Objectives To examine the patterns of social inequalities in anaemia over time among women of reproductive age in India.
Design Repeated cross-sectional study using nationally representative data from the 1998/1999 and 2005/2006 National Family Health Surveys of India. Multivariate modified Poisson regression models were used to assess trends and social inequalities in anaemia.
Population 164 600 ever-married women aged 15–49 years (n=79 197 in 1998/1999 and n=85 403 in 2005/2006) from 25 Indian states.
Main outcome measure Anaemia status defined by haemoglobin level (<12 g/dl in non-pregnant women, haemoglobin<11 g/dl for pregnant women).
Results Over the 7-year period, anaemia prevalence increased significantly from 51.3% (95% CI 50.6% to 52%) to 56.1% (95% CI 55.4% to 56.8%) among Indian women. This corresponded to a 1.11-fold increase in anaemia prevalence (95% CI 1.09 to 1.13) after adjustment for age and parity, and 1.08-fold increase (95% CI 1.06 to 1.10) after further adjustment for wealth, education and caste. There was marked state variation in anaemia prevalence; in only 4 of the 25 states did anaemia prevalence significantly decline. In both periods, anaemia was socially patterned, being positively associated with lower wealth status, lower education and belonging to scheduled tribes and scheduled castes. In this context of overall increasing anaemia prevalence, adjusted relative and absolute socioeconomic inequalities in anaemia by wealth, education and caste have narrowed significantly over time.
Conclusions The significant increase in anaemia among India's women during this recent period is a matter of concern, and in contrast to secular improvements in other markers of women's health and nutritional status. While socioeconomic inequalities in anaemia persist, the relative and absolute inequalities in anaemia have decreased over time. Future research should explore the causes for these changing patterns, and inform the policy and programmatic response to address anaemia and its inequalities in this vulnerable population.
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Anaemia is a major global health issue, primarily affecting women of reproductive age and children. In India, more than half of women of reproductive age are anaemic.
While anaemia disproportionally affects disadvantaged groups, it is not known how patterns of inequalities in anaemia have changed over time.
India's prevalence of anaemia among women of reproductive age has increased significantly over a recent 7-year period, even after adjustment for age, parity, wealth, education, caste and residence.
Socioeconomic inequalities in anaemia (by wealth, education and caste), using both absolute and relative metrics, have decreased over time.
Future studies, which explore the causes for narrowing inequalities in anaemia and explore why trends in anaemia are increasing, are needed to guide the policy response.
Strengths and limitations of this study
Used large population-based nationally representative data with haemoglobin testing.
Measured both relative and absolute inequalities across different dimensions of social inequalities to assess changing patterns over time.
Unable to distinguish between the different causes of anaemia that may explain the changing patterns of inequalities.
Used repeated cross-sectional surveys standardised across the two time periods.
Anaemia persists as a significant public health problem affecting 1.62 billion of the world's population.1 The burden falls predominantly on Asia and Africa with the complex interplay of dietary factors, infectious disease, genetics and other factors determining anaemia status.2 Anaemia contributes to almost 120 000 maternal deaths globally; in low and middle income countries, 18% of maternal mortality is attributed to iron deficiency.3 Several adverse health outcomes have been associated with anaemia including maternal morbidity and mortality, perinatal and neonatal mortality, low birth weight and poor cognitive development.4 Women of reproductive age are physiologically more vulnerable to anaemia because of recurrent menstrual loss and the demands of pregnancy and repeated childbearing; global estimates suggest that the prevalence of anaemia is 41.8% among pregnant women and 30.2% among non-pregnant women.1
South Asia suffers from some of the highest rates of anaemia worldwide,5 and in India, more than half are anaemic and around one-third of women of reproductive age are underweight.6 In this region burdened by high maternal mortality,7 around 13% of maternal deaths are attributable to anaemia.8 Given the importance of addressing the nutritional and health needs of women of reproductive age, understanding the trends and patterns of anaemia within this population is important for meeting Millennium Development Goal 5, which pertains to improving maternal health.
