf-a11e-74e3bf99de4e.pdf"},{"id":68540991,"identity":"b1e98d53-8871-459d-9c33-82495618bde7","added_by":"auto","created_at":"2024-11-08 10:50:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42535,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.docx","url":"https://assets-eu.researchsquare.com/files/rs-5280307/v1/2df6f2f813bac44fa6c2d7aa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaternal and child malnutrition is a leading risk factor for disease burden, contributing to 11.6% of all annual global disability-adjusted life years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Anaemia during pregnancy is a major public health concern due to its impact on maternal and child health such as increased risk of infection, preterm delivery, low birth weight, infant mortality, and poor growth and development of children [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Globally, 38% of pregnant women are anaemic [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] with South Asian countries carrying the largest burden.\u003c/p\u003e \u003cp\u003eIn India, home to one-fifth of the world\u0026rsquo;s population, the most recent National Family Health Survey (NFHS-5) conducted in 2019\u0026ndash;2021 indicated that 52.2% of pregnant women were anaemic [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Most worrying is that the prevalence of anemia among pregnant women has increased, from 50.4% in 2015\u0026ndash;2016, [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] despite widespread iron supplementation. These findings support the complex aetiology of maternal anaemia, with factors other than micronutrient intake playing a role. The study on subnational variations showed that several states in India are not on-track to meet National Nutrition Program (NNM) targets by 2022 or the UN Sustainable Development Goals by 2030 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similar to the national estimates, the NFHS-4 to NFHS- 5 reports show an increase in anaemia prevalence for pregnant women in Tamil Nadu state as well[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA main driver of anaemia among pregnant women is an increased physiological iron requirement during pregnancy, combined with inadequate iron intake and absorption, especially among those following vegetarian diets. Social drivers of anaemia in rural pregnant women include socio-economic conditions, maternal education, age at pregnancy. Environmental drivers of anaemia include poor sanitation conditions, including the practice of open defaecation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], inadequate access to safe water [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and use of biomass fuels for cooking [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These environmental conditions can lead to chronic systemic inflammation developed by long-term exposure to pathogens and pollutants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Anaemia of inflammation, like iron deficiency anaemia, is among the most prevalent forms of anaemia worldwide [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGaps remain in our knowledge of the relative contribution of these factors to the prevalence of anaemia among pregnant women in rural India. To date, studies of anaemia in rural India have focused on individual determinants of anaemia in isolation rather than looking at them jointly [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Evidence is also lacking specific to anaemia among pregnant women in rural Tamil Nadu state. Therefore, the objective of our study was to assess the prevalence of anaemia and to identify any coexisting determinants among exclusive biomass-using pregnant women in two rural sites in Tamil Nadu state using a conceptual framework for anaemia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe used baseline data from pregnant women who were recruited to participate in the India site of the Household Air Pollution Intervention Network (HAPIN) trial, a multi-centre randomised controlled trial implemented in four countries (Guatemala, India, Peru and Rwanda) to assess the effect of a liquefied petroleum gas stove intervention. Details of the trial protocol [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and rationale for the selection of study sites in India [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] are provided elsewhere. For the India site, study participants were identified from lists of pregnant women registered for routine antenatal care at health facilities in two districts in Tamil Nadu: Kallakurichi (formerly Villupuram) and Nagapattinam. A total of 799 pregnant women were enrolled between April 2018 and November 2019. Eligibility criteria included: aged 18\u0026ndash;35 years, ultrasound-confirmed gestational age between 9 and 20 weeks and lived in a household that was using biomass as their primary cooking fuel.\u003c/p\u003e \u003cp\u003eSociodemographic information was self-reported through surveys and included mother\u0026rsquo;s education, occupation, father\u0026rsquo;s education, household assets, access to sanitation facility, access to safe drinking water, self-reported treatment of drinking water, observable hand washing area with soap, and women\u0026rsquo;s autonomy in decision making. The household wealth categories were calculated based on household asset ownership and classified them into five national wealth indices [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Parity, birth spacing and details of consumption of micronutrient supplements were captured from the medical records.\u003c/p\u003e \u003cp\u003eHaemoglobin (Hb) was measured in capillary blood using point-of-care testing (HemoCue\u0026reg; 201\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e using standard procedure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. WHO criteria for anaemia in pregnancy were used to categorize participants as normal (Hb\u0026thinsp;\u0026ge;\u0026thinsp;11g/dL), mild (Hb 10.0\u0026ndash;10.9 g/dL), moderate (Hb 7.0\u0026ndash;9.9 g/dL), and severe anemia (Hb\u0026thinsp;\u0026lt;\u0026thinsp;7.0 g/dL) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Anaemia was treated as a categorical variable in all analyses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeight (Seca 213 stadiometer) and weight (Seca 876 digital scale) were measured by trained enumerators following standardised procedures. Two readings of height were recorded to 0.1 cm and a third measurement was taken if the difference between the first two readings exceeded 1 cm. Similarly, two weight measurements were taken and recorded to 0.1 kg. If the difference between two measurements exceeded 0.5 kg, a third measurement was performed. Averages of the height and weight measurements were used to calculate body mass index (BMI) and