Trends, regional disparities, and projected burden of anemia among women of reproductive age in Ghana, 2000–2030

Abstract

Background

Anemia remains a significant public health concern among women of reproductive age (WRA) in Ghana, contributing to adverse health and socioeconomic outcomes. Despite national nutrition and malaria control interventions, progress has been modest with persistent regional disparities. This study assessed national and regional trends in anemia prevalence among WRA from 2000 to 2019, quantified regional inequalities, and forecasted national prevalence to 2030 to measure progress toward global reduction targets.

Methods

Population-representative estimates of anemia among WRA were obtained from the WHO Health Equity Assessment Toolkit (HEAT) database. National and regional prevalence were analyzed using four inequality measures: difference (D), ratio (R), population attributable risk (PAR), and population attributable fraction (PAF). Time-series forecasting was conducted using an ARIMA (1, 1, 0) model to project prevalence from 2020 to 2030.

Results

Anemia prevalence among WRA declined modestly from 47.8% in 2000 to 44.3% in 2019, a 3.5%-point reduction. The Ashanti region recorded the greatest decline (43.3% to 37.3%), while the Upper West region observed the highest increase (41.0% to 45.2%). Regional inequalities widened from 2000 to 2019 across several measures: D (18.1 to 22.2), R (1.5 to 1.6), and PAF (− 16.2 to − 18.4). ARIMA forecasting suggests a plateauing effect, with national prevalence projected to reach 43.6% (95% CI: 40.4–46.9) by 2030.

Conclusion

The modest decline in prevalence, coupled with widening regional inequalities and a projected plateau through 2030, indicates that current progress is insufficient to achieve WHO global targets. Addressing these gaps requires geographically targeted, multi-sectoral interventions, such as improving food storage infrastructure and integrating malaria control with nutritional counseling, to accelerate progress and ensure equitable health outcomes for WRA across all regions of Ghana.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12978-026-02345-6.

Introduction

Anemia, characterized by a reduction in hemoglobin concentration that impairs oxygen-carrying capacity, remains a persistent and multifaceted global health challenge disproportionately affecting women of reproductive age (WRA) [15–49 years] [1–3]. In 2019, anemia affected approximately half a billion women and 269 million children globally, with the most profound burdens concentrated in Africa and South-East Asia [4]. By 2023, the global prevalence among WRA was estimated at 30.7%, with significantly higher rates among pregnant women (35.5%) compared to non-pregnant women (30.5%) [5]. Defined by hemoglobin thresholds of < 12.0 g/dL for non-pregnant women and < 11.0 g/dL for pregnant women [6], anemia is a critical driver of maternal and infant mortality, stillbirth, low birth weight, and impaired cognitive development [1, 7]. Beyond the clinical burden, the global economic cost of anemia among women and adolescent girls is estimated at USD 113 billion, reflecting massive losses in human capital and productivity [8–10].

In the African context, anemia is classified as a severe public health crisis, with prevalence rates exceeding 20% in most nations [4]. The etiology in this region is complex, involving a synergy of nutritional deficiencies (iron, folate, and vitamin B₁₂), infectious diseases such as malaria and helminthiasis, and genetic hemoglobinopathies [4, 7, 11–13]. These biological drivers are compounded by deep-seated socioeconomic inequalities, including inequitable household food distribution and limited healthcare access in rural and underserved populations [10, 14, 15]. West Africa, in particular, bears a disproportionate burden due to high malaria transmission, undernutrition, and frequent pregnancies [16, 17]. Emerging evidence suggests that climate change is further destabilizing food systems and healthcare access in the region [17]. Environmental shocks, such as extreme flooding and unpredictable rainfall, directly exacerbate anemia by triggering acute food shortages and creating stagnant water bodies that intensify malaria transmission, a primary driver of hematological deficiency in West Africa [18–21]. Ghana exemplifies the complexities of addressing anemia within this changing landscape. Despite sustained socioeconomic development and the implementation of various public health interventions [18], anemia remains a dominant health concern, with national prevalence rates consistently hovering above 40% [19]. Significant regional disparities persist; the Northern, Upper East, and Upper West regions frequently report higher burdens compared to the Greater Accra and Ashanti regions, reflecting entrenched differences in poverty, food security, and malaria endemicity [22, 23]. While the government and non-governmental organizations have deployed iron-folic acid supplementation, food fortification, and malaria control programs, progress toward national and global targets has been slow and geographically uneven [24, 25].

