Trends, Regional Inequalities, and Future Projections of Anemia among Women of Reproductive Age (15-49 years) in Ghana (2000–2030)

Trends, Regional Inequalities, and Future Projections of Anemia among Women of Reproductive Age (15-49 years) in Ghana (2000–2030) | 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 Trends, Regional Inequalities, and Future Projections of Anemia among Women of Reproductive Age (15-49 years) in Ghana (2000–2030) Abdul-Wahab Inusah, Temple Jagha, Michael Head, Abdul‑Aziz Seidu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7215917/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Introduction Anemia, characterized by a deficiency in haemoglobin, remains a public health problem in Ghana among women of reproductive age (WRA) and children under five, as it hinders cognitive development, physical growth and well-being. This study examines sub-national prevalence and inequalities from 2000 to 2019. It also forecast the prevalence of anemia through to 2030 to inform Ghana’s efforts towards achieving the global anemia reduction targets. Methods We used population-representative estimates of anemia among WRA drawn from the WHO Equity Database. Using the WHO Health Equity Assessment Toolkit, we calculated both prevalence and regional inequalities across four inequality dimensions: difference(D), ratio(R), population-attributable risk(PAR), and population-attributable fraction(PAF). An Autoregressive Integrated Moving Average (ARIMA) model, specifically ARIMA(1, 1, 0), was used to forecast anemia prevalence from 2020 to 2030 using Python package. Results Anemia prevalence in Ghana among WRA age showed a modest decline from 47.8% in 2000 to 44.3% in 2019, representing a 3.5% reduction. The Ashanti region experienced the highest decline, from 43.3% (95% CI: 27.6%, 60.6%) in 2000 to 37.3% (95% CI: 28.7%, 46.3%) in 2019. Paradoxically, Upper West observed the highest increase in prevalence from 41% (95% CI: 26.1%, 56.8%) to 45.2% (95%: 36.6%, 53.5%). The gap in prevalence between the region with the highest burden and one with the lowest burden keeps widening across the four inequality dimensions from 2000 to 2019; D(18.1% to 22.2%), PAF(-16.2% to -18.4%), R (1.5 to 1.6) and PAR (7.8 to -8.2). Forecasting results revealed an insignificant decline, as prevalence was projected to decrease marginally from 44.1% (95% CI: 43.8% – 44.4%) in 2020 to 43.6% (95% CI: 40.4% – 46.9%) in 2030. Overall, the study shows absolute and relative fluctuation in inequalities across regions over-time. Conclusion The marginal declines in the anemia prevalence over the two decades and the widening inequalities between the highest and lowest burdened regions required urgent public health intervention to avert the trends. Without intensified, equity-focused strategies such as those addressing socio-economic inequalities, health system strengthening and scaling-up of nutrition-sensitive and malaria control interventions, Ghana is not on track to achieve the WHO Target 50% anemia reduction by 2030. Anemia Ghana Regional Disparities Forecasting ARIMA Model Public Health Nutrition Health Equity Women of Reproductive Age Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Anemia, characterized by reduced hemoglobin concentration and impaired oxygen transport, is a persistent and multifaceted global health challenge that disproportionately affects women of reproductive age (15–49 years) ( 1 – 3 ). Globally, in 2019, anemia affected about half a billion women aged 15–49 years and 269 million children under five year, with the highest burdens in Africa (106 million women, 103 million children) and South-East Asia (244 million women, 83 million children)( 4 ). Globally, by 2023, anemia affected 30.7% of women aged 15–49 years, with higher prevalence among pregnant women (35.5%) compared to non-pregnant women (30.5%), and 39.8% of children aged 6–59 months, with the highest prevalence observed in low- and middle-income countries (LMICs)( 5 ). Anemia is defined as hemoglobin concentrations below 12.0 g/dL for non-pregnant women and below 11.0 g/dL for pregnant women( 6 ) It is associated with heightened risks of adverse health outcomes, including increased maternal and infant mortality, poor pregnancy outcomes such as stillbirth and low birth weight, impaired cognitive and physical performance in babies, and greater susceptibility to infections( 1 , 7 ). The economic cost of anemia among adolescent girls and women is estimated at USD 113 billion globally, reflecting substantial losses in productivity and increased healthcare expenditures( 8 – 10 ). In Africa, anemia among women of reproductive age (WRA) is classified as a moderate-to-severe public health problem, with prevalence rates exceeding 20% in most countries( 4 ). The etiology of anemia in African women is complex and multifactorial, involving nutritional deficiencies (especially iron, folate, and vitamin B12), infectious diseases such as malaria and helminthiasis, chronic inflammation, genetic hemoglobinopathies, and blood loss from menstruation and childbirth( 4 , 7 , 11 – 13 ). Socioeconomic disparities, unequal household food allocation, and limited access to health services further exacerbate the risk, particularly in rural and underserved populations( 10 , 14 , 15 ). Severe anemia is especially dangerous in these settings, where access to timely and adequate healthcare is often limited, increasing the risk of life-threatening complications Within Africa, West Africa bears a disproportionate burden of anemia among WRA( 16 ). The region is characterized by high rates of malaria transmission, undernutrition, frequent pregnancies, and limited access to quality maternal healthcare, all of which contribute to the elevated risk of both moderate and severe anemia( 17 ). Across West Africa, there are also increasing links between climate change and ability to access healthcare( 18 ). Studies from countries such as Nigeria, Burkina Faso, and Côte d’Ivoire report anemia prevalence rates among WRA frequently exceeding 40% among women of reproductive age, with anemia accounting for a significant share of maternal morbidity and mortality( 19 ). The interplay of nutritional, infectious, and socioeconomic factors is further compounded by cultural practices and environmental conditions that limit women’s access to nutrient-rich foods and healthcare services are pronounced in the region( 20 ). Ghana exemplifies the challenges and complexities of addressing anemia in West Africa. Despite notable progress in socioeconomic development and public health interventions( 21 ), anemia remains a major health concern among Ghanaian women of reproductive age. National surveys consistently report anemia prevalence rates above 40% in this population, with anemia posing a significant threat to maternal and child health, particularly in resource-limited settings( 22 ). Regional disparities are pronounced: the Northern, Upper East, and Upper West regions consistently report higher anemia prevalence compared to Greater Accra and Ashanti, reflecting underlying differences in poverty, food security, malaria burden, and access to healthcare( 23 , 24 ) Environmental and behavioral factors, such as high malaria transmission, poor dietary diversity, inadequate water, hygiene, and sanitation, and limited access to health education, further exacerbate the risk, especially among women in rural and impoverished communities( 25 ) Despite the implementation of public health interventions such as iron and folic acid supplementation, food fortification, and malaria control programs by both the National government and non-governmental organizations, progress towards reducing anemia prevalence among Ghanaian women has been slow and uneven( 26 – 29 ). National averages often obscure substantial subnational and regional inequalities, which, if unaddressed, may hinder the effectiveness of targeted interventions and impede progress toward global nutrition targets. The WHO and other international bodies have called for a 50% reduction in anemia among women of reproductive age by 2025, with this target extended to 2030 and incorporated as an indicator for the Sustainable Development Goals (SDG 2.2)( 4 ). Different ecological zones and regions experience disproportionate prevalence of anemia in Ghana, highlighting the critical importance of understanding these regional disparities to inform the design and implementation of targeted nutritional and clinical interventions for effective anemia management( 24 , 26 , 28 ) Previous studies in Ghana primarily focused on anemia among children under five and pregnant women, leaving a gap in understanding the regional inequalities of anemia among all women of reproductive age( 15 , 23 , 30 , 31 ). This study addresses this gap by examining regional-level disparities in using data from the Institute for Health Metrics and Evaluation (IHME), which provides annual, subnational estimates of anemia prevalence from 2000 to 2019. The study also used the Auto Regressive Integrated Moving Average (ARIMA) model to forecast the prevalence of anemia for 2020 to 2030. Such evidence is crucial for targeted nutritional and clinical interventions, and for effective planning towards achieving the WHO 2030 goal of reducing anemia by 50%. Methods Study Design and Data Source This study employed a time trend design to analyse the regional-standardized prevalence of anemia among women of reproductive age (WRA) in Ghana from 2000 to 2019. The data was sourced from the WHO Health Equity Assessments Toolkit(HEAT) and the WHO Equity Database, which provides accessible, harmonized estimates of health indicators disaggregated by inequality dimensions across multiple countries( 32 ). Anemia prevalence estimates used in this study were derived from the Institute for Health Metrics and Evaluation (IHME), which is part of the Global Burden of Disease(GBD) geospatial estimates( 33 ). IHME uses various statistical methods including Bayesian geostatistical modeling that incorporates household surveys data from Demographic and Health Surveys(DHS) and Multiple Cluster Surveys(MICs), administrative health records, and spatial covariates to generate subnational level estimates across countries( 34 ). IHME are designed to be representative both nationally and subnational; the design accounts for data gaps and reporting biases, thereby ensuring the reliability and generalizability of their estimates. WHO incorporates IHME estimates into the WHO Equity Database and uses these estimates to monitor trends in disease burden, including anemia prevalence globally, and conduct inequality analysis to support evidence-based decision-making Outcome Measure and Dimensions of Inequality The primary outcome was the prevalence of anemia among women of reproductive age (15–49 years), defined according to WHO criteria as haemoglobin concentration < 12.0 g/dL for non-pregnant women and < 11.0 g/dL for pregnant women. Prevalence was expressed as a percentage of all women in the specified age group( 6 ). The analysis included data from the following 10 regions: Greater Accra, Ashanti, Central, Eastern, Western, Northern, Upper East, Upper West, Volta, and Brong Ahafo(As shown in Fig. 1 ). Although six new regions were created in 2019, the dataset from HEAT platform retained the original 10-region structure for consistency in time trend analysis over the 2000–2019 period. Inequality Analysis The WHO Health Equity Assessment Toolkit (HEAT) platform provides standardized methods for quantifying and visualizing health inequalities. Four summary measures of inequalities were used to evaluate both absolute and relative disparities anemia prevalence across the study sites. The Difference (D) captured the absolute gap in prevalence between the regions with the highest and lowest prevalence of anemia, while the Ratio (R) represented the relative disparity between these two extremes. The Population Attributable Risk (PAR) estimated the absolute reduction in national anemia prevalence that would occur if all regions achieved the same rate as the best-performing region. Lastly, the Population Attributable Fraction (PAF) indicated the proportion of the national burden of anemia attributable to inter-regional inequality. Together, these metrics provided a comprehensive understanding of the geographic inequities in anemia among women of reproductive age. Time Series Forecasting Approach This study applied a time-series modeling approach to forecast the national anemia prevalence among women of reproductive age for 2020 to 2030. Data spanning from 2000 to 2019 were first examined to understand historical trends and assess model suitability. The data were then aggregated at the national level with one observation per year, and each data point represented the percentage prevalence of anemia. To determine whether the series was stationary, the Augmented Dickey-Fuller (ADF) test was conducted. The test statistic was − 1.51 with a p -value of 0.51, indicating non-stationarity. Consequently, stationarity analysis was performed using the series differenced once, and stationarity was confirmed through visual inspection and autocorrelation diagnostics. The autocorrelation function (ACF) of the differenced series exhibited a slow decay, while the partial autocorrelation function (PACF) showed a sharp cutoff after lag one, suggesting an autoregressive process of order one. Based on these diagnostics, an ARIMA(1,1,0) model was specified and fitted to the entire 20-year dataset. The model included one autoregressive term, one order of differencing, and no moving average component. Residual diagnostics were performed to validate model assumptions, including normality (Jarque–Bera test), absence of autocorrelation (Ljung–Box test), and homoskedasticity. Model performance was assessed using in-sample error metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Forecasts were generated for 2020 to 2030, with corresponding 95% confidence intervals computed for each projected value. All analyses were