Does urban living contribute to better nutrition? An ecological study on urban–rural disparities in Indonesia

Does urban living contribute to better nutrition? 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An ecological study on urban–rural disparities in Indonesia Muhammad Iqhrammullah, Nuril Farid Abshori, Derren DCH Rampengan, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7177814/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Indonesia faces a double burden of malnutrition, with urban children generally less affected by undernutrition but increasingly prone to being overweight. However, national trends may mask sub-provincial disparities driven by uneven access to health services, food quality, and socioeconomic conditions—patterns that remain underexplored. Objectives To assess disparities in child nutritional and maternal care indicators between urban and rural areas at the national level and to conduct sub-provincial analyses in selected districts to uncover patterns masked by aggregated national data. Methods We performed an ecological analysis using data from the 2024 Indonesian Nutritional Status Survey. First, we assessed national-level disparities in child nutritional and maternal care indicators between urban and rural areas using odds ratios (OR) and chi-square tests. To capture localized patterns hidden by national aggregates, we then conducted sub-provincial case studies in selected districts of Central Java and South Sulawesi, comparing outcomes across different urban and rural settings. Results Nationally, urban children had lower odds of undernutrition—including severely underweight (OR 0.78; 95% CI: 0.75–0.81), underweight (OR 0.82; 95% CI: 0.80–0.84), and stunting (OR 0.77; 95% CI: 0.75–0.78)—but higher odds of being at risk of overweight (OR 1.35; 95% CI: 1.31–1.40) and consuming unhealthy foods (OR 1.22; 95% CI: 1.19–1.25). Rural areas consistently showed worse access to dietary diversity and antenatal care. In Central Java, Kota Magelang showed lower risk of severe underweight compared to Kota Surakarta (OR 0.25; 95% CI: 0.09–0.70; p = 0.008) and Kota Tegal (OR 0.15; 95% CI: 0.06–0.39; p = 0.001). In South Sulawesi, Kota Makassar had lower odds of severe underweight than Kota Pare-pare (OR 0.42; 95% CI: 0.23–0.77; p = 0.005), but higher underweight risk than Tana Toraja (OR 1.79; 95% CI: 1.30–2.48; p < 0.001). Conclusions While urban areas generally have lower undernutrition, this study reveals that substantial disparities also exist between and within urban and rural districts. Kota Magelang, a small urban city, shows more favorable outcomes—possibly due to proximity to referral centers—while cities like Palopo and surrounding rural areas remain vulnerable. Child malnutrition dietary diversity antenatal care Indonesia health inequity 1. Introduction Child undernutrition remains a critical public health challenge in Indonesia, contributing to increased morbidity, impaired cognitive development, and long-term productivity losses. On the other hand, nutrition transitions where lower activities with increased consumption of sweet drinks and food containing too much fat causes obesity and increase the risk of non-communicable disease [1]. Evidence from a cohort study in Democratic Republic Congo has consistently linked early-life undernutrition with heightened risks of chronic diseases later in life, including cardiovascular disease, type 2 diabetes, and metabolic syndrome [2]. The Developmental Origins of Health and Disease (DOHaD) framework further reinforces that suboptimal nutrition during the first 1,000 days can lead to permanent physiological changes, predisposing individuals to non-communicable diseases across the life course [3]. In Indonesia, where stunting and wasting remain prevalent, the public health implications extend beyond childhood and into adult economic productivity and health system burden. Despite decades of national efforts to improve child nutrition, disparities persist across geographic, socioeconomic, and service-access dimensions [4]. While rural areas are often the focus of nutritional interventions due to historically lower service availability and food security, recent data suggest that these challenges are not exclusive to rural settings. In fact, substantial variation may exist within urban areas, warranting closer examination of sub-provincial patterns [4]. Urban environments are traditionally associated with better access to health services, improved dietary diversity, and higher socioeconomic status. These assumptions are grounded in the urban advantage hypothesis, which posits that urban children benefit from proximity to healthcare facilities, higher parental education, and greater availability of diverse foods through market integration [5]. However, emerging evidence—particularly from low- and middle-income countries (LMICs)—suggests that this advantage is neither uniform nor guaranteed. The urban penalty hypothesis offers a countervailing perspective, emphasizing that rapid, unregulated urbanization can lead to overcrowded living conditions, inadequate sanitation, environmental degradation, and fragmented health systems that disproportionately affect the urban poor [6]. In such contexts, urban living may expose children to nutritional risk through food insecurity, sedentary behavior, and poor dietary choices driven by the proliferation of ultra-processed foods and limited access to nutritious options—especially in informal or peri-urban settlements [7, 8]. The UNICEF conceptual framework of child undernutrition identifies the interplay of immediate (dietary intake, illness), underlying (household food security, maternal care, health services), and basic causes (sociopolitical context and resources) in shaping nutritional outcomes [9]. Geographic location—whether urban or rural—modulates these determinants through its influence on infrastructure, service delivery, and exposure to socioeconomic inequality. Additionally, the social determinants of health model underscores that structural inequities, including housing, income, and education, can differentially impact child nutrition in urban versus rural contexts [10]. Together, these frameworks justify the inclusion of urban–rural stratification in nutritional analysis and highlight the need to examine intra-urban heterogeneity to avoid masking disparities with aggregate classifications. Understanding the determinants of child undernutrition requires a multi-level framework that accounts for both structural and behavioral drivers. From