categorised as normal (18.5 to 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25 to 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e), and obese (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) as per WHO classification [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHousehold food insecurity and dietary diversity were measured using the food insecurity experience scale (FIES) and minimum dietary diversity score for women (MDD-W), respectively [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. FIES consists of eight questions on access to adequate food in the participant\u0026rsquo;s household in the past 30 days. Scores for the eight questions were summed and households were classified as being food secure (0), moderately food insecure (1 to 3), or severely food insecure (\u0026gt;\u0026thinsp;3). Data collection for the MDD-W involves an open recall of foods consumed in the previous day or night. For HAPIN, the MDD-W questionnaire was adapted to cover the previous 30 days, to align with the FIES. In MDD-W, ten food groups are included in the score: grains, white roots and tubers, and plantains; pulses (beans, peas, and lentils); nuts and seeds; dairy; meat, poultry, and fish; eggs; dark green leafy vegetables; other vitamin A-rich fruits and vegetables; other vegetables; and other fruits. A score of \u0026ldquo;1\u0026rdquo; was entered for those who reported a daily intake of each food group and others were given with the scores of \u0026ldquo;0\u0026rdquo;. The total is the sum of the scores for the above ten food groups and that could take any value between minimum of 0 to maximum of 10. Further, the women with total scores\u0026thinsp;\u0026ge;\u0026thinsp;5 were categorised as achieving minimum dietary diversity and those with scores of \u0026lt;\u0026thinsp;5 were categorised as not achieving minimum dietary diversity. Physical activity of women was recorded using International Physical Activity Questionnaire \u0026ndash;short form. The total metabolic equivalent of task (METs) per week was calculated. Other risk factors such as alcohol and tobacco use were self-reported.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eAll analyses were conducted using R software (R Foundation for Statistical Computing, Vienna, Austria). Bivariate logistic regression was used to identify variables that were associated with anaemia at a level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.2, which were subsequently included in multivariable analysis. Multinomial logistic regressions were used to examine associations between these variables and any anaemia in general, and with grades of anaemia, respectively. Adjusted odds ratios (OR) are reported with 95% confidence intervals (CI).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eSociodemographic and obstetric details of pregnant women are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD participant\u0026rsquo;s age was 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8 years. Overall, 72% of the study participants were either in the first or second lowest categories of the National wealth index. Half of the pregnant women in Villupuram had neither any formal education nor completed primary school, whereas over 45% of the participants from Nagapattinam had completed secondary school and higher. Similar differences were also observed in father\u0026rsquo;s education. Agriculture (87%) was reported to be the major occupation of participants in Villupuram whereas almost all (97%) were homemakers in Nagapattinam. The mean gestational weeks at the time of assessment was 16 weeks. Around 50% were primi gravidae; 40% of non-primi had children\u0026thinsp;\u0026lt;\u0026thinsp;3 years old.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio Demographic and obstetric details of the pregnant women living in rural areas of two districts in Tamil Nadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillupuram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagapattinam\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;400\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;399\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;799\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years) [Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation-self [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education/ Primary school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e207(51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78(19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(31.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227(28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school complete and other higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194(48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation -Spouse [n%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education or Primary school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183(45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e304(38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school complete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school complete or Vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77(19.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126(15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83(20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135(16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation-self [n%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337(84.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387(97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e432(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Wealth indices [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond Lowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e232(58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e169(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e401(50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105(26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176(22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond highest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43(5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age at baseline (Weeks) [Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn vitamin supplements [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e390(97.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394(98.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e784(98.