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 <!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.0 20120330//EN" "http://jats.nlm.nih.gov/publishing/1.0/JATS-journalpublishing1.dtd"> <article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.0" xml:lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">IJN</journal-id>
      <journal-title-group>
        <journal-title>International Journal of Nutrition</journal-title>
      </journal-title-group>
      <issn pub-type="epub">2379-7835</issn>
      <publisher>
        <publisher-name>Open Access Pub</publisher-name>
        <publisher-loc>United States</publisher-loc>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">IJN-24-5360</article-id>
      <article-id pub-id-type="doi">10.14302/issn.2379-7835.ijn-24-5360</article-id>
      <article-categories>
        <subj-group>
          <subject>research-article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Assessing The Nutritional Status and Health Outcomes of Women and Children in Rajshahi, Bangladesh: A Comprehensive Study</article-title>
        <alt-title alt-title-type="running-head">nutritional status and health outcomes of women and children in rajshahi</alt-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Rashed</surname>
            <given-names>Alam</given-names>
          </name>
          <xref ref-type="aff" rid="idm1840837628">1</xref>
          <xref ref-type="aff" rid="idm1840843604">*</xref>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Most</surname>
            <given-names>Tanziman Ara</given-names>
          </name>
          <xref ref-type="aff" rid="idm1840844324">2</xref>
        </contrib>
      </contrib-group>
      <aff id="idm1840837628">
        <label>1</label>
        <addr-line>Department of Population Science and Human Resource Development, University of Rajshahi. </addr-line>
      </aff>
      <aff id="idm1840844324">
        <label>2</label>
        <addr-line>Post-Doctoral Research Fellow, Institute of Biological Sciences, University of Rajshahi.</addr-line>
      </aff>
      <aff id="idm1840843604">
        <label>*</label>
        <addr-line>Corresponding Author </addr-line>
      </aff>
      <author-notes>
        <corresp>
  Rashed Alam, <addr-line>Department of Population Science and Human Resource Development, University of Rajshahi</addr-line>, <email>mrasps29@ru.ac.bd</email></corresp>
        <fn fn-type="conflict" id="idm1842594652">
          <p>The author declares that there is no conflict of interest.</p>
        </fn>
      </author-notes>
      <pub-date pub-type="epub" iso-8601-date="2025-11-30">
        <day>30</day>
        <month>11</month>
        <year>2025</year>
      </pub-date>
      <volume>8</volume>
      <issue>4</issue>
      <fpage>1</fpage>
      <lpage>14</lpage>
      <history>
        <date date-type="received">
          <day>21</day>
          <month>11</month>
          <year>2024</year>
        </date>
        <date date-type="accepted">
          <day>08</day>
          <month>02</month>
          <year>2025</year>
        </date>
        <date date-type="online">
          <day>30</day>
          <month>11</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>©</copyright-statement>
        <copyright-year>2025</copyright-year>
        <copyright-holder>Rashed Alam, et al.</copyright-holder>
        <license xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <self-uri xlink:href="http://openaccesspub.org/ijn/article/2356">This article is available from http://openaccesspub.org/ijn/article/2356</self-uri>
      <abstract>
        <sec id="idm1840688196">
          <title>Background</title>
          <p>Malnutrition is a significant public health issue in Bangladesh, particularly                   impacting women and children. Rajshahi, marked by socio-economic disparities, offers a distinctive context to explore the nutritional status and health outcomes of these vulnerable groups.</p>
        </sec>
        <sec id="idm1840686540">
          <title>Objectives</title>
          <p>This study aims to assess the nutritional status of women and children in Rajshahi and investigate associated health outcomes. Additionally, it seeks to identify              socio-economic and cultural factors that influence nutrition.</p>
        </sec>
        <sec id="idm1840685820">
          <title>Methods</title>
          <p>A mixed-methods approach was utilized, incorporating a cross-sectional survey of 460 households and in-depth interviews with mothers and caregivers. Anthropometric measurements were taken to evaluate the nutritional status of women and children, while dietary assessments measured nutrient intake and diversity.                 Logistic regression analysis was performed to determine the likelihood of                     malnutrition based on socio-economic characteristics, thereby identifying key risk factors.</p>
        </sec>
        <sec id="idm1840687764">
          <title>Results </title>
          <p>The findings indicate a troubling prevalence of malnutrition, with 36% of children under five classified as stunted and 25% as underweight. The analysis highlights critical factors contributing to chronic undernutrition, including maternal                       education, employment, and dietary diversity. Notably, mothers aged 27-37                exhibit a lower risk of undernutrition, and urban households with secure food access demonstrate better nutritional outcomes. </p>
        </sec>
        <sec id="idm1840687620">
          <title>Discussion</title>
          <p>Maternal education and employment were positively associated with better                  nutritional outcomes, as educated and employed mothers had higher chances of maintaining a normal BMI. Regular ANC visits (≥4 visits) were crucial for improved maternal                      nutrition. Household food security emerged as a significant determinant, with food-secure households showing better maternal nutritional status. Safe water access and adequate dietary diversity were also linked to improved maternal BMI. Additionally, factors such as child birth weight, exclusive                       breastfeeding, and childhood diarrhea significantly influenced maternal nutrition. Moreover, frequent antenatal care visits and a diverse diet are vital in mitigating undernutrition risks among children.</p>
        </sec>
        <sec id="idm1840686468">
          <title>Conclusion</title>
          <p>This study emphasizes the urgent need for targeted interventions to combat malnutrition in Rajshahi. Recommendations include implementing community-based nutrition education programs and improving access to healthcare services. By addressing the socio-economic and cultural determinants of                  nutrition, stakeholders can enhance health outcomes for women and children in the region, ultimately contributing to broader public health objectives in Bangladesh.</p>
        </sec>
      </abstract>
      <kwd-group>
        <kwd>Socioeconomic inequalities</kwd>
        <kwd>nutritional status</kwd>
        <kwd>women</kwd>
        <kwd>children and Rajshahi District</kwd>