a life-course and ecological perspective, child nutritional status is influenced not only by food intake but also by maternal health, healthcare access, sanitation, and social determinants such as poverty and education [11]. This study includes anthropometric indicators—weight-for-age, height-for-age, and weight-for-height—as they are internationally recognized proxies for acute and chronic malnutrition. We also assess dietary practices (e.g., Minimum Dietary Diversity, Minimum Meal Frequency, Minimum Acceptable Diet), which reflect both availability and caregiver behavior. Lastly, we incorporate maternal care variables, particularly antenatal care (ANC) coverage, given its well-established role in improving birth outcomes, early growth, and feeding practices. Central Java and South Sulawesi were selected for case studies due to their contrasting geography, urbanization patterns, and health system structures. Central Java includes mature urban centers with proximity to referral hospitals and education hubs [12, 13], while South Sulawesi offers a mix of highland and coastal districts with variable infrastructure [14]. By comparing nutritional indicators across selected districts within these provinces, we aim to investigate whether urban status alone ensures nutritional advantage or whether localized determinants—such as health system capacity, geographic isolation, and local governance—shape outcomes more significantly. At the national level, we further examine urban–rural disparities in dietary and maternal health variables using the Survei Status Gizi Indonesia 2024 (SSGI; Indonesian Nutritional Status Survey). This integration of subnational and national data allows us to explore how micro-level variation aligns or diverges from national trends, and to identify specific districts or population subgroups that require differentiated policy responses. This present study seeks to move beyond binary urban–rural comparisons and contribute evidence for district-sensitive nutrition policies in Indonesia—highlighting the importance of context, behavior, and service delivery in tackling child undernutrition. 2. Methods 2.1 Study Design This ecological, cross-sectional study examined nutritional disparities among children under five in Indonesia, using data from the 2024 SSGI. The analysis focused on both national urban–rural differences and district-level variation in selected areas of Central Java and South Sulawesi. These provinces were chosen as case studies to enrich our understanding of how urban–rural disparities manifest at the sub-provincial level and influence child nutritional status across diverse geographic and service contexts. 2.2 Data Source The 2024 SSGI was a nationally representative survey conducted by the Indonesian Ministry of Health in collaboration with Statistics Indonesia (BPS). The target population included all households with children under five years of age across the country. A total of 345,000 under-five households were selected from 34,500 census blocks covering all 514 districts/cities, with each block contributing 10 households. Sampling followed a two-stage, one-phase stratified design. In the first stage, census blocks were selected using probability proportional to size with replacement. In the second stage, under-five households were selected systematically from updated household listings conducted prior to sampling. The survey achieved high response rates: 92.5% for households, 97.1% for children visited, and 98.7% for completed interviews. In total, 42,893 under-five children were successfully included in the final dataset. 2.3 Urban–Rural Classification The 2024 SSGI used urban–rural classification criteria established by Statistics Indonesia (BPS), which are applied at the level of census blocks, not at the level of individual households or respondents. Each census block was categorized as either urban or rural based on a composite scoring system developed by BPS. This classification system considers the following indicators: (1) Population density; (2) Percentage of households working in the agricultural sector; (3) Access to urban-type facilities (such as schools or universities, health services, roads, markets, electricity, and telecommunication); and (4) Percentage of households working in agriculture. Census blocks meeting the urban threshold across these criteria were labeled as urban; all others were considered rural. This classification was applied uniformly in both national-level comparisons and sub-provincial case studies. For intra-urban comparisons in this present study, only districts composed predominantly of urban-designated census blocks were included to ensure consistency and comparability across urban settings. 2.4 National-Level Urban–Rural Comparison At the national level, this study assessed disparities in nutritional and maternal–child health indicators between urban and rural populations using aggregate data from the 2024 Survei Status Gizi Indonesia (SSGI). All indicators were originally reported in percentages and were converted to absolute counts by multiplying each proportion by the number of under-five children with available data for each respective item. Nutritional status was assessed using three anthropometric indices—weight-for-age (severely underweight, underweight, normal, at-risk overweight), height-for-age (stunting, severely stunting, normal), and weight-for-height (wasting, severely wasting, normal, overweight/obese)—which were derived from direct measurements of children’s body weight and length/height performed by trained enumerators using standardized equipment. In contrast, dietary indicators such as Minimum Dietary Diversity (MDD), Minimum Meal Frequency (MMFF), and Minimum Acceptable Diet (MAD) were estimated based on caregiver-reported 24-hour recall of food intake. Additional dietary variables captured whether the child consumed specific food types (protein-rich foods, sweet beverages, unhealthy foods such as fried snacks or instant noodles, and vegetables or fruits) in the preceding 24 hours. Service-related variables reflected health service coverage and utilization, also collected through caregiver interviews. These included whether the child received routine growth monitoring and whether the mother accessed antenatal care (ANC) during pregnancy. ANC utilization was categorized into four levels: basic coverage (at least one visit with a skilled provider), first-trimester ANC, adequate ANC (four or more visits with appropriate spacing), and comprehensive ANC (six or more visