European Journal of Cardiovascular Medicine

Anemia during pregnancy is a global public health concern, particularly in low- and middle-income countries, where it significantly contributes to maternal morbidity, adverse pregnancy outcomes, and increased perinatal mortality [1]. The World Health Organization (WHO) estimates that approximately 40% of pregnant women worldwide are anemic, with the highest prevalence reported in South Asia and Sub-Saharan Africa [2]. Anemia in pregnancy, predominantly caused by iron deficiency, can impair oxygen delivery to maternal and fetal tissues, resulting in complications such as intrauterine growth restriction, preterm labour, and low birth weight [3,4].

The spatial and temporal distribution of anemia is influenced by several determinants, including socio-economic status, dietary patterns, healthcare access, sanitation, and regional disparities in public health programs [5,6]. Traditional epidemiological approaches often fail to capture the geographic heterogeneity and time-based fluctuations in disease burden. Geographic Information System (GIS) technology has emerged as a powerful tool to analyze spatial trends in health outcomes, offering visual insights into disease clustering and aiding in evidence-based decision-making [7]. GIS-based epidemiology enables the integration of health data with demographic and environmental variables, facilitating the identification of high-risk areas and gaps in service delivery [8].

In recent years, studies have demonstrated the utility of GIS in tracking the prevalence of vector-borne diseases, malnutrition, and maternal health indicators [9,10]. However, there remains limited evidence on the application of GIS for mapping anemia burden among pregnant women in a spatiotemporal context. The ability to identify geographic clusters and assess time trends can significantly improve the design and implementation of targeted interventions, particularly in resource-limited settings [11].

This study aims to conduct a comprehensive spatiotemporal analysis of anemia among pregnant women using GIS tools. By analyzing district-level data over a five-year period, the study seeks to highlight regional variations, detect anemia hotspots, and evaluate changes in prevalence over time. The findings are expected to contribute to strategic planning and targeted delivery of maternal health services, ultimately improving pregnancy outcomes and reducing the anemia burden.

This retrospective cross-sectional study was conducted using secondary data collected from antenatal clinics located in 10 administrative districts. The study population consisted of pregnant women attending routine antenatal check-ups at primary health centres, community health centres, and district hospitals. Ethical approval for the study was obtained from the institutional ethics committee, and the data used was anonymized to protect patient confidentiality.

Data Collection: Hemoglobin levels were extracted from digital and paper-based health records of pregnant women in their first or second trimester. Anemia was defined and categorized according to World Health Organization (WHO) criteria: mild (10–10.9 g/dL), moderate (7–9.9 g/dL), and severe (<7 g/dL). Demographic details such as age, gestational age, parity, and area of residence (urban, rural, or tribal) were also recorded. Records with incomplete data or duplicate entries were excluded from analysis.

GIS Mapping and Spatial Analysis: Each district was geo-referenced using administrative shapefiles obtained from the national GIS data repository. Hemoglobin data were linked to the corresponding geographical locations using ArcGIS 10.8 software. Hotspot analysis was carried out using the Getis-Ord Gi* statistic to identify spatial clustering of anemia cases. Maps were generated to visually represent anemia prevalence, intensity of hotspots, and temporal changes.

Temporal Trend Analysis: Anemia prevalence was calculated annually for each district to observe temporal trends from 2018 to 2022. Seasonal variation was assessed by grouping data into four quarters based on collection dates. A chi-square test for trend was applied to assess the statistical significance of changes in anemia prevalence over time.

Statistical Analysis: Data were entered in Microsoft Excel and analyzed using SPSS version 25.0. Descriptive statistics were used to summarize the demographic characteristics and anemia categories. Comparative analysis between regions was performed using the chi-square test, and p-values less than 0.05 were considered statistically significant. Correlation analysis was conducted between anemia prevalence and selected indicators such as maternal age, nutritional program coverage, and healthcare access metrics.

A total of 25,600 pregnant women were included in the study across the 10 districts from 2018 to 2022. The overall prevalence of anemia was found to be 57.3%, with moderate anemia being the most common category (32.5%), followed by mild (20.8%) and severe (4%). The distribution of anemia according to severity and district is summarized in Table 1.

