Trend and determinants of anaemia among reproductive-age women in Tanzania (2004- 2022): A generalised Poisson regression analysis of demographic and health surveys

Trend and determinants of anaemia among reproductive-age women in Tanzania (2004- 2022): A generalised Poisson regression analysis of demographic and health surveys | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trend and determinants of anaemia among reproductive-age women in Tanzania (2004- 2022): A generalised Poisson regression analysis of demographic and health surveys Elihuruma Eliufoo Stephano, Theresia Wenati Ngunguru, Jacktan Josephat Ruhighira, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6931713/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Dec, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted 11 You are reading this latest preprint version Abstract Background Globally, approximately 30% of women of reproductive age are affected by anaemia. Anaemia is of major public health concern due to its strong association with increased morbidity and mortality among women of reproductive age. This study aimed to examine the trends and factors influencing anaemia among women of childbearing age in Tanzania. Methods An analytical cross-sectional study was conducted using data from the Tanzania Demographic and Health Surveys collected between 2004/05, 2010, 2016, and 2022. The study included 40,632 women of reproductive age who were selected for haemoglobin measurements. Two stage sampling was used to select survey participants. A Generalised Poisson regression model was used to identify factors associated with anaemia. Adjusted prevalence ratios (APR) with 95% Confidence Intervals (CI) were calculated to estimate the strength of the association. Results The overall pooled prevalence of anaemia was 44.0% (95% CI: 43.0–44.9) among women of reproductive age. Among these women, 29.2% (95% CI: 28.4–29.9) had mild anaemia, 13.4% (95% CI: 12.9–13.9) had moderate anaemia, and 1.4% (95% CI: 1.3–1.6) had severe anaemia. Looking at the trend over time, the prevalence of anaemia was 48.4% IN 2004/05 (95%CI:46.2–50.5), 40.1% in 2010 (95% CI: 38.4–41.9), 44.8% in 2015/16 (95% CI: 43.4–46.4), and 41.5% in 2022 (95% CI: 39.8–43.3). Conclusion The findings demonstrate anaemia as both a clinical and public health challenge, requiring multi-sectoral approaches. The persistent associations across demographic, socioeconomic, and reproductive domains suggest that singular interventions are unlikely to address this prevalence sufficiently. Reducing the burden of anaemia will require coordinated efforts across clinical care, public health programming, and social policy to address both immediate nutritional needs and underlying determinants. Anaemia Reproductive-age Women Trend Tanzania Figures Figure 1 Background Anaemia is a condition in which the concentration of haemoglobin or the number of red blood cells is below the normal level to meet physiological needs [ 1 ]. This condition remains one of the most significant public health challenges globally, affecting approximately 1.8 billion people worldwide, with 50.3 million years lost to disability, while women of reproductive age (WRA) bear a disproportionate burden [ 2 , 3 ]. According to WHO data, anaemia affects 39.8% of children aged 6–59 months globally, while 29.9% of WRA suffer from this condition worldwide [ 2 , 4 ]. Despite being recognised as a critical health concern for decades, global progress in reducing anaemia has been disappointingly slow, with prevalence declining only marginally from 31.2% in 2000 to 29.9% in 2019 [ 3 ]. This stagnation persists despite the WHO's global nutrition target of reducing anaemia in WRA by 50% by 2025 [ 2 – 4 ]. A significant difference exists in anaemia prevalence between developed and developing nations, with rates of 9% and 43%, respectively [ 5 ]. The stagnation in lowering the prevalence of anaemia has led the WHO and UNICEF to extend the proposed target to 2030, aligning with the Sustainable Development Goals (SDGs). Meanwhile, the prevalence of anaemia among WRA was included as an indicator in 2020 [ 5 – 7 ]. Pregnant women in developed countries show moderate rates, for example, 20% in Australia and 18% in the United States [ 8 ]. Developing nations report substantially higher figures, including Ethiopia at 50.1%, Pakistan at 76.7%, and Indonesia at 35.5% [ 4 , 8 ]. The burden of anaemia is particularly pronounced in Sub-Saharan Africa (SSA), where approximately 41.74% of non-pregnant WRA and 54% of pregnant women are anaemic [ 9 ]. The reported prevalence is significantly higher than the global average. In some SSA countries, the prevalence exceeds 50%, making it one of the most severely affected regions by this condition worldwide [ 9 , 10 ]. The high anaemia burden in SSA results from a complex interplay of factors including widespread nutritional deficiencies (particularly iron, folate, and vitamin B12), endemic infectious diseases (malaria, hookworm, schistosomiasis), genetic hemoglobinopathies (sickle cell disease, thalassemias) [ 11 , 12 ], and structural determinants such as food insecurity, inadequate dietary diversity, limited healthcare access, and gender inequities [ 13 ]. Regional initiatives, such as the Comprehensive Africa Agriculture Development Programme and the African Regional Nutrition Strategy, have incorporated anaemia reduction as a key objective [ 14 , 15 ]. Many SSA countries have implemented integrated anaemia control strategies, including iron supplementation through antenatal care, mass deworming campaigns, insecticide-treated bed net distribution, and food fortification programs [ 9 , 16 ]. Despite these efforts, progress has been inconsistent across the region, with some countries showing modest improvements while others experience stagnation or even increases in anaemia prevalence [ 9 ]. Anaemia is associated with reduced work capacity and productivity, compromised immune function, impaired cognitive performance, and a decreased quality of life [ 12 , 17 ]. During pregnancy, anaemic women face increased risks of maternal mortality, postpartum haemorrhage, preterm birth, and delivering low birth weight infants [ 18 ]. The intergenerational impact is significant, as maternal anaemia contributes to poor fetal development, increased infant mortality, and compromised cognitive and physical development in children [ 16 , 18 ]. Tanzania faces a significant anaemia challenge, a rate that surpasses global and regional averages, particularly in rural areas where prevalence reaches 47.3% in Moshi and exceeds 68% in Dar es Salaam [ 19 ]. Despite the implementation of various interventions, such as iron and folic acid supplementation during antenatal care [ 20 , 21 ], food fortification programs, school-based deworming, malaria prevention strategies [ 22 ], and nutritional education campaigns [ 23 ], challenges persist. Anaemia has remained an indirect cause of 14.5% of maternal deaths in Tanzania [ 24 ]. These challenges include limited healthcare access in rural areas, inconsistent supply chains for supplements, low awareness of anaemia causes, and persistent factors like poverty and food insecurity [ 24 , 25 ]. Cultural dietary practices, high fertility rates, and insufficient dietary diversity further exacerbate the problem [ 25 ]. Additionally, decades of interventions have not sufficiently addressed critical knowledge gaps, as current research primarily offers cross-sectional snapshots without analysing long-term trends and the interaction of different determinants affecting anaemia prevalence, hence the rationale for this study. Given the significant geographical variations in prevalence and the limited understanding of the effectiveness of anaemia control programs, further research is needed to inform more effective and context-specific interventions that can contribute to national health targets and global sustainable development goals, ultimately reducing anaemia prevalence and its associated health burdens for WRA in Tanzania. Therefore, this study aimed to assess the trend and determinants of anaemia status among reproductive-age women in Tanzania using the 2004–2022 Tanzania Demographic and Health Surveys (TDHS). Materials and methods Study design, data source, population and sampling procedure A nationally representative, cross-sectional study was conducted using secondary data from the TDHS spanning the years 2004/05, 2010, 2016, and 2022. The TDHS, carried out every five years, collects updated information on health and health-related topics. The target population for the TDHS includes women of reproductive age (15-49 years), men, children and households. However, this study solely focused on women of reproductive age. The sample design for the DHS followed a two-stage sampling procedure aimed at providing national, urban, and rural estimates for both the Tanzania Mainland and Zanzibar. The study used a stratified, two-stage sampling method. In the first stage, sampling points (clusters) were chosen from enumeration areas (EAs) based on the previous Tanzania Population and Housing Census (PHC), using a probability proportional to size (PPS) sampling technique. Regions were grouped into zones to minimise sampling errors and allow for more accurate estimates at the zonal level. Tanzania was divided into strata based on geographical regions and urban/rural classification, with each stratum treated as a separate sampling domain. In the second stage, households were systematically selected from each cluster. To adjust for unequal selection probabilities and non-responses, individual weights were calculated for each respondent. For this study, we utilised the individual records (IR) file extracted from each survey round. We considered women from households that were selected for haemoglobin measurement, ensuring that haemoglobin results were available for analysis. In 2004/05, 10,139 women were selected, in 2010, 9,875; in 2015/16, 13,064 women; and 2022, 7,554 women (weighted sample). Overall, a total of 40,632 weighted women were included in this study. Variable measurements Dependent variable: The outcome variable measured was the level of anaemia. In TDHS, blood samples were collected from women who voluntarily provided their consent to undertake the haemoglobin test. After a finger prick, blood was drawn into a microcuvette for on-site analysis using a battery-operated, portable HemoCue analyser. Blood haemoglobin values were adjusted by altitude among women of childbearing age using this equation [26]. Hb-adjustment (g/dl) = -0.032 x (altitude × 0.0032808) + 0.022 x (altitude × 0.0032808) 2 For this study, anaemia was defined as a blood haemoglobin level below 12.0 g/dL, aligning with the World Health Organisation (WHO) definition. The WHO categorises anaemia severity as mild (11.0–11.9 g/dL), moderate (8.0–10.9 g/dL), and severe (< 8.0 g/dL). However, for the purpose of our analysis, the outcome variable was recoded into a binary response: "Not anaemic" (coded as 0) for individuals with a blood haemoglobin level of 12.0 g/dL or higher, and "Anaemic" (coded as 1) for those with a blood haemoglobin level below 12.0 g/dL. Altitude correction was applied to haemoglobin levels, with the threshold for anaemia set under 12 g/dL after this adjustment. Independent variables: We examined variables based on the available data and literature. The following variables were included under the individual level; age in years(15-24, 25-34 or 35-49), education level (no formal