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimi gravidae [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175(43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e217(54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e392(49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmong multi gravidae [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas children (\u0026lt;\u0026thinsp;3-year-old)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70(38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194(47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad C-Section\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad Still born babies [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad pre term babies [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHad abortion [n %]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChi-square or t-test were used appropriately as test of significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows anthropometry, diet and physical activity details of the study participants. The mean and standard deviation of BMI was 19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2 kg/m\u003csup\u003e2\u003c/sup\u003e. Half of participants were in underweight category in Villupuram. Overall, 80% (Villupuram 72%; Nagapattinam 90%) were food secure. Majority of the participants met the pregnancy period physical activity recommendation of \u0026gt;\u0026thinsp;600METs/week. None of them reported any tobacco smoking or alcohol consumption. Overall, 89% of participants (Villupuram 99%, Nagapattinam 79%) did not achieve minimum dietary diversity, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometry, Diet and Physical activity details of the pregnant women living in rural areas of two districts in Tamil Nadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillupuram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagapattinam\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet Diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot achieving minimum dietary diversity (\u0026lt;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395(98.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e317(79.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e712(89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAchieved minimum dietary diversity (\u0026ge;\u0026thinsp;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82(20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold food insecurity scale assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo food insecurity (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286(71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359(90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e645(80.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild food insecurity present (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114(14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate/Severe food insecurity present (\u0026gt;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal METs per week Mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1386(4851)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5832 (1680)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5040(5334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeeting PA recommendation in pregnancy (\u0026gt;\u0026thinsp;600METs per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e396(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e747(93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthropometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199(49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e313(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5 to 24.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194(48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235(58.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429(53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (25 to 29.9 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI \u0026ndash;Body Mass Index\u003c/p\u003e \u003cp\u003eChi-square or t-test were used appropriately as test of significance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAround 75% had access to safe drinking water and 98% reported following some method to treat their drinking water in Villupuram. On the other hand, 98% had access to safe drinking water and only 13% reported treating their drinking water in Nagapattinam. Open defaecation was common (Villupuram 93%, Nagapattinam 53%). Hand wash facility was observed in most of the houses in Villupuram, in contrast it was noted only in 18% of the houses in Nagapattinam as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails on water, sanitation and hand hygiene practices among the pregnant women living in rural areas of two districts in Tamil Nadu, India\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillupuram\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNagapattinam\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess to safe drinking water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e303(75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390(97.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e693(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety method to process the drinking water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e393(98.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444(55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;00.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence of hand wash area in /around the house\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e287(71.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e289(36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToilet - No facility Bush/Fields\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e372(93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e211(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e583(73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvolved in decision making in\u003c/p\u003e \u003cp\u003eMajor Purchase\u003c/p\u003e \u003cp\u003eDaily Purchase\u003c/p\u003e \u003cp\u003eVisiting the relative's and other places\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e191(47.8)\u003c/p\u003e \u003cp\u003e194(48.6)\u003c/p\u003e \u003cp\u003e180(45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e181(45.3)\u003c/p\u003e \u003cp\u003e181(45.3)\u003c/p\u003e \u003cp\u003e172(43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e372(46.6)\u003c/p\u003e \u003cp\u003e375(46.9)\u003c/p\u003e \u003cp\u003e352(44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003cp\u003e0.41\u003c/p\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eChi-square or t-test were used appropriately as test of significance\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the mean Hb levels (10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3gm/dl; p-value- 0.06) were similar between the study sites. The overall prevalence of anaemia was 66.7% (95% CI: 63.3\u0026ndash;70.0%), and the comparisons by site and severity are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In Villupuram, 27.5% of participants were mildly anaemic, 35.8% moderately anaemic and 0.8% severely anaemic. Similarly, from Nagapattinam, 36.3% had mild, 31.3% had moderate and 1.8% had severe anaemia (p\u0026thinsp;=\u0026thinsp;0.03)\u003c/p\u003e \u003cp\u003eResults from bivariate logistic regressions examining associations of each variable with total anaemia and three group comparisons (Normal, mild and moderate and severe anaemia combined together) are illustrated in supplementary tables ST1 to ST4.