conducted in Python using pandas, statsmodels, matplotlib, and scikit-learn packages. Results National Trends in Anemia Prevalence among Women of Reproductive Age in Ghana (2000–2019) From Figure 2, between 2000 and 2019, Ghana experienced a slow decline in the national prevalence of anemia among women of reproductive age. In 2000, the prevalence stood at 47.8%, and by 2019, it had reduced to 44.3%, marking an absolute decrease of approximately 3.5 percentage points over two decades. This equates to an average annual decline of about 0.2 percentage points, reflecting a relatively slow pace of improvement in anemia reduction at the national level. The trend over this period can be categorized into two general phases. From 2000 to 2010, the prevalence declined modestly from 47.8% to 46.30%. This decade-long period was characterized by a near-static pattern with minimal year-to-year improvements. From 2010 onwards, a slightly accelerated decline was observed, with prevalence reducing from 46.3% in 2010 to 44.4% by 2019. Despite some fluctuations, such as slight increases in 2013 and 2015, the overall trend remained downward. Regional Anemia Inequalities in Ghana, 2000–2019 Figure 3 shows that a substantial regional disparity in the prevalence of anemia among women of reproductive age persisted across the ten regions. While the national average exhibited a modest decline over the two-decade period, some regions consistently recorded higher burdens than others did. Throughout the years, the Volta region consistently reported the highest anemia prevalence, peaking at 59.1% in 2017 and remaining above 50% in most years. Similarly, the Northern, Upper East, and Upper West regions exhibited elevated prevalence levels, often exceeding 50% during the early to mid-2000s. In contrast, the Brong Ahafo and Ashanti regions registered the lowest anemia prevalence levels, typically ranging between 36% and 44% during the latter part of the period. By 2019, Brong Ahafo recorded the lowest prevalence at 36.1%, while Volta remained the highest at 58.3%, reflecting a persistent inter-regional gap of over 22 percentage points. In terms of percentage reduction in anemia prevalence, the Ashanti region experienced the highest decline, from 43.3% (95% CI: 27.6%, 60.6%) in 2000 to 37.3% (95% CI: 28.7%, 46.3%) in 2019, representing 6% fall in prevalence. This was followed by a 5.5% from the Upper East region, thus 55.1% (95% CI: 41.3%, 70.5%) to 49.6% (95% CI: 41.9%, 56.9%). Paradoxically, Upper West, Northern and Volta regions observed a percentage increase in anemia over the same period; 41%(95% CL: 26.1%, 56.8%) to 45.2%(95% CL:36.6%, 53.5%), 51.1%(95% CL:38.4%, 64.1%) to 52.5%(95% CL:45.8%, 59.3%) and 58.1%(95% CL:45.8%, 70.1%) to 58.3%(95% CL: 53.0%, 63.4%) respectively as illustrated in supplementary file 1. The magnitude of regional inequality remained relatively stable over time, with no significant convergence between high- and low-burden regions. Although some regions, such as Greater Accra and Western, showed moderate declines, the overall pattern indicates slow progress with unevenly distribution. Inequality Analysis Regional Inequalities in Anemia Prevalence among Reproductive Age Women (2000–2019) An analysis of inequality in anemia prevalence among women of reproductive age across regions reveals persistent and substantial disparities over the two-decade period (Table 1). The absolute inequality measured by the Difference (D), the gap in prevalence between regions with the highest and lowest burdens, ranged from 9.6 percentage points in 2013 to a peak of 23.7 percentage points in 2010. Similarly, the relative inequality captured by the Ratio (R) remained elevated, fluctuating between 1.2 and 1.7, indicating that women in high-burden regions were consistently 20% to 70% more likely to experience anemia compared to those in low-burden regions. The Population Attributable Risk (PAR), which estimates the reduction in national prevalence of anemia if all regions achieved the level of the best-performing region, ranged from -5.1% in 2004 to -10.4% in 2010. This indicates that up to 10.4 percentage points of national anemia prevalence could have been avoided in 2010 by closing the regional gap. Likewise, the Population Attributable Fraction (PAF) the proportion of the national burden attributable to regional inequality, was as high as -22.5% in 2010, signifying that nearly a quarter of the national burden could be averted through equitable distribution. Over time, the data show a fluctuating but non-linear trend in both absolute and relative inequality. While the difference and ratio indicators narrowed slightly during the mid-2010s (e.g., D = 12.2 and R = 1.3 in 2014), inequalities widened again by 2019, with D = 22.2 and R = 1.6. These findings underscore that while national prevalence showed a marginal decline, the benefits were not evenly shared across regions, and this is illustrated in Table 1. Table 1 : Regional inequalities of Prevalence of anemia among reproductive age women (2000-2019) Year D R PAR PAF 2019 22.2 1.6 -8.2 -18.4 2018 22.3 1.6 -8.2 -18.4 2017 22.4 1.6 -8.2 -18.2 2016 18.4 1.5 -6.5 -14.5 2015 15.0 1.4 -5.9 -13.0 2014 12.2 1.3 -4.5 -9.9 2013 9.6 1.2 -5.4 -11.8 2012 14.8 1.4 -8.3 -18.2 2011 17.8 1.5 -8.5 -18.5 2010 23.7 1.7 -10.4 -22.5 2009 19.4 1.5 -9.2 -19.7 2008 15.2 1.4 -7.0 -14.9 2007 11.1 1.3 -6.5 -14.0 2006 14.0 1.3 -5.9 -12.7 2005 16.0 1.4 -5.4 -11.5 2004 12.5 1.3 -5.1 -10.9 2003 14.6 1.4 -6.2 -13.2 2002 15.8 1.4 -7.2 -15.2 2001 16.9 1.4 -7.1 -15.0 2000 18.1 1.5 -7.8 -16.2 D: Absolute Difference; R: Ratio; PAR: Population Attributable Risk; PAF: Population Attributable Fraction Forecasting Anemia Prevalence (2020–2030) The time series analysis of national anemia prevalence revealed a gradual downward trend from 2000 to 2019. Prevalence declined from 47.8% in 2000 to 44.1% in 2019, reflecting an overall reduction of 3.7 percentage points over two decades, or approximately 0.19 percentage points per year. The decline was more pronounced in the latter part of the period, particularly from 2010 to 2019. Figure 4 illustrates how well the ARIMA (1,1,0) model fits the dataset, demonstrating strong predictive capacity and meeting key statistical assumptions. The autoregressive parameter was estimated at 0.7116 and was statistically significant ( p < 0.001), indicating a strong dependency on prior year changes in prevalence. As shown in Figure 4, Residual diagnostics showed no evidence of model misspecification: the residuals were normally distributed (Jarque–Bera test p = 0.70), exhibited no significant autocorrelation (Ljung–Box test p = 1.00), and were homoskedastic (heteroskedasticity test p = 0.31) . In-sample forecasting accuracy was high, with a MAPE of 0.32% and an RMSE of 0.183. As shown in Figure 5, forecasts for the 2020–2030 period revealed a continuation of the historical decline, but at a much slower rate. The projected prevalence for 2020 was 44.1%, nearly identical to the 2019 value. By 2030, the forecasted prevalence is expected to reach 43.6%, representing a marginal decrease of 0.5 percentage points over the entire decade. The model indicates a stabilization trend, with prevalence converging toward a lower asymptote. This plateau effect may reflect diminishing returns from existing public health interventions or the presence of structural factors limiting further reductions. As seen in Table 2 in the Appendix, the forecast’s 95% confidence intervals widen progressively over time, from [43.75%, 44.44%] in 2020 to [40.41%, 46.87%] in 2030, indicating growing uncertainty as the forecast horizon extends. These intervals underscore the importance of updating forecasts with new data and implementing responsive policy adjustments. These national trends are further supported by region-specific forecasts (see Appendix Figure 6), which reinforce the persistence of spatial disparities in anemia prevalence across Ghana. Overall, the ARIMA (1,1,0) model suggests that while anemia prevalence among reproductive-age women in Ghana may continue to decline slightly, substantial additional reductions are unlikely in the absence of intensified or innovative interventions. Discussion The study highlights the importance of reducing anemia prevalence among reproductive women aged 15 to 49 years in Ghana. It revealed a persistent decline in the overall national prevalence of anemia among reproductive age women from 47.8% in 2000 to 44.2% in 2019, representing a slow but positive downward trend. This represents a 3.6% reduction in the national prevalence of anemia over the 2-decade period. This finding is consistent with previous studies in Ghana and similar countries in Sub-Saharan Africa that reported a very modest reduction in the prevalence of anemia among reproductive age women( 35 , 36 ). A study conducted in 29 Sub-Saharan African countries reported a pooled prevalence of 40.5%, Rwanda had the lowest prevalence of 13%, while Mali reported the highest prevalence of 62%( 37 ). Another study conducted in 10 East African countries reported a pooled prevalence of 42%, again 23.4% in Rwanda to 57.1% in Tanzania( 38 ). This finding was, however, higher than previous studies conducted in Ethiopia, Uganda, Rwanda, Pakistan that reported 37.5%, 32% 19% and 18% respectively( 37 , 39 – 41 ). These discrepancies could be explained partly by the difference in countries' levels of development and strategic national public health and nutritional intervention implemented at different levels in these countries. On the contrary, other studies reported higher prevalence than the current finding, including Burkina Faso (55%)( 42 ), Asia (53%)( 43 ), Afghanistan (52%)( 44 ). The observed discrepancies may stem from variations in study context, period of data collection, sample sizes, reporting and data quality, and demographic profiles of the study populations. While the Ghana national anemia prevalence saw a modest decline, the study revealed substantial disparities in anemia prevalence at the subnational level over the two decades. The Volta region was consistently identified as having the highest prevalence of anemia among women of reproductive age in Ghana, with rates reaching approximately 60% in 2017 and remaining above 50% in most years. Consistent with previous studies, for example, a study conducted at the Hohoe Municipal Hospital in the Volta region reported anemia prevalence among pregnant women 33%( 45 ). At the Adaklu District, in the Volta region, the prevalence of anemia was as high as 78.5% among pregnant women( 31 ), exceeding the regional average of 49%. Similarly, the Northern regions of Ghana, including Northern, Upper East, and Upper West, have also recorded elevated levels anemia prevalence, often exceeding 50% during the early to mid-2000s. Previous studies conducted in 12 health facilities in the Tatale-Sanguli and Zabzugu district, West Gonja District Hospital and the Tamale Teaching Hospital of the northern region reported anemia prevalence of 72.1%, 52.2% and 50.8% respectively. These figures exceed the highest regional prevalence ever reported within the two decades of data analysed( 46 – 48 ). Interestingly, these regions received various public health interventions from both government and non-governmental organizations to combat both maternal and child malnutrition( 27 , 29 ); however, the regions are still experiencing higher prevalence. One contributing factor may be the challenges with addressing all four pillars of food insecurity (availability, access, utilization, stability)( 49 ). Interventions often focus purely on availability but do not consider factors that affect actual access to nutrition or food, or other aspects such as storage; thus moderate and severe food insecurity in areas such as the Northern Region are extremely high, despite the implementation of interventions and emergency measures( 50 ). Regions with the lowest anemia prevalence were the Brong Ahafo and Ashanti regions. This is consistent with an earlier study in the Kintampo Municipal Hospital of the Brong Ahafo region, which reported a prevalence of 28.5%( 51 ). Differences in anemia prevalence reported across studies may partly result from variations in sample population and size. The current analysis uses regional-level, nationally representative data, whereas many previous studies (some cited in this discussion) were district- or facility-based, focusing on smaller, specific populations such as antenatal clinic attendees. Therefore, comparisons between these studies and our findings should be made cautiously, as differences in study design, population characteristics, and settings can affect prevalence estimates and limit direct comparability. The above findings were further supported by the four measure of measures of inequalities at the regional level. In this study, we found persistent regional-level inequalities in the prevalence of anemia among women of reproductive age. The northern regions of Ghana, both in absolute and relative terms, recorded the highest prevalence compared to those in the southern Ghana, except the Volta. These regional disparities are attributed to complex interactions of socioeconomic, environmental, and health system factors, including poverty, limited access to healthcare, poor nutrition, and high malaria endemicity in the Northern and Volta regions( 15 , 22 , 52 ). Ghana Living Standards Survey (GLSS6&7) indicates that the three regions in the north have the lowest mean annual incomes, which likely affects women of reproductive age consumption of nutrient-rich foods essential for preventing anemia( 53 , 54 ). Similar findings were reported in other low- and middle-income countries where geographic inequalities are a critical dimension of health equity, with some regions experiencing disproportionately higher burdens of anemia. For example, in Benin, due to cultural practices, certain foods rich in nutrients were set aside by both mothers and children, exacerbating their anemia condition( 20 ). Also in Uganda, geographical