Despite the abundance of maternal and child health research in Ghana, a significant gap remains in the longitudinal analysis of regional-level inequalities among the broader population of WRA. Most previous studies have focused exclusively on children under five or specifically on pregnant women at the facility level, leaving the wider demographic of reproductive-age women understudied in the context of long-term spatial trends [15, 22, 26–29]. Furthermore, there is a lack of subnational forecasting to determine if current trajectories are sufficient to meet the World Health Organization (WHO) target of a 50% reduction in anemia by 2030 [4]. To address these gaps, this study utilizes subnational estimates from the Institute for Health Metrics and Evaluation (IHME) to examine regional-level disparities in anemia prevalence in Ghana from 2000 to 2019. Additionally, an AutoRegressive Integrated Moving Average (ARIMA) model is employed to forecast prevalence from 2020 to 2030. This evidence is intended to guide high-precision policy planning and targeted nutritional interventions necessary to achieve the WHO 2030 targets and ensure equitable health outcomes across all regions.

Methods

Study design and data source

This study employed a time-trend ecological design to analyze the regional-standardized prevalence of anemia among WRA in Ghana from 2000 to 2019. Data were obtained from the WHO Health Equity Assessments Toolkit (HEAT) and the WHO Equity Database, which provide accessible, harmonized estimates of health indicators disaggregated by inequality dimensions across multiple countries [30]. Anemia prevalence estimates used in this study is originally derived from the Institute for Health Metrics and Evaluation(IHME), which is part of the Global Burden of Disease(GBD) geospatial estimates [31]. 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 [32]. IHME are designed to be representative at both nationally and subnational levels; 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 WRA, defined according to WHO criteria hemoglobin 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 (Fig. 1). Although six additional regions were created in 2019, the dataset retained the original 10-region classification to ensure consistency in time-trend analysis over the 2000–2019 period. Specifically, data reported for the “Northern” region encompasses the present-day Northern, Savannah, and North East regions; the “Volta” region includes the current Volta and Oti regions; and the “Brong Ahafo” region comprises the current Bono, Bono East, and Ahafo regions. The “Western” region similarly includes the present-day Western and Western North regions. This consolidation was necessary to maintain a stable geographic unit of analysis across the two-decade study horizon.

Fig. 1.

Regional prevalence of anemia among reproductive age women in Ghana: 2019

Inequality analysis

The WHO HEAT platform provides standardized methods for quantifying and visualizing health inequalities. Four summary measures were used to assess both absolute and relative inequalities in anemia prevalence across regions. 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 [33].

Time series forecasting approach

This study applied a time-series modeling approach to forecast the national anemia prevalence among WRA 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 [34]. 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)

The analysis of national-level data indicates a modest downward trajectory in the prevalence of anemia among WRA in Ghana over the twenty-year study period. In 2000, the baseline prevalence was recorded at 47.8%, which declined to 44.3% by 2019, reflecting a total absolute reduction of 3.5% points. This equates to an average annual rate of approximately 0.2% points reduction. The longitudinal trend was characterized by two primary phases: a decade of relative stagnation between 2000 and 2010, during which prevalence only shifted from 47.8% to 46.3%, followed by a period of minor annual fluctuations between 2011 and 2019 that ultimately led to the terminal prevalence of 44.3%, Fig. 2.

Fig. 2.

National anemia inequalities among reproductive age women (2000–2019)

Regional anemia inequalities in Ghana, 2000–2019

The subnational analysis identified substantial geographic variation in the burden and trajectory of anemia across the study period. Historically high-burden areas, including the Volta, Northern, Upper East, and Upper West regions, frequently exceeded a 50% prevalence threshold during the early 2000s. The Volta region, in particular, consistently reported the highest prevalence, reaching a peak of 59.1% in 2017. Conversely, the Brong Ahafo and Ashanti regions consistently recorded the lowest prevalence rates. By the conclusion of the study period in 2019, an inter-regional gap of 22.2% points was observed between Brong Ahafo (36.1%) and the Volta region (58.3%).

Progress across regions was markedly non-uniform. The Ashanti region demonstrated the most significant improvement, with prevalence decreasing from 43.3% (95% CI: 27.6–60.6) in 2000 to 37.3% (95% CI: 28.7–46.3) in 2019. The Upper East region also recorded a reduction of 5.5% points. In contrast, net increases in prevalence over the twenty-year period were observed in the Upper West, Northern, and Volta regions. Specifically, the Upper West region rose from 41.0% to 45.2%, while the Northern and Volta regions experienced marginal increases from 51.1% to 52.5% and 58.1% to 58.3%, respectively, as shown in Fig. 3 and supplementary file 3.