\u003c/p\u003e\u003cp\u003eResults from logistic regression for total anemia unadjusted and adjusted for study sites are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Multi gravidae [OR (CI; p-value)], [1.6 (1.1 to 2.28; p\u0026thinsp;=\u0026thinsp;0.03)] and underweight [1.5 (1.1 to 2.03); p\u0026thinsp;=\u0026thinsp;0.02] had higher odds of anaemia. In contrast, being classified as overweight or obese was associated with lower odds of anaemia [0.5 (0.3 to 1.0; p\u0026thinsp;=\u0026thinsp;XXX]. Participants in the middle wealth index had lower prevalence in this population compared to all other national wealth indices (p\u0026thinsp;=\u0026thinsp;0.02) when not adjusted for study sites. After adjusting for study site, two additional variables were significantly associated with anaemia: no observable hand wash facilities in the dwelling place [1.8 (1.1 to 2.9); p\u0026thinsp;=\u0026thinsp;0.02] and having children\u0026thinsp;\u0026gt;\u0026thinsp;3 years old [1.6 (1.0 to 2.4); p\u0026thinsp;=\u0026thinsp;0.04]. Gestational age, self-reported treatment of drinking water, minimum dietary diversity score, food insecurity, and occupation were not associated with anaemia in either model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Regression analysis-normal versus any form of anaemia as dependent variable\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOdds ratio with (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA (weeks) at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1(1.0 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(1.0 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand washing facility not observed \u003cb\u003e^\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.9to 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1.1 to 3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo treatment of drinking water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.7 to 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8 (1 to 3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold food insecurity scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.9 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.9 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Wealth indices Lowest (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond Lowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.7 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.7 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.5 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.5 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond highest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (0.9 to 4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2 (0.9 to 5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum diet diversity score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.9 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.9 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI \u0026ndash;Normal (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight*\u003cb\u003e^\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (1.1 to 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (1 to 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight \u0026amp; obesity*\u003cb\u003e^\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.3 to 0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5 (0.3 to 1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot a primigravida*^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (1.1to 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.1 to 2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation-Agriculture (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.6 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.9 to 3.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.6 to 3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.6 to 3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas no children less than 3 years \u003cb\u003e^\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5(1.0 to 2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.0 to 2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA- gestational age, BMI \u0026ndash; Body Mass Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel-1 is unadjusted for study sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel-2 is adjusted for study sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e* Represents the variables with statistical significance at 0.05 level for model-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e^ Represents the variables with statistical significance at 0.05 level for model-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFactors responsible for mild, moderate and severe (combined together) anaemia are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The participants in second highest wealth index was associated with mild anaemia [2.9 (1.2 to 7.2); p\u0026thinsp;=\u0026thinsp;0.03)] and women who were in underweight category had [1.6 (1.1 to 2.4); p\u0026thinsp;=\u0026thinsp;0.08)] higher odds of developing moderate and severe anaemia compared to those who had normal BMI. Hand wash area not observed [2.0 (1.1 to 3.6); p\u0026thinsp;=\u0026thinsp;0.02] was significantly associated with moderate and severe anaemia after adjusting for study sites.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial regression analysis- normal vs mild, moderate and severe grades of anaemia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOdds ratio (95% confidence interval)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMild Anaemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel \u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(1 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum dietary diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (1 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(0.9 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u0026thinsp;=\u0026thinsp;Higher education (reference category)\u003c/p\u003e \u003cp\u003eNo formal education/Primary school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.6 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.6 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school complete or Secondary school incomplete\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.5 to 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8(0.5 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation \u0026ndash; Agriculture (reference category)\u003c/p\u003e \u003cp\u003eHousehold work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.5 to 4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4 (0.4 to 4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 (0.5 to 3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5 (0.5 to 4.