While several small epidemiological studies have focused on determining the prevalence and causes of anaemia among specific subpopulations of Indian women,9–14 population-based surveys such as the National Family Health Surveys (NFHS) and District Level Household and Facility Surveys (DLHS) have been more useful in monitoring the national burden of anaemia.15–20 Understanding the social patterning of anaemia and heterogeneity among different groups and across states, and exploring the changing patterns of anaemia distribution over time may help elucidate which factors drive these changes, and identify interventions to address this significant health burden.
Therefore, we set out to examine the national and state trends in anaemia prevalence and examine how social inequalities in anaemia among women of reproductive age have changed over time during a recent 7-year period. This study adds to the existing literature in several ways: first, by using nationally representative data of 164 600 women from two repeated cross-sectional surveys to examine trends in social inequalities over time; second, by using both relative and absolute metrics to capture different dimensions of social inequalities over time; third, by conducting analyses at both the national and state levels to investigate anaemia trends over time and fourth, by using modified Poisson regression models to more accurately estimate prevalence ratios.
We used data from the NFHS of India carried out in 1998/2009 (NFHS-2) and 2005/2006 (NFHS-3).21 These nationally representative cross-sectional surveys collect detailed information relating to population, health and nutrition. The surveys use a stratified cluster multistage sampling design, with generally two stages in rural areas and three stages in urban areas. In brief, in rural areas, in the first stage, primary sampling units (PSU) are selected using a probability proportional to population size; in the second stage, households are systematically selected for inclusion in the survey. In urban areas, in the first stage, wards are selected using a probability proportional to population size; in the second stage, a census enumeration block is selected randomly from each ward; and in the third stage, households are selected randomly from each census enumeration block for inclusion in the survey. More details of the sampling methodology are described elsewhere.21 NFHS-2 was restricted to ever-married women aged 15–49 years, whereas NFHS-3 included all women aged 15–49 years.
Both surveys were carried out by trained fieldworkers using standardised questionnaires and methodologies, and the household response rate was greater than 95% in both surveys. NFHS-2 surveyed 90 303 ever-married women from 92 486 households from the 26 states; NFHS-3 surveyed 124 385 women from 109 041 households in all 29 states of India (between the time of the surveys, three new states were formed: Jharkhand was created out of Bihar, Uttaranchal (Uttarakhand) from Uttar Pradesh and Chhattisgarh from Madhya Pradesh).
Informed consent was obtained from participants at the time of interview, and further consent was obtained prior to blood testing. All survey participants received an informational leaflet at the time of anaemia testing; women diagnosed with severe anaemia were asked if they could be referred to local health services. This analysis was approved by the Institutional Review Board of the Harvard School of Public Health.
In NFHS-2, 80 851(89.5%) agreed to haemoglobin (Hb) testing, 3252 (3.6%) refused and 6200 (6.9%) were not sampled. Of those women who agreed to testing, Hb measurements were available for 80 672 women (99.8%) (89.3% of the total sample). In NFHS-3, Hb was measured on 90.6% of women surveyed (n=112 714): anaemia (and HIV) testing was not conducted in the state of Nagaland due to local opposition (3.13%; n=3 896); in the other states, 4.45% (n=5538) refused, 0.86% (n=1069) were not present for testing and in 0.94% (n=1168), Hb was not measured for other reasons. Extreme and physiologically implausible measurements (Hb<4g/dl and >20g/dl) were excluded (n=84 in NFHS-2; n=69 in NFHS-3). A further 674 in NFHS-2 and 376 in NFHS-3 were excluded due to missing data on other covariates used in this analysis.
In order to ensure comparability between the surveys, state boundaries from the time of the earlier survey were used, and the state of Nagaland was excluded from the analysis as it was not represented in the later survey, thus yielding a total of 25 states. The sample was restricted further to ever-married women to ensure comparability between the two surveys. Therefore, the final analytical sample consisted of 164 600 ever-married women from 25 states in India; with 79 197 women from NFHS-2 and 85 403 women from NFHS-3.