Fig. 3.

Regional anemia inequalities among reproductive age women (2000-2019)

Inequality analysis

Regional inequalities in anemia prevalence among reproductive age women (2000–2019)

The analysis of health inequality metrics demonstrated that geographic disparities in anemia prevalence remained substantial throughout the two-decade study period. Absolute inequality, as measured by the D between the regions with the highest and lowest burdens, exhibited fluctuations, reaching a peak of 23.7% points in 2010 before settling at 22.2% points by 2019. Relative inequality, represented by the R, consistently ranged between 1.2 and 1.7. These values indicate that women residing in the highest-burden regions faced a risk of anemia that was 20% to 70% higher than those in the lowest-burden regions.

Further assessment using population-weighted measures, specifically the PAR and PAF, quantified the potential impact of these inequalities on the national burden. The PAR reached its highest absolute value in 2010 at -10.4% points, indicating the reduction in national prevalence achievable if all regions performed at the level of the best-performing region. Correspondingly, the PAF reached its maximum in 2010 at -22.5% and was recorded at -18.4% in 2019. These findings reflect a non-linear trend in regional health disparities, with inequalities widening toward the end of the study period following a temporary narrowing in the mid-2010s.

as 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 study used time series ARIMA(1,1,0) model to project national anemia prevalence from 2020 to 2030, demonstrating high predictive accuracy with a MAPE of 0.32% and a RMSE of 0.183. The model identified a significant autoregressive parameter of 0.7116 (p < 0.001), indicating that past prevalence rates strongly influence future trends, (Fig. 4).

Fig. 4.

Autocorrelation diagnostics for determining model selection

Autocorrelation diagnostics for determining model selection

The forecasting results suggest a continuation of the historical downward trend, albeit at a notably decelerating rate. National prevalence is projected to reach 43.6% by 2030, representing a marginal absolute decline of 0.7% points from the 2019 baseline. Analysis of the forecast intervals shows a progressive widening of the 95% confidence intervals, ranging from 43.75% to 44.44% in 2020 to 40.41%–46.87% by 2030. These projections indicate a plateauing effect, wherein the national prevalence is expected to remain above 40% throughout the next decade under current historical trajectories (Fig. 5).These national trends are further supported by region-specific forecasts (see Appendix Table 2; Fig. 6), which reinforce the persistence of spatial disparities in anemia prevalence across Ghana.

Fig. 5.

Forecast of national anemia prevalence among reproductive age women (2020–2030)

Fig. 6.

Regional forecast of anemia prevalence among reproductive age women (2020-2030)

Discussion

The findings of this study underscore a persistent public health challenge in Ghana, characterized by a slow and non-linear decline in anemia prevalence among WRA. The observed reduction from 47.8% in 2000 to 44.2% in 2019 represents a modest downward trend, a 3.6% absolute reduction over two decades, that aligns with previous longitudinal data in Ghana and broader findings across Sub-Saharan Africa (SSA) reporting similarly constrained progress [35, 36]. Our findings are consistent with pooled analyses from 29 SSA countries reporting a prevalence of 40.5%, reflecting the systemic nature of nutritional and hematological deficiencies in the region [37]. However, Ghana’s prevalence remains substantially higher than that of regional counterparts such as Rwanda (13%), Ethiopia (37.5%), and Uganda (32%) [37–39]. While some nations like Burkina Faso (55%) and Mali (62%) report higher burdens, the discrepancies between Ghana and better-performing nations likely stem from variations in the scale of national public health interventions and differing demographic profiles [37, 40–43]. These variations may also be attributed to differences in data quality, reporting standards, and study contexts across the continent. Beyond these regional comparisons, the projected plateau in Ghana’s anemia reduction may be attributed to entrenched structural barriers within the national health and food systems. Specifically, frequent stock-outs and inconsistencies in the iron-folic acid (IFA) supplement supply chain at the primary healthcare level limit the reach of maternal health programs [41]. Furthermore, socio-cultural dietary habits in many Ghanaian communities, particularly the high consumption of phytate-rich, plant-based diets which inhibit iron absorption, remain a significant challenge [42]. These factors, coupled with the high cost of animal-source foods (heme iron) for low-income households, create a multifaceted barrier that current interventions have yet to fully overcome. Addressing these specific structural constraints is essential for moving beyond the current stagnation in anemia reduction and achieving long-term targets.