      </kwd-group>
      <counts>
        <fig-count count="0"/>
        <table-count count="3"/>
        <page-count count="14"/>
      </counts>
    </article-meta>
  </front>
  <body>
    <sec id="idm1840684596" sec-type="intro">
      <title>Introduction</title>
      <p>Bangladesh faces significant challenges in addressing the poor nutritional status of women and children, which is exacerbated by socioeconomic inequalities. The nutritional status of women, who are primarily responsible for food preparation and caregiving, directly impacts the health of their children <xref ref-type="bibr" rid="ridm1842509508">1</xref><xref ref-type="bibr" rid="ridm1842512172">2</xref>. Socioeconomic disparities, particularly in rural and marginalized areas, contribute to substantial gaps in nutrition, influenced by factors such as gender, religion, culture, and economic status. Gender inequality remains a key driver, with women historically facing systemic discrimination that limits their access to education, employment, and healthcare. In Bangladesh's patriarchal society, traditional gender roles restrict women’s social and economic mobility, making them economically dependent on men first their fathers, then their husbands, and later their sons <xref ref-type="bibr" rid="ridm1842521684">3</xref>. This dependence, combined with cultural norms that prioritize male well-being, results in gender-based nutritional disparities, with girls often receiving less nutritious food than boys, further deepening these inequalities <xref ref-type="bibr" rid="ridm1842509508">1</xref><xref ref-type="bibr" rid="ridm1842619308">4</xref>.</p>
      <p>The status of women in Bangladesh is also shaped by a patrilineal kinship system, which enforces their social and economic dependence on men. This system perpetuates the subordination and inequality of women, limiting their opportunities and health outcomes. Women are primarily seen as caregivers and mothers, with little attention paid to their own nutritional needs. As a result, poor maternal nutrition, including high rates of anemia, zinc deficiency, and lack of vitamin B12, contributes to poor pregnancy outcomes, low birth weight, and higher infant mortality rates <xref ref-type="bibr" rid="ridm1842357164">5</xref>. Poor maternal nutrition not only impacts fetal development but also contributes to intergenerational cycles of malnutrition, as malnourished mothers are more likely to give birth to undernourished children <xref ref-type="bibr" rid="ridm1842361196">6</xref>.</p>
      <p>Undernutrition remains a major public health issue in Bangladesh, contributing to millions of deaths annually. Globally, undernutrition is linked to 3.5 million deaths each year, accounting for 35% of the disease burden in children under five <xref ref-type="bibr" rid="ridm1842353036">7</xref>. Bangladesh is among the 20 countries where 80% of the world’s undernourished children live <xref ref-type="bibr" rid="ridm1842349868">8</xref>. High rates of undernutrition, especially among women of childbearing age, contribute to poor maternal and child health, leading to adverse birth outcomes and higher infant mortality. However, rising obesity rates are also emerging as a concern, with 16% of ever-married women and 10% of unmarried women aged 15–19 classified as overweight or obese <xref ref-type="bibr" rid="ridm1842512172">2</xref>. This dual burden of undernutrition and overnutrition reflects complex dietary shifts, particularly in urban areas.</p>
      <p>Gender-based food allocation preferences play a significant role in malnutrition, particularly in Bangladesh, where boys often receive preferential treatment in food distribution, leaving girls with inadequate nutrition. This bias exacerbates the nutritional challenges faced by female children, who are more                 vulnerable to food insecurity and malnutrition <xref ref-type="bibr" rid="ridm1842619308">4</xref>. Such gender-based disparities in food allocation contribute to higher malnutrition rates among girls, reinforcing broader gender inequalities in health outcomes. Beyond food distribution, the nutritional status of women and children is also influenced by the availability and quality of healthcare services. In rural areas like Rajshahi, limited access to healthcare compounds the problem, preventing timely diagnosis and treatment of nutritional deficiencies. Poor healthcare infrastructure further hinders the ability of women and children to access essential services, while low-income households often struggle to afford balanced diets, relying on cheaper, less nutritious food options that exacerbate food insecurity and malnutrition (Ahmed et al., 2022; Rahman &amp; Rahman, 2020).</p>
      <p>Education, particularly among women, is crucial for improving nutritional outcomes. Educated mothers are more likely to adopt health-promoting behaviors, such as breastfeeding and providing nutritious meals for their children <xref ref-type="bibr" rid="ridm1842324420">9</xref>. However, in areas with limited access to education and healthcare, like rural Rajshahi, these benefits are less pronounced, perpetuating nutritional disparities. Addressing these gaps requires targeted interventions that enhance access to nutritious food, healthcare, and education. Improving maternal nutrition, especially during pregnancy, is a key intervention to reduce poor pregnancy outcomes and promote better health for mothers and children <xref ref-type="bibr" rid="ridm1842361196">6</xref>. Studies highlight the need for region-specific strategies in districts like Rajshahi, where socioeconomic disparities and limited resources exacerbate health challenges <xref ref-type="bibr" rid="ridm1842328308">10</xref><xref ref-type="bibr" rid="ridm1842315236">11</xref>.</p>
      <p>The nutritional status of women and children in Bangladesh is strongly shaped by socioeconomic inequalities, including income, education, employment, and healthcare access. Despite economic progress, significant disparities persist, especially in rural and marginalized areas. Addressing these inequalities through targeted policies is essential for improving health and nutrition, and for advancing toward the SDGs. This study aims to assess the impact of socioeconomic status and healthcare access on the nutritional outcomes of women and children in Rajshahi district, offering evidence-based guidance for future interventions (SDGs).</p>
    </sec>
    <sec id="idm1840677012" sec-type="methods">
      <title>Methods</title>
      <sec id="idm1840676724">
        <title>Data</title>