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI-Normal (reference category)\u003c/p\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0 (0.7 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.7 to 1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight \u0026amp; obese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7 (0.4 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (0.4 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo safety methods followed to process the water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4(0.8 to 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6(0.82 to 3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas no child\u0026thinsp;\u0026lt;\u0026thinsp;3 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.7 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1(0.7 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand wash area not observable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5 (0.9 to 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6(0.9 to 2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Wealth indices \u0026ndash; Lowest (reference category)\u003c/p\u003e \u003cp\u003e2nd higher*^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9 (1.2 to 7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 (1.2 to 7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.5 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.5 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.7 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1 (0.7 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eModerate /Severe anaemia\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.9 to 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1 (0.95 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum dietary diversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.8 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.86 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation- Higher education (reference category)\u003c/p\u003e \u003cp\u003eNo formal education or Primary school incomplete]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.8 to 1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(0.7 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school complete or Secondary school incomplete]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (1 to 2.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5(1 to 2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation \u0026ndash; Agriculture (reference category)\u003c/p\u003e \u003cp\u003eHousehold work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.2 to 1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4 (0.2 to 1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.2 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (0.3 to 2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI- Normal (reference category)\u003c/p\u003e \u003cp\u003eUnderweight*^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (1.1 to 2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (1.1 to 2.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight \u0026amp; obese category*^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3 (0.2 to 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.2 to 0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo safety methods followed to process the water\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.6 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6(0.8 to 3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHas no child\u0026thinsp;\u0026lt;\u0026thinsp;3 years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.2 (0.8 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2(0.8 to 1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand wash area not observable^\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.6 (0.9 to 2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0(1.1 to 3.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational Wealth indices \u0026ndash; Lowest (reference category)\u003c/p\u003e \u003cp\u003eSecond highest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3(0.5 to 3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4(0.5 to 3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8 (0.5 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8(0.5 to 1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond lowest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7 to 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7 to 1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBMI \u0026ndash;Body Mass Index\u003c/p\u003e \u003cp\u003eModel-1 is unadjusted for study sites\u003c/p\u003e \u003cp\u003eModel-2 is adjusted for study sites\u003c/p\u003e \u003cp\u003e* Represents the variables with statistical significance at 0.05 level for model-1\u003c/p\u003e \u003cp\u003e^ represents the variables with statistical significance at 0.05 level for model-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe current study showed that 66.7% of biomass stove using women in two rural districts of Tamil Nadu were anaemic in pregnancy (9\u0026ndash;20 weeks). Our results indicate that lower socioeconomic status, low BMI, absence of a handwashing facility, being multigravida, and having children more than 3 years old were all significantly associated with anaemia in this population. Inadequate dietary diversity, limited access to safe drinking water and unimproved sanitation are highly prevalent in these communities but were not found to be associated with anaemia in our models. The participants in the middle wealth index showed a lower risk compared to the rest of the indices. The direction of the association of the wealth index is in par with other studies, except second highest category showing reverse association with mild anaemia in these biomass users.\u003c/p\u003e \u003cp\u003eOur study findings are consistent with the predictors of anaemia identified from a larger set of data analyses of high burden countries of South East Asia: women of low socioeconomic status, lack of education, limited access to safe drinking water sources and poor sanitation and hygienic practices [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Similar studies among rural settings of Tamil Nadu have shown lower prevalence 17.5%[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and 41.5%\u003csup\u003e[\u003c/sup\u003e14] The differences could be attributed to variability in sampling frame, the pregnancy period assessment, haemoglobin estimation methods used and also the mixed socioeconomic status in that settings. This study finding is similar (70%) with that of Salem study [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] because of comparable rates of almost universal biomass use and was even considered as a potential study site for the HAPIN study.