The main outcome variable was Hb level measured in g/dl, operationalised as a categorical variable by predefined cut-off points for mild, moderate and severe anaemia recommended by the WHO for women above age 15 years. For non-pregnant women, any anaemia was defined as Hb<12 g/dl, and for pregnant women as <11 g/dl: this included the categories of mild, moderate and severe anaemia. Mild anaemia was defined as 10–11.9 g/dl for non-pregnant women and 10–10.9 g/dl for pregnant women. Moderate/severe anaemia was defined as Hb<10 g/dl.22 Both non-pregnant and pregnant women were included in the analysis. Hb levels were adjusted for altitude and smoking status.23
Anaemia testing was conducted by specially trained personnel who were part of the survey team. The finger prick tests were carried out in the homes of the respondents, and blood samples were tested immediately using a portable hand-held HemoCue testing system. In NFHS-2, the HemoCue B-Hb system was used, whereas the newer HemoCue Hb 201+ analyser was used in NFHS-3.
The demographic, socioeconomic, cultural and behavioural covariates included in the analysis were age, marital status, parity, religion, caste, place of residence, wealth, education, occupation, body mass index (BMI), contraceptive use, vegetarianism and alcohol consumption, based on a review of the existing literature.
We focused on wealth, education and caste as key measures of socioeconomic status, based on previous work on social patterning of disease in India.24 We constructed a new wealth index from the 24 household asset data and household characteristics available in both surveys, and used this to categorise individuals into wealth quintiles (poorest, poorer, middle, rich, richest). Principal components analysis was used to assign each of the 24 assets and housing characteristics a factor score or weight derived from a covariance matrix. These weights were then used to estimate an asset score for each household.25 ,26 Households were ranked on their asset score for each survey, and individuals were then ranked according to their household.
Education was categorised based on years of schooling into none, 1–5 years, 6–10 years, 11–12 years, >=13 years, representing milestones in educational attainment. Caste was classified in to scheduled caste (SC), scheduled tribe (ST), other backward caste, general, other/missing, reflecting the categories most routinely used for population-based monitoring.27 Caste represents a social stratification based on hereditary status in Indian society, although such classification masks substantial heterogeneity within these groups.28 Place of residence was defined as either urban or rural.
Age was categorised into 5 years age groups. Parity, defined as the number of children ever born, was categorised as 0, 1, 2, 3, 4, 5 or more. Religion was categorised into Hindu, Muslim, Christian, Sikh, Buddhist and other/missing. BMI (kg/m2) was categorised using standard WHO classification into underweight <18.5 kg/m2, normal 18.5–24.9 kg/m2, overweight 25.0–29.9 kg/m2 and obese ≥30.0 kg/m2.29 Contraceptive use was categorised as no use, use of contraceptive pills, use of intrauterine device, female sterilisation or other. Vegetarianism (yes/no) was based on a self-report of never eating fish or meat products. Any alcohol consumption (yes/no) was based on a self-report of drinking alcohol.
We estimated the weighted prevalences of any anaemia and moderate/severe anaemia for each of the covariates using the survey-specific sample weights clustered at the PSU level for both surveys taking into account the survey design.
Trends in anaemia
We examined both relative and absolute changes in anaemia prevalence. We estimated adjusted prevalence ratios and 95% CIs from modified Poisson regression models, using the time of the first survey as the reference group. Given that the outcome of anaemia was not rare (>10%), the use of logistic models is less appropriate as the resultant ORs are a poor approximation of the prevalence ratio.30 ,31 Alternative approaches for estimating the prevalence ratio include log-binomial models or modified Poisson models. Owing to the failure of the log-binomial model to converge, we adopted a modified Poisson regression approach, although this approach is less efficient. Poisson models are considered appropriate for modelling rare events over time; however, when applied to binomial data, they can overestimate the error-term associated with the prevalence ratio. Therefore, the alternative approach of modified Poisson models, using a robust error variance procedure to more accurately estimate the SEs, was adopted.32 In these modified Poisson models, we first adjusted for age and parity, and then additionally controlled for wealth, education, caste and place of residence to estimate relative change. We estimated absolute change in anaemia prevalence and 95% CIs from linear regression models, using the time of the first survey as the reference group, adjusting for the same covariates. These modelling procedures were then carried out independently (25 models) for each state to examine the anaemia trends for each state.