Table 1: Distribution of Anemia among Pregnant Women by District (2018–2022)

Hotspot analysis using GIS tools revealed that Districts A, D, and G consistently showed high clustering of anemia cases over the five-year period (p < 0.05). These areas also had lower institutional delivery rates and limited antenatal care access. Conversely, Districts B and E emerged as cold spots, indicating relatively lower anemia prevalence, possibly due to better healthcare infrastructure and nutrition supplementation programs.

Temporal analysis showed a gradual decline in anemia prevalence from 61.2% in 2018 to 52.8% in 2022, with seasonal peaks noted during the monsoon months (June–September) (Figure not shown). The annual trend of anemia prevalence is summarized in Table 2.

Table 2: Year-wise Prevalence of Anemia among Pregnant Women (2018–2022)

As shown in Table 2, the declining trend was statistically significant (χ² for trend = 12.48, p = 0.003). These findings support the gradual impact of nutritional interventions and maternal health programs implemented in high-burden areas.

The present study provides a comprehensive spatiotemporal analysis of anemia among pregnant women using GIS-based epidemiological tools across 10 districts over five years. The overall anemia prevalence of 57.3% is consistent with national estimates reported in India and other South Asian countries, where maternal anemia continues to be a significant public health burden [1,2]. While there was a modest decline in anemia rates over time, the persistence of high-burden clusters, particularly in Districts A, D, and G, suggests that spatial inequalities in healthcare access and nutrition continue to impact maternal health outcomes [3,4].

Several factors could contribute to the observed regional differences in anemia prevalence. Rural and tribal populations, as found in this study, are often characterized by low socio-economic status, limited access to antenatal care, poor dietary intake, and higher prevalence of parasitic infections — all of which contribute to iron deficiency anemia [5,6]. Previous research has also shown that geographic location significantly affects maternal health indicators, with peripheral regions often lagging behind in infrastructure and service utilization [7,8].

The utilization of GIS tools allowed for better visualization and identification of high-risk areas. The hotspot analysis revealed significant clusters in certain districts that remained consistent throughout the study period. Such mapping techniques have proven effective in prior studies for tracking malaria, tuberculosis, and malnutrition [9,10], and their integration in anemia surveillance can support better programmatic interventions [11].

Temporal trends in this study indicate a gradual decline in anemia prevalence from 2018 to 2022, which aligns with national efforts under programs such as the Anemia Mukt Bharat (AMB) initiative and increased awareness through community health workers [12]. However, the decline was not uniform across districts, indicating potential issues in program delivery and outreach efficiency. Studies have emphasized the importance of community-based monitoring and targeted supplementation to reduce maternal anemia more effectively [13,14].

Interestingly, seasonal variation was observed, with higher anemia prevalence during the monsoon season, likely due to increased infections, reduced food availability, and decreased health-seeking behaviour in adverse weather conditions. Similar findings have been reported in other studies where environmental and seasonal factors influenced nutritional and hemoglobin status [15].

The strength of this study lies in its integration of clinical and spatial data, which provides a multidimensional understanding of anemia patterns. By identifying persistent hotspots, health authorities can allocate resources more efficiently and tailor strategies to localized needs. Moreover, the downward trend in anemia prevalence, although modest, highlights the positive impact of sustained public health efforts.

Nonetheless, this study has limitations. Being based on secondary data, it may be subject to reporting biases and data entry errors. The lack of biochemical indicators for other micronutrient deficiencies or infections (such as malaria or helminthiasis) limits the ability to explore the multifactorial etiology of anemia in this population. Future research should incorporate a broader range of determinants and consider longitudinal cohort designs to better understand causality and intervention effectiveness.

In conclusion, the findings underscore the utility of GIS in public health surveillance and the need for geographically focused anemia control programs. Spatial tools, when integrated with maternal health services, can enhance the precision of intervention delivery, ultimately contributing to improved maternal and neonatal outcomes.

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