location(region), household income were significantly associated with anemia in women( 40 ). These challenges around addressing inequalities are likely to continue to be challenging, due to the instability from high-impact events such as pandemics and conflict. There will also be issues directly associated with extreme weather events such as flooding and heat. Ghana is described as a ‘hot spot’ for climate change by the Intergovernmental Panel on Climate Change( 55 ). Climate change will likely negatively impact upon vector-borne disease such as yellow fever, and increase food insecurity, both risk factors for worsening anemia and women’s health more widely( 56 , 57 ). The regional differences across Ghana also reflect vulnerability associated with climate shocks, for example income across the Northern Region being mostly from agriculture, and around 40000 people being displaced by unexpected flooding from the Akosombo Dam in the Volta Region in 2023( 58 ). The Population Attributable Risk (PAR) and Fraction (PAF) analyses highlight that a significant proportion of national anemia cases could be prevented by reducing regional inequalities, reflecting entrenched socio-economic and healthcare access disparities. This is consistent with other studies in SSA showing that rural and poorer regions, such as Northern Ghana, experience an increase anemia prevalence due to limited healthcare access, poverty, and infectious disease burden, including malaria( 10 , 14 , 17 , 20 , 24 , 37 ). Knowledge-sharing partnerships between regions with high and low anemia burden has the potential to at least partially reduce the inequalities gap. The World Health Organization (WHO) originally targeted a 50% reduction in anemia prevalence by 2025, a goal later shifted to 2030. Against this backdrop, our study forecasted anemia prevalence in Ghana from 2020 to 2030 using historical data from 2000 to 2019 to measure progress toward this target and inform policy decisions and strategies. The forecasts reveal a likely continuous but slow decline in the prevalence of anemia, with the projected national prevalence virtually unchanged from 44.1% in 2020 to 43.6% in 2030. This represents a marginal decrease of only 0.5 percentage points over the decade. This plateau suggests that structural barriers may be impeding further reductions, and novel approaches to public health interventions are needed. Region-specific forecasts further confirm persistent spatial disparities and marginal decline in anemia prevalence, reinforcing that national averages mask substantial heterogeneity in anemia burden across Ghana’s regions. Our findings align closely with global trends in recent literature. According to a comprehensive global study, the prevalence of anemia in non-pregnant women aged 15–49 years changed very little between 2000 and 2019, from 31% (95% UI 28–34) to 30% ( 27 – 33 ), while in pregnant women aged 15–49 years it decreased from 41% ( 39 – 43 ) to 36% ( 34 – 39 )( 59 ). The findings reinforce the need to design public health interventions that prioritize resource allocation to high-burden Northern and Volta regions, enhance malaria prevention, improve nutrition-sensitive interventions and strengthen healthcare access. These approaches are essential tools in closing the regional gaps in anemia prevalence and achieving more equitable health outcomes among women of reproductive age in Ghana, consistent with findings from other sub-Saharan African Countries( 17 , 19 , 60 ). Research and policy implications The regional inequalities in anemia prevalence, coupled with a slow decline in the prevalence, have clear implications for both policy and research in Ghana’s efforts to reduce anemia. From a policy perspective, strengthened multisectoral strategies are crucial to address the complex and interrelated causes of anemia, which include poverty, malaria, nutrition, and healthcare access. The Government current commitments to reduce iron deficiency anemia (IDA) in children under five, and pregnant women, and to scale up nutrition interventions such as iron-folic acid supplementation and vitamin A distribution provide a strong foundation but require accelerated implementation and expanded coverage, particularly in high-burden regions( 27 , 29 , 61 ). Also, enhancing malaria prevention and treatment, improving laboratory capacity, and addressing socio-cultural barriers to care are essential for reducing anemia among vulnerable groups, including as pregnant women( 62 ). Addressing the challenges of climate change is vital, particularly how to deliver healthcare is areas that are prone to flooding, extreme heat, and have limited capacity to adapt their livelihoods to evolving and inconsistent rainfall patterns. From a research perspective, it is crucial to conduct further studies to explore why anemia prevalence remains high in the northern regions of Ghana despite ongoing efforts by the government and non-governmental organizations. Such research should investigate underlying factors, including health system challenges, socio-cultural barriers, nutritional practices, and the effectiveness and reach of current interventions. Given there will always be limited resources to address these health needs, longitudinal datasets can provide insight around temporal changes, and these datasets can then inform granular social network analyses, geospatial approaches, and risk modelling to consider vulnerability at local community and even individual household level. These datasets could be combined with qualitative studies that provide more nuanced local context around social and structural barriers to health protection and access to health services. Understanding these dynamics will help identify knowledge gaps and inform more targeted, context-specific strategies to reduce anemia in these high-burden areas effectively. Strengths and limitations The study has some limitations that should be considered when interpreting its findings. The study relies on secondary data, which are limited to subnational disaggregation, thereby restricting a deeper analysis of other variables, such as sex, age, socio-economic status, dietary intake, or iron deficiency measures. The study does not fully explore the underlying health system or socio-cultural factors associated with the high prevalence of anemia in high-burden regions. Finally, forecasting uncertainty increases in the ARIMA model over time, as observed in a gradual widening of the confidence intervals. However, the use of nationally representative data over two decades in the study enables a robust trend analysis and forecasting. It projects regional disparities and quantifies the impact of inequalities using advanced measures like Population Attributable Risk. The use of ARIMA modelling adds rigor to future prevalence projections, providing valuable insights for policy and intervention planning. Conclusion The findings of this study highlight that, while Ghana experienced a modest decline in anemia prevalence over the past two decades, significant regional disparities persist, particularly in the three Northern and Volta regions, where the burden remains very high. The forecast analysis indicates that without intensified and targeted interventions, the prevalence of anemia is likely to plateau, making it unlikely for Ghana to achieve the WHO 2030 anemia reduction target. Overcoming these challenges requires a comprehensive, equity-based approach that combines strengthened healthcare systems, multi-sectoral collaborations, and regional-specific strategies to tackle the socio-economic and health system inequalities. A sustained commitment to data-driven policy and longitudinal research will be crucial to accelerate progress and ensure that gains made in anemia reduction among women of reproductive age are shared equitably and maintained across all regions in the country. Abbreviations R – Relative Ratio D – Absolute Difference PAR – Population Attributable Risk PAF – Population Attributable Fraction ARIMA – Autoregressive Integrated Moving Average DHS – Demographic and Health Survey IHME – Institute for Health Metrics and Evaluation WRA – Women of Reproductive Age NHIS – National Health Insurance Scheme SSA – Sub-Saharan Africa IDA – Iron Deficiency Anemia GLSS – Ghana Living Standards Survey SDG Sustainable Development Goal WASH – Water, Sanitation, and Hygiene WHO – World Health Organization Declarations Acknowledgements We are grateful to IHME and the WHO for making the dataset and the HEAT software accessible. Authors’ contributions AI and AS conceived the study. AI and AS wrote the methods section and performed the data analysis. AI, SZ, MH, TJ, and AS were responsible for the initial draft of the manuscript. All the authors reviewed and approved the final version of the manuscript. Funding This study received no funding. Data availability The dataset used can be accessed at : https://www.who.int/data/inequality-monitor/data Declarations Ethics approval and consent to participate No ethical clearance was sought for this study due to the public availability of the dataset. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Author details Abdul-Wahab Inusah, MPH¹ ✉ (Corresponding Author) ORCID: 0000-0002-8184-7355 ¹ Department of Global and International Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana Email: [email protected] Temple O. Jagha, DrPH, FAPH 2 2 Global and International Development Expert, Gaithersburg, MD 20878, USA Email: [email protected] Michael G. Head PhD 3 3 Faculty of Medicine, University of Southampton, Southampton, United Kingdom Email: [email protected] Abdul‑Aziz Seidu PhD 4 4 Public Health and Tropical Medicine, James Cook University, Townsville, QLD 4811, Australia. Shamsu-Deen Ziblim, PhD 5 5 Directorate of Academic Planning & Quality Assurance (DAPQA), University for Development Studies, Box TL1350, Tamale, Ghana Email: [email protected] References Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, et al. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995-2011: a systematic analysis of population-representative data. Lancet Glob Health. 2013 July;1(1):e16-25. Kassebaum NJ, Jasrasaria R, Naghavi M, Wulf SK, Johns N, Lozano R, et al. A systematic analysis of global anemia burden from 1990 to 2010. Blood. 2014 Jan 30;123(5):615–24. Jeevan J, Karun KM, Puranik A, Deepa C, MK L, Barvaliya M. Prevalence of anemia in India: a systematic review, meta-analysis and geospatial analysis. BMC Public Health. 2025 Apr 4;25(1):1270. WHO. WHO fact sheet on anaemia, including definitions, symptoms, causes, treatments and WHO response. [Internet]. 2025 [cited 2025 July 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/anaemia WHO. Anaemia in women and children [Internet]. 2023 [cited 2025 July 8]. Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children WHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity [Internet]. WHO; 2011. Report No.: WHO/NMH/NHD/MNM/11.1. Available from: http://www.who.int Wang R, Xu S, Hao X, Jin X, Pan D, Xia H, et al. Anemia during pregnancy and adverse pregnancy outcomes: a systematic review and meta-analysis of cohort studies. Front Glob Womens Health [Internet]. 2025 Jan 31 [cited 2025 July 8];6. Available from: https://www.frontiersin.org/articles/10.3389/fgwh.2025.1502585/full Anaemia Action Alliance. The economic cost of anaemia | Anaemia Action Alliance [Internet]. 2025 [cited 2025 July 8]. Available from: https://anaemiaalliance.who.int/about-anaemia/cost-of-inaction Nissenson AR, Wade S, Goodnough T, Knight K, Dubois RW. Economic Burden of Anemia in an Insured Population. JMCP. 2005 Sept;11(7):565–74. Balarajan Y, Ramakrishnan U, Özaltin E, Shankar AH, Subramanian S. Anaemia in low-income and middle-income countries. The Lancet. 2011 Dec;378(9809):2123–35. Andersen CT, Tadesse AW, Bromage S, Fekadu H, Hemler EC, Passarelli S, et al. Anemia Etiology in Ethiopia: Assessment of Nutritional, Infectious Disease, and Other Risk Factors in a Population-Based Cross-Sectional Survey of Women, Men, and Children. The Journal of Nutrition. 2022 Feb 1;152(2):501–12. Chaparro CM, Suchdev PS. Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries. Ann N Y Acad Sci. 2019 Aug;1450(1):15–31. Wegmüller R, Bentil H, Wirth JP, Petry N, Tanumihardjo SA, Allen L, et al. Anemia, micronutrient deficiencies, malaria, hemoglobinopathies and malnutrition in young children and non-pregnant women in Ghana: Findings from a national survey. PLOS ONE. 2020 Jan 30;15(1):e0228258. Addis Alene K, Mohamed Dohe A. Prevalence of Anemia and Associated Factors among Pregnant Women in an Urban Area of Eastern Ethiopia. Anemia. 2014;2014:561567. Klu D, Kyei-Arthur F, Appiah M, Odame ML. Multilevel predictors of anaemia among pregnant women in Ghana: New evidence from the 2019 Ghana Malaria Indicator Survey. PLOS Glob Public Health. 2024 Sept 5;4(9):e0003673. Belay DG, Amlak BT, Chilot D, Alem AZ, Merid MW. Pooled prevalence of anaemia and its associated factors among adolescent girls in East and West Africa: a systematic review and meta-analysis. BMC Public Health. 2025 July 17;25(1):2482. Nyarko SH, Boateng ENK, Dickson KS, Adzrago D, Addo IY, Acquah E, et al. Geospatial disparities and predictors of anaemia among pregnant women in Sub-Saharan Africa. BMC Pregnancy and Childbirth. 2023 Oct 20;23(1):743. Head M, Boxall J, Atiwoto W, Atengble K, Dotse-Gborgbortsi W, Agyemang E. Addressing climate change and health in Ghana and West Africa: a Summit report [Internet]. figshare; 2025 [cited 2025 July 23]. Available from: https://figshare.com/articles/preprint/_b_Addressing_climate_change_and_health_in_Ghana_and_West_Africa_a_Summit_report_b_/27894951/1 Salifu MG, Da-Costa Vroom FB, Guure C. Anaemia among women of reproductive age in selected sub-Saharan African countries: multivariate decomposition analyses of the demographic and health surveys data 2008–2018. Front Public Health. 2024 Jan 5;11:1128214. Lokossou YUA, Tambe AB, Azandjèmè C, Mbhenyane X. Socio-cultural beliefs influence feeding practices of mothers and their children in Grand Popo, Benin. Journal of Health, Population and Nutrition. 