        <p>The study employs a combination of primary and secondary data to provide a comprehensive view of the nutritional landscape in Rajshahi district, focusing on three areas: Charghat, Mohonpur, and Rajshahi City. Primary data is collected through field surveys and interviews, using a structured questionnaire designed to gather quantitative information on socioeconomic factors, dietary intake, and nutritional practices. Separate questionnaires are used for women and caregivers of children to address gender-specific and age-related issues. A stratified random sampling method ensures diverse representation across socioeconomic strata. Field enumerators conduct face-to-face interviews, ensuring accuracy and confidentiality, while additional qualitative data is gathered through focus group discussions and in-depth interviews with key stakeholders, such as community health workers and local policymakers. The data collection process is designed for reliability and clarity, with all questionnaires reviewed and translated into Bengali for ease of understanding. The study will also incorporate a cross-sectional design, recruiting healthcare professionals to interview respondents and ensuring high response rates through interviewer training and multiple household visits. A representative sample of 600 households will be selected from two Upazilas and Rajshahi City, with a specific focus on gathering data from 460 women. Data collection will take place from February 1, 2024, to April 30, 2024. Regarding ethical considerations, we obtained approval from the Institute of Biological Sciences, Rajshahi University, before beginning the study.</p>
      </sec>
      <sec id="idm1840676076">
        <title>Variables</title>
        <p>This study analyzes Body Mass Index (BMI) for women and the prevalence of undernutrition in children as dependent variables. For children, three growth indices are used: the height-for-age z-score (HAZ), which reflects linear growth impairment and cumulative growth deficits; the weight-for-height z-score (WHZ), which indicates recent or severe weight loss; and the weight-for-age z-score (WAZ), a composite measure combining height-for-age and weight-for-height. WAZ can indicate both acute weight loss (wasting) and chronic growth failure (stunting) but does not differentiate between the two. Growth indices were calculated using the World Health Organization (WHO) 2006 growth standards, derived from the WHO Multicentre Growth Reference Study. Children with Z-scores below -2 standard deviations (SD) from the reference median were classified as undernourished.</p>
        <p>The study includes three broad categories of independent variables: characteristics of children, mothers, and households. Children’s characteristics include sex, age (in months), birth weight (in grams), and whether the child has suffered from diarrhea. Maternal characteristics include age, education, working status, antenatal care (ANC) attendance, marital status, and exclusive breastfeeding. Household characteristics include religion, place of residence, family type, food security, water sources, and minimum dietary diversity.</p>
      </sec>
      <sec id="idm1840675356">
        <title>Statistical analysis</title>
        <p>Both univariate and multivariate techniques were used for data analysis. In the univariate analysis, categorized independent variables were cross-tabulated with dichotomized BMI to examine their association with severe malnutrition. Logistic regression was performed with dichotomized BMI and chronic undernutrition as dependent variables, and all independent variables as categorized variables. A forward stepwise selection method was used to identify significant variables and construct the final model.</p>
      </sec>
    </sec>
    <sec id="idm1840676292" sec-type="results">
      <title>Results </title>
      <p>This study results <xref ref-type="table" rid="idm1842397092">Table 1</xref> analyzed the associations between maternal and child characteristics and the nutritional status of mothers in Rajshahi, Bangladesh, as indicated by Body Mass Index (BMI). Several factors were found to significantly influence maternal BMI, including maternal age, education, employment, antenatal care (ANC), food security, and water access. Younger mothers (17-26 years) were more likely to be underweight, indicating vulnerability due to early marriage and limited education. Maternal education and employment were also linked to better nutritional outcomes, with educated and working mothers more likely to have a normal BMI. The number of ANC visits was a key factor, as mothers with regular check-ups had better nutritional status. Household food security emerged as a critical determinant; secure food access was associated with higher BMI, while food insecurity led to higher rates of underweight mothers. Safe water access and adequate dietary diversity were also correlated with better nutritional outcomes. Additionally, factors such as child birth weight, exclusive breastfeeding, and childhood diarrhea significantly impacted maternal BMI. The study emphasizes the need for targeted interventions in Rajshahi, focusing on improving education, healthcare access, food security, and water quality to reduce maternal malnutrition, particularly among younger, rural, and food-insecure populations.</p>
      <table-wrap id="idm1842397092">
        <label>Table 1.</label>
        <caption>
          <title> Association Between Maternal and Child Characteristics and Nutritional Status of Women (BMI) in Rajshahi, Bangladesh (n=460)</title>
        </caption>
        <table rules="all" frame="box">
          <tbody>
            <tr>
              <th>
                <bold>Characteristics</bold>
              </th>
              <td>
                <bold>Level</bold>
              </td>
              <td colspan="2">
                <bold>BMI</bold>
              </td>
              <td colspan="2">
                <bold>p-value </bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td/>
              <td>
                <bold>underweight</bold>
              </td>
              <td>
                <bold>Normal weight</bold>
              </td>
              <td>
                <bold>Total</bold>
              </td>
              <td>
                <bold> </bold>
              </td>
            </tr>
            <tr>
              <td>Maternal age</td>
              <td>17-26</td>
              <td>47(35.1%)</td>
              <td>87(64.9%)</td>
              <td>134(29.1%)</td>
              <td>
                <bold>0.009</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>27-37</td>
              <td>106(37.1%)</td>
              <td>180(62.9%)</td>
              <td>286(62.2%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>   ≥38</td>
              <td>18(45.0%)</td>
              <td>22(55.0%)</td>
              <td>40(8.7%)</td>
              <td/>
            </tr>
            <tr>
              <td>Religion</td>
              <td>Muslim</td>
              <td>67(23.2%)</td>
              <td>222(76.8%)</td>
              <td>289(62.8%)</td>
              <td>
                <bold>0.039</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Non-Muslim</td>
              <td>35(20.5%)</td>
              <td>136(79.5%)</td>
              <td>171(37.2%)</td>
              <td/>
            </tr>
            <tr>
              <td>Education</td>
              <td>Illiterate</td>
              <td>26(59.1%)</td>
              <td>18(40.9%)</td>
              <td>44(9.6%)</td>
              <td>
                <bold>0.05</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Primary</td>