education, primary, secondary, or higher), marital status (Currently married, cohabiting or previously married), literacy (illiterate or literate), wealth index (poorest, poorer, middle, richer or richest), parity (none, 1-2, ≥3), working status (working or not working), sex of household head (male or female), current pregnant status (yes or no), ever terminated a pregnancy (yes or no), current contraceptive use (none or any method), currently breastfeeding (yes or nor), covered by health insurance (yes or no), place of residence (urban or rural) and geographical zones (western, northern, central, lake, southern, eastern or Zanzibar). Data collection procedure Blood specimens for anaemia testing were collected from eligible women aged 15–49 years who consented to the testing. Haemoglobin analysis was carried out on-site using a battery-operated portable analysis device (HemoCue® 201+ photometer), which produces a result in less than 1 minute. HemoTrol® controls were used as quality control materials for haemoglobin measurements using a HemoCue® 201+ photometer. Results were provided verbally and in writing to those being tested. Women were referred for follow-up care if their haemoglobin levels were <8 g/dl. Statistical analysis Prior to performing any statistical analysis, the data were weighted by incorporating the individual sampling weight (v005/1,000,000), primary sampling unit, and stratum. All analyses were performed using STATA version 18.5 (STATA Corp, College Station, TX). Descriptive statistics were summarised using means, standard deviation (SD) and frequency and proportion for categorical variables. A generalised Poisson regression model with robust variance estimator was used to identify factors associated with anaemia, and the results were presented as a prevalence ratio with corresponding 95% Confidence Intervals (CI). We used this model as the odds ratio estimated using logistic regression in a cross-sectional study might overestimate the association when the outcome is common (>10%) [27]. Univariate analyses were performed by fitting each explanatory variable against the dependent variable (anaemia) to produce a Crude prevalence ratio (CPR). A p-value of <0.2 was used to identify covariates to be included in the final multivariable analyses. A variance inflation factor (VIF) was used to assess multicollinearity between independent variables before fitting a multivariable regression model. Finally, a multivariable generalised Poisson regression model was fitted to estimate the adjusted Prevalence Ratio (APR) with corresponding 95% Confidence Intervals (CI) to assess the strength and magnitude of the association. Given this, DHS surveys are conducted approximately every five years in the same settings, so some women may have participated in multiple survey rounds. To avoid potential issues with non-independent observations, our regression analysis focused solely on the most recent data (the 2022 TDHS). Statistical significance was set at p-value <0.05. Results Sociodemographic characteristics A total of 30,493 women were analysed, with an average age of 28.9 years (standard deviation = 9.8). More than one-third of the participants were aged 15-24 years: 41.1% in 2004/05, 40.4% in 2010, 40.75% in 2015/16, and 38.8% in 2022. Over half of the women had completed primary education, with 67.2% in 2004/05, 65.0% in 2010, 62.1% in 2015/16, and 53.7% in 2022. Additionally, the majority (≥70.0%) were literate in each survey round, and a quarter (25.0%) were nulliparous. Approximately 70.0% of households were headed by men. Less than one-third of the women used contraceptives in each of the survey rounds, and fewer than 10% had health insurance coverage. Most women lived in rural areas, with the smallest proportion coming from Zanzibar (Table 1). Table 1: Sociodemographic characteristics of women of reproductive age across Tanzania demographic and health survey years [N=30,493] Characteristics TDHS Year 2004/05 [N=10,139] 2010 [N=9,875] 2015/16 [N=13,064] 2022 [N=7,554] Age (years) 15-24 4168 (41.1) 3986 (40.4) 5315 (40.7) 2930 (38.8) 25-34 3362 (33.2) 2980 (30.2) 3827 (29.3) 2273 (30.1) 35-49 2609 (25.7) 2909 (29.5) 3922 (30) 2352 (31.1) Mean (±SD) 28.3 (9.4) 28.7 (9.7) 28.6 (9.7( 29.2 (9.9) Education No formal education 2461 (24.3) 1891 (19.1) 1910 (14.6) 1216 (16.1) Primary 6817 (67.2) 6415 (65) 8117 (62.1) 4059 (53.7) Secondary/Higher 861 (8.5) 1570 (15.9) 3037 (23.2) 2279 (30.2) Marital status Never married 2321 (22.9) 2452 (24.8) 3309 (25.3) 2063 (27.3) Married/Cohabiting 6841 (67.5) 6261 (63.4) 8086 (61.9) 4544 (60.2) Separated/Divorced 977 (9.6) 1162 (11.8) 1670 (12.8) 947 (12.5) Literacy level Illiterate 3325 (32.8) 2918 (29.6) 3021 (23.1) 1503 (19.9) Literate 6814 (67.2) 6957 (70.4) 10043 (76.9) 6051 (80.1) BMI category ≥18.5 kg/m 2 9184 (90.6) 8832 (89.4) 11934 (91.3) 6858 (90.8) <18.5 kg/m 2 955 (9.4) 1044 (10.6) 1130 (8.7) 696 (9.2) Occupation Not working 2155 (21.3) 2150 (21.8) 3600 (27.6) 3175 (42) Working 7984 (78.7) 7725 (78.2) 9457 (72.4) 4379 (58) Parity None 2501 (24.7) 2470 (25) 3317 (25.4) 1968 (26.1) 1-2 2931 (28.9) 2718 (27.5) 3975 (30.4) 2269 (30) 3+ 4707 (46.4) 4687 (47.5) 5772 (44.2) 3317 (43.9) Currently pregnant No 9064 (89.4) 8917 (90.3) 11945 (91.4) 6971 (92.3) Yes 1075 (10.6) 958 (9.7) 1119 (8.6) 583 (7.7) Ever terminated a pregnancy No 8409 (82.9) 8190 (82.9) 10969 (84.0) 6512 (86.2) Yes 1730 (17.1) 1685 (17.1) 2094 (16.0) 1042 (13.8) Currently breastfeeding No 7117 (70.2) 7099 (71.9) 9568 (73.2) 5825 (77.1) Yes 3022 (29.8) 2776 (28.1) 3496 (26.8) 1729 (22.9) Wealth index Poorest 1815 (17.9) 1652 (16.7) 2225 (17) 1176 (15.6) Poorer 1928 (19.0) 1917 (19.4) 2254 (17.3) 1326 (17.6) Middle 1913 (18.9) 1960 (19.8) 2309 (17.7) 1491 (19.7) Richer 1983 (19.6) 2062 (20.9) 2781 (21.3) 1671 (22.1) Richest 2500 (24.6) 2284 (23.1) 3495 (26.8) 1889 (25) Residence Urban 2830 (27.9) 2758 (27.9) 4682 (35.8) 2640 (34.9) Rural 7309 (72.1) 7118 (72.1) 8382 (64.2) 4914 (65.1) Geographical zones Western 1007 (9.9) 882 (8.9) 1274 (9.8) 642 (8.5) Northern 1170 (11.5) 1272 (12.9) 1540 (11.8) 796 (10.5) Central 1077 (10.6) 1022 (10.4) 1320 (10.1) 809 (10.7) Southern 2292 (22.6) 2268 (23) 2735 (20.9) 1543 (20.4) Lake 2679 (26.4) 2597 (26.3) 3429 (26.3) 2229 (29.5) Eastern 1609 (15.9) 1512 (15.3) 2366 (18.1) 1282 (17) Zanzibar 305 (3.0) 322 (3.3) 400 (3.1) 252 (3.3) Prevalence and Trend of anaemia among women of reproductive age in Tanzania The overall weighted prevalence of anaemia was 44.0% (95%CI: 43.0–44.9) among women of reproductive age. Among these women, 28.0% (95% CI: 27.2–28.8) had mild anaemia, 13.0% (95% CI: 12.4–13.6) had moderate anaemia, and 1.5% (95% CI: 1.3–1.7) had severe anaemia. Looking at the trend over time, the prevalence of anaemia was 48.4% in 2004/05 (95%CI:46.2-50.5), 40.1% in 2010 (95% CI: 38.4–41.9), 44.8% in 2015/16 (95% CI: 43.4–46.4), and 41.5% in 2022 (95% CI: 39.8–43.3), (Figure 01). Generalised Poisson regression analysis for factors associated with anaemia In the adjusted analysis, women with 1-2 children (APR=0.85, 95% CI: 0.76-0.96) and those with more than two children (APR=0.82, 95% CI: 0.72- 0.94) were less likely to have anaemia compared to their counterparts. Furthermore, women who were currently pregnant (APR = 1.40, 95% CI: 1.25-1.55) were more likely to have anaemia compared to their counterparts. In terms of socioeconomic status, women in the richest quintile were more likely to have anaemia compared to those in the poorest quintile (APR = 0.85, 95% CI: 0.74-0.98) (Table 2). Table 2: Generalised Poisson regression model for factors associated with anaemia among women of reproductive age in Tanzania, TDHS 2022 (N=7,554) Characteristics Crude p-value Adjusted p-value PR (95%CI) PR (95%CI) Age (years) 15-24 1.00 1.00 25-34 0.93 (0.86-1.01) 0.105 1.06 (0.95-1.17) 0.294 35-49 0.92 (0.85-0.99) 0.033 1.08 (0.96-1.22) 0.182 Education No formal education 1.00 1.00 Primary 0.91 (0.83-0.99) 0.041 0.92 (0.84-1.01) 0.064 Secondary/Higher 0.99 (0.89-1.09) 0.822 0.89 (0.80-1.01) 0.060 Marital status Never married 1.00 1.00 Married/Cohabiting 0.89 (0.83-0.96) 0.003 0.95 (0.85-1.06) 0.340 Separated/Divorced 0.97 (0.87-1.08) 0.609 1.08 (0.94-1.24) 0.287 BMI category ≥18.5 kg/m 2 1.00 1.00 =3 0.83 (0.77-0.89) <0.001 0.82 (0.72-0.94) 0.005 Occupation Not working 1.00 1.00 Working 0.85 (0.80-0.91) <0.001 0.87 (0.81-0.93) <0.001 Currently pregnant No 1.00 1.00 Yes 1.38 (1.25-1.52) <0.001 1.40 (1.25-1.55) <0.001 Ever terminated a pregnancy No 1.00 1.00 Yes 1.09 (0.98-1.19) 0.095 1.06 (0.96-1.17) 0.206 Currently breastfeeding No 1.00 1.00 Yes 0.89 (0.83-0.96) 0.011 1.04 (0.99-1.08) 0.066 Wealth index Poorest 1.00 1.00 Poorer 0.84 (0.76-0.94) 0.002 0.85 (0.76-0.94) 0.002 Middle 0.85 (0.77-0.95) 0.004 0.84 (0.76-0.94) 0.003 Richer 0.99 (0.89-1.09) 0.838 0.93 (0.83-1.05) 0.247 Richest 0.96 (0.87-1.07) 0.480 0.85 (0.74-0.98) 0.024 Residence Urban 1.00 1.00 Rural 0.92 (0.86-0.99) 0.023 1.01 (0.91-1.11) 0.857 Geographical zones Western 1.00 1.00 Northern 1.12 (0.96-1.29) 0.151 1.15 (0.99-1.34) 0.062 Central 0.92 (0.78-1.08) 0.320 0.95 (0.80-1.11) 0.499 Southern 0.86 (0.76-0.98) 0.028 0.92 (0.80-1.05) 0.215 Lake 1.13 (0.99-1.28) 0.073 1.16 (1.02-1.32) 0.024 Eastern 1.42 (1.25-1.62) <0.001 1.52 (1.33-1.74) <0.001 Zanzibar 1.58 (1.40-1.79) <0.001 1.32 (1.24-1.40) <0.001 Discussion This study aimed to assess the trend and determinants of anaemia status among reproductive-age women in Tanzania, using data collected from the TDHS from 2004 to 2022. The results present a significant public health concern regarding anaemia among women of reproductive age, which becomes more pronounced when examining the severity distributions of women who experienced mild, moderate, and severe anaemia. These stratified findings suggest that while the most severe form affects a relatively small percentage, the combined burden of mild and moderate anaemia impacts over four in ten women of reproductive age. This distribution pattern is critical for public health planning, as different severities may require distinct intervention approaches. These figures align closely with global data that indicate anaemia poses a significant health challenge for women worldwide, particularly in low- and middle-income countries [ 28 ]. The pooled prevalence from 19 SSA countries showed a higher prevalence than the pooled prevalence of this study [ 16 ]. Another study highlighted that the prevalence of anaemia in low- and middle-income countries among women of reproductive age was slightly lower than this study’s reported prevalence [ 8 ]. The trend analysis reveals notable fluctuations in anaemia prevalence over a twelve-year period, suggesting complex underlying factors affecting women's anaemia status. Previous studies emphasise the fluctuating nature of anaemia prevalence, often linked to changes in economic conditions, dietary habits, and public health interventions that target nutritional deficits [ 4 , 19 ]. This nonlinear pattern raises important questions about intervening factors associated with these timeframes. The persistence of high rates despite potential interventions highlights the multifactorial nature of anaemia and suggests that existing approaches may be insufficient or inconsistently implemented [ 24 ]. These findings highlight the need for sustained, comprehensive strategies that address not only iron supplementation but also the underlying socioeconomic determinants and healthcare access that influence women's nutritional status. This study revealed several factors