Beyond national averages, this study reveals substantial subnational disparities that remained entrenched over the study period. The Volta region consistently emerged as the highest-burden area, with prevalence rates reaching 59.1% in 2017, supported by localized studies in Hohoe and Adaklu reporting rates between 33% and 78.5% [29, 31]. Similarly, the Northern, Upper East, and Upper West regions frequently exceeded 50% prevalence. These findings are echoed by facility-based data from the Zabzugu and Tamale districts, which reported anemia rates as high as 72.1% [43–45]. Interestingly, these regions have been the focus of numerous government and non-governmental interventions aimed at maternal and child malnutrition [25, 27]. The persistence of high prevalence despite these efforts suggests that current programs may be failing to address all four pillars of food insecurity: availability, access, utilization, and stability [46]. In regions like Northern Ghana, moderate to severe food insecurity remains high despite emergency measures, often because interventions focus on food availability while neglecting infrastructure for storage and economic access [47].Other factors worth mentioning is the impact of climate change or weather variations and communities resilience to these shocks. Conversely, the lower prevalence observed in the Brong Ahafo and Ashanti regions [48], highlights the protective effect of better economic stability and food access. To address these gaps, policy should shift from generalized iron-supplementation to region-specific, nutrition-sensitive social protection, such as targeted cash transfers for nutrient-rich food in the North and intensified malaria-anemia integrated control in the Volta region.

These regional disparities are further confirmed by the four measures of inequality utilized in this study. The persistent absolute and relative inequalities between Northern and Southern Ghana (excluding the Volta region) are driven by complex interactions of poverty, limited healthcare access, and high malaria endemicity [15, 19, 49]. Data from the Ghana Living Standards Survey (GLSS6 & 7) confirms that Northern regions possess the lowest mean annual incomes, directly limiting the consumption of nutrient-rich foods [50, 51]. Similar geographic inequalities are seen in Benin and Uganda, where cultural dietary practices and household income significantly influence anemia outcomes [20, 39].

Furthermore, the emerging threat of climate change, specifically flooding in the Volta region and agricultural instability in the North, poses a significant risk to future progress [52, 53]. As Ghana is considered a climate change ‘hot spot,’ these environmental shocks exacerbate food insecurity and vector-borne diseases, both of which are critical risk factors for anemia [52, 54].

The PAR and PAF analyses highlight that a significant proportion of national anemia cases could be prevented by reducing these regional socio-economic and healthcare access disparities [10, 14, 23, 37, 55]. Policy interventions must therefore prioritize climate-resilient agricultural support and cross-regional knowledge-sharing partnerships to close these equity gaps.

Finally, our time-series forecasting indicates a looming plateau in anemia reduction. The ARIMA model projects a national prevalence of 43.6% by 2030, a marginal decline of only 0.5% points from 2020. This suggests that the World Health Organization’s target of a 50% reduction in anemia is unlikely to be met under current historical trajectories. This plateau is mirrored in global literature, which shows that anemia prevalence in non-pregnant women changed very little between 2000 and 2019 [53].

To overcome this stagnation, a radical shift in public health strategy is required. Instead of continuing with current structural barriers, Ghana must prioritize resource allocation to the high-burden Northern and Volta regions, enhance malaria prevention through environmental management, and strengthen healthcare access for the most vulnerable populations [55–57].Without these targeted, actionable policy shifts, the regional inequalities identified in this study will likely persist, hindering Ghana’s ability to achieve equitable health outcomes for women of reproductive age.

Implications for policy and practice

The persistent regional inequalities and the projected plateau in anemia reduction carry significant implications for Ghana’s public health architecture. From a policy perspective, the “one-size-fits-all” approach must be replaced by geographic-targeted, multisectoral strategies that account for the convergence of poverty, malaria endemicity, and food insecurity. While current government commitments, such as scaling up IFA supplementation and Vitamin A distribution [25, 27, 58], provide a foundation, their impact is limited by gaps in subnational coverage. Policy must therefore prioritize “high-burden zones,” specifically the Volta and Northern regions, by integrating nutritional interventions with strengthened laboratory diagnostic capacity and localized malaria control [59]. Furthermore, given that Ghana is a climate change hotspot, health policy must evolve to include climate-adaptive healthcare delivery. This includes developing resilient supply chains for iron supplements in flood-prone areas and protecting agricultural livelihoods against inconsistent rainfall patterns to ensure stable access to nutrient-dense foods.