              <td>32(32.3%)</td>
              <td>67(67.7%)</td>
              <td>99(21.5%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>Secondary</td>
              <td>97(38.5%)</td>
              <td>160(61.5%)</td>
              <td>257(58.8%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>Higher</td>
              <td>16(26.6%)</td>
              <td>44(73.4%)</td>
              <td>60(13.1%)</td>
              <td/>
            </tr>
            <tr>
              <td>Respondents Working status</td>
              <td>Working</td>
              <td>43(40.9%)</td>
              <td>62(59.1%)</td>
              <td>105(22.8%)</td>
              <td>
                <bold>0.04</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Not working</td>
              <td>128(36.1%)</td>
              <td>227(63.9%)</td>
              <td>355(77.2%)</td>
              <td/>
            </tr>
            <tr>
              <td>Place of living</td>
              <td>Rajshahi city</td>
              <td>49(31.6%)</td>
              <td>106(68.4%)</td>
              <td>155(33.6%)</td>
              <td>
                <bold>0.057</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Charghat</td>
              <td>60(39.5%)</td>
              <td>92(60.5%)</td>
              <td>152(33.0%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>Mohonpur</td>
              <td>62(40.5%)</td>
              <td>91(59.5%)</td>
              <td>153(33.3%)</td>
              <td/>
            </tr>
            <tr>
              <td>Maternal ANC</td>
              <td>No visit</td>
              <td>13(30.9%)</td>
              <td>29(69.1%)</td>
              <td>42(9.1%)</td>
              <td>
                <bold>0.000</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>1-3 visit</td>
              <td>87(36.6%)</td>
              <td>151(63.4%)</td>
              <td>238(51.8%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>   ≥4 visit</td>
              <td>71(39.4%)</td>
              <td>109(60.6%)</td>
              <td>180(39.1%)</td>
              <td/>
            </tr>
            <tr>
              <td>Marital status</td>
              <td>Married</td>
              <td>158(36.5%)</td>
              <td>275(63.5%)</td>
              <td>433((94.1%)</td>
              <td>0.464</td>
            </tr>
            <tr>
              <td/>
              <td>Widowed</td>
              <td>5(45.5%)</td>
              <td>6(54.5%)</td>
              <td>11(2.3%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>Divorced</td>
              <td>8(50.0%)</td>
              <td>8(50.0%)</td>
              <td>16(3.6%)</td>
              <td/>
            </tr>
            <tr>
              <td>Type of family</td>
              <td>Single-family</td>
              <td>112(32.7%)</td>
              <td>230(67.3%)</td>
              <td>342(74.3%)</td>
              <td>0.069</td>
            </tr>
            <tr>
              <td/>
              <td>Joint family</td>
              <td>59(50.0%)</td>
              <td>59(50.0%)</td>
              <td>118(25.7%)</td>
              <td/>
            </tr>
            <tr>
              <td>Household food security status</td>
              <td>unsecure</td>
              <td>111(63.4%)</td>
              <td>64(36.6%)</td>
              <td>175(38.0%)</td>
              <td>
                <bold>0.016</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>secure</td>
              <td>60(21.1%)</td>
              <td>225(78.9%)</td>
              <td>285(62.0%)</td>
              <td/>
            </tr>
            <tr>
              <td>Water sources</td>
              <td>Safe</td>
              <td>26(9.4%)</td>
              <td>251(90.6%)</td>
              <td>277(60.2%)</td>
              <td>
                <bold>0.005</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Unsafe</td>
              <td>145(79.2%)</td>
              <td>38(20.8%)</td>
              <td>183(39.8%)</td>
              <td/>
            </tr>
            <tr>
              <td>Minimum dietary diversity</td>
              <td>Adequate</td>
              <td>36(12.9%)</td>
              <td>243(87.1%)</td>
              <td>279(60.7%)</td>
              <td>
                <bold>0.025</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>Inadequate</td>
              <td>135(74.6%)</td>
              <td>46(25.4%)</td>
              <td>181(39.3%)</td>
              <td/>
            </tr>
            <tr>
              <td>Child age, months</td>
              <td>12-23</td>
              <td>67(32.8%)</td>
              <td>137(67.2%)</td>
              <td>204(43.3%)</td>
              <td>0.057</td>
            </tr>
            <tr>
              <td/>
              <td>24-35</td>
              <td>25(33.8%)</td>
              <td>49(66.2%)</td>
              <td>74(16.1%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>36-47</td>
              <td>36(39.6%)</td>
              <td>55(60.4)</td>
              <td>91(19.8%)</td>
              <td/>
            </tr>
            <tr>
              <td/>
              <td>48-59</td>
              <td>43(47.3)</td>
              <td>48(52.7%)</td>
              <td>91(19.8%)</td>
              <td/>
            </tr>
            <tr>
              <td>Child gender</td>
              <td>Male</td>
              <td>104(42.4%)</td>
              <td>141(57.6%)</td>
              <td>245(53.3%)</td>
              <td>0.008</td>
            </tr>
            <tr>
              <td/>
              <td>Female</td>
              <td>67(31.2%)</td>
              <td>148(68.8%)</td>
              <td>215(46.7%)</td>
              <td/>
            </tr>
            <tr>
              <td>Birth weight, gm</td>
              <td>≥2500</td>
              <td>20(35.1%)</td>
              <td>37(64.9%)</td>
              <td>57(12.4%)</td>
              <td>0.049</td>
            </tr>
            <tr>
              <td/>
              <td>&lt;2500</td>
              <td>151(37.5%)</td>
              <td>252(62.5%)</td>
              <td>403(87.6%)</td>
              <td/>
            </tr>
            <tr>
              <td>Exclusive breastfeeding</td>
              <td>Yes</td>
              <td>67(34.4%)</td>
              <td>128(65.6%)</td>
              <td>195(42.4%)</td>
              <td>0.004</td>
            </tr>
            <tr>
              <td/>
              <td>No</td>
              <td>104(39.2%)</td>
              <td>161(60.8%)</td>
              <td>265(57.6%)</td>
              <td/>
            </tr>
            <tr>
              <td>Child suffering from diarrhea</td>
              <td>Yes</td>
              <td>15(44.1%)</td>
              <td>19(55.9%)</td>
              <td>34(7.4)</td>