that are associated with anaemia among WRA. Age appears to be a significant determinant, with younger women exhibiting a slightly lower prevalence of anaemia compared to their older counterparts. This finding is consistent with other studies indicating that younger women generally have better nutritional statuses, potentially due to a lower incidence of chronic diseases and fewer reproductive complications compared to older age groups [ 8 , 13 ]. These findings were contrary to those of other studies as well [ 24 , 28 ]. Educational attainment emerges as another crucial factor, with women lacking formal education experiencing a higher anaemia prevalence than those with secondary or higher education, similar to other studies [ 8 , 24 ], but contrary to the study by Let [ 29 ]. This educational gradient likely reflects broader socioeconomic disparities in nutritional knowledge, healthcare access, and dietary quality. Nutritional status also plays a key role, as underweight women (BMI < 18.5 kg/m²) demonstrated a higher prevalence of anaemia compared to women with normal weight, suggesting that overall nutritional inadequacy may contribute to iron deficiency [ 30 ]. Reproductive factors appear to substantially influence the risk of anaemia among women, similar to the study in Northern Tanzania [ 18 ]. Current pregnancy stands out as the strongest risk factor identified in this analysis, with pregnant women experiencing a 27% higher prevalence of anaemia compared to non-pregnant women. This pronounced association reflects the increased iron demands during pregnancy to support fetal development and maternal blood volume expansion [ 18 , 24 ]. A finding similar to the WHO, which identifies pregnant women as one of the most vulnerable groups for anaemia due to increased nutritional requirements [ 2 ]. Women with a history of pregnancy termination also showed a higher anaemia prevalence, which may indicate the cumulative effect of reproductive blood loss or potentially underlying health conditions [ 31 ]. Though the findings showed increased prevalence of anaemia among breastfeeding women, likely reflecting the nutritional demands of lactation. Several studies, which were pooled in the narrative review in Indonesia, showed that breastfeeding was associated with anaemia and warrant further investigation on the impact on neonatal wellbeing [ 32 ]. Socioeconomic disparities emerge as significant determinants of anaemia risk, with women in the poorest wealth quintile experiencing a higher prevalence compared to those in the richest quintile, similar to the study conducted in Namibia [ 33 ]. This substantial socioeconomic gradient likely reflects multiple intersecting disadvantages, including limited access to iron-rich foods, inadequate healthcare services, a higher burden of infectious diseases, and poorer sanitation conditions [ 34 ]. The findings collectively paint a picture of anaemia as a condition deeply rooted in social determinants of health, disproportionately affecting vulnerable populations, including the poor, undereducated, and those with greater reproductive demands [ 11 , 19 ]. A significant study in Nigeria found similar correlations, wherein economic disparities led to pronounced differences in health outcomes [ 35 ]. These results emphasise the need for targeted interventions that address not only iron supplementation but also the underlying social, economic, and educational factors that contribute to anaemia risk among WRA. Strengths and limitations of the study This study is one of the few to report on the trends in prevalence and predictors of anaemia among reproductive-aged women in Tanzania at a national level, utilising a large enough sample. The measurements were taken using standardised national tools and methods, and trained experts conducted data collection. However, since the data are cross-sectional, it is not possible to establish cause-and-effect relationships. Additionally, most of the responses were self-reported, and recall and socio-desirability biases may be present. Implications for clinical and community practice The clinical implications of these findings are substantial for healthcare providers working with WRA. The strong associations between anaemia and pregnancy, education level, nutritional status, and socioeconomic position highlight the need for targeted screening protocols. Clinicians should implement more vigilant haemoglobin monitoring for high-risk groups, particularly pregnant women and those from lower socioeconomic backgrounds. The identified risk factors suggest that effective clinical management must extend beyond iron supplementation to include comprehensive nutritional counselling, especially for underweight women. Healthcare providers should also recognise that women with a history of pregnancy termination require additional attention, as they demonstrate a 5% higher anaemia prevalence. These findings call for the integration of anaemia prevention into reproductive health services, with special consideration for women with multiple risk factors who may benefit from more intensive interventions. Conclusion In conclusion, these results highlight anaemia as both a clinical and public health challenge requiring multi-sectoral approaches. The persistent associations across demographic, socioeconomic, and reproductive domains suggest that singular interventions are unlikely to sufficiently address the 42.5% prevalence among WRA. While iron supplementation remains important, especially during pregnancy, sustainable reductions in anaemia will likely require broader social interventions addressing educational disparities and poverty. The findings also emphasise the importance of longitudinal monitoring, considering the fluctuating trends observed between 2004 and 2022. Future clinical guidelines should incorporate these risk stratifications to ensure efficient resource allocation and targeted care, particularly in settings where healthcare resources are limited. Ultimately, reducing the burden of anaemia will require coordinated efforts across clinical care, public health programming, and social policy to address both immediate nutritional needs and underlying determinants. Abbreviations APR Adjusted Prevalence ratio BMI Body mass index CI Confidence intervals CPR Crude prevalence ratio DHS Demographic Health Survey EA Enumeration areas PHC Population and Housing Census PSU Primary sampling unit PPS Probability proportional to size SD Standard deviations SSA Sub-Saharan Africa SDG Sustainable Development Goals TDHS Tanzania Demographic and Health Surveys UNICEF United Nations International Children's Emergency Fund VIF Variance inflation factor WHO World Health Organization WRA Women of reproductive age Declarations Acknowledgements We thank the DHS program for making the data available for this study, and TILAM International for statistical consultation. Authors’ Contribution MJM and EES conceptualised the idea and conducted the formal analysis. EES, TWN, JJR, VGM, LKK and MJM supported interpretation and reviewed subsequent versions of the manuscript. All authors read and approved the final manuscript. Funding No funding received. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com Ethics approval and consent to participate This study utilized publicly available, de-identified data from the 2004 to 2022 TDHS, accessible online through the DHS program. The original study received ethical clearance and research permit from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X. Consent for publication Not applicable. Competing interests The authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Zavala E, Adler S, Wabyona E, Ahimbisibwe M, Doocy S. Trends and determinants of anemia in children 6–59 months and women of reproductive age in Chad from 2016 to 2021. BMC Nutr. 2023;9:117. WHO. Global nutrition targets 2025: anaemia policy brief [Internet]. 2025 [cited 2025 Mar 12]. Available from: https://www.who.int/publications/i/item/WHO-NMH-NHD-14.4 Mildon A, Lopez de Romaña D, Jefferds MED, Rogers LM, Golan JM, Arabi M. Integrating and coordinating programs for the management of anemia across the life course. Ann N Y Acad Sci. 2023;1525:160–72. Gardner WM, Razo C, McHugh TA, Hagins H, Vilchis-Tella VM, Hennessy C, et al. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10:e713–34. Stevens GA, Paciorek CJ, Flores-Urrutia MC, Borghi E, Namaste S, Wirth JP, et al. National, regional, and global estimates of anaemia by severity in women and children for 2000–19: a pooled analysis of population-representative data. Lancet Glob Health. 2022;10:e627. Arora A. WHO/UNICEF discussion paper: The extension of the 2025 maternal, infant and young child nutrition targets to 2030 [Internet]. UNICEF DATA. 2019 [cited 2025 Mar 12]. Available from: https://data.unicef.org/resources/who-unicef-discussion-paper-nutrition-targets/ UNGA. Transforming our world: the 2030 Agenda for Sustainable Development | Department of Economic and Social Affairs [Internet]. [cited 2025 Mar 12]. Available from: https://sdgs.un.org/2030agenda Alem AZ, Efendi F, McKenna L, Felipe-Dimog EB, Chilot D, Tonapa SI, et al. Prevalence and factors associated with anemia in women of reproductive age across low- and middle-income countries based on national data. Sci Rep. 2023;13:20335. Tirore LL, Areba AS, Habte A, Desalegn M, Kebede AS. Prevalence and associated factors of severity levels of anemia among women of reproductive age in sub-Saharan Africa: a multilevel ordinal logistic regression analysis. Front Public Health. 2024;11:1349174. Ali SA, Khan U, Feroz A. Prevalence and Determinants of Anemia among Women of Reproductive Age in Developing Countries. J Coll Physicians Surg--Pak JCPSP. 2020;30:177–86. Chaparro CM, Suchdev PS. Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries. Ann N Y Acad Sci. 2019;1450:15–31. Brittenham GM, Moir-Meyer G, Abuga KM, Datta-Mitra A, Cerami C, Green R, et al. Biology of Anemia: A Public Health Perspective. J Nutr. 2023;153:S7–28. Tesema GA, Worku MG, Tessema ZT, Teshale AB, Alem AZ, Yeshaw Y, et al. Prevalence and determinants of severity levels of anemia among children aged 6–59 months in sub-Saharan Africa: A multilevel ordinal logistic regression analysis. PLoS ONE. 2021;16:e0249978. CAAD. CAADP – Comprehensive Africa Agricultural Development Programme [Internet]. 2025 [cited 2025 Mar 12]. Available from: https://caadp.org/ Africa Union. Africa Regional Nutrition Strategy 2015-2025 | African Union [Internet]. 2022 [cited 2025 Mar 12]. Available from: https://au.int/en/documents/20220401/africa-regional-nutrition-strategy-2015-2025 Zegeye B, Anyiam FE, Ahinkorah BO, Ameyaw EK, Budu E, Seidu A-A, et al. Prevalence of anemia and its associated factors among married women in 19 sub-Saharan African countries. Arch Public Health. 2021;79:214. Pasricha S-R, Low M, Thompson J, Farrell A, De-Regil L-M. Iron Supplementation Benefits Physical Performance in Women of Reproductive Age: A Systematic Review and Meta-Analysis. J Nutr. 2014;144:906–14. Stephen G, Mgongo M, Hussein Hashim T, Katanga J, Stray-Pedersen B, Msuya SE. Anaemia in Pregnancy: Prevalence, Risk Factors, and Adverse Perinatal Outcomes in Northern Tanzania. Anemia. 2018;2018:1846280. Abdallah F, John SE, Hancy A, Paulo HA, Sanga A, Noor R, et al. Prevalence and factors associated with anaemia among pregnant women attending reproductive and child health clinics in Mbeya region, Tanzania. PLOS Glob Public Health. 