\u003c/p\u003e \u003cp\u003eThe rural cohorts who were part of the air pollution (2010\u0026ndash;2012) study had a much higher prevalence of mild (65% versus 31.9% in our study) and slightly lower moderate (30% versus 33.9% in our study) anaemia [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. A modest decline in the anaemia prevalence in the state was observed till 2016 that could be attributed to the outreach and increased utilisation of national programme [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA comparative assessment between biomass stove users with that of clean fuel users in rural pregnant women in India showed a higher relative risk for mild (1.4) and moderate to severe (1.8) anaemia [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] among the biomass users indicating the possible role of inflammation in the aetiology of anaemia. Hence, the higher prevalence observed in the current study may be attributed to the additive effect of low socioeconomic condition and use of unclean fuel. Similar findings were reported in a recent study conducted in China [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to time bound high target rate for study enrollment of the pregnant women who met the eligible criteria, the expected time gap from antenatal registration to haemoglobin assessment would have rarely exceeded one month indicating the receipt of supplement is less likely to have influenced the estimated prevalence. This shows that the targeting adolescent girls and young women pre-conceptionally must be of top priority and more operational indicators and achievable targets are required to reduce anaemia prevalence prior to pregnancy.\u003c/p\u003e \u003cp\u003eUnderweight pregnant women with high rates of moderate anaemia indicate the poor dietary intake in terms of quality and quantity. The results are supported as majority of them have not achieved minimum diet diversity. Hence, in addition to fortification, multi sectoral coordination to improve the local agricultural yields, procurements and coordinating with the public distribution system to plan and establish a continuous supply of healthy dietary alternatives at an affordable cost can improve women\u0026rsquo;s nutritional status as measured by BMI in such rural communities.\u003c/p\u003e \u003cp\u003eThe relatively better economic condition, improved education, and underweight in the Nagapattinam have not spared them from higher rates of anaemia than the district average. A comparatively better diet diversity, lack of treatment of drinking water and lack of observed handwashing facilities were observed in this site. So the maternal and child care service providers while reiterating on awareness on the WASH components and dietary advices to the pregnant women should emphasize on region specific challenges.\u003c/p\u003e \u003cp\u003eAn important strength of our study is the contribution of data on anaemia prevalence among women in two rural districts of Tamil Nadu. The NFHS-5 estimate of anaemia prevalence among pregnant women in Nagapattinam district was 44.8%, but this was based on a very small, unweighted sample of between 25\u0026ndash;49 women (exact N not specified). The NFHS-5 did not collect data from Villupuram. Therefore, our results represent a larger and more representative sample than is offered by NFHS. Our study also has several limitations. These include the lack of information on duration of consumption of iron supplementation, biomarkers to understand the other aetiologies of anemia (e.g., micronutrient deficiencies, infection, and genetic disorders) and on intake of enhancers and inhibitors of iron absorption. In addition, inadequate access to health services is considered to be an underlying cause of anaemia, but given that our study population was drawn from pregnant women who were registered for routine antenatal care at health facilities, we were not able to include this factor in our models. Similarly, an inclusion criterion for our study was the use of biomass as a primary cooking fuel, hence we were not able to assess associations between cooking fuel type and anaemia.\u003c/p\u003e \u003cp\u003eThe current study findings reveal that the existing burden of pregnancy anaemia in a few set of populations are quite higher than that of the NFHS estimate for state and the identified factors are amenable and offer a greater room for improvement with enhancement of socio economic components. Moreover, the study points out that there are regions similar to the current study sites even in states with high development index like Tamil Nadu, indicating the need of identification of such poor performing communities with respect to maternal and child malnutrition and its associated determinants.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e \u003cb\u003eEthics Declarations\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe HAPIN trial is registered with ClinicalTrials.Gov (Identifier: NCT02944682) dated 17th October 2016. Institutional Review Board approval was obtained from Emory University (Ref- no -00089799) and also from the Ethics committee of Sri Ramachandra Higher Institute of Education and Research (IECN1/16/JUL/54/49) and the Indian Council of Medical Research\u0026ndash;Health Ministry Screening Committee (5/8/4\u0026ndash;30/(Env)/Indo-US/2016-NCDI).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Sources\u003c/h2\u003e \u003cp\u003eThis study was funded by the U.S. National Institutes of Health [cooperative agreement 1UM1HL134590] in collaboration with the Bill \u0026amp; Melinda Gates Foundation [OPP1131279]. The funding agency has no role in design, data analysis and reports presented in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVA conceptualised, designed, analysed, and initial drafting. SS, GT, had made substantial contribution to the design and analysis. KB, UR are the senior authors had made remarkable contributions to the conception of the work, analysis, and interpretation of data for the work; KR, RR, DB, PN, MSG, PK were part of design and data acquisition. NP, SJ, SGS, SSM, KM, critically reviewed the draft. LJ, TC, WC, JP revised the article critically for the content and approved the final version\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the relentless service rendered by our field team. We are thankful to the study participants for their constant cooperation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data that support the findings of this study are secured in REDCap and available from the corresponding author upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1223\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal WHA, Targets N. Box 1: What is anaemia? 