              <td>.001</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p><xref ref-type="table" rid="idm1842115260">Table 2</xref> presents the adjusted odds ratios (OR) for associations between sociodemographic, anthropometric, and health-related characteristics and child chronic undernutrition in a study of 460 children. Maternal age was not significantly associated with child chronic undernutrition, with the odds ratios for mothers aged 27-37 years (OR = 1.05, 95% CI: 0.66–1.67) and ≥38 years (OR = 0.84, 95% CI: 0.47–1.49) showing no statistical significance (p = 0.748 and p = 0.556, respectively). Older child age (24-59 months) was significantly associated with higher odds of chronic undernutrition, with children aged 24-35 months (OR = 2.40, 95% CI: 1.20–4.98), 36-47 months (OR = 2.65, 95% CI: 1.29–5.92), and 48-59 months (OR = 2.79, 95% CI: 1.34–5.96) having significantly higher odds compared to children aged 12-23 months (p = 0.004, p = 0.002, and p = 0.001, respectively). No significant association was found between child gender (OR = 0.91, 95% CI: 0.66–1.78, p = 0.448) or water source safety (OR = 0.87, 95% CI: 0.71–1.22, p = 0.281) and chronic undernutrition. However, maternal education was protective, with children of mothers with secondary education (OR = 0.78, 95% CI: 0.56–0.87, p = 0.009) or higher education (OR = 0.59, 95% CI: 0.43–0.74, p &lt; 0.001) having significantly lower odds of chronic undernutrition compared to children of mothers with pre-primary or no education. Employed mothers also had lower odds of child chronic undernutrition (OR = 0.69, 95% CI: 0.43–0.85, p = 0.030). Maternal antenatal care (ANC) visits showed that women with ≥4 visits had lower odds of chronic undernutrition in their children (OR = 0.67, 95% CI: 0.53–0.89, p &lt; 0.001). Children from food-secure households had significantly lower odds of chronic undernutrition (OR = 0.77, 95% CI: 0.64–0.91, p &lt; 0.001). Low birth weight (&lt;2500 gm) was associated with higher odds of chronic undernutrition (OR = 1.23, 95% CI: 1.08–1.44, p = 0.008), as was maternal chronic undernutrition (OR = 1.81, 95% CI: 1.61–2.51, p = 0.007). Adequate dietary diversity was also protective (OR = 0.68, 95% CI: 0.55–0.94, p = 0.006). Exclusive breastfeeding showed no significant effect on child chronic undernutrition (OR = 0.97, 95% CI: 0.78–1.87, p = 0.654), while diarrhea in children was a significant risk factor (OR = 1.61, 95% CI: 1.31–1.95, p = 0.015). These findings underscore the importance of maternal education, employment, ANC visits, food security, dietary diversity, and birth weight in mitigating child chronic undernutrition, while highlighting the risks posed by maternal undernutrition and child diarrhea.</p>
      <table-wrap id="idm1842115260">
        <label>Table 2.</label>
        <caption>
          <title> Intensity of the effects of odds ratio of various child, maternal, and household characteristics on child chronic undernutrition: A logistic regression analysis (n = 460).</title>
        </caption>
        <table rules="all" frame="box">
          <tbody>
            <tr>
              <th>
                <bold>Characteristics</bold>
              </th>
              <td>
                <bold>Child chronic undernutrition</bold>
              </td>
              <td>
                <bold>  P-value</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>
                <bold>Adjusted odds ratio (95% CI)</bold>
              </td>
              <td/>
            </tr>
            <tr>
              <td>
                <bold>Maternal age, yrs (ref=17–26)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>27–37</td>
              <td>1.05 (0.66–1.67)</td>
              <td>0.748</td>
            </tr>
            <tr>
              <td>≥38</td>
              <td>0.84 (0.47–1.49)</td>
              <td>0.556</td>
            </tr>
            <tr>
              <td>
                <bold>Place of residence (ref = Charghat)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Rajshahi City area</td>
              <td>0.55 (0.42, 0.78)</td>
              <td>0.005</td>
            </tr>
            <tr>
              <td>Mohonpur</td>
              <td>0.97 (0.38, 0.99)</td>
              <td>0.001</td>
            </tr>
            <tr>
              <td>
                <bold>Child age, months (ref = 12–23)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>24–35</td>
              <td>2.40 (1.20, 4.98)</td>
              <td>0.004</td>
            </tr>
            <tr>
              <td>  36–47</td>
              <td>2.65 (1.29, 5.92)</td>
              <td>0.002</td>
            </tr>
            <tr>
              <td>  48–59</td>
              <td>2.79 (1.34, 5.96)</td>
              <td>0.001</td>
            </tr>
            <tr>
              <td>
                <bold>Child gender (ref = female)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Male</td>
              <td>0.91 (0.66, 1.78)</td>
              <td>0.448</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal education (ref = pre-primary or none)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Primary</td>
              <td>0.98 (0.78, 1.77)</td>
              <td>0.361</td>
            </tr>
            <tr>
              <td>  Secondary</td>
              <td>0.78 (0.56, 0.87)</td>
              <td>0.009</td>
            </tr>
            <tr>
              <td>  Higher</td>
              <td>0.59 (0.43–0.74)</td>
              <td>&lt;0.001</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal current job (ref = no)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Yes</td>
              <td>0.69 (0.43, 0.85)</td>
              <td>0.030</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal ANC (ref = no visit)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  1–3 visit</td>
              <td>0.93 (0.79, 1.94)</td>
              <td>0.312</td>
            </tr>
            <tr>
              <td>  ≥4 visit</td>
              <td>0.67 (0.53, 0.89)</td>
              <td>&lt;0.001</td>
            </tr>
            <tr>
              <td>
                <bold>Household food security status (ref = unsecure)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Secure</td>
              <td>0.77 (0.64, 0.91)</td>
              <td>&lt;0.001</td>
            </tr>
            <tr>
              <td>
                <bold>Water sources (ref = unsafe)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Safe</td>
              <td>0.87 (0.71, 1.22)</td>
              <td>0.281</td>
            </tr>
            <tr>
              <td>
                <bold>Birth weight, gm (ref = ≥2500)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  &lt;2500</td>
              <td>1.23 (1.08, 1.44)</td>
              <td>0.008</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal chronic undernutrition (ref = no)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Yes</td>
              <td>1.81 (1.61, 2.51)</td>
              <td>0.007</td>
            </tr>
            <tr>
              <td>
                <bold>Minimum dietary diversity (ref = inadequate)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Adequate</td>
              <td>0.68 (0.55, 0.94)</td>
              <td>0.006</td>
            </tr>
            <tr>
              <td>
                <bold>Exclusive breastfeeding (ref = no)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Yes</td>
              <td>0.97 (0.78, 1.87)</td>