2022;2:e0000280. Munyogwa MJ, Gibore NS, Ngowi AF, Mwampagatwa IH. Routine uptake of prenatal iron-folic acid supplementation and associated factors among pregnant women in peri-urban areas of Dodoma City, Tanzania: a cross-sectional study. BMC Pregnancy Childbirth. 2024;24:673. Eliufoo E, Majengo V, Tian Y, Bintabara D, Moshi F, Li Y. Determinants of adequate antenatal care visits among pregnant women in low-resource setting: evidence from Tanzania national survey. BMC Pregnancy Childbirth. 2024;24:790. Ngasala B, Matata F, Mwaiswelo R, Mmbando BP. Anemia among Schoolchildren with Malaria and Soil-Transmitted Helminth Coinfections after Repeated Rounds of Mass Drug Administration in Muheza District, Tanzania. Am J Trop Med Hyg. 2019;101:1148–55. Shija AE, Rumisha SF, Oriyo NM, Kilima SP, Massaga JJ. Effect of Moringa Oleifera leaf powder supplementation on reducing anemia in children below two years in Kisarawe District, Tanzania. Food Sci Nutr. 2019;7:2584–94. Sunguya BF, Ge Y, Mlunde L, Mpembeni R, Leyna G, Huang J. High burden of anemia among pregnant women in Tanzania: a call to address its determinants. Nutr J. 2021;20:65. Lema EJ, Seif SA. Prevalence of anemia and its associated factors among pregnant women in Ilala Municipality - Tanzania: Analytical cross-sectional study. Medicine (Baltimore). 2023;102:e33944. Determinants of anemia among women of childbearing age: analysis of the 2018 Mali demographic and health survey | Archives of Public Health [Internet]. [cited 2025 May 22]. Available from: https://link.springer.com/article/10.1186/s13690-023-01023-4 Tamhane AR, Westfall AO, Burkholder GA, Cutter GR. Prevalence odds ratio versus prevalence ratio: choice comes with consequences: Prevalence odds ratio versus prevalence ratio. Stat Med. 2016;35:5730–5. Merid MW, Chilot D, Alem AZ, Aragaw FM, Asratie MH, Belay DG, et al. An unacceptably high burden of anaemia and it’s predictors among young women (15–24 years) in low and middle income countries; set back to SDG progress. BMC Public Health. 2023;23:1292. Let S, Tiwari S, Singh A, Chakrabarty M. Prevalence and determinants of anaemia among women of reproductive age in Aspirational Districts of India: an analysis of NFHS 4 and NFHS 5 data. BMC Public Health. 2024;24:437. Paramastri R, Hsu C-Y, Lee H-A, Lin L-Y, Kurniawan AL, Chao JC-J. Association between Dietary Pattern, Lifestyle, Anthropometric Status, and Anemia-Related Biomarkers among Adults: A Population-Based Study from 2001 to 2015. Int J Environ Res Public Health. 2021;18:3438. Yosef T, Gizachew A, Fetene G, Girma D, Setegn M, Tesfaw A, et al. Infectious and obstetric determinants of anemia among pregnant women in Southwest Ethiopia. Front Glob Womens Health [Internet]. 2024 [cited 2025 Mar 12];5. Available from: https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2024.1421884/full Basrowi RW, Zulfiqqar A, Sitorus NL. Anemia in Breastfeeding Women and Its Impact on Offspring’s Health in Indonesia: A Narrative Review. Nutrients. 2024;16:1285. Shimanda PP, Amukugo HJ, Norström F. Socioeconomic factors associated with anemia among children aged 6-59 months in Namibia. J Public Health Afr. 2020;11:1131. Alemu TG, Fentie EA, Belay DG, Asmamaw DB, Shewarega ES, Negash WD, et al. Socioeconomic inequality in the co-occurrence of anemia and stunting among adolescent girls aged 15–19 years in Sub-Saharan African countries: a decomposition analysis. BMC Public Health. 2025;25:573. Azinge IE, Ogunyemi A, Ogamba CF, Jimoh RO. Prevalence of anemia and associated factors among adults in a select population in Lagos, Southwest Nigeria. J Public Health Afr. 2023;14:2224. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Dec, 2025 Read the published version in Journal of Health, Population and Nutrition → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 07 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviewers invited by journal 06 Jul, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 20 Jun, 2025 First submitted to journal 19 Jun, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-6931713","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":482527373,"identity":"0dc8695f-b240-47e9-97d4-2448f21ad6b5","order_by":0,"name":"Elihuruma Eliufoo Stephano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDAC5gNAokCChx/ESSggRgtbApAwkJCRbABpMSBeC4ONAcg2BmK0GBxjPvjgg4EFj/H51YkfHhgwyPOLHSCkhS3ZcIaBBI/ZjbebJYAOM5w5O4GAlvs9ZtI8YC1nN4C0JBjcJqTlGI+Z9B+gFuMZZzf/IF4LMMR4DPh7txFniyTILz1ALRI3eLdZJBhIEPYLHyjEflTU2fP3n91880eFjTy/NAEtCCABVilBrHIQ4D9AiupRMApGwSgYSQAApSo968jTgS0AAAAASUVORK5CYII=","orcid":"","institution":"University of Dodoma","correspondingAuthor":true,"prefix":"","firstName":"Elihuruma","middleName":"Eliufoo","lastName":"Stephano","suffix":""},{"id":482527374,"identity":"18396322-00b1-48f3-b8c9-0f58c5688f6e","order_by":1,"name":"Theresia Wenati Ngunguru","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Theresia","middleName":"Wenati","lastName":"Ngunguru","suffix":""},{"id":482527375,"identity":"b12d7453-c3e8-4de6-99bd-d78d0ef6dda8","order_by":2,"name":"Jacktan Josephat Ruhighira","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Jacktan","middleName":"Josephat","lastName":"Ruhighira","suffix":""},{"id":482527376,"identity":"5a631b72-f247-47fa-9c91-324d2eea347e","order_by":3,"name":"Victoria Godfrey Majengo","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"Godfrey","lastName":"Majengo","suffix":""},{"id":482527377,"identity":"9454c067-cffc-4eb1-aa37-f34f6e1f941a","order_by":4,"name":"Leonard Kamanga Katalambula","email":"","orcid":"","institution":"University of Dodoma","correspondingAuthor":false,"prefix":"","firstName":"Leonard","middleName":"Kamanga","lastName":"Katalambula","suffix":""},{"id":482527378,"identity":"cc788491-170f-4c2a-a926-d76f3e90710f","order_by":5,"name":"Mtoro Jabar Mtoro","email":"","orcid":"","institution":"Dodoma Regional Referral Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mtoro","middleName":"Jabar","lastName":"Mtoro","suffix":""}],"badges":[],"createdAt":"2025-06-19 13:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6931713/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6931713/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41043-025-01177-7","type":"published","date":"2025-12-14T15:58:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86642772,"identity":"2396b0b7-45a2-4ef7-bb2d-ad177b707aee","added_by":"auto","created_at":"2025-07-14 08:31:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40845,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of anaemia among women of reproductive age in Tanzania\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6931713/v1/02910a1e73390f0febb4160c.png"},{"id":98243877,"identity":"46c4a9c6-c38c-4574-90ee-0cdd386ca31c","added_by":"auto","created_at":"2025-12-15 16:11:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1275231,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6931713/v1/5605b648-2e41-4978-9147-03332ce67789.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trend and determinants of anaemia among reproductive-age women in Tanzania (2004- 2022): A generalised Poisson regression analysis of demographic and health surveys","fulltext":[{"header":"Background","content":"\u003cp\u003eAnaemia is a condition in which the concentration of haemoglobin or the number of red blood cells is below the normal level to meet physiological needs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This condition remains one of the most significant public health challenges globally, affecting approximately 1.8\u0026nbsp;billion people worldwide, with 50.3\u0026nbsp;million years lost to disability, while women of reproductive age (WRA) bear a disproportionate burden [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to WHO data, anaemia affects 39.8% of children aged 6\u0026ndash;59 months globally, while 29.9% of WRA suffer from this condition worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite being recognised as a critical health concern for decades, global progress in reducing anaemia has been disappointingly slow, with prevalence declining only marginally from 31.2% in 2000 to 29.9% in 2019 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This stagnation persists despite the WHO's global nutrition target of reducing anaemia in WRA by 50% by 2025 [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A significant difference exists in anaemia prevalence between developed and developing nations, with rates of 9% and 43%, respectively [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The stagnation in lowering the prevalence of anaemia has led the WHO and UNICEF to extend the proposed target to 2030, aligning with the Sustainable Development Goals (SDGs).\u003c/p\u003e\u003cp\u003eMeanwhile, the prevalence of anaemia among WRA was included as an indicator in 2020 [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Pregnant women in developed countries show moderate rates, for example, 20% in Australia and 18% in the United States [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Developing nations report substantially higher figures, including Ethiopia at 50.1%, Pakistan at 76.7%, and Indonesia at 35.5% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe burden of anaemia is particularly pronounced in Sub-Saharan Africa (SSA), where approximately 41.74% of non-pregnant WRA and 54% of pregnant women are anaemic [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The reported prevalence is significantly higher than the global average. In some SSA countries, the prevalence exceeds 50%, making it one of the most severely affected regions by this condition worldwide [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The high anaemia burden in SSA results from a complex interplay of factors including widespread nutritional deficiencies (particularly iron, folate, and vitamin B12), endemic infectious diseases (malaria, hookworm, schistosomiasis), genetic hemoglobinopathies (sickle cell disease, thalassemias) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and structural determinants such as food insecurity, inadequate dietary diversity, limited healthcare access, and gender inequities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Regional initiatives, such as the Comprehensive Africa Agriculture Development Programme and the African Regional Nutrition Strategy, have incorporated anaemia reduction as a key objective [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Many SSA countries have implemented integrated anaemia control strategies, including iron supplementation through antenatal care, mass deworming campaigns, insecticide-treated bed net distribution, and food fortification programs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Despite these efforts, progress has been inconsistent across the region, with some countries showing modest improvements while others experience stagnation or even increases in anaemia prevalence [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnaemia is associated with reduced work capacity and productivity, compromised immune function, impaired cognitive performance, and a decreased quality of life [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. During pregnancy, anaemic women face increased risks of maternal mortality, postpartum haemorrhage, preterm birth, and delivering low birth weight infants [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The intergenerational impact is significant, as maternal anaemia contributes to poor fetal development, increased infant mortality, and compromised cognitive and physical development in children [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTanzania faces a significant anaemia challenge, a rate that surpasses global and regional averages, particularly in rural areas where prevalence reaches 47.3% in Moshi and exceeds 68% in Dar es Salaam [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite the implementation of various interventions, such as iron and folic acid supplementation during antenatal care [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], food fortification programs, school-based deworming, malaria prevention strategies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and nutritional education campaigns [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], challenges persist. Anaemia has remained an indirect cause of 14.5% of maternal deaths in Tanzania [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These challenges include limited healthcare access in rural areas, inconsistent supply chains for supplements, low awareness of anaemia causes, and persistent factors like poverty and food insecurity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Cultural dietary practices, high fertility rates, and insufficient dietary diversity further exacerbate the problem [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, decades of interventions have not sufficiently addressed critical knowledge gaps, as current research primarily offers cross-sectional snapshots without analysing long-term trends and the interaction of different determinants affecting anaemia prevalence, hence the rationale for this study. Given the significant geographical variations in prevalence and the limited understanding of the effectiveness of anaemia control programs, further research is needed to inform more effective and context-specific interventions that can contribute to national health targets and global sustainable development goals, ultimately reducing anaemia prevalence and its associated health burdens for WRA in Tanzania. Therefore, this study aimed to assess the trend and determinants of anaemia status among reproductive-age women in Tanzania using the 2004\u0026ndash;2022 Tanzania Demographic and Health Surveys (TDHS).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design, data source, population and sampling procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA nationally representative, cross-sectional study was conducted using secondary data from the TDHS spanning the years 2004/05, 2010, 2016, and 2022. The TDHS, carried out every five years, collects updated information on health and health-related topics. The target population for the TDHS includes women of reproductive age (15-49 years), men, children and households. However, this study solely focused on women of reproductive age. The sample design for the DHS followed a two-stage sampling procedure aimed at providing national, urban, and rural estimates for both the Tanzania Mainland and Zanzibar. The study used a stratified, two-stage sampling method. In the first stage, sampling points (clusters) were chosen from enumeration areas (EAs) based on the previous Tanzania Population and Housing Census (PHC), using a probability proportional to size (PPS) sampling technique. Regions were grouped into zones to minimise sampling errors and allow for more accurate estimates at the zonal level. Tanzania was divided into strata based on geographical regions and urban/rural classification, with each stratum treated as a separate sampling domain. In the second stage, households were systematically selected from each cluster. To adjust for unequal selection probabilities and non-responses, individual weights were calculated for each respondent. For this study, we utilised the individual records (IR) file extracted from each survey round. We considered women from households that were selected for haemoglobin measurement, ensuring that haemoglobin results were available for analysis. In 2004/05, 10,139 women were selected, in 2010, 9,875; in 2015/16, 13,064 women; and 2022, 7,554 women (weighted sample). Overall, a total of 40,632 weighted women were included in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable measurements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variable:\u003c/strong\u003e The outcome variable measured was the level of anaemia. In TDHS, blood samples were collected from women who voluntarily provided their consent to undertake the haemoglobin test. After a finger prick, blood was drawn into a microcuvette for on-site analysis using a battery-operated, portable HemoCue analyser.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBlood haemoglobin values were adjusted by altitude among women of childbearing age using this equation [26].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHb-adjustment (g/dl)\u0026thinsp;=\u0026thinsp;-0.032 x (altitude\u0026thinsp;\u0026times;\u0026thinsp;0.0032808)\u0026thinsp;+\u0026thinsp;0.022 x (altitude\u0026thinsp;\u0026times;\u0026thinsp;0.0032808)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFor this study, anaemia was defined as a blood haemoglobin level below 12.0 g/dL, aligning with the World Health Organisation (WHO) definition. The WHO categorises anaemia severity as mild (11.0\u0026ndash;11.9 g/dL), moderate (8.0\u0026ndash;10.9 g/dL), and severe (\u0026lt; 8.0 g/dL). However, for the purpose of our analysis, the outcome variable was recoded into a binary response: \u0026quot;Not anaemic\u0026quot; (coded as 0) for individuals with a blood haemoglobin level of 12.0 g/dL or higher, and \u0026quot;Anaemic\u0026quot; (coded as 1) for those with a blood haemoglobin level below 12.0 g/dL. Altitude correction was applied to haemoglobin levels, with the threshold for anaemia set under 12 g/dL after this adjustment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndependent variables:\u0026nbsp;\u003c/strong\u003eWe examined variables based on the available data and literature. The following variables were included under the individual level; age in years(15-24, 25-34 or 35-49), education level (no formal education, primary, secondary, or higher), marital status (Currently married, cohabiting or previously married), literacy (illiterate or literate), wealth index (poorest, poorer, middle, richer or richest), parity (none, 1-2, \u0026ge;3), working status (working or not working), sex of household head (male or female), current pregnant status (yes or no), ever terminated a pregnancy (yes or no), current contraceptive use (none or any method), currently breastfeeding (yes or nor), covered by health insurance (yes or no), place of residence (urban or rural) and geographical zones (western, northern, central, lake, southern, eastern or Zanzibar).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection procedure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood specimens for anaemia testing were collected from eligible women aged 15\u0026ndash;49 years who consented to the testing. Haemoglobin analysis was carried out on-site using a battery-operated portable analysis device (HemoCue\u0026reg; 201+ photometer), which produces a result in less than 1 minute. HemoTrol\u0026reg; controls were used as quality control materials for haemoglobin measurements using a HemoCue\u0026reg; 201+ photometer. Results were provided verbally and in writing to those being tested. Women were referred for follow-up care if their haemoglobin levels were \u0026lt;8 g/dl.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to performing any statistical analysis, the data were weighted by incorporating the individual sampling weight (v005/1,000,000), primary sampling unit, and stratum. All analyses were performed using STATA version 18.5 (STATA Corp, College Station, TX). Descriptive statistics were summarised using means, standard deviation (SD) and frequency and proportion for categorical variables. A generalised Poisson regression model with robust variance estimator was used to identify factors associated with anaemia, and the results were presented as a prevalence ratio with corresponding 95% Confidence Intervals (CI). We used this model as the odds ratio estimated using logistic regression in a cross-sectional study might overestimate the association when the outcome is common (\u0026gt;10%) [27]. Univariate analyses were performed by fitting each explanatory variable against the dependent variable (anaemia) to produce a Crude prevalence ratio (CPR). A p-value of \u0026lt;0.2 was used to identify covariates to be included in the final multivariable analyses. A variance inflation factor (VIF) was used to assess multicollinearity between independent variables before fitting a multivariable regression model. Finally, a multivariable generalised Poisson regression model was fitted to estimate the adjusted Prevalence Ratio (APR) with corresponding 95% Confidence Intervals (CI) to assess the strength and magnitude of the association. Given this, DHS surveys are conducted approximately every five years in the same settings, so some women may have participated in multiple survey rounds. To avoid potential issues with non-independent observations, our regression analysis focused solely on the most recent data (the 2022 TDHS). Statistical significance was set at p-value \u0026lt;0.05.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 30,493 women were analysed, with an average age of 28.9 years (standard deviation = 9.8). More than one-third of the participants were aged 15-24 years: 41.1% in 2004/05, 40.4% in 2010, 40.75% in 2015/16, and 38.8% in 2022. Over half of the women had completed primary education, with 67.2% in 2004/05, 65.0% in 2010, 62.1% in 2015/16, and 53.7% in 2022. Additionally, the majority (\u0026ge;70.0%) were literate in each survey round, and a quarter (25.0%) were nulliparous. Approximately 70.0% of households were headed by men. Less than one-third of the women used contraceptives in each of the survey rounds, and fewer than 10% had health insurance coverage. Most women lived in rural areas, with the smallest proportion coming from Zanzibar (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Sociodemographic characteristics of women of reproductive age across Tanzania demographic and health survey years [N=30,493]\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"687\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 517px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTDHS Year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004/05 [N=10,139]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010 [N=9,875]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015/16 [N=13,064]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022 [N=7,554]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4168 (41.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3986 (40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e5315 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2930 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3362 (33.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2980 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3827 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2273 (30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2609 (25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2909 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3922 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2352 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Mean (\u0026plusmn;SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e28.3 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e28.7 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e28.6 (9.7(\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e29.2 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; No formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2461 (24.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1891 (19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1910 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1216 (16.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6817 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6415 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8117 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4059 (53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Secondary/Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e861 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1570 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3037 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2279 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2321 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2452 (24.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3309 (25.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2063 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Married/Cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6841 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6261 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8086 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4544 (60.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Separated/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e977 (9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1162 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1670 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e947 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiteracy level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Illiterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3325 (32.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2918 (29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3021 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1503 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Literate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e6814 (67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6957 (70.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10043 (76.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6051 (80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9184 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e8832 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11934 (91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6858 (90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e955 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1044 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1130 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e696 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Not working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2155 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2150 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3600 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3175 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7984 (78.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e7725 (78.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9457 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4379 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2501 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2470 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3317 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1968 (26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2931 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2718 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3975 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2269 (30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; 3+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e4707 (46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4687 (47.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e5772 (44.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e3317 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently pregnant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e9064 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e8917 (90.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e11945 (91.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6971 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1075 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e958 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1119 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e583 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver terminated a pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e8409 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e8190 (82.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e10969 (84.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e6512 (86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1730 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1685 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2094 (16.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1042 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently breastfeeding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7117 (70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e7099 (71.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e9568 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e5825 (77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e3022 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2776 (28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3496 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1729 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1815 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1652 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2225 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1176 (15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1928 (19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1917 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2254 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1326 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1913 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1960 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2309 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1491 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1983 (19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2062 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2781 (21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1671 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2500 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2284 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3495 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1889 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2830 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2758 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e4682 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2640 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e7309 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e7118 (72.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e8382 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e4914 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Western\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1007 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e882 (8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1274 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e642 (8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Northern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1170 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1272 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1540 (11.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e796 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1077 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1022 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e1320 (10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e809 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Southern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2292 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2268 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2735 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1543 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Lake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e2679 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2597 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e3429 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e2229 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e1609 (15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1512 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e2366 (18.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e1282 (17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u0026nbsp; Zanzibar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e305 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e322 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 145px;\"\u003e\n \u003cp\u003e400 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e252 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePrevalence and Trend of anaemia among women of reproductive age in Tanzania\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall weighted prevalence of anaemia was 44.0% (95%CI: 43.0\u0026ndash;44.9) among women of reproductive age. Among these women, 28.0% (95% CI: 27.2\u0026ndash;28.8) had mild anaemia, 13.0% (95% CI: 12.4\u0026ndash;13.6) had moderate anaemia, and 1.5% (95% CI: 1.3\u0026ndash;1.7) had severe anaemia. Looking at the trend over time, the prevalence of anaemia was 48.4% in 2004/05 (95%CI:46.2-50.5), 40.1% in 2010 (95% CI: 38.4\u0026ndash;41.9), 44.8% in 2015/16 (95% CI: 43.4\u0026ndash;46.4), and 41.5% in 2022 (95% CI: 39.8\u0026ndash;43.3), (Figure 01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralised Poisson regression analysis for factors associated with anaemia\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the adjusted analysis, women with 1-2 children (APR=0.85, 95% CI: 0.76-0.96) and those with more than two children (APR=0.82, 95% CI: 0.72- 0.94) were less likely to have anaemia compared to their counterparts. Furthermore, women who were currently pregnant (APR = 1.40, 95% CI: 1.25-1.55) were more likely to have anaemia compared to their counterparts. In terms of socioeconomic status, women in the richest quintile were more likely to have anaemia compared to those in the poorest quintile (APR = 0.85, 95% CI: 0.74-0.98) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Generalised Poisson regression model for factors associated with anaemia among women of reproductive age in Tanzania, TDHS 2022 (N=7,554)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; 15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; 25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.93 (0.86-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.06 (0.95-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; 35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.92 (0.85-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.08 (0.96-1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; No formal education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Primary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.91 (0.83-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.92 (0.84-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Secondary/Higher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.99 (0.89-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.89 (0.80-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Married/Cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.89 (0.83-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.95 (0.85-1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Separated/Divorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.97 (0.87-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.08 (0.94-1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.08 (0.97-1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.02 (0.91-1.