2021 July 23;40(1):33. Oxfam International. Oxfam International. 2022 [cited 2025 July 23]. Ghana: extreme inequality in numbers. Available from: https://www.oxfam.org/en/ghana-extreme-inequality-numbers Zegeye B, Anyiam FE, Ahinkorah BO, Ameyaw EK, Budu E, Seidu AA, et al. Prevalence of anemia and its associated factors among married women in 19 sub-Saharan African countries. Archives of Public Health. 2021 Nov 29;79(1):214. Ewusie JE, Ahiadeke C, Beyene J, Hamid JS. Prevalence of anemia among under-5 children in the Ghanaian population: estimates from the Ghana demographic and health survey. BMC Public Health. 2014 June 19;14(1):626. 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 Nov 20;13(1):20335. Okesanya OJ, Eshun G, Ukoaka BM, Manirambona E, Olabode ON, Adesola RO, et al. Water, sanitation, and hygiene (WASH) practices in Africa: exploring the effects on public health and sustainable development plans. Tropical Medicine and Health. 2024 Oct 9;52(1):68. Awuuh VA, Appiah CA, Mensah FO. Impact of nutrition education intervention on nutritional status of undernourished children (6-24 months) in East Mamprusi district of Ghana. Nutrition & Food Science. 2018 Oct 26;49(2):262–72. Azagba-Nyako JM, Tortoe C, Akonor PT, Padi A, Boateng J, Otwey R. Review of Current Strategies to Address Micronutrient Deficiencies (MNDs) in Ghana: A Scoping Review. Journal of Nutrition and Metabolism. 2025;2025(1):6652716. Yawson AE, Amoaful EO, Senaya LK, Yawson AO, Aboagye PK, Mahama AB, et al. The lancet series nutritional interventions in Ghana: a determinants analysis approach to inform nutrition strategic planning. BMC Nutr. 2017 Mar 15;3(1):27. Dalaba MA, Nonterah EA, Chatio ST, Adoctor JK, Dambayi E, Nonterah EW, et al. Engaging community members in setting priorities for nutrition interventions in rural northern Ghana. PLOS Global Public Health. 2022 Sept 16;2(9):e0000447. Petry N, Wirth JP, Adu‐Afarwuah S, Wegmuller R, Woodruff BA, Tanumihardjo SA, et al. Risk factors for anaemia among Ghanaian women and children vary by population group and climate zone. Matern Child Nutr. 2020 Sept 18;17(2):e13076. Tettegah E, Hormenu T, Ebu-Enyan NI. Risk factors associated with anaemia among pregnant women in the Adaklu District, Ghana. Front Glob Womens Health [Internet]. 2024 Feb 15 [cited 2025 July 9];4. Available from: https://www.frontiersin.org/articles/10.3389/fgwh.2023.1140867/full WHO. Health Equity Assessment Toolkit [Internet]. 2025 [cited 2025 July 3]. Available from: https://www.who.int/data/inequality-monitor/assessment_toolkit IHME. Institute of Health Metrics and Evaluation and the global burden of disease - Canadian Institute of Actuaries | Institut canadien des actuaires [Internet]. 2024 [cited 2025 July 23]. Available from: https://www.cia-ica.ca/news/institute-of-health-metrics-and-evaluation-and-the-global-burden-of-disease/ 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. The Lancet Haematology. 2023 Sept 1;10(9):e713–34. Tandoh MA, Agyemang WO, Brago ET, Attu SS, Domfeh EA. Prevalence And Risk Factors Of Anaemia Among Women Of Reproductive Age In A Ghanaian University. 1. 2024 Dec 18;42(4):80–93. Tetteh, Adanu, Adanu R, Folson G, Agyabeng K, Dwomoh D, et al. Drivers of anaemia reduction among women of reproductive age in the eastern and upper west regions of Ghana: A secondary data analysis of the Ghana demographic and health surveys. AJFAND. 2023 Jan 23;23(116):22248–74. Mare KU, Aychiluhm SB, Sabo KG, Tadesse AW, Kase BF, Ebrahim OA, et al. Determinants of anemia level among reproductive-age women in 29 Sub-Saharan African countries: A multilevel mixed-effects modelling with ordered logistic regression analysis. PLOS ONE. 2023 Nov 29;18(11):e0294992. Liyew AM, Tesema GA, Alamneh TS, Worku MG, Teshale AB, Alem AZ, et al. Prevalence and determinants of anemia among pregnant women in East Africa; A multi-level analysis of recent Demographic and Health Surveys. PloS one. 2021;16(4):e0250560. Animut K, Berhanu G. Determinants of anemia status among pregnant women in ethiopia: using 2016 ethiopian demographic and health survey data; application of ordinal logistic regression models. BMC Pregnancy Childbirth [Internet]. 2022 Aug 26 [cited 2025 July 11];22(1). Available from: https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-04990-8 Nankinga O, Aguta D. Determinants of Anemia among women in Uganda: further analysis of the Uganda demographic and health surveys. BMC Public Health. 2019 Dec 30;19(1):1757. Habib MA, Raynes-Greenow C, Soofi SB, Ali N, Nausheen S, Ahmed I, et al. Prevalence and determinants of iron deficiency anemia among non-pregnant women of reproductive age in Pakistan. Asia Pacific Journal of Clinical Nutrition. 2018 Jan 1;27(1):195–203. Ouedraogo I, Zamané H, Sawadogo AY, Kiemtoré S, Kain DP, Ouattara A, et al. Anaemia in Pregnancy in an African Setting after Preventive Measures. OJOG. 2019;09(01):10–20. Sunuwar DR, Singh DR, Chaudhary NK, Pradhan PMS, Rai P, Tiwari K. Prevalence and factors associated with anemia among women of reproductive age in seven South and Southeast Asian countries: Evidence from nationally representative surveys. Cardoso MA, editor. PLoS ONE. 2020 Aug 13;15(8):e0236449. Zewar M, Chakraborty S. Perspectives of Health Care Professionals and Patients on Anemia in Afghanistan’s Women of Reproductive Age. International Journal of Clinical Skills. 2022;16(3):230. Kofie P, Tarkang EE, Manu E, Amu H, Ayanore MA, Aku FY, et al. Prevalence and associated risk factors of anaemia among women attending antenatal and post-natal clinics at a public health facility in Ghana. BMC Nutr [Internet]. 2019 Dec [cited 2025 July 11];5(1). Available from: https://bmcnutr.biomedcentral.com/articles/10.1186/s40795-019-0303-x Adokiya MN, Abodoon GN, Boah M. Prevalence and determinants of anaemia during third trimester of pregnancy: a retrospective cohort study of women in the northern region of Ghana. Women & Health. 2022 Feb 7;62(2):168–79. Wemakor A. Prevalence and determinants of anaemia in pregnant women receiving antenatal care at a tertiary referral hospital in Northern Ghana. BMC Pregnancy and Childbirth. 2019 Dec 11;19(1):495. Malick M. Prevalence and factors associated with anemia in pregnancy among women receiving antenatal care at the West Gonja District Hospital of Northern Ghana. Auctores Publishing LLC, editor. JOGRS. 2020 Dec 14;4(3):01–12. GFSC. Food Security Cluster - Cluster Coordinators Handbook [Internet]. 2023 [cited 2025 July 23]. Available from: https://handbook.fscluster.org Head M, Boxall J. Exploring the impact of climate change on food insecurity and health in a Last Mile district of rural Ghana [Internet]. figshare; 2024 [cited 2025 July 23]. Available from: https://figshare.com/articles/preprint/Exploring_the_impact_of_climate_change_on_food_insecurity_and_health_in_a_Last_Mile_district_of_rural_Ghana/25245082/3 Sumaila I, Manu A, Hallidu M. Prevalence and associated factors of anaemia among antenatal care attendants in the Kintampo municipality. PAMJ-OH [Internet]. 2022 [cited 2025 July 11];9. Available from: https://www.one-health.panafrican-med-journal.com/content/article/9/16/full Anaemia and its determinants among reproductive age women (15–49 years) in the Gambia: a multi-level analysis of 2019–20 Gambian Demographic and Health Survey Data | Archives of Public Health [Internet]. [cited 2025 July 11]. Available from: https://link.springer.com/article/10.1186/s13690-022-00985-1 GSS. Ghana - Ghana Living Standards Survey 6 (With a Labour Force Module) 2012-2013 [Internet]. 2012 [cited 2025 July 12]. Available from: https://microdata.statsghana.gov.gh/index.php/catalog/72 GSS. Ghana Living Standards Survey (GLSS7) [Internet]. 2020 [cited 2025 July 12]. Available from: https://open.africa/dataset/38102d96-1393-4918-a1a7-556eca8491ad/resource/839a1758-146c-40cd-957d-37d26aa84fb6/download/glss7-main-report_final.pdf Pachauri RK, Mayer L, Intergovernmental Panel on Climate Change, editors. Climate change 2014: synthesis report. Geneva, Switzerland: Intergovernmental Panel on Climate Change; 2015. 151 p. Gaythorpe KA, Hamlet A, Cibrelus L, Garske T, Ferguson NM. The effect of climate change on yellow fever disease burden in Africa. eLife [Internet]. 2020 July 28 [cited 2025 July 23];9. Available from: https://elifesciences.org/articles/55619 Semba RD, Askari S, Gibson S, Bloem MW, Kraemer K. The Potential Impact of Climate Change on the Micronutrient-Rich Food Supply. Adv Nutr. 2021 Oct 4;13(1):80–100. Benjamin KAD. The Akosombo Dam Spillage | UNICEF Ghana [Internet]. 2023 [cited 2025 July 23]. Available from: https://www.unicef.org/ghana/blog/akosombo-dam-spillage 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. The Lancet Global Health. 2022 May 1;10(5):e627–39. Nti J, Afagbedzi S, da-Costa Vroom FB, Ibrahim NA, Guure C. Variations and Determinants of Anemia among Reproductive Age Women in Five Sub-Saharan Africa Countries. BioMed Research International. 2021;2021(1):9957160. NAF. NAF Tracker - Nutrition for Growth Commitments - Global Nutrition Report [Internet]. 2021 [cited 2025 July 12]. Available from: https://globalnutritionreport.org/resources/naf/tracker/commitment/nutrition-for-growth-commitments/ Asobuno C, Adjei-Gyamfi S, Aabebe FG, Hammond J, Taikeophithoun C, Amuna NN, et al. Risk factors for anaemia among pregnant women: A cross-sectional study in Upper East Region, Ghana. PLoS One. 2024;19(11):e0301654. Additional Declarations No competing interests reported. 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by reduced hemoglobin concentration and impaired oxygen transport, is a persistent and multifaceted global health challenge that disproportionately affects women of reproductive age (15–49 years) (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e–\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Globally, in 2019, anemia affected about half a billion women aged 15–49 years and 269\u0026nbsp;million children under five year, with the highest burdens in Africa (106\u0026nbsp;million women, 103\u0026nbsp;million children) and South-East Asia (244\u0026nbsp;million women, 83\u0026nbsp;million children)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Globally, by 2023, anemia affected 30.7% of women aged 15–49 years, with higher prevalence among pregnant women (35.5%) compared to non-pregnant women (30.5%), and 39.8% of children aged 6–59 months, with the highest prevalence observed in low- and middle-income countries (LMICs)(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Anemia is defined as hemoglobin concentrations below 12.0 g/dL for non-pregnant women and below 11.0 g/dL for pregnant women(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) It is associated with heightened risks of adverse health outcomes, including increased maternal and infant mortality, poor pregnancy outcomes such as stillbirth and low birth weight, impaired cognitive and physical performance in babies, and greater susceptibility to infections(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The economic cost of anemia among adolescent girls and women is estimated at USD 113\u0026nbsp;billion globally, reflecting substantial losses in productivity and increased healthcare expenditures(\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Africa, anemia among women of reproductive age (WRA) is classified as a moderate-to-severe public health problem, with prevalence rates exceeding 20% in most countries(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The etiology of anemia in African women is complex and multifactorial, involving nutritional deficiencies (especially iron, folate, and vitamin B12), infectious diseases such as malaria and helminthiasis, chronic inflammation, genetic hemoglobinopathies, and blood loss from menstruation and childbirth(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Socioeconomic disparities, unequal household food allocation, and limited access to health services further exacerbate the risk, particularly in rural and underserved populations(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Severe anemia is especially dangerous in these settings, where access to timely and adequate healthcare is often limited, increasing the risk of life-threatening complications\u003c/p\u003e\u003cp\u003eWithin Africa, West Africa bears a disproportionate burden of anemia among WRA(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The region is characterized by high rates of malaria transmission, undernutrition, frequent pregnancies, and limited access to quality maternal healthcare, all of which contribute to the elevated risk of both moderate and severe anemia(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Across West Africa, there are also increasing links between climate change and ability to access healthcare(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Studies from countries such as Nigeria, Burkina Faso, and Côte d’Ivoire report anemia prevalence rates among WRA frequently exceeding 40% among women of reproductive age, with anemia accounting for a significant share of maternal morbidity and mortality(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The interplay of nutritional, infectious, and socioeconomic factors is further compounded by cultural practices and environmental conditions that limit women’s access to nutrient-rich foods and healthcare services are pronounced in the region(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGhana exemplifies the challenges and complexities of addressing anemia in West Africa. Despite notable progress in socioeconomic development and public health interventions(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), anemia remains a major health concern among Ghanaian women of reproductive age. National surveys consistently report anemia prevalence rates above 40% in this population, with anemia posing a significant threat to maternal and child health, particularly in resource-limited