Patterns of socioeconomic inequalities in anaemia
We first examined the correlates of anaemia for the covariates listed above using multivariable multilevel Poisson regression models, for each survey separately and then for the pooled sample. Adjusted prevalence ratios and 95% CIs for each covariate were estimated from these models.
We measured socioeconomic anaemia patterns using wealth, education and caste, making the normative judgement that these are important social constructs which influence the distribution of opportunities and, therefore, access to goods and services which can reduce exposure to the determinants of anaemia.33 Wealth, education and caste have shown to be important axes of stratification in Indian society,24 with independent associations with nutrition outcomes in this context.34 In unadjusted analysis, we assessed inequalities by wealth (poorest vs richest (reference)), education (no education vs most educated ≥13 years (reference)) and caste (ST vs general (reference)) for both surveys. Trends in relative and absolute inequalities over time were calculated using ((rate ratio (RR)2005−RR1998))/((RR 1998−1)) and ((rate difference (RD)2005−RD1998))/((RD1998−1)), respectively.35 In adjusted analysis, we applied the modified Poisson regression modelling method described above to pooled data from both surveys and tested for interactions between wealth, education and caste and the survey year, each in separate models, using Wald tests.
For the continuous variable of Hb, we then estimated the independent effects of wealth, education and caste on Hb level from multivariable multilevel linear regression models to see whether these associations have changed over time. (This analysis was restricted to non-pregnant women due to the variation in Hb during pregnancy.) Again, this was carried out for the pooled sample including interaction terms between socioeconomic variables and the survey year.
In sensitivity analyses, we also used an alternate stepwise modelling approach, using the significance level of p<0.05, to justify the inclusion of covariates into the regression models.
Statistical significance was considered at p<0.05; all tests were double-sided. Survey sample weights were applied. Regression models included a random effect at the PSU level to account for clustering at the PSU and state fixed effects. Analyses were carried out using STATA: Release V.10 (StataCorp LP, CollegeStation, Texas, USA).
Table 1 shows the weighted distributions of anaemia prevalence among women of reproductive age in India for both surveys. Between 1998/1999 and 2005/2006, the prevalence of all severities of anaemia increased among ever-married women in India. The prevalence of any anaemia increased from 51.3% (95% CI 50.6% to 52.0%) in 1998/1999 to 56.1% (95% CI 55.4% to 56.8%) in 2005/2006. The prevalence of mild anaemia increased from 35.0% (95% CI 34.4% to 35.5%) to 38.9% (95% CI 38.3% to 39.5%), while the prevalence of moderate/severe anaemia increased from 16.3% (95% CI 15.9% to 16.8%) to 17.2% (95% CI 16.8% to 17.7%). The prevalence of anaemia among pregnant women increased from 49.2% (95% CI 47.5% to 51.0%) in 1998/1999 to 57.8% (95% CI 56.1% to 59.6%) in 2005/2006, and among non-pregnant women, it increased from 51.4% (95% CI 50.7% to 52.2%) in 1998/1999 to 56.0% (95% CI 55.3% to 56.7%) in 2005/2006.
After adjustment for age and parity, the prevalence of anaemia increased 1.11-fold, (95% CI 1.09 to 1.13), and 1.08-fold (95% CI 1.06 to 1.10) after further adjustment for wealth, education and caste. Correspondingly, between the two periods, there was a significant decline in the mean Hb of women of reproductive age (p<0.001) from 11.7 (95% CI 11.7 to 11.7) to 11.5g/dl (95% CI 11.5 to 11.6) among non-pregnant women (p<0.001), and from to 10.9 (95% CI 10.8 to 10.9) to 10.6 (95% CI 10.5 to 10.6) g/dl among pregnant women (p<0.001).
Next, we examined the changes in the prevalence of anaemia (table 2; see online supplementary figure S1) and changes in Hb level by state (see online supplementary table S1). We found marked regional variation in anaemia prevalence, ranging in 2005/2006 from 69.4% in Assam, 68.7% in Bihar and 67.5% in Tripura, to 32.7% in Kerala, 38.4% in Punjab and 38.8% in Goa. In 14 of the 25 states, there was a significant increase in the adjusted relative risk of anaemia over time, with a significant increase in the adjusted absolute difference in 13 states. In the states of Himachal Pradesh, Manipur, Sikkim, Andhra Pradesh and Kerala, the adjusted absolute difference in prevalence between the two surveys was over 10% points. Only in the four states of Punjab, Arunachal Pradesh, Meghalaya and Mizoram did the relative prevalence of anaemia decrease and absolute prevalence of anaemia decrease significantly over this 7-year period.
We analysed the correlates of anaemia for both surveys (table 3; see online supplementary table S2). Adolescent girls (age 15–19 years) had the highest prevalence of anaemia compared with the other age groups. Anaemia was positively associated with increasing parity (adjusted p for trend<0.001). Muslims and Christians were significantly less likely to have any anaemia, in comparison to Hindus. There was a negative association between BMI and any anaemia (adjusted p for trend<0.001).
At both timepoints, the prevalence of anaemia was positively associated with decreasing relative wealth and decreasing educational level (adjusted p for trend<0.001). Members of STs and SCs were more likely to have any anaemia, even after controlling for wealth and education. The wealth gradient in anaemia was consistently the strongest of these socioeconomic correlates.
Examining the inequalities in anaemia over time, in unadjusted analyses, both relative and absolute inequalities in anaemia status decreased over time for wealth (by 19% and 11%, respectively), education (by 21% and 8%, respectively) and by caste groups (see online supplementary table S3). This pattern was also observed in adjusted analyses. Relative inequalities in anaemia had decreased over time by wealth (p for interaction ≤ 0.001), education (p for interaction=0.0013) and caste (p for interaction=0.0001) (see online supplementary table S4) The adjusted differential in Hb between women in the highest and lowest wealth quintiles was greater in 1998/1999 (0.35 g/dl) than in 2005/2006 (0.25 g/dl), and the differential between the most educated and least educated was 0.16 g/dl in 1998/1999 compared with 0.11 g/dl in 2005/6 (see online supplementary table S5).
The prevalence of anaemia among women of reproductive age has increased significantly during a recent 7-year period in India. This increase was apparent across socioeconomic groups. There was geographical variation in anaemia trends, with a significant increase seen in 14 of the 25 states and significant decrease in four states. Anaemia remains socially patterned; however, both relative and absolute inequalities in anaemia, along the axes of wealth, education and caste, have decreased over this time.
Comparison with existing studies
Trends in anaemia prevalence
The increasing trends are of concern, more so as, since the mid-1980s, the anaemia burden was predicted to improve in India.36 Based on an interpolation from regression models, the predicted change in anaemia prevalence was −0.38% points/year for non-pregnant women and −0.36% points/year for pregnant women36 between 1995 and 2000. However, extrapolating from our analysis, the anaemia prevalence in India is increasing by 0.65% points/year for non-pregnant women and 1.23% points/year for pregnant women. Moreover, this increase in anaemia prevalence has occurred in the context of a comprehensive anaemia policy framework that has existed in India since the 1970s (see online supplementary table S6), as well as rapid economic development and increased investment in health through national health initiatives.
Furthermore, over this time period, there have been improvements in several indicators relating to women's nutrition and health, such increasing BMI, increasing utilisation of antenatal care and iron and folic acid supplementation, increasing use of contraception, as well as increased age at marriage and decreased total fertility rate.27 However, such a pattern of anaemia increase is consistent with studies from other transitional countries37 and from other developing countries where Demographic and Health Surveys have been carried out.21
Social inequalities in anaemia
We demonstrated that anaemia continues to be socially patterned, and such findings build on and are consistent with other studies from India. For example, Bharati et al19 ,20, have highlighted the higher prevalence of anaemia in women with lower education, lower wealth and for select caste groups. This picture has also been supported by an analysis of seven states of India,18 and an analysis of the burden of anaemia in the state of Andhra Pradesh.15 Recent analysis of the five Eastern states of India using NFHS-3 survey corroborated the socioeconomic gradient of anaemia, and highlighted the vulnerability of the urban poor.16
We found that wealth is consistently the strongest marker of anaemia status, more so than education and caste. This finding is consistent with a previous study of social inequalities in child survival in India, which, in a decomposition analysis, identified poor household wealth as the greatest factor contributing to social inequalities (46%), followed by mother's illiteracy and rural residence.38
From regression models testing for interactions between wealth, education, caste and survey year, we found that the change in relative inequalities in anaemia for each of these predictors was significant. Our finding of narrowing inequalities in anaemia over time was unexpected. While considerable attention has been paid to understanding socioeconomic inequalities in health in developing countries,39 fewer studies have examined trends in inequalities and these have mostly focused on child health. The existing literature from developing countries suggests that the socioeconomic inequalities in health appear to be widening, or remaining the same.34 ,40–45 An exception is trends in inequalities in under five mortality in Brazil, where inequalities by household wealth narrowed, but widened by mother's education.45 In the Indian context, a previous study which examined trends in social inequalities in child undernutrition found that inequalities either widened or remained the same between 1992/1993 and 2005/2006.34 Our findings are driven by the fact that, among the more advantaged, trends in anaemia have been increasing at a faster rate than among the more disadvantaged.
Interpretation of findings
Anaemia is determined by several factors, but inadequate dietary intake of bioavailable iron is by far the most important cause of anaemia2 and therefore likely to be the key factor behind these changing patterns. As data from the National Nutrition Monitoring Board (NNMB) Surveys demonstrate, Indian women have a sustained low intake of iron.46 The 2004–2005 NNMB survey conducted among rural populations residing in nine states indicate that women's iron intake is around half of the recommended daily allowance.47 Factors related to the Indian diet, such as its reliance on less bioavailable non-haem iron, higher phytate and polyphenols levels and low ascorbic/iron ratios which constrain iron absorption,48 and possibly lower basal acidity of gastric fluid found in the Indian population,49 may account for the high prevalence of anaemia in this population. However, there is limited evidence from such surveys as to how dietary intake of iron has changed due to changes in the estimation methods used over time.
Other aetiological factors such as recurrent infections with intestinal parasites, primarily hookworm and schistosoma, as well as malaria, tuberculosis and HIV/AIDS may be important. However, the limited monitoring systems and population-based studies make it difficult to assess whether changing exposure to these risk factors may be contributing to rising anaemia prevalence. Although hereditary haemoglobinopathies may help explain some of the geographical variation in high anaemia prevalence, especially in specific tribal populations,50 ,51 this is unlikely to have changed over this time frame. Future work which explores state differences in anaemia trends, especially in the four states where significant improvements occurred, may help further understand which factors drive changes in anaemia prevalence. Given the importance of nutrition in determining anaemia status, it is quite likely that the low iron bioavailability in the Indian diet and the changes in dietary intake are the key drivers of the change and variation seen in this analysis.
India's nutrition transition with its rapid changes to diet and lifestyles is likely to be affecting social groups differentially. For example, with increasing purchasing power for food, there may be shifts in food preferences with increased consumption of convenience and processed foods.52 There is evidence that when poorer households gain more money to spend on food, they prefer to buy more expensive, yet not necessarily more nutritious, food such as sugar, salt and processed food.53Also, richer households are twice more likely to eat out (although eating out is not uncommon among low-income households (monthly per capita expenditure less than 500 rupees), where 17% eat out).52 These preferences may shift diets towards less iron bioavailable food sources, with less consumption of green leafy vegetables and fruits, offering some explanation of why the anaemia trend has been more marked in the socially advantaged.
Over this time period, the prevalence of overweight and obesity among women of reproductive age has increased significantly.54 The fact that the prevalence of anaemia exceeds 40% even among overweight and obese women with caloric excess indicates that food quality and diversity are critical, as well as food availability and supply. Interestingly urbanisation, which is a strong determinant of overweight and obesity,54 does not appear to be as significant for anaemia, which may reflect anaemia's different aetiology.
Future work that explores the primary determinants of anaemia may help understand which factors are driving changes in anaemia prevalence. Also, understanding why significant improvements occurred in select states may help further understand which factors drive changes in anaemia prevalence at the population level.
Limitations of the study
There are limitations to this study. First, we explicitly chose to measure inequalities by groups (wealth, education and caste) as we deem the social, political and historical significance of such groupings important.33 Yet an extensive literature discusses issues relating to measurement of inequalities in health,55–57 which necessitates technical and normative judgements in the choice of metrics.39 An alternative measure of anaemia distribution among individuals within the population, such as concentrations curves and indices, may have given rise to different, albeit complementary, results.
Second, we note that the strong association between socioeconomic status and anaemia detected here fails to determine the direction of causality which may be bidirectional. A consequence of morbidity associated with chronic anaemia is loss of productivity as a result of impaired work capacity, cognitive impairment and increased susceptibility to infection.58 Thus, anaemia itself may contribute to and perpetuate poverty. The aggregate effect of these individual losses has a large impact on human capital; for example, iron deficiency anaemia leads to an estimated $4.2 billion annual loss in the South Asia region.59
Third, it is not possible to distinguish between the different types and causes of anaemia. Consequently, we are limited in our ability to understand the reasons for these trends and the different nutritional and infectious disease factors that may drive these trends, as this analysis focused on examining the differences between geographical and socioeconomic groups.
Fourth, this analysis used repeated cross-sectional surveys to assess trends, and it was not possible to follow Hb levels in individual women over time. However, given that these surveys are nationally representative and use consistent methodologies in the assessment of anaemia and classification of anaemia across time, this is likely to yield representative population-based estimates of anaemia prevalence. Furthermore, we attempted to make comparisons over time as robust as possible by restricting to ever-married women, using previous state boundaries and constructing a new-wealth index. It is unlikely that restricting to ever-married women led to significance bias in the findings, given that most women (>98%) above age 20 are married in this context. We also controlled for time-invariant state effects in examining the inequalities over time.
Fifth, despite the use of consistent sampling and methodology in the NFHS surveys over time, the HemoCue analyser used to measure Hb changed between the two surveys as an upgraded model came out. It is unlikely though this was responsible for the increase in trends, as both systems are calibrated against a standard.
Implications of this study
The severe and worsening trends in anaemia among women of reproductive age over time have a number of implications for both policy makers and health professionals, as anaemia continues to be a major public health problem in the country. These increasing trends have occurred in the context of a well-established anaemia policy framework. Indeed, India was the first country in the developing world to establish a national anaemia programme, which has continued to evolve over time57 (see online supplementary table S6). In the 1970s, the Nutritional Anaemia Prophylaxis Programme was constituted by the Ministry of Health and Family Welfare to target specific populations at high risk of anaemia, namely pregnant and lactating women and preschool children between 1 and 5 years of age.60 Over time, anaemia policies have expanded to cover more population groups: initially, pregnant and lactating women and preschool children between 1 and 5 years of age deemed at ‘high-risk’, as defined by severe anaemia status, were targeted, but now both anaemic and non-anaemic adolescents, pregnant and lactating women and children above 6 months are covered. There has also been further integration of anaemia prevention and control into existing health strategies and programmes. India's latest 11th Five Year Plan sets out an ambitious time-bound goal of reducing anaemia among women and girls by 50% by 2012.61 However, this goal is unlikely to be reached without improved capacity to translate policy commitments into practice. Variation in state capacity to implement policies relating to health and nutrition may in part explain the variable performance in responding to anaemia in this population.
Improving the implementation of the current policy response to anaemia prevention and control, together with addressing knowledge gaps behind these increasing trends, will improve our ability to address the multifactorial aetiology of anaemia within this population and alleviate India's anaemia burden. The finding of a narrowing of socioeconomic relative and absolute inequalities in anaemia warrants further research to understand why this is occurring in this population.
We thank Measure DHS, ICF Macro for access to the data used in this analysis. We also thank Ajay Mahal for helpful comments on the design of the study and manuscript; and Donald Halstead for comments on the manuscript.
Review history and Supplementary material
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Contributors The authors’ responsibilities were as follows: YB, WWF and SVS designed and conceptualised the study; planned the data analysis; interpreted the results; drafted the manuscript; reviewed and approved the final manuscript. YB performed the data analysis and had access to the data and has primary responsibility for the final content.
Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None.
Ethics approval This analysis of secondary data of a publically available dataset was approved by the IRB of the Harvard School of Public Health.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement Dataset available at http://www.measuredhs.com.
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