              <td>0.654</td>
            </tr>
            <tr>
              <td>
                <bold>Child suffering from diarrhea (ref = no)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>  Yes</td>
              <td>1.61 (1.31, 1.95)</td>
              <td>0.015</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="idm1840385636">
            <label/>
            <p>(Note: CI = confidence interval)</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
      <p><xref ref-type="table" rid="idm1841974252">Table 3</xref> presents the adjusted odds ratios (OR) for sociodemographic, anthropometric, and health-related characteristics associated with maternal chronic undernutrition (BMI &lt;18.5) in a study of 460 women. Maternal age was a significant factor, with women aged 27-37 years having significantly lower odds of chronic undernutrition (OR = 0.15, 95% CI: 0.03–0.93, p = 0.041) compared to women aged 17-26 years. However, no significant association was found for women aged ≥38 years (OR = 0.87, 95% CI: 0.46–1.94, p = 0.739). Regarding place of residence, women in Rajshahi City had lower odds of chronic undernutrition compared to those in Charghat (OR = 0.95, 95% CI: 0.82–0.98, p = 0.005), while those in Mohonpur had significantly higher odds (OR = 0.57, 95% CI: 0.28–0.89, p = 0.021). Maternal employment did not show a significant association with chronic undernutrition (OR = 0.87, 95% CI: 0.39–1.94, p = 0.740). Women with ≥4 antenatal care (ANC) visits had significantly lower odds of chronic undernutrition (OR = 0.37, 95% CI: 0.16–0.86, p = 0.021), while those with 1-3 visits showed no significant difference (OR = 0.65, 95% CI: 0.22–1.91, p = 0.445). Household food security was a protective factor, with food-secure women having significantly lower odds of chronic undernutrition (OR = 0.34, 95% CI: 0.17–0.67, p = 0.002). No significant association was found between access to safe water and maternal chronic undernutrition (OR = 0.98, 95% CI: 0.33–2.88, p = 0.138). Women with adequate dietary diversity had significantly lower odds of chronic undernutrition (OR = 0.18, 95% CI: 0.04–0.76, p = 0.020). In summary, maternal chronic undernutrition is influenced by factors such as maternal age, place of residence, ANC visits, household food security, and dietary diversity. Protective factors include older maternal age (27-37 years), living in Rajshahi City, and having adequate ANC visits, food security, and dietary diversity. In contrast, living in Mohonpur and inadequate dietary diversity were associated with higher odds of undernutrition, while maternal employment and access to safe water did not significantly affect chronic undernutrition.</p>
      <table-wrap id="idm1841974252">
        <label>Table 3.</label>
        <caption>
          <title> Intensity of the effects of odds ratio of various child, maternal, and household characteristics on maternal chronic undernutrition: A Logistic Regression Analysis (BMI&lt;18.5) (n = 460).</title>
        </caption>
        <table rules="all" frame="box">
          <tbody>
            <tr>
              <th>
                <bold>Characteristics</bold>
              </th>
              <td>
                <bold>Maternal chronic undernutrition</bold>
              </td>
              <td>
                <bold>P-value</bold>
              </td>
            </tr>
            <tr>
              <td/>
              <td>
                <bold>Adjusted odds ratio (95% CI)</bold>
              </td>
              <td/>
            </tr>
            <tr>
              <td>
                <bold>Maternal age, yrs (ref = 17–26)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>27–37</td>
              <td>0.15 (0.03–0.93)</td>
              <td>0.041</td>
            </tr>
            <tr>
              <td>≥38</td>
              <td>0.87 (0.46–1.94)</td>
              <td>0.739</td>
            </tr>
            <tr>
              <td>
                <bold>Place of residence (ref = Charghat)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Rajshahi City area</td>
              <td>0.95 (0.82, 0.98)</td>
              <td>0.005</td>
            </tr>
            <tr>
              <td>Mohonpur</td>
              <td>0.57 (0.28, 0.89)</td>
              <td>0.021</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal current job (ref = no)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Yes</td>
              <td>0.87 (0.39, 1.94)</td>
              <td>0.740</td>
            </tr>
            <tr>
              <td>
                <bold>Maternal ANC (ref = no visit)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>1–3 visit</td>
              <td>0.65 (0.22, 1.91)</td>
              <td>0.445</td>
            </tr>
            <tr>
              <td>≥4 visit</td>
              <td>0.37 (0.16, 0.86)</td>
              <td>0.021</td>
            </tr>
            <tr>
              <td>
                <bold>Household food security status (ref = unsecure)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Secure</td>
              <td>0.34 (0.17, 0.67)</td>
              <td>0.002</td>
            </tr>
            <tr>
              <td>
                <bold>Water sources (ref = unsafe)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Safe</td>
              <td>0.98 (0.33, 2.88)</td>
              <td>0.138</td>
            </tr>
            <tr>
              <td>
                <bold>Minimum dietary diversity (ref = inadequate)</bold>
              </td>
              <td> </td>
              <td> </td>
            </tr>
            <tr>
              <td>Adequate</td>
              <td>0.18 (0.04, 0.76)</td>
              <td>0.020</td>
            </tr>
          </tbody>
        </table>
        <table-wrap-foot>
          <fn id="idm1840340996">
            <label/>
            <p>(Note: CI = confidence interval)</p>
          </fn>
        </table-wrap-foot>
      </table-wrap>
    </sec>
    <sec id="idm1840342868" sec-type="discussion">
      <title>Discussion</title>
      <p>This study explores several key factors influencing maternal and child nutrition in Bangladesh, with a particular focus on maternal age, education, employment, antenatal care, food security, dietary diversity, birth weight, and childhood diarrhea. Below is an analysis of the findings, with supporting references that highlight global trends and context-specific challenges.</p>
      <p>The study finds that mothers aged 27-37 years have significantly lower odds of chronic undernutrition compared to younger mothers (AOR = 0.15, p = 0.041). This suggests that women in this age group are better equipped to handle the nutritional demands of pregnancy. In contrast, mothers aged 38 years or older do not show a significant difference in undernutrition (AOR = 0.87, p = 0.739). This aligns with research in Bangladesh, where younger maternal age (17-26 years) is linked to higher risks of undernutrition due to biological, socioeconomic, and experiential factors <xref ref-type="bibr" rid="ridm1842313508">12</xref>. Similarly, studies in India and other South Asian countries have highlighted that younger mothers are more vulnerable to poor health outcomes, including low birth weight and maternal anemia <xref ref-type="bibr" rid="ridm1842308612">13</xref><xref ref-type="bibr" rid="ridm1842306956">14</xref>. These findings emphasize the need for targeted nutrition interventions for younger mothers, as their age and related vulnerabilities significantly impact their nutritional status and child health.</p>