settings(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Regional disparities are pronounced: the Northern, Upper East, and Upper West regions consistently report higher anemia prevalence compared to Greater Accra and Ashanti, reflecting underlying differences in poverty, food security, malaria burden, and access to healthcare(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Environmental and behavioral factors, such as high malaria transmission, poor dietary diversity, inadequate water, hygiene, and sanitation, and limited access to health education, further exacerbate the risk, especially among women in rural and impoverished communities(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eDespite the implementation of public health interventions such as iron and folic acid supplementation, food fortification, and malaria control programs by both the National government and non-governmental organizations, progress towards reducing anemia prevalence among Ghanaian women has been slow and uneven(\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). National averages often obscure substantial subnational and regional inequalities, which, if unaddressed, may hinder the effectiveness of targeted interventions and impede progress toward global nutrition targets. The WHO and other international bodies have called for a 50% reduction in anemia among women of reproductive age by 2025, with this target extended to 2030 and incorporated as an indicator for the Sustainable Development Goals (SDG 2.2)(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Different ecological zones and regions experience disproportionate prevalence of anemia in Ghana, highlighting the critical importance of understanding these regional disparities to inform the design and implementation of targeted nutritional and clinical interventions for effective anemia management(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e\u003cp\u003ePrevious studies in Ghana primarily focused on anemia among children under five and pregnant women, leaving a gap in understanding the regional inequalities of anemia among all women of reproductive age(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This study addresses this gap by examining regional-level disparities in using data from the Institute for Health Metrics and Evaluation (IHME), which provides annual, subnational estimates of anemia prevalence from 2000 to 2019. The study also used the Auto Regressive Integrated Moving Average (ARIMA) model to forecast the prevalence of anemia for 2020 to 2030. Such evidence is crucial for targeted nutritional and clinical interventions, and for effective planning towards achieving the WHO 2030 goal of reducing anemia by 50%.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Data Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a time trend design to analyse the regional-standardized prevalence of anemia among women of reproductive age (WRA) in Ghana from 2000 to 2019. The data was sourced from the WHO Health Equity Assessments Toolkit(HEAT) and the WHO Equity Database, which provides accessible, harmonized estimates of health indicators disaggregated by inequality dimensions across multiple countries(\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e). Anemia prevalence estimates used in this study were derived from the Institute for Health Metrics and Evaluation (IHME), which is part of the Global Burden of Disease(GBD) geospatial estimates(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). IHME uses various statistical methods including Bayesian geostatistical modeling that incorporates household surveys data from Demographic and Health Surveys(DHS) and Multiple Cluster Surveys(MICs), administrative health records, and spatial covariates to generate subnational level estimates across countries(\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). IHME are designed to be representative both nationally and subnational; the design accounts for data gaps and reporting biases, thereby ensuring the reliability and generalizability of their estimates. WHO incorporates IHME estimates into the WHO Equity Database and uses these estimates to monitor trends in disease burden, including anemia prevalence globally, and conduct inequality analysis to support evidence-based decision-making\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Measure and Dimensions of Inequality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was the prevalence of anemia among women of reproductive age (15\u0026ndash;49 years), defined according to WHO criteria as haemoglobin concentration\u0026thinsp;\u0026lt;\u0026thinsp;12.0 g/dL for non-pregnant women and \u0026lt;\u0026thinsp;11.0 g/dL for pregnant women. Prevalence was expressed as a percentage of all women in the specified age group(\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). The analysis included data from the following 10 regions: Greater Accra, Ashanti, Central, Eastern, Western, Northern, Upper East, Upper West, Volta, and Brong Ahafo(As shown in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Although six new regions were created in 2019, the dataset from HEAT platform retained the original 10-region structure for consistency in time trend analysis over the 2000\u0026ndash;2019 period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInequality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe WHO Health Equity Assessment Toolkit (HEAT) platform provides standardized methods for quantifying and visualizing health inequalities. Four summary measures of inequalities were used to evaluate both absolute and relative disparities anemia prevalence across the study sites. The Difference (D) captured the absolute gap in prevalence between the regions with the highest and lowest prevalence of anemia, while the Ratio (R) represented the relative disparity between these two extremes. The Population Attributable Risk (PAR) estimated the absolute reduction in national anemia prevalence that would occur if all regions achieved the same rate as the best-performing region. Lastly, the Population Attributable Fraction (PAF) indicated the proportion of the national burden of anemia attributable to inter-regional inequality. Together, these metrics provided a comprehensive understanding of the geographic inequities in anemia among women of reproductive age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTime Series Forecasting Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study applied a time-series modeling approach to forecast the national anemia prevalence among women of reproductive age for 2020 to 2030. Data spanning from 2000 to 2019 were first examined to understand historical trends and assess model suitability. The data were then aggregated at the national level with one observation per year, and each data point represented the percentage prevalence of anemia.\u003c/p\u003e\n\u003cp\u003eTo determine whether the series was stationary, the Augmented Dickey-Fuller (ADF) test was conducted. The test statistic was \u0026minus;\u0026thinsp;1.51 with a \u003cem\u003ep\u003c/em\u003e-value of 0.51, indicating non-stationarity. Consequently, stationarity analysis was performed using the series differenced once, and stationarity was confirmed through visual inspection and autocorrelation diagnostics. The autocorrelation function (ACF) of the differenced series exhibited a slow decay, while the partial autocorrelation function (PACF) showed a sharp cutoff after lag one, suggesting an autoregressive process of order one.\u003c/p\u003e\n\u003cp\u003eBased on these diagnostics, an ARIMA(1,1,0) model was specified and fitted to the entire 20-year dataset. The model included one autoregressive term, one order of differencing, and no moving average component. Residual diagnostics were performed to validate model assumptions, including normality (Jarque\u0026ndash;Bera test), absence of autocorrelation (Ljung\u0026ndash;Box test), and homoskedasticity. Model performance was assessed using in-sample error metrics: Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). Forecasts were generated for 2020 to 2030, with corresponding 95% confidence intervals computed for each projected value. All analyses were conducted in Python using pandas, statsmodels, matplotlib, and scikit-learn packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eNational Trends in Anemia Prevalence among Women of Reproductive Age in Ghana (2000\u0026ndash;2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom Figure 2, between 2000 and 2019, Ghana experienced a slow decline in the national prevalence of anemia among women of reproductive age. In 2000, the prevalence stood at 47.8%, and by 2019, it had reduced to 44.3%, marking an absolute decrease of approximately 3.5 percentage points over two decades. This equates to an average annual decline of about 0.2 percentage points, reflecting a relatively slow pace of improvement in anemia reduction at the national level. The trend over this period can be categorized into two general phases. From 2000 to 2010, the prevalence declined modestly from 47.8% to 46.30%. This decade-long period was characterized by a near-static pattern with minimal year-to-year improvements. From 2010 onwards, a slightly accelerated decline was observed, with prevalence reducing from 46.3% in 2010 to 44.4% by 2019. Despite some fluctuations, such as slight increases in 2013 and 2015, the overall trend remained downward.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Anemia Inequalities in Ghana, 2000\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 shows that a substantial regional disparity in the prevalence of anemia among women of reproductive age persisted across the ten regions. While the national average exhibited a modest decline over the two-decade period, some regions consistently recorded higher burdens than others did. Throughout the years, the Volta region consistently reported the highest anemia prevalence, peaking at 59.1% in 2017 and remaining above 50% in most years. Similarly, the Northern, Upper East, and Upper West regions exhibited elevated prevalence levels, often exceeding 50% during the early to mid-2000s. In contrast, the Brong Ahafo and Ashanti regions registered the lowest anemia prevalence levels, typically ranging between 36% and 44% during the latter part of the period. By 2019, Brong Ahafo recorded the lowest prevalence at 36.1%, while Volta remained the highest at 58.3%, reflecting a persistent inter-regional gap of over 22 percentage points. In terms of percentage reduction in anemia prevalence, the Ashanti region experienced the highest decline, from 43.3% (95% CI: 27.6%, 60.6%) in 2000 to 37.3% (95% CI: 28.7%, 46.3%) in 2019, representing 6% fall in prevalence. This was followed by a 5.5% from the Upper East region, thus 55.1% (95% CI: 41.3%, 70.5%) to 49.6% (95% CI: 41.9%, 56.9%). Paradoxically, Upper West, Northern and Volta regions observed a percentage increase in anemia over the same period; 41%(95% CL: 26.1%, 56.8%) to 45.2%(95% CL:36.6%, 53.5%), 51.1%(95% CL:38.4%, 64.1%) to 52.5%(95% CL:45.8%, 59.3%) and 58.1%(95% CL:45.8%, 70.1%) to 58.3%(95% CL: 53.0%, 63.4%) respectively as illustrated in supplementary file 1. The magnitude of regional inequality remained relatively stable over time, with no significant convergence between high- and low-burden regions. Although some regions, such as Greater Accra and Western, showed moderate declines, the overall pattern indicates slow progress with unevenly distribution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInequality Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegional Inequalities in Anemia Prevalence among Reproductive Age Women (2000\u0026ndash;2019)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn analysis of inequality in anemia prevalence among women of reproductive age across regions reveals persistent and substantial disparities over the two-decade period (Table 1).\u003c/p\u003e\n\u003cp\u003eThe absolute inequality measured by the Difference (D), the gap in prevalence between regions with the highest and lowest burdens, ranged from 9.6 percentage points in 2013 to a peak of 23.7 percentage points in 2010. Similarly, the relative inequality captured by the Ratio (R) remained elevated, fluctuating between 1.2 and 1.7, indicating that women in high-burden regions were consistently 20% to 70% more likely to experience anemia compared to those in low-burden regions. The Population Attributable Risk (PAR), which estimates the reduction in national prevalence of anemia if all regions achieved the level of the best-performing region, ranged from -5.1% in 2004 to -10.4% in 2010. This indicates that up to 10.4 percentage points of national anemia prevalence could have been avoided in 2010 by closing the regional gap. Likewise, the Population Attributable Fraction (PAF) the proportion of the national burden attributable to regional inequality, was as high as -22.5% in 2010, signifying that nearly a quarter of the national burden could be averted through equitable distribution.\u003c/p\u003e\n\u003cp\u003eOver time, the data show a fluctuating but non-linear trend in both absolute and relative inequality. While the difference and ratio indicators narrowed slightly during the mid-2010s (e.g., D = 12.2 and R = 1.3 in 2014), inequalities widened again by 2019, with D = 22.2 and R = 1.6. These findings underscore that while national prevalence showed a marginal decline, the benefits were not evenly shared across regions, and this is illustrated in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e: Regional inequalities of Prevalence of anemia among reproductive age women (2000-2019)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e12.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e17.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e16.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-15.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e-16.