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.86 (0.79-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.85 (0.76-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026gt;=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.83 (0.77-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.82 (0.72-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Not working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.85 (0.80-0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.87 (0.81-0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently pregnant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.38 (1.25-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.40 (1.25-1.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEver terminated a pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.09 (0.98-1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.06 (0.96-1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCurrently breastfeeding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.89 (0.83-0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.04 (0.99-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWealth index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.84 (0.76-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.85 (0.76-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.85 (0.77-0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.84 (0.76-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.99 (0.89-1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.93 (0.83-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.96 (0.87-1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.85 (0.74-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.92 (0.86-0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.01 (0.91-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographical zones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Western\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Northern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.12 (0.96-1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.15 (0.99-1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Central\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.92 (0.78-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.95 (0.80-1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Southern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e0.86 (0.76-0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e0.92 (0.80-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Lake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.13 (0.99-1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.16 (1.02-1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.42 (1.25-1.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.52 (1.33-1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003e\u0026nbsp; Zanzibar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 134px;\"\u003e\n \u003cp\u003e1.58 (1.40-1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 125px;\"\u003e\n \u003cp\u003e1.32 (1.24-1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to assess the trend and determinants of anaemia status among reproductive-age women in Tanzania, using data collected from the TDHS from 2004 to 2022. The results present a significant public health concern regarding anaemia among women of reproductive age, which becomes more pronounced when examining the severity distributions of women who experienced mild, moderate, and severe anaemia. These stratified findings suggest that while the most severe form affects a relatively small percentage, the combined burden of mild and moderate anaemia impacts over four in ten women of reproductive age. This distribution pattern is critical for public health planning, as different severities may require distinct intervention approaches. These figures align closely with global data that indicate anaemia poses a significant health challenge for women worldwide, particularly in low- and middle-income countries [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The pooled prevalence from 19 SSA countries showed a higher prevalence than the pooled prevalence of this study [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another study highlighted that the prevalence of anaemia in low- and middle-income countries among women of reproductive age was slightly lower than this study\u0026rsquo;s reported prevalence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe trend analysis reveals notable fluctuations in anaemia prevalence over a twelve-year period, suggesting complex underlying factors affecting women's anaemia status. Previous studies emphasise the fluctuating nature of anaemia prevalence, often linked to changes in economic conditions, dietary habits, and public health interventions that target nutritional deficits [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This nonlinear pattern raises important questions about intervening factors associated with these timeframes. The persistence of high rates despite potential interventions highlights the multifactorial nature of anaemia and suggests that existing approaches may be insufficient or inconsistently implemented [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings highlight the need for sustained, comprehensive strategies that address not only iron supplementation but also the underlying socioeconomic determinants and healthcare access that influence women's nutritional status.\u003c/p\u003e\u003cp\u003eThis study revealed several factors that are associated with anaemia among WRA. Age appears to be a significant determinant, with younger women exhibiting a slightly lower prevalence of anaemia compared to their older counterparts. This finding is consistent with other studies indicating that younger women generally have better nutritional statuses, potentially due to a lower incidence of chronic diseases and fewer reproductive complications compared to older age groups [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings were contrary to those of other studies as well [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Educational attainment emerges as another crucial factor, with women lacking formal education experiencing a higher anaemia prevalence than those with secondary or higher education, similar to other studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], but contrary to the study by Let [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This educational gradient likely reflects broader socioeconomic disparities in nutritional knowledge, healthcare access, and dietary quality. Nutritional status also plays a key role, as underweight women (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;) demonstrated a higher prevalence of anaemia compared to women with normal weight, suggesting that overall nutritional inadequacy may contribute to iron deficiency [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eReproductive factors appear to substantially influence the risk of anaemia among women, similar to the study in Northern Tanzania [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Current pregnancy stands out as the strongest risk factor identified in this analysis, with pregnant women experiencing a 27% higher prevalence of anaemia compared to non-pregnant women. This pronounced association reflects the increased iron demands during pregnancy to support fetal development and maternal blood volume expansion [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A finding similar to the WHO, which identifies pregnant women as one of the most vulnerable groups for anaemia due to increased nutritional requirements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Women with a history of pregnancy termination also showed a higher anaemia prevalence, which may indicate the cumulative effect of reproductive blood loss or potentially underlying health conditions [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThough the findings showed increased prevalence of anaemia among breastfeeding women, likely reflecting the nutritional demands of lactation. Several studies, which were pooled in the narrative review in Indonesia, showed that breastfeeding was associated with anaemia and warrant further investigation on the impact on neonatal wellbeing [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSocioeconomic disparities emerge as significant determinants of anaemia risk, with women in the poorest wealth quintile experiencing a higher prevalence compared to those in the richest quintile, similar to the study conducted in Namibia [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This substantial socioeconomic gradient likely reflects multiple intersecting disadvantages, including limited access to iron-rich foods, inadequate healthcare services, a higher burden of infectious diseases, and poorer sanitation conditions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The findings collectively paint a picture of anaemia as a condition deeply rooted in social determinants of health, disproportionately affecting vulnerable populations, including the poor, undereducated, and those with greater reproductive demands [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A significant study in Nigeria found similar correlations, wherein economic disparities led to pronounced differences in health outcomes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These results emphasise the need for targeted interventions that address not only iron supplementation but also the underlying social, economic, and educational factors that contribute to anaemia risk among WRA.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and limitations of the study\u003c/h2\u003e\u003cp\u003eThis study is one of the few to report on the trends in prevalence and predictors of anaemia among reproductive-aged women in Tanzania at a national level, utilising a large enough sample. The measurements were taken using standardised national tools and methods, and trained experts conducted data collection. However, since the data are cross-sectional, it is not possible to establish cause-and-effect relationships. Additionally, most of the responses were self-reported, and recall and socio-desirability biases may be present.