From a research perspective, the paradox of high anemia prevalence in regions receiving intensive interventions, such as the Northern and Volta regions, demands a shift in investigative focus. Future research should move beyond cross-sectional prevalence studies toward implementation science to identify why existing interventions are failing to reach or resonate with the most vulnerable populations. Investigating socio-cultural barriers to IFA compliance and the effectiveness of current fortification programs at the household level is essential. Additionally, the use of longitudinal datasets should be expanded to facilitate granular geospatial mapping and social network analysis. Such approaches would allow for the identification of “vulnerability clusters” at the community level, enabling high-precision targeting of resources. Combining these quantitative models with qualitative assessments of structural barriers will provide the nuanced context needed to design the “novel approaches” our forecasting suggests are required to break the current stagnation in anemia reduction.

Strengths and limitations

This study has some limitations that warrant consideration. First, the analysis relies on secondary data from the WHO HEAT, which restricts the ability to disaggregate findings beyond the regional level. Consequently, the study could not account for individual-level determinants such as dietary diversity, specific iron-deficiency biomarkers, or detailed socio-economic and health-seeking behaviors. Second, while the study identifies regional variations it does not qualitatively explore the underlying health system or socio-cultural drivers, such as traditional dietary taboos or local healthcare infrastructure, that may sustain high prevalence in the Volta and Northern regions. Finally, as is inherent in time-series forecasting, the uncertainty of the ARIMA (1,1,0) model increases over the projection horizon, as reflected in the widening 95% confidence intervals toward 2030.

Despite these limitations, the study possesses significant strengths. By utilizing two decades of nationally representative, population-level data, it provides a robust longitudinal assessment of anemia trends that is less susceptible to the seasonal or localized biases often found in facility-based studies. The application of standardized inequality metrics, specifically the PAR and PAF, offers a mathematically rigorous quantification of how much the national burden could be reduced by addressing regional disparities. Furthermore, the use of ARIMA modeling adds predictive depth to the analysis, shifting the narrative from historical observation to future policy planning. These strengths make this study a valuable tool for stakeholders in Ghana and other sub-Saharan African contexts seeking to transition toward high-precision public health interventions.

Conclusion

The findings of this study demonstrate that while Ghana achieved a modest absolute reduction in anemia prevalence of 3.5% points between 2000 and 2019, this national progress masks significant and persistent regional inequalities. The disproportionately high burden in the Volta and Northern regions, frequently exceeding 50% prevalence, contrasts sharply with the progress observed in the Ashanti and Brong Ahafo regions, where prevalence fell to as low as 36.1%. The ARIMA (1,1,0) forecast analysis indicates a projected national plateau of 43.6% by 2030, confirming that under current historical trajectories, Ghana is not on track to meet the WHO Global Nutrition Targets for anemia reduction.

To overcome this stagnation, a strategic shift toward an equity-based, subnational framework is required. Addressing the entrenched disparities identified by the PAR necessitates multisectoral interventions that move beyond traditional health silos. For example, improving food storage infrastructure in the Northern region could stabilize the availability of iron-rich crops during the lean season, directly addressing nutritional gaps. Simultaneously, intensifying the distribution of long-lasting insecticidal nets (LLINs) and integrating malaria prevention with community-level nutritional counseling in the Volta region would address the dual clinical and dietary drivers of the disease. Ultimately, a sustained commitment to data-driven policy and implementation science will be essential to accelerate progress, bridge the geographic health divide, and ensure equitable health outcomes for WRA across all regions of Ghana.

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

Appendix

Table 2.

Forecasted anemia prevalence among women of reproductive age (2020-2030)

Year	Forecast (%)	Lower 95% CI	Upper 95% CI
2020	44.1	43.8	44.4
2021	44.0	43.3	44.7
2022	43.9	42.8	44.9
2023	43.8	42.4	45.2
2024	43.8	42.1	45.4
2025	43.7	41.7	45.7
2026	43.7	41.4	46.0
2027	43.7	41.1	46.2
2028	43.7	40.9	46.4
2029	43.7	40.6	46.7
2030	43.6	40.4	46.9

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

No datasets were generated or analysed during the current study.

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.