      <p>The study highlights the urban advantage in reducing child undernutrition, with Rajshahi City outperforming rural areas like Charghat. Urban areas generally offer better healthcare, nutrition programs, and socioeconomic resources, all of which contribute to improved health outcomes (Ruel et al., 2014) <xref ref-type="bibr" rid="ridm1842303860">15</xref>. This is consistent with global trends, where urban children often have better access to healthcare and nutrition, resulting in lower rates of undernutrition <xref ref-type="bibr" rid="ridm1842288868">16</xref>. However, the study also finds that children in Mohonpur, another rural area, do not show significant differences in undernutrition compared to those in Charghat, suggesting that some rural communities may benefit from local resources, community networks, or agricultural activities that help mitigate nutritional risks. While urbanization generally improves access to services, it can also introduce challenges, such as pollution and dietary shifts, which could negatively affect nutritional outcomes <xref ref-type="bibr" rid="ridm1842287788">17</xref>. This complex interplay between urban and rural health highlights the need for context-specific interventions that consider local resources and disparities in healthcare access and food security <xref ref-type="bibr" rid="ridm1842284332">18</xref>.</p>
      <p>Maternal education is a powerful determinant of child nutritional status. The study reveals that mothers with secondary and higher education are significantly less likely to have chronically undernourished children. Specifically, mothers with secondary education have an adjusted odds ratio (OR) of 0.78 (p = 0.009), and those with higher education show even lower odds (OR = 0.59, p &lt; 0.001). This indicates that educated mothers are more likely to make informed decisions about nutrition and healthcare <xref ref-type="bibr" rid="ridm1842281884">19</xref>. Educated mothers are also more likely to secure better employment, increasing their ability to afford nutritious food and healthcare. Studies from Bangladesh, India, and other countries have consistently shown that maternal education reduces child undernutrition and improves health outcomes <xref ref-type="bibr" rid="ridm1842278932">20</xref><xref ref-type="bibr" rid="ridm1842291604">21</xref><xref ref-type="bibr" rid="ridm1842265340">22</xref>.</p>
      <p>However, primary education did not show a significant effect on reducing child chronic undernutrition (OR = 0.98, p = 0.361). This suggests that while primary education offers some benefits, it may not be as impactful as secondary or higher education in shaping maternal and child nutrition outcomes. Investing in maternal education is thus critical not only for individual families but also for public health improvements across society.</p>
      <p>Both maternal education and employment significantly reduce the likelihood of child chronic undernutrition. The study finds that employed mothers are 31% less likely to have undernourished children compared to non-employed mothers (OR = 0.69, p = 0.030). Employed mothers tend to have higher incomes, which enhances food security and enables them to purchase nutritious food and access healthcare services more readily <xref ref-type="bibr" rid="ridm1842261524">23</xref>. They are also more likely to attend antenatal care (ANC) and ensure their children receive necessary health services, including vaccinations <xref ref-type="bibr" rid="ridm1842259940">24</xref>.</p>
      <p>Similar results have been observed in India and Ethiopia, where maternal employment is associated with better nutritional outcomes for children <xref ref-type="bibr" rid="ridm1842291604">21</xref><xref ref-type="bibr" rid="ridm1842254540">25</xref>. Employment also provides economic empowerment, which helps alleviate food scarcity and increases dietary diversity for children <xref ref-type="bibr" rid="ridm1842252668">26</xref>. These                findings highlight the importance of increasing maternal employment opportunities, especially in               low-income settings like Bangladesh, where such opportunities can help break the cycle of                           malnutrition.</p>
      <p>The study also highlights the role of antenatal care in reducing maternal chronic undernutrition. Attending four or more ANC visits is associated with significantly lower odds of maternal undernutrition (AOR = 0.37, p = 0.021). In Bangladesh, ANC is crucial in providing essential nutritional guidance, healthcare, and medical support during pregnancy <xref ref-type="bibr" rid="ridm1842324420">9</xref>. Women who attend fewer than four ANC visits (1-3 visits) do not show significant improvements in nutritional status (AOR = 0.65, p = 0.445), indicating that more frequent and comprehensive ANC visits are necessary to address maternal nutrition adequately. This finding is supported by studies in India, where women with more ANC visits experienced better nutritional outcomes and health <xref ref-type="bibr" rid="ridm1842240436">27</xref>.</p>
      <p>The study finds that households with secure food status significantly reduce the likelihood of maternal chronic undernutrition (AOR = 0.34, p = 0.002). This is a critical finding in Bangladesh, where food insecurity is a major barrier to maternal and child nutrition, leading to poor health outcomes <xref ref-type="bibr" rid="ridm1842313508">12</xref>.               Secure food environments are crucial in providing the necessary nutrients for pregnant and lactating women, which in turn supports their health and the health of their infants. Research in other countries, such as Ethiopia, supports the direct link between food security and maternal health outcomes <xref ref-type="bibr" rid="ridm1842235180">28</xref>. Targeted interventions to improve food security can play a critical role in reducing maternal undernutrition globally.</p>
      <p>Maternal undernutrition is a significant factor influencing child nutritional status. The study finds that undernourished mothers are 81% more likely to have chronically undernourished children (OR = 1.81, 95% CI: 1.61-2.51, P = 0.007). In Bangladesh, where maternal undernutrition affects over 30% of women, this relationship is especially concerning, as it perpetuates intergenerational cycles of malnutrition (NIPORT, 2020). Undernourished mothers are more likely to give birth to low birth weight (LBW) infants, who are at higher risk for chronic undernutrition during childhood <xref ref-type="bibr" rid="ridm1842232084">29</xref>. Addressing maternal undernutrition through comprehensive interventions is essential for breaking the cycle of malnutrition and improving child health outcomes.</p>