2012; 1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung MF, Ramakrishnan U. How to Feed the Fetus Maternal Undernutrition before and during Pregnancy and Offspring Health and Development. Metab [Internet]. 2020;76:41\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalarajan Y, Ramakrishnan U, Ozaltin E, Shankar A, Subramanian SV. Anaemia in low-income and middle-income countries. Lancet [Internet]. 2011;378:2123\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaru J, Zamora J, Fern\u0026aacute;ndez-F\u0026eacute;lix 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. Lancet Glob Heal. 2018;6:e548\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNFHS Fact sheet \u0026ndash; Tamil Nadu state. National Family Health Survey \u0026ndash; 5 Report [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rchiips.org/nfhs/factsheet_NFHS-5.shtml\u003c/span\u003e\u003cspan address=\"http://rchiips.org/nfhs/factsheet_NFHS-5.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e -.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNFHS Fact sheet. National Family Health Survey \u0026ndash;\u0026thinsp;4 State Fact Sheet Tamil Nadu. 2015 [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://rchiips.org/nfhs/factsheet_NFHS-5.shtml\u003c/span\u003e\u003cspan address=\"http://rchiips.org/nfhs/factsheet_NFHS-5.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e -.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwaminathan S, Hemalatha R, Pandey A, Kassebaum NJ, Laxmaiah A, Longvah T, et al. The burden of child and maternal malnutrition and trends in its indicators in the states of India: the Global Burden of Disease Study 1990\u0026ndash;2017. Lancet Child Adolesc Heal. 2019;3:855\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakrabarti S, George N, Majumder M, Raykar N, Scott S. Identifying sociodemographic, programmatic and dietary drivers of anaemia reduction in pregnant Indian women over 10 years. Public Health Nutr [Internet]. 2018;21:2424\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKothari MT, Coile A, Huestis A, Pullum T, Garrett D, Engmann C. Exploring associations between water, sanitation, and anemia through 47 nationally representative demographic and health surveys. Ann N Y Acad Sci. 2019;1450:249\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage CM, Patel A, Hibberd PL. Does smoke from biomass fuel contribute to Anemia in pregnant women in Nagpur, India? A cross-sectional study. PLoS ONE. 2015;10:1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndarge SD, Areba AS, Kabthymer RH, Legesse MT, Kanno GG. Is Indoor Air Pollution from Different Fuel Types Associated with the Anemia Status of Pregnant Women in Ethiopia? J Prim Care Community Heal 2021;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss G, Ganz T, Goodnough LT. Anemia of inflammation. Blood. 2019;133:40\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbiselvi A, Gopalakrishnan S, Umadevi R, Rama R. Anaemia among pregnant women in a rural area of Kancheepuram district, Tamil Nadu. Int J Community Med Public Heal. 2017;4:2400.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan MS, Srivastav A, Dixit AK. The study of anaemia \u0026amp; its related socio-demographic factors amongst pregnant women in rural community of Uttar Pradesh. J Evol Med Dent Sci [Internet]. 2014;3:14\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMangla M, Singla D. Prevalence of anaemia among pregnant women in rural India: a longitudinal observational study. Int J Reprod Contracept Obstet Gynecol. 2016;5:3500\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel 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 [Internet]. 2018;8:21623.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad N, Kalakoti P, Bano R, Aarif SMM. The prevalence of anaemia and associated factors in pregnant women in a rural Indian community. Australas Med J. 2010;1:276\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarney A, Abraham V, Danda S, Cherian A, Vanitha S. Prevalence of Vitamin B12 deficiency and its associated risk factors among pregnant women of rural South India: A community-based cross-sectional study. Indian J Community Med. 2020;45:399.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnand Rao C, Reddy S, Pambi S. Study on Prevalence of Anemia among Pregnant Women attending Antenatal Clinic at Rural Health Training Centre (RHTC) and. Int J Contemp Med Res [Internet]. 2016;343:2393\u0026ndash;915.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunuwar 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 [Internet]. 2020;15:e0236449.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGopalan VS. A study on prevalence of anemia in pregnancy in South India. Int J Reprod Contracept Obstet Gynecol. 2019;9:34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinha A, Adhikary M, Phukan JP, Kedia S. A study on anemia and its risk factors among pregnant women attending antenatal clinic of a rural medical college of West Bengal. J Fam Med Prim Care. 2021;10:1327\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnyeneho NG, Ozumba BC, Chalupowski M, Subramanian SV. Risk Factors of Childhood and Maternal Anemia in India. Int Q Community Health Educ [Internet]. 2020;40:209\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSAID. Multisectoral Anemia Partners Meeting Conceptual Frameworks for Anemia, Washingt DC. 2013;(October):1\u0026ndash;4. [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.spring-nutrition.org/sites/default/files/events/multisectoral_anemia_meeting_diagrams.pdf\u003c/span\u003e\u003cspan address=\"https://www.spring-nutrition.org/sites/default/files/events/multisectoral_anemia_meeting_diagrams.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClasen T, Checkley W, Peel JL, Balakrishnan K, McCracken JP, Rosa G et al. Design and rationale of the HAPIN study: A multicountry randomized controlled trial to assess the effect of liquefied petroleum gas stove and continuous fuel distribution. Environ Health Perspect 2020; 128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSambandam S, Mukhopadhyay K, Sendhil S, Ye W, Pillarisetti A, Thangavel G et al. Exposure contrasts associated with a liquefied petroleum gas (LPG) intervention at potential field sites for the multi-country household air pollution intervention network (HAPIN) trial in India: results from pilot phase activities in rural Tamil Nadu. BMC Public Health [Internet]. 