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 556px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD:\u003c/strong\u003e Absolute Difference; \u003cstrong\u003eR:\u003c/strong\u003e Ratio; \u003cstrong\u003ePAR:\u003c/strong\u003e Population Attributable Risk; \u003cstrong\u003ePAF:\u003c/strong\u003e Population Attributable Fraction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003eForecasting Anemia Prevalence (2020\u0026ndash;2030)\u003c/h3\u003e\n\u003cp\u003eThe time series analysis of national anemia prevalence revealed a gradual downward trend from 2000 to 2019. Prevalence declined from 47.8% in 2000 to 44.1% in 2019, reflecting an overall reduction of 3.7 percentage points over two decades, or approximately 0.19 percentage points per year. The decline was more pronounced in the latter part of the period, particularly from 2010 to 2019. Figure 4 illustrates how well the ARIMA (1,1,0) model fits the dataset, demonstrating strong predictive capacity and meeting key statistical assumptions. The autoregressive parameter was estimated at 0.7116 and was statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), indicating a strong dependency on prior year changes in prevalence. As shown in Figure 4, Residual diagnostics showed no evidence of model misspecification: the residuals were normally distributed (Jarque\u0026ndash;Bera test \u003cem\u003ep\u003c/em\u003e = 0.70), exhibited no significant autocorrelation (Ljung\u0026ndash;Box test \u003cem\u003ep\u003c/em\u003e = 1.00), and were homoskedastic (heteroskedasticity test \u003cem\u003ep\u003c/em\u003e = 0.31) . In-sample forecasting accuracy was high, with a MAPE of 0.32% and an RMSE of 0.183.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 5, forecasts for the 2020\u0026ndash;2030 period revealed a continuation of the historical decline, but at a much slower rate. The projected prevalence for 2020 was 44.1%, nearly identical to the 2019 value. By 2030, the forecasted prevalence is expected to reach 43.6%, representing a marginal decrease of 0.5 percentage points over the entire decade. The model indicates a stabilization trend, with prevalence converging toward a lower asymptote. This plateau effect may reflect diminishing returns from existing public health interventions or the presence of structural factors limiting further reductions.\u003c/p\u003e\n\u003cp\u003eAs seen in Table 2 in the Appendix, the forecast\u0026rsquo;s 95% confidence intervals widen progressively over time, from [43.75%, 44.44%] in 2020 to [40.41%, 46.87%] in 2030, indicating growing uncertainty as the forecast horizon extends. These intervals underscore the importance of updating forecasts with new data and implementing responsive policy adjustments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese national trends are further supported by region-specific forecasts (see Appendix Figure 6), which reinforce the persistence of spatial disparities in anemia prevalence across Ghana.\u003c/p\u003e\n\u003cp\u003eOverall, the ARIMA (1,1,0) model suggests that while anemia prevalence among reproductive-age women in Ghana may continue to decline slightly, substantial additional reductions are unlikely in the absence of intensified or innovative interventions.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study highlights the importance of reducing anemia prevalence among reproductive women aged 15 to 49 years in Ghana. It revealed a persistent decline in the overall national prevalence of anemia among reproductive age women from 47.8% in 2000 to 44.2% in 2019, representing a slow but positive downward trend. This represents a 3.6% reduction in the national prevalence of anemia over the 2-decade period. This finding is consistent with previous studies in Ghana and similar countries in Sub-Saharan Africa that reported a very modest reduction in the prevalence of anemia among reproductive age women(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). A study conducted in 29 Sub-Saharan African countries reported a pooled prevalence of 40.5%, Rwanda had the lowest prevalence of 13%, while Mali reported the highest prevalence of 62%(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Another study conducted in 10 East African countries reported a pooled prevalence of 42%, again 23.4% in Rwanda to 57.1% in Tanzania(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This finding was, however, higher than previous studies conducted in Ethiopia, Uganda, Rwanda, Pakistan that reported 37.5%, 32% 19% and 18% respectively(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). These discrepancies could be explained partly by the difference in countries' levels of development and strategic national public health and nutritional intervention implemented at different levels in these countries. On the contrary, other studies reported higher prevalence than the current finding, including Burkina Faso (55%)(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), Asia (53%)(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), Afghanistan (52%)(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The observed discrepancies may stem from variations in study context, period of data collection, sample sizes, reporting and data quality, and demographic profiles of the study populations.\u003c/p\u003e\u003cp\u003eWhile the Ghana national anemia prevalence saw a modest decline, the study revealed substantial disparities in anemia prevalence at the subnational level over the two decades. The Volta region was consistently identified as having the highest prevalence of anemia among women of reproductive age in Ghana, with rates reaching approximately 60% in 2017 and remaining above 50% in most years. Consistent with previous studies, for example, a study conducted at the Hohoe Municipal Hospital in the Volta region reported anemia prevalence among pregnant women 33%(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). At the Adaklu District, in the Volta region, the prevalence of anemia was as high as 78.5% among pregnant women(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), exceeding the regional average of 49%. Similarly, the Northern regions of Ghana, including Northern, Upper East, and Upper West, have also recorded elevated levels anemia prevalence, often exceeding 50% during the early to mid-2000s. Previous studies conducted in 12 health facilities in the Tatale-Sanguli and Zabzugu district, West Gonja District Hospital and the Tamale Teaching Hospital of the northern region reported anemia prevalence of 72.1%, 52.2% and 50.8% respectively. These figures exceed the highest regional prevalence ever reported within the two decades of data analysed(\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Interestingly, these regions received various public health interventions from both government and non-governmental organizations to combat both maternal and child malnutrition(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e); however, the regions are still experiencing higher prevalence. One contributing factor may be the challenges with addressing all four pillars of food insecurity (availability, access, utilization, stability)(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Interventions often focus purely on availability but do not consider factors that affect actual access to nutrition or food, or other aspects such as storage; thus moderate and severe food insecurity in areas such as the Northern Region are extremely high, despite the implementation of interventions and emergency measures(\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegions with the lowest anemia prevalence were the Brong Ahafo and Ashanti regions. This is consistent with an earlier study in the Kintampo Municipal Hospital of the Brong Ahafo region, which reported a prevalence of 28.5%(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDifferences in anemia prevalence reported across studies may partly result from variations in sample population and size. The current analysis uses regional-level, nationally representative data, whereas many previous studies (some cited in this discussion) were district- or facility-based, focusing on smaller, specific populations such as antenatal clinic attendees. Therefore, comparisons between these studies and our findings should be made cautiously, as differences in study design, population characteristics, and settings can affect prevalence estimates and limit direct comparability.\u003c/p\u003e\u003cp\u003eThe above findings were further supported by the four measure of measures of inequalities at the regional level. In this study, we found persistent regional-level inequalities in the prevalence of anemia among women of reproductive age. The northern regions of Ghana, both in absolute and relative terms, recorded the highest prevalence compared to those in the southern Ghana, except the Volta. These regional disparities are attributed to complex interactions of socioeconomic, environmental, and health system factors, including poverty, limited access to healthcare, poor nutrition, and high malaria endemicity in the Northern and Volta regions(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Ghana Living Standards Survey (GLSS6\u0026amp;7) indicates that the three regions in the north have the lowest mean annual incomes, which likely affects women of reproductive age consumption of nutrient-rich foods essential for preventing anemia(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Similar findings were reported in other low- and middle-income countries where geographic inequalities are a critical dimension of health equity, with some regions experiencing disproportionately higher burdens of anemia. For example, in Benin, due to cultural practices, certain foods rich in nutrients were set aside by both mothers and children, exacerbating their anemia condition(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Also in Uganda, geographical location(region), household income were significantly associated with anemia in women(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese challenges around addressing inequalities are likely to continue to be challenging, due to the instability from high-impact events such as pandemics and conflict. There will also be issues directly associated with extreme weather events such as flooding and heat. Ghana is described as a \u0026lsquo;hot spot\u0026rsquo; for climate change by the Intergovernmental Panel on Climate Change(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Climate change will likely negatively impact upon vector-borne disease such as yellow fever, and increase food insecurity, both risk factors for worsening anemia and women\u0026rsquo;s health more widely(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The regional differences across Ghana also reflect vulnerability associated with climate shocks, for example income across the Northern Region being mostly from agriculture, and around 40000 people being displaced by unexpected flooding from the Akosombo Dam in the Volta Region in 2023(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Population Attributable Risk (PAR) and Fraction (PAF) analyses highlight that a significant proportion of national anemia cases could be prevented by reducing regional inequalities, reflecting entrenched socio-economic and healthcare access disparities. This is consistent with other studies in SSA showing that rural and poorer regions, such as Northern Ghana, experience an increase anemia prevalence due to limited healthcare access, poverty, and infectious disease burden, including malaria(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Knowledge-sharing partnerships between regions with high and low anemia burden has the potential to at least partially reduce the inequalities gap.\u003c/p\u003e\u003cp\u003eThe World Health Organization (WHO) originally targeted a 50% reduction in anemia prevalence by 2025, a goal later shifted to 2030. Against this backdrop, our study forecasted anemia prevalence in Ghana from 2020 to 2030 using historical data from 2000 to 2019 to measure progress toward this target and inform policy decisions and strategies. The forecasts reveal a likely continuous but slow decline in the prevalence of anemia, with the projected national prevalence virtually unchanged from 44.1% in 2020 to 43.6% in 2030. This represents a marginal decrease of only 0.5 percentage points over the decade. This plateau suggests that structural barriers may be impeding further reductions, and novel approaches to public health interventions are needed. Region-specific forecasts further confirm persistent spatial disparities and marginal decline in anemia prevalence, reinforcing that national averages mask substantial heterogeneity in anemia burden across Ghana\u0026rsquo;s regions.