      <p>The study reveals that dietary diversity is significantly associated with reduced chronic undernutrition. Individuals with more diverse diets have a 32% lower likelihood of experiencing chronic undernutrition (OR = 0.68, p = 0.006). In Bangladesh, where malnutrition is a significant public health challenge, promoting dietary diversity is essential for improving nutrition, particularly for children (NIPORT, 2020). Increased dietary diversity ensures adequate intake of essential vitamins and minerals that are often missing in less varied diets <xref ref-type="bibr" rid="ridm1842244900">30</xref>. Studies in India and Nigeria show similar findings, where children with more diverse diets are less likely to suffer from malnutrition (Kumar et al., 2019; Ihedioha et al., 2020).</p>
      <p>The study also finds that dietary diversity significantly reduces maternal chronic undernutrition (AOR = 0.18, p = 0.020). Women with diverse diets are more likely to meet their nutritional needs during pregnancy and lactation, reducing the risk of undernutrition and promoting maternal and fetal health <xref ref-type="bibr" rid="ridm1842242668">31</xref>. Similar studies in Ethiopia have found that pregnant women with higher dietary diversity had better nutritional outcomes and lower rates of anemia <xref ref-type="bibr" rid="ridm1842219524">32</xref>. These findings support the need for global interventions to improve dietary diversity as a strategy to reduce undernutrition and improve maternal and child health outcomes.</p>
      <p>Birth weight is another critical determinant of child nutrition. The study found that low birth weight (LBW) is significantly associated with higher odds of chronic undernutrition (OR = 1.23, 95% CI: 1.08-1.44, P = 0.008). In Bangladesh, where approximately 28% of infants are born with LBW, addressing the determinants of LBW is crucial for improving child nutrition and health (NIPORT, 2020). Globally, LBW is a well-established risk factor for developmental delays, malnutrition, and mortality <xref ref-type="bibr" rid="ridm1842216716">33</xref><xref ref-type="bibr" rid="ridm1842232084">29</xref>. Research from India and sub-Saharan Africa supports this link between LBW and chronic undernutrition <xref ref-type="bibr" rid="ridm1842213476">34</xref><xref ref-type="bibr" rid="ridm1842211892">35</xref>. Targeted maternal health interventions to improve nutrition during pregnancy can help improve birth outcomes and reduce the incidence of LBW.</p>
      <p>Childhood diarrhea is a significant public health issue that is closely linked to malnutrition, with an odds ratio of 1.61 (95% CI: 1.31-1.95, P = 0.015) indicating that children suffering from diarrhea are 61% more likely to experience chronic undernutrition compared to those who do not. In Bangladesh, diarrhea remains a leading cause of morbidity and mortality among children under five, exacerbated by inadequate access to clean water, sanitation, and hygiene practices (NIPORT, 2020). The frequent loss of nutrients due to diarrheal illness can lead to a vicious cycle of malnutrition, where undernourished children are more susceptible to infections, and the resulting illnesses further compromise their nutritional status <xref ref-type="bibr" rid="ridm1842205484">36</xref>. Globally, this relationship is well-documented. For example, studies in sub-Saharan Africa and South Asia have shown that children with diarrhea experience higher rates of stunting and wasting, with recurrent episodes contributing to long-term growth deficits <xref ref-type="bibr" rid="ridm1842203900">37</xref><xref ref-type="bibr" rid="ridm1842200732">38</xref>. Interventions aimed at reducing diarrheal diseases, such as improved sanitation, access to clean water, and education on hygiene, are essential for breaking this cycle and improving child health outcomes. Addressing the dual challenges of diarrhea and malnutrition is crucial for enhancing child survival and development, particularly in resource-limited settings like Bangladesh.</p>
      <sec id="idm1840338476">
        <title>Policy Implication</title>
        <p>The study’s findings highlight critical areas for policy intervention to address nutritional disparities in Rajshahi, Bangladesh. Policymakers should prioritize educational advancement, especially for mothers, as higher education is linked to improved nutritional practices and health outcomes. Expanding healthcare accessibility, particularly maternal and child health services, is essential for providing timely interventions. Strengthening food security strategies through government and community programs is necessary to ensure consistent access to nutritious food for all households. Additionally, investments in sanitation and hygiene infrastructure are crucial to mitigating health risks associated with undernutrition, particularly among children. The recommendations emphasize targeted educational programs to empower women with knowledge, enhancing healthcare services in underserved areas, supporting local food production initiatives, fostering community engagement in health programs, and establishing robust monitoring and evaluation frameworks to track progress. By implementing these strategies, policymakers can reduce socioeconomic inequalities in nutritional status and promote better health outcomes for women and children in the region, contributing to a more equitable society.</p>
      </sec>
    </sec>
    <sec id="idm1840339412" sec-type="conclusions">
      <title>Conclusion</title>
      <p>This study investigates the socioeconomic factors impacting the nutritional status of women and children in Rajshahi District, Bangladesh, highlighting persistent malnutrition despite public health progress. The study, employing a mixed-methods approach, finds that women from lower socioeconomic backgrounds face higher malnutrition rates, adversely affecting their children's health. Maternal education was identified as a key factor in improving child nutrition, while cultural norms and gender roles were barriers to accessing adequate nutrition and healthcare. Disparities in healthcare access, exacerbated by financial constraints and poor infrastructure, further worsen the situation. The study calls for policies focused on improving women's education, healthcare access, and community-based nutrition programs. It advocates for a multi-sectoral approach to address both immediate and underlying factors contributing to malnutrition, offering a framework for tackling these issues and promoting health               equity.</p>
    </sec>
  </body>
  <back>
    <ack>
      <p>This study acknowledges the invaluable contributions of the participants from Rajshahi, Bangladesh, whose willingness to share their experiences made the research possible. Gratitude is extended to the academic and field teams, local health authorities, and community leaders for their guidance and support. Additionally, I thank to the Dean, Faculty of Science for financial assistance and our families for their continuous encouragement.</p>
    </ack>
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