2020;20(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe W, Thangavel G, Pillarisetti A, Steenland K, Peel JL, Balakrishnan K et al. Association between personal exposure to household air pollution and gestational blood pressure among women using solid cooking fuels in rural Tamil Nadu, India. Environ Res [Internet] 2022; 208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeufeld L, Garc\u0026iacute;a-Guerra A, S\u0026aacute;nchez-Francia D, Newton-S\u0026aacute;nchez O, Ram\u0026iacute;rez-Villalobos MD, Rivera-Dommarco J. Hemoglobin measured by Hemocue and a reference method in venous and capillary blood: A validation study. Salud Publica Mex. 2002;44:219\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan M. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. Geneva, Switz World Heal Organ [Internet]. 2011; 1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO Consultation on Obesity (\u0026lrm;1999. Geneva, Switzerland)\u0026lrm; \u0026amp; World Health Organization. (\u0026lrm;2000)\u0026lrm;. Obesity: preventing and managing the global epidemic: report of a WHO consultation. World Health Organization. [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/42330\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/42330\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHousehold food insecurity experience-based scale. [cited 2022 Oct 23]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/3/i5486e/i5486e.pdf\u003c/span\u003e\u003cspan address=\"https://www.fao.org/3/i5486e/i5486e.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunuwar 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 [Internet]. 2020;15:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRasappan M, Rangasamy S, Kumarasamy SL. Study of Prevalence of Anemia among Pregnant Women Attending Antenatal Clinic in a Tertiary. Int J Sci Res. 2021;10:2019\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalakrishnan K, Ghosh S, Thangavel G, Sambandam S, Mukhopadhyay K, Puttaswamy N, et al. Exposures to fine particulate matter (PM2.5) and birthweight in a rural-urban, mother-child cohort in Tamil Nadu. India. Environ Res. 2018;161:524\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajpal S, Joe W, Subramanyam MA, Sankar R, Sharma S, Kumar A et al. Utilization of Integrated Child Development Services in India: Programmatic Insights from National Family Health Survey, 2016. Int J Environ Res Public Health. 2020; 3197. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph17093197\u003c/span\u003e\u003cspan address=\"10.3390/ijerph17093197\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. P.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Liu X, Zheng Y, Zhai Z, Wu X, Kang N, et al. Lower socioeconomic status strengthens the effect of cooking fuel use on anemia risk and anemia-related parameters: Findings from the Henan Rural Cohort. Sci Total Environ [Internet]. 2022;831:154958.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anemia, Pregnant women, South India, Rural, Biomass users","lastPublishedDoi":"10.21203/rs.3.rs-5280307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5280307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAnaemia during pregnancy is a major public health concern in India. Biomass fuel use is common in a few rural areas of Tamil Nadu state and there is growing evidence that its use is associated with anaemia. The aim of this study was to estimate the prevalence of anaemia and to identify the coexisting determinants of anaemia among exclusive biomass-using rural pregnant women.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used baseline data collected from 799 rural pregnant women with a gestational age between 9 and 20 weeks from two districts of Tamil Nadu, India who were enrolled in a multi-country randomised controlled trial of a liquefied petroleum gas intervention. Haemoglobin (Hb) was measured in capillary blood using the Haemocue-201 point of case device. Hb was categorized as normal (Hb\u0026thinsp;\u0026ge;\u0026thinsp;11g/dL), mild anaemia (Hb 10.0-10.9g/dL), moderate anaemia (Hb 7.0-9.9g/dL), or severe anaemia (Hb\u0026thinsp;\u0026lt;\u0026thinsp;7.0g/dL). Multinomial logistic regression was used to identify factors associated with anaemia.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOverall prevalence of any anaemia was 66.7% (95% confidence interval: 63.4\u0026ndash;70%) and that of mild anaemia was 31.9% (28.7\u0026ndash;35.1%), moderate anaemia 33.7% (30.4\u0026ndash;36.9%), and severe anaemia was 1.1% (0.6\u0026ndash;2.2%) Wealth index, body mass index classified as underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e), being a multigravida, and hand wash area not observed were associated with a significantly higher odds of anaemia.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis exclusive biomass using pregnant women had quite higher rates of anaemia and underweight than that of rural estimates from recent NFHS reports for Tamil Nadu state indicating the need to improve their overall anthropometric status and anaemia together emphasizing on healthy dietary habits.\u003c/p\u003e","manuscriptTitle":"Prevalence and determinants of anaemia among pregnant women who use biomass for cooking in rural parts of Tamil Nadu","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-08 10:50:11","doi":"10.21203/rs.3.rs-5280307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T12:23:43+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-24T06:38:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"263587449365556851237422300044511659857","date":"2025-12-10T05:56:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-21T10:30:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281630694555228396974445289760367710670","date":"2024-12-15T15:01:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58405772104400288561441469294623438724","date":"2024-12-08T03:40:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-28T07:12:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-24T17:00:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-22T03:50:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-22T03:50:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2024-10-17T06:51:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f9033d35-a10f-4499-a448-b7b90b987758","owner":[],"postedDate":"November 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:27:21+00:00","versionOfRecord":{"articleIdentity":"rs-5280307","link":"https://doi.org/10.1186/s12884-026-08999-1","journal":{"identity":"bmc-pregnancy-and-childbirth","isVorOnly":false,"title":"BMC Pregnancy and Childbirth"},"publishedOn":"2026-03-27 16:10:19","publishedOnDateReadable":"March 27th, 2026"},"versionCreatedAt":"2024-11-08 10:50:11","video":"","vorDoi":"10.1186/s12884-026-08999-1","vorDoiUrl":"https://doi.org/10.1186/s12884-026-08999-1","workflowStages":[]},"version":"v1","identity":"rs-5280307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5280307","identity":"rs-5280307","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}