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImplications for clinical and community practice\u003c/h2\u003e\u003cp\u003eThe clinical implications of these findings are substantial for healthcare providers working with WRA. The strong associations between anaemia and pregnancy, education level, nutritional status, and socioeconomic position highlight the need for targeted screening protocols. Clinicians should implement more vigilant haemoglobin monitoring for high-risk groups, particularly pregnant women and those from lower socioeconomic backgrounds. The identified risk factors suggest that effective clinical management must extend beyond iron supplementation to include comprehensive nutritional counselling, especially for underweight women. Healthcare providers should also recognise that women with a history of pregnancy termination require additional attention, as they demonstrate a 5% higher anaemia prevalence. These findings call for the integration of anaemia prevention into reproductive health services, with special consideration for women with multiple risk factors who may benefit from more intensive interventions.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, these results highlight anaemia as both a clinical and public health challenge requiring multi-sectoral approaches. The persistent associations across demographic, socioeconomic, and reproductive domains suggest that singular interventions are unlikely to sufficiently address the 42.5% prevalence among WRA. While iron supplementation remains important, especially during pregnancy, sustainable reductions in anaemia will likely require broader social interventions addressing educational disparities and poverty. The findings also emphasise the importance of longitudinal monitoring, considering the fluctuating trends observed between 2004 and 2022. Future clinical guidelines should incorporate these risk stratifications to ensure efficient resource allocation and targeted care, particularly in settings where healthcare resources are limited. Ultimately, reducing the burden of anaemia will require coordinated efforts across clinical care, public health programming, and social policy to address both immediate nutritional needs and underlying determinants.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eAdjusted Prevalence ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eConfidence intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eCPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eCrude prevalence ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eDemographic Health Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eEnumeration areas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003ePopulation and Housing Census\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePSU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003ePrimary sampling unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eProbability proportional to size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eStandard deviations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eSub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSDG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eSustainable Development Goals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eTDHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eTanzania Demographic and Health Surveys\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eUNICEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eUnited Nations International Children\u0026apos;s Emergency Fund\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eVariance inflation factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 516px;\"\u003e\n \u003cp\u003eWomen of reproductive age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the DHS program for making the data available for this study, and TILAM International for statistical consultation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMJM and EES conceptualised the idea and conducted the formal analysis. EES, TWN, JJR, VGM, LKK and MJM supported interpretation and reviewed subsequent versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors without undue reservation. The complete dataset is available at https://dhsprogram.com\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized publicly available, de-identified data from the 2004 to 2022 TDHS, accessible online through the DHS program. The original study received ethical clearance and research permit from both the National Institute of Medical Research Ethics Committee in Tanzania and the ICF Macro Ethics Committee in Calverton, New York. Permission to use the data for this secondary analysis was granted by the DHS program upon acceptance of the proposed analysis plan under the designated account, with credentials available upon request via https://dhsprogram.com/data/dataset_admin/index.cfm. As this study involved secondary data analysis of publicly accessible datasets, no additional ethical approval was required. Informed consent was obtained from all participants during the initial survey, and all procedures adhered strictly to relevant guidelines and regulations. Further details regarding DHS data usage and ethical standards can be found at http://goo.gl/ny8T6X.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZavala E, Adler S, Wabyona E, Ahimbisibwe M, Doocy S. Trends and determinants of anemia in children 6\u0026ndash;59 months and women of reproductive age in Chad from 2016 to 2021. BMC Nutr. 2023;9:117. \u003c/li\u003e\n\u003cli\u003eWHO. Global nutrition targets 2025: anaemia policy brief [Internet]. 2025 [cited 2025 Mar 12]. 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Front Glob Womens Health [Internet]. 2024 [cited 2025 Mar 12];5. Available from: https://www.frontiersin.org/journals/global-womens-health/articles/10.3389/fgwh.2024.1421884/full\u003c/li\u003e\n\u003cli\u003eBasrowi RW, Zulfiqqar A, Sitorus NL. Anemia in Breastfeeding Women and Its Impact on Offspring\u0026rsquo;s Health in Indonesia: A Narrative Review. Nutrients. 2024;16:1285. \u003c/li\u003e\n\u003cli\u003eShimanda PP, Amukugo HJ, Norstr\u0026ouml;m F. Socioeconomic factors associated with anemia among children aged 6-59 months in Namibia. J Public Health Afr. 2020;11:1131. \u003c/li\u003e\n\u003cli\u003eAlemu TG, Fentie EA, Belay DG, Asmamaw DB, Shewarega ES, Negash WD, et al. Socioeconomic inequality in the co-occurrence of anemia and stunting among adolescent girls aged 15\u0026ndash;19 years in Sub-Saharan African countries: a decomposition analysis. BMC Public Health. 2025;25:573. \u003c/li\u003e\n\u003cli\u003eAzinge IE, Ogunyemi A, Ogamba CF, Jimoh RO. Prevalence of anemia and associated factors among adults in a select population in Lagos, Southwest Nigeria. J Public Health Afr. 2023;14:2224. \u003c/li\u003e\n\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":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anaemia, Reproductive-age, Women, Trend, Tanzania","lastPublishedDoi":"10.21203/rs.3.rs-6931713/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6931713/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eGlobally, approximately 30% of women of reproductive age are affected by anaemia. Anaemia is of major public health concern due to its strong association with increased morbidity and mortality among women of reproductive age. This study aimed to examine the trends and factors influencing anaemia among women of childbearing age in Tanzania.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eAn analytical cross-sectional study was conducted using data from the Tanzania Demographic and Health Surveys collected between 2004/05, 2010, 2016, and 2022. The study included 40,632 women of reproductive age who were selected for haemoglobin measurements. Two stage sampling was used to select survey participants. A Generalised Poisson regression model was used to identify factors associated with anaemia. Adjusted prevalence ratios (APR) with 95% Confidence Intervals (CI) were calculated to estimate the strength of the association.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe overall pooled prevalence of anaemia was 44.0% (95% CI: 43.0\u0026ndash;44.9) among women of reproductive age. Among these women, 29.2% (95% CI: 28.4\u0026ndash;29.9) had mild anaemia, 13.4% (95% CI: 12.9\u0026ndash;13.9) had moderate anaemia, and 1.4% (95% CI: 1.3\u0026ndash;1.6) had severe anaemia. Looking at the trend over time, the prevalence of anaemia was 48.4% IN 2004/05 (95%CI:46.2\u0026ndash;50.5), 40.1% in 2010 (95% CI: 38.4\u0026ndash;41.9), 44.8% in 2015/16 (95% CI: 43.4\u0026ndash;46.4), and 41.5% in 2022 (95% CI: 39.8\u0026ndash;43.3).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe findings demonstrate anaemia as both a clinical and public health challenge, requiring multi-sectoral approaches. The persistent associations across demographic, socioeconomic, and reproductive domains suggest that singular interventions are unlikely to address this prevalence sufficiently. Reducing the burden of anaemia will require coordinated efforts across clinical care, public health programming, and social policy to address both immediate nutritional needs and underlying determinants.\u003c/p\u003e","manuscriptTitle":"Trend and determinants of anaemia among reproductive-age women in Tanzania (2004- 2022): A generalised Poisson regression analysis of demographic and health surveys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 08:31:00","doi":"10.21203/rs.3.rs-6931713/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-18T08:59:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T04:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207536453303365329822099747667907002344","date":"2025-07-07T16:03:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247408709321953218874412061341142400696","date":"2025-07-07T01:20:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T13:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338178431340213334682995483168826198765","date":"2025-07-06T13:02:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22777029595031238932186420829922770139","date":"2025-07-06T12:40:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-06T12:15:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T07:18:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T07:15:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Health, Population and Nutrition","date":"2025-06-19T13:24:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-health-population-and-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"johp","sideBox":"Learn more about [Journal of Health, Population and Nutrition](http://jhpn.biomedcentral.com/)","snPcode":"41043","submissionUrl":"https://submission.nature.com/new-submission/41043/3","title":"Journal of Health, Population and Nutrition","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5a28bd43-8c40-4505-956b-2995019c9b5d","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:03:45+00:00","versionOfRecord":{"articleIdentity":"rs-6931713","link":"https://doi.org/10.1186/s41043-025-01177-7","journal":{"identity":"journal-of-health-population-and-nutrition","isVorOnly":false,"title":"Journal of Health, Population and Nutrition"},"publishedOn":"2025-12-14 15:58:33","publishedOnDateReadable":"December 14th, 2025"},"versionCreatedAt":"2025-07-14 08:31:00","video":"","vorDoi":"10.1186/s41043-025-01177-7","vorDoiUrl":"https://doi.org/10.1186/s41043-025-01177-7","workflowStages":[]},"version":"v1","identity":"rs-6931713","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6931713","identity":"rs-6931713","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}