\u003c/p\u003e\u003cp\u003eOur findings align closely with global trends in recent literature. According to a comprehensive global study, the prevalence of anemia in non-pregnant women aged 15\u0026ndash;49 years changed very little between 2000 and 2019, from 31% (95% UI 28\u0026ndash;34) to 30% (\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), while in pregnant women aged 15\u0026ndash;49 years it decreased from 41% (\u003cspan additionalcitationids=\"CR40 CR41 CR42\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) to 36% (\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). The findings reinforce the need to design public health interventions that prioritize resource allocation to high-burden Northern and Volta regions, enhance malaria prevention, improve nutrition-sensitive interventions and strengthen healthcare access. These approaches are essential tools in closing the regional gaps in anemia prevalence and achieving more equitable health outcomes among women of reproductive age in Ghana, consistent with findings from other sub-Saharan African Countries(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch and policy implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe regional inequalities in anemia prevalence, coupled with a slow decline in the prevalence, have clear implications for both policy and research in Ghana\u0026rsquo;s efforts to reduce anemia. From a policy perspective, strengthened multisectoral strategies are crucial to address the complex and interrelated causes of anemia, which include poverty, malaria, nutrition, and healthcare access. The Government current commitments to reduce iron deficiency anemia (IDA) in children under five, and pregnant women, and to scale up nutrition interventions such as iron-folic acid supplementation and vitamin A distribution provide a strong foundation but require accelerated implementation and expanded coverage, particularly in high-burden regions(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Also, enhancing malaria prevention and treatment, improving laboratory capacity, and addressing socio-cultural barriers to care are essential for reducing anemia among vulnerable groups, including as pregnant women(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Addressing the challenges of climate change is vital, particularly how to deliver healthcare is areas that are prone to flooding, extreme heat, and have limited capacity to adapt their livelihoods to evolving and inconsistent rainfall patterns. From a research perspective, it is crucial to conduct further studies to explore why anemia prevalence remains high in the northern regions of Ghana despite ongoing efforts by the government and non-governmental organizations. Such research should investigate underlying factors, including health system challenges, socio-cultural barriers, nutritional practices, and the effectiveness and reach of current interventions. Given there will always be limited resources to address these health needs, longitudinal datasets can provide insight around temporal changes, and these datasets can then inform granular social network analyses, geospatial approaches, and risk modelling to consider vulnerability at local community and even individual household level. These datasets could be combined with qualitative studies that provide more nuanced local context around social and structural barriers to health protection and access to health services. Understanding these dynamics will help identify knowledge gaps and inform more targeted, context-specific strategies to reduce anemia in these high-burden areas effectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study has some limitations that should be considered when interpreting its findings. The study relies on secondary data, which are limited to subnational disaggregation, thereby restricting a deeper analysis of other variables, such as sex, age, socio-economic status, dietary intake, or iron deficiency measures. The study does not fully explore the underlying health system or socio-cultural factors associated with the high prevalence of anemia in high-burden regions. Finally, forecasting uncertainty increases in the ARIMA model over time, as observed in a gradual widening of the confidence intervals. However, the use of nationally representative data over two decades in the study enables a robust trend analysis and forecasting. It projects regional disparities and quantifies the impact of inequalities using advanced measures like Population Attributable Risk. The use of ARIMA modelling adds rigor to future prevalence projections, providing valuable insights for policy and intervention planning.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study highlight that, while Ghana experienced a modest decline in anemia prevalence over the past two decades, significant regional disparities persist, particularly in the three Northern and Volta regions, where the burden remains very high. The forecast analysis indicates that without intensified and targeted interventions, the prevalence of anemia is likely to plateau, making it unlikely for Ghana to achieve the WHO 2030 anemia reduction target. Overcoming these challenges requires a comprehensive, equity-based approach that combines strengthened healthcare systems, multi-sectoral collaborations, and regional-specific strategies to tackle the socio-economic and health system inequalities. A sustained commitment to data-driven policy and longitudinal research will be crucial to accelerate progress and ensure that gains made in anemia reduction among women of reproductive age are shared equitably and maintained across all regions in the country.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eR \u0026ndash; Relative Ratio\u003c/p\u003e\n\u003cp\u003eD \u0026ndash; Absolute Difference\u003c/p\u003e\n\u003cp\u003ePAR \u0026ndash; Population Attributable Risk\u003c/p\u003e\n\u003cp\u003ePAF \u0026ndash; Population Attributable Fraction\u003c/p\u003e\n\u003cp\u003eARIMA \u0026ndash; Autoregressive Integrated Moving Average\u003c/p\u003e\n\u003cp\u003eDHS \u0026ndash; Demographic and Health Survey\u003c/p\u003e\n\u003cp\u003eIHME \u0026ndash; Institute for Health Metrics and Evaluation\u003c/p\u003e\n\u003cp\u003eWRA \u0026ndash; Women of Reproductive Age\u003c/p\u003e\n\u003cp\u003eNHIS \u0026ndash; National Health Insurance Scheme\u003c/p\u003e\n\u003cp\u003eSSA \u0026ndash; Sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003eIDA \u0026ndash; Iron Deficiency Anemia\u003c/p\u003e\n\u003cp\u003eGLSS \u0026ndash; Ghana Living Standards Survey\u003c/p\u003e\n\u003cp\u003eSDG Sustainable Development Goal\u003c/p\u003e\n\u003cp\u003eWASH \u0026ndash; Water, Sanitation, and Hygiene\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to IHME and the WHO for making the dataset and the HEAT software accessible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI and AS conceived the study. AI and AS wrote the methods section and performed the data analysis. AI, SZ, MH, TJ, and AS were responsible for the initial draft of the manuscript. All the authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset used can be accessed at : https://www.who.int/data/inequality-monitor/data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eNo ethical clearance was sought for this study due to the public availability of\u003c/p\u003e\n\u003cp\u003ethe dataset.\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 no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbdul-Wahab Inusah, MPH\u0026sup1; ✉ (Corresponding Author)\u003c/p\u003e\n\u003cp\u003eORCID: 0000-0002-8184-7355\u003c/p\u003e\n\u003cp\u003e\u0026sup1; Department of Global and International Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eTemple O. Jagha, DrPH, FAPH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e 2\u003c/sup\u003eGlobal and International Development Expert, Gaithersburg, MD 20878, USA\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\n\u003cp\u003eMichael G. Head PhD\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eFaculty of Medicine, University of Southampton, Southampton, United Kingdom\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n\u003cp\u003eAbdul‑Aziz Seidu PhD\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003ePublic Health and Tropical Medicine, James Cook University,\u003c/p\u003e\n\u003cp\u003eTownsville, QLD 4811, Australia.\u003c/p\u003e\n\u003cp\u003eShamsu-Deen Ziblim, PhD\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDirectorate of Academic Planning \u0026amp; Quality Assurance (DAPQA), University for Development Studies, Box TL1350, Tamale, Ghana\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F, et al. Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995-2011: a systematic analysis of population-representative data. Lancet Glob Health. 2013 July;1(1):e16-25. \u003c/li\u003e\n\u003cli\u003eKassebaum NJ, Jasrasaria R, Naghavi M, Wulf SK, Johns N, Lozano R, et al. A systematic analysis of global anemia burden from 1990 to 2010. Blood. 2014 Jan 30;123(5):615\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eJeevan J, Karun KM, Puranik A, Deepa C, MK L, Barvaliya M. Prevalence of anemia in India: a systematic review, meta-analysis and geospatial analysis. BMC Public Health. 2025 Apr 4;25(1):1270. \u003c/li\u003e\n\u003cli\u003eWHO. WHO fact sheet on anaemia, including definitions, symptoms, causes, treatments and WHO response. [Internet]. 2025 [cited 2025 July 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/anaemia\u003c/li\u003e\n\u003cli\u003eWHO. Anaemia in women and children [Internet]. 2023 [cited 2025 July 8]. Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children\u003c/li\u003e\n\u003cli\u003eWHO. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity [Internet]. WHO; 2011. Report No.: WHO/NMH/NHD/MNM/11.1. Available from: http://www.who.int\u003c/li\u003e\n\u003cli\u003eWang R, Xu S, Hao X, Jin X, Pan D, Xia H, et al. Anemia during pregnancy and adverse pregnancy outcomes: a systematic review and meta-analysis of cohort studies. Front Glob Womens Health [Internet]. 2025 Jan 31 [cited 2025 July 8];6. Available from: https://www.frontiersin.org/articles/10.3389/fgwh.2025.1502585/full\u003c/li\u003e\n\u003cli\u003eAnaemia Action Alliance. The economic cost of anaemia | Anaemia Action Alliance [Internet]. 2025 [cited 2025 July 8]. Available from: https://anaemiaalliance.who.int/about-anaemia/cost-of-inaction\u003c/li\u003e\n\u003cli\u003eNissenson AR, Wade S, Goodnough T, Knight K, Dubois RW. Economic Burden of Anemia in an Insured Population. JMCP. 2005 Sept;11(7):565\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eBalarajan Y, Ramakrishnan U, \u0026Ouml;zaltin E, Shankar AH, Subramanian S. Anaemia in low-income and middle-income countries. The Lancet. 2011 Dec;378(9809):2123\u0026ndash;35. \u003c/li\u003e\n\u003cli\u003eAndersen CT, Tadesse AW, Bromage S, Fekadu H, Hemler EC, Passarelli S, et al. Anemia Etiology in Ethiopia: Assessment of Nutritional, Infectious Disease, and Other Risk Factors in a Population-Based Cross-Sectional Survey of Women, Men, and Children. The Journal of Nutrition. 2022 Feb 1;152(2):501\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eChaparro CM, Suchdev PS. Anemia epidemiology, pathophysiology, and etiology in low- and middle-income countries. Ann N Y Acad Sci. 2019 Aug;1450(1):15\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eWegm\u0026uuml;ller R, Bentil H, Wirth JP, Petry N, Tanumihardjo SA, Allen L, et al. Anemia, micronutrient deficiencies, malaria, hemoglobinopathies and malnutrition in young children and non-pregnant women in Ghana: Findings from a national survey. PLOS ONE. 2020 Jan 30;15(1):e0228258. \u003c/li\u003e\n\u003cli\u003eAddis Alene K, Mohamed Dohe A. Prevalence of Anemia and Associated Factors among Pregnant Women in an Urban Area of Eastern Ethiopia. Anemia. 2014;2014:561567. \u003c/li\u003e\n\u003cli\u003eKlu D, Kyei-Arthur F, Appiah M, Odame ML. Multilevel predictors of anaemia among pregnant women in Ghana: New evidence from the 2019 Ghana Malaria Indicator Survey. PLOS Glob Public Health. 2024 Sept 5;4(9):e0003673. \u003c/li\u003e\n\u003cli\u003eBelay DG, Amlak BT, Chilot D, Alem AZ, Merid MW. Pooled prevalence of anaemia and its associated factors among adolescent girls in East and West Africa: a systematic review and meta-analysis. BMC Public Health. 2025 July 17;25(1):2482. \u003c/li\u003e\n\u003cli\u003eNyarko SH, Boateng ENK, Dickson KS, Adzrago D, Addo IY, Acquah E, et al. Geospatial disparities and predictors of anaemia among pregnant women in Sub-Saharan Africa. BMC Pregnancy and Childbirth. 2023 Oct 20;23(1):743. \u003c/li\u003e\n\u003cli\u003eHead M, Boxall J, Atiwoto W, Atengble K, Dotse-Gborgbortsi W, Agyemang E. Addressing climate change and health in Ghana and West Africa: a Summit report [Internet]. figshare; 2025 [cited 2025 July 23]. Available from: https://figshare.com/articles/preprint/_b_Addressing_climate_change_and_health_in_Ghana_and_West_Africa_a_Summit_report_b_/27894951/1\u003c/li\u003e\n\u003cli\u003eSalifu MG, Da-Costa Vroom FB, Guure C. Anaemia among women of reproductive age in selected sub-Saharan African countries: multivariate decomposition analyses of the demographic and health surveys data 2008\u0026ndash;2018. Front Public Health. 2024 Jan 5;11:1128214. \u003c/li\u003e\n\u003cli\u003eLokossou YUA, Tambe AB, Azandj\u0026egrave;m\u0026egrave; C, Mbhenyane X. Socio-cultural beliefs influence feeding practices of mothers and their children in Grand Popo, Benin. Journal of Health, Population and Nutrition. 2021 July 23;40(1):33. \u003c/li\u003e\n\u003cli\u003eOxfam International. Oxfam International. 2022 [cited 2025 July 23]. Ghana: extreme inequality in numbers. Available from: https://www.oxfam.org/en/ghana-extreme-inequality-numbers\u003c/li\u003e\n\u003cli\u003eZegeye B, Anyiam FE, Ahinkorah BO, Ameyaw EK, Budu E, Seidu AA, et al. Prevalence of anemia and its associated factors among married women in 19 sub-Saharan African countries. Archives of Public Health. 2021 Nov 29;79(1):214. \u003c/li\u003e\n\u003cli\u003eEwusie JE, Ahiadeke C, Beyene J, Hamid JS. Prevalence of anemia among under-5 children in the Ghanaian population: estimates from the Ghana demographic and health survey. BMC Public Health. 2014 June 19;14(1):626. \u003c/li\u003e\n\u003cli\u003eAlem 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 Nov 20;13(1):20335. \u003c/li\u003e\n\u003cli\u003eOkesanya OJ, Eshun G, Ukoaka BM, Manirambona E, Olabode ON, Adesola RO, et al. Water, sanitation, and hygiene (WASH) practices in Africa: exploring the effects on public health and sustainable development plans. Tropical Medicine and Health. 2024 Oct 9;52(1):68. \u003c/li\u003e\n\u003cli\u003eAwuuh VA, Appiah CA, Mensah FO. Impact of nutrition education intervention on nutritional status of undernourished children (6-24 months) in East Mamprusi district of Ghana. Nutrition \u0026amp;amp; Food Science. 2018 Oct 26;49(2):262\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eAzagba-Nyako JM, Tortoe C, Akonor PT, Padi A, Boateng J, Otwey R. Review of Current Strategies to Address Micronutrient Deficiencies (MNDs) in Ghana: A Scoping Review. Journal of Nutrition and Metabolism. 2025;2025(1):6652716. \u003c/li\u003e\n\u003cli\u003eYawson AE, Amoaful EO, Senaya LK, Yawson AO, Aboagye PK, Mahama AB, et al. The lancet series nutritional interventions in Ghana: a determinants analysis approach to inform nutrition strategic planning. BMC Nutr. 2017 Mar 15;3(1):27. \u003c/li\u003e\n\u003cli\u003eDalaba MA, Nonterah EA, Chatio ST, Adoctor JK, Dambayi E, Nonterah EW, et al. Engaging community members in setting priorities for nutrition interventions in rural northern Ghana. PLOS Global Public Health. 2022 Sept 16;2(9):e0000447. \u003c/li\u003e\n\u003cli\u003ePetry N, Wirth JP, Adu‐Afarwuah S, Wegmuller R, Woodruff BA, Tanumihardjo SA, et al. Risk factors for anaemia among Ghanaian women and children vary by population group and climate zone. Matern Child Nutr. 2020 Sept 18;17(2):e13076. \u003c/li\u003e\n\u003cli\u003eTettegah E, Hormenu T, Ebu-Enyan NI. Risk factors associated with anaemia among pregnant women in the Adaklu District, Ghana. Front Glob Womens Health [Internet]. 2024 Feb 15 [cited 2025 July 9];4. Available from: https://www.frontiersin.org/articles/10.3389/fgwh.2023.1140867/full\u003c/li\u003e\n\u003cli\u003eWHO. Health Equity Assessment Toolkit [Internet]. 2025 [cited 2025 July 3]. Available from: https://www.who.int/data/inequality-monitor/assessment_toolkit\u003c/li\u003e\n\u003cli\u003eIHME. Institute of Health Metrics and Evaluation and the global burden of disease - Canadian Institute of Actuaries | Institut canadien des actuaires [Internet]. 2024 [cited 2025 July 23]. Available from: https://www.cia-ica.ca/news/institute-of-health-metrics-and-evaluation-and-the-global-burden-of-disease/\u003c/li\u003e\n\u003cli\u003eGardner 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\u0026ndash;2021: findings from the Global Burden of Disease Study 2021. The Lancet Haematology. 2023 Sept 1;10(9):e713\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eTandoh MA, Agyemang WO, Brago ET, Attu SS, Domfeh EA. Prevalence And Risk Factors Of Anaemia Among Women Of Reproductive Age In A Ghanaian University. 1. 2024 Dec 18;42(4):80\u0026ndash;93. \u003c/li\u003e\n\u003cli\u003eTetteh, Adanu, Adanu R, Folson G, Agyabeng K, Dwomoh D, et al. Drivers of anaemia reduction among women of reproductive age in the eastern and upper west regions of Ghana: A secondary data analysis of the Ghana demographic and health surveys. AJFAND. 2023 Jan 23;23(116):22248\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eMare KU, Aychiluhm SB, Sabo KG, Tadesse AW, Kase BF, Ebrahim OA, et al. Determinants of anemia level among reproductive-age women in 29 Sub-Saharan African countries: A multilevel mixed-effects modelling with ordered logistic regression analysis. PLOS ONE. 2023 Nov 29;18(11):e0294992. \u003c/li\u003e\n\u003cli\u003eLiyew AM, Tesema GA, Alamneh TS, Worku MG, Teshale AB, Alem AZ, et al. Prevalence and determinants of anemia among pregnant women in East Africa; A multi-level analysis of recent Demographic and Health Surveys. PloS one. 2021;16(4):e0250560. \u003c/li\u003e\n\u003cli\u003eAnimut K, Berhanu G. Determinants of anemia status among pregnant women in ethiopia: using 2016 ethiopian demographic and health survey data; application of ordinal logistic regression models. BMC Pregnancy Childbirth [Internet]. 2022 Aug 26 [cited 2025 July 11];22(1). Available from: https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-022-04990-8\u003c/li\u003e\n\u003cli\u003eNankinga O, Aguta D. Determinants of Anemia among women in Uganda: further analysis of the Uganda demographic and health surveys. BMC Public Health. 2019 Dec 30;19(1):1757. \u003c/li\u003e\n\u003cli\u003eHabib MA, Raynes-Greenow C, Soofi SB, Ali N, Nausheen S, Ahmed I, et al. Prevalence and determinants of iron deficiency anemia among non-pregnant women of reproductive age in Pakistan. Asia Pacific Journal of Clinical Nutrition. 2018 Jan 1;27(1):195\u0026ndash;203. \u003c/li\u003e\n\u003cli\u003eOuedraogo I, Zaman\u0026eacute; H, Sawadogo AY, Kiemtor\u0026eacute; S, Kain DP, Ouattara A, et al. Anaemia in Pregnancy in an African Setting after Preventive Measures. OJOG. 2019;09(01):10\u0026ndash;20. \u003c/li\u003e\n\u003cli\u003eSunuwar DR, Singh DR, Chaudhary NK, Pradhan PMS, Rai P, Tiwari K. Prevalence and factors associated with anemia among women of reproductive age in seven South and Southeast Asian countries: Evidence from nationally representative surveys. Cardoso MA, editor. PLoS ONE. 2020 Aug 13;15(8):e0236449. \u003c/li\u003e\n\u003cli\u003eZewar M, Chakraborty S. Perspectives of Health Care Professionals and Patients on Anemia in Afghanistan\u0026rsquo;s Women of Reproductive Age. International Journal of Clinical Skills. 2022;16(3):230. \u003c/li\u003e\n\u003cli\u003eKofie P, Tarkang EE, Manu E, Amu H, Ayanore MA, Aku FY, et al. Prevalence and associated risk factors of anaemia among women attending antenatal and post-natal clinics at a public health facility in Ghana. BMC Nutr [Internet]. 2019 Dec [cited 2025 July 11];5(1). Available from: https://bmcnutr.biomedcentral.com/articles/10.1186/s40795-019-0303-x\u003c/li\u003e\n\u003cli\u003eAdokiya MN, Abodoon GN, Boah M. Prevalence and determinants of anaemia during third trimester of pregnancy: a retrospective cohort study of women in the northern region of Ghana. Women \u0026amp; Health. 2022 Feb 7;62(2):168\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eWemakor A. Prevalence and determinants of anaemia in pregnant women receiving antenatal care at a tertiary referral hospital in Northern Ghana. BMC Pregnancy and Childbirth. 2019 Dec 11;19(1):495. \u003c/li\u003e\n\u003cli\u003eMalick M. Prevalence and factors associated with anemia in pregnancy among women receiving antenatal care at the West Gonja District Hospital of Northern Ghana. Auctores Publishing LLC, editor. JOGRS. 2020 Dec 14;4(3):01\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eGFSC. Food Security Cluster - Cluster Coordinators Handbook [Internet]. 2023 [cited 2025 July 23]. Available from: https://handbook.fscluster.org\u003c/li\u003e\n\u003cli\u003eHead M, Boxall J. Exploring the impact of climate change on food insecurity and health in a Last Mile district of rural Ghana [Internet]. figshare; 2024 [cited 2025 July 23]. Available from: https://figshare.com/articles/preprint/Exploring_the_impact_of_climate_change_on_food_insecurity_and_health_in_a_Last_Mile_district_of_rural_Ghana/25245082/3\u003c/li\u003e\n\u003cli\u003eSumaila I, Manu A, Hallidu M. Prevalence and associated factors of anaemia among antenatal care attendants in the Kintampo municipality. PAMJ-OH [Internet]. 2022 [cited 2025 July 11];9. Available from: https://www.one-health.panafrican-med-journal.com/content/article/9/16/full\u003c/li\u003e\n\u003cli\u003eAnaemia and its determinants among reproductive age women (15\u0026ndash;49 years) in the Gambia: a multi-level analysis of 2019\u0026ndash;20 Gambian Demographic and Health Survey Data | Archives of Public Health [Internet]. [cited 2025 July 11]. Available from: https://link.springer.com/article/10.1186/s13690-022-00985-1\u003c/li\u003e\n\u003cli\u003eGSS. Ghana - Ghana Living Standards Survey 6 (With a Labour Force Module) 2012-2013 [Internet]. 2012 [cited 2025 July 12]. Available from: https://microdata.statsghana.gov.gh/index.php/catalog/72\u003c/li\u003e\n\u003cli\u003eGSS. Ghana Living Standards Survey (GLSS7) [Internet]. 2020 [cited 2025 July 12]. Available from: https://open.africa/dataset/38102d96-1393-4918-a1a7-556eca8491ad/resource/839a1758-146c-40cd-957d-37d26aa84fb6/download/glss7-main-report_final.pdf\u003c/li\u003e\n\u003cli\u003ePachauri RK, Mayer L, Intergovernmental Panel on Climate Change, editors. Climate change 2014: synthesis report. Geneva, Switzerland: Intergovernmental Panel on Climate Change; 2015. 151 p. \u003c/li\u003e\n\u003cli\u003eGaythorpe KA, Hamlet A, Cibrelus L, Garske T, Ferguson NM. The effect of climate change on yellow fever disease burden in Africa. eLife [Internet]. 2020 July 28 [cited 2025 July 23];9. Available from: https://elifesciences.org/articles/55619\u003c/li\u003e\n\u003cli\u003eSemba RD, Askari S, Gibson S, Bloem MW, Kraemer K. The Potential Impact of Climate Change on the Micronutrient-Rich Food Supply. Adv Nutr. 2021 Oct 4;13(1):80\u0026ndash;100. \u003c/li\u003e\n\u003cli\u003eBenjamin KAD. The Akosombo Dam Spillage | UNICEF Ghana [Internet]. 2023 [cited 2025 July 23]. Available from: https://www.unicef.org/ghana/blog/akosombo-dam-spillage\u003c/li\u003e\n\u003cli\u003eStevens 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\u0026ndash;19: a pooled analysis of population-representative data. The Lancet Global Health. 2022 May 1;10(5):e627\u0026ndash;39. \u003c/li\u003e\n\u003cli\u003eNti J, Afagbedzi S, da-Costa Vroom FB, Ibrahim NA, Guure C. Variations and Determinants of Anemia among Reproductive Age Women in Five Sub-Saharan Africa Countries. BioMed Research International. 2021;2021(1):9957160. \u003c/li\u003e\n\u003cli\u003eNAF. NAF Tracker - Nutrition for Growth Commitments - Global Nutrition Report [Internet]. 2021 [cited 2025 July 12]. Available from: https://globalnutritionreport.org/resources/naf/tracker/commitment/nutrition-for-growth-commitments/\u003c/li\u003e\n\u003cli\u003eAsobuno C, Adjei-Gyamfi S, Aabebe FG, Hammond J, Taikeophithoun C, Amuna NN, et al. Risk factors for anaemia among pregnant women: A cross-sectional study in Upper East Region, Ghana. PLoS One. 2024;19(11):e0301654. \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":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Anemia, Ghana, Regional Disparities, Forecasting, ARIMA Model, Public Health, Nutrition, Health Equity, Women of Reproductive Age","lastPublishedDoi":"10.21203/rs.3.rs-7215917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7215917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnemia, characterized by a deficiency in haemoglobin, remains a public health problem in Ghana among women of reproductive age (WRA) and children under five, as it hinders cognitive development, physical growth and well-being. This study examines sub-national prevalence and inequalities from 2000 to 2019. It also forecast the prevalence of anemia through to 2030 to inform Ghana’s efforts towards achieving the global anemia reduction targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used population-representative estimates of anemia among WRA drawn from the WHO Equity Database. Using the WHO Health Equity Assessment Toolkit, we calculated both prevalence and regional inequalities across four inequality dimensions: difference(D), ratio(R), population-attributable risk(PAR), and population-attributable fraction(PAF). An Autoregressive Integrated Moving Average (ARIMA) model, specifically ARIMA(1, 1, 0), was used to forecast anemia prevalence from 2020 to 2030 using Python package.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnemia prevalence in Ghana among WRA age showed a modest decline from 47.8% in 2000 to 44.3% in 2019, representing a 3.5% reduction. The Ashanti region experienced the highest decline, from 43.3% (95% CI: 27.6%, 60.6%) in 2000 to 37.3% (95% CI: 28.7%, 46.3%) in 2019. Paradoxically, Upper West observed the highest increase in prevalence from 41% (95% CI: 26.1%, 56.8%) to 45.2% (95%: 36.6%, 53.5%). The gap in prevalence between the region with the highest burden and one with the lowest burden keeps widening across the four inequality dimensions from 2000 to 2019; D(18.1% to 22.2%), PAF(-16.2% to -18.4%), R (1.5 to 1.6) and PAR (7.8 to -8.2). Forecasting results revealed an insignificant decline, as prevalence was projected to decrease marginally from 44.1% (95% CI: 43.8% – 44.4%) in 2020 to 43.6% (95% CI: 40.4% – 46.9%) in 2030. Overall, the study shows absolute and relative fluctuation in inequalities across regions over-time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe marginal declines in the anemia prevalence over the two decades and the widening inequalities between the highest and lowest burdened regions required urgent public health intervention to avert the trends. Without intensified, equity-focused strategies such as those addressing socio-economic inequalities, health system strengthening and scaling-up of nutrition-sensitive and malaria control interventions, Ghana is not on track to achieve the WHO Target 50% anemia reduction by 2030.\u003c/p\u003e","manuscriptTitle":"Trends, Regional Inequalities, and Future Projections of Anemia among Women of Reproductive Age (15-49 years) in Ghana (2000–2030)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 06:50:10","doi":"10.21203/rs.3.rs-7215917/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-31T19:04:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-28T15:03:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T20:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162847278895064379011176920223178708621","date":"2026-03-21T12:53:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56723562242094136797792492261766710187","date":"2026-03-06T12:19:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T10:36:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T22:04:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-27T22:03:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Reproductive Health","date":"2025-07-25T16:01:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"reproductive-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reph","sideBox":"Learn more about [Reproductive Health](http://reproductive-health-journal.biomedcentral.com)","snPcode":"12978","submissionUrl":"https://submission.nature.com/new-submission/12978/3","title":"Reproductive Health","twitterHandle":"@Reprod_Health","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9abc5456-97b2-41d8-beb7-d5e932c2b7fb","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T23:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 06:50:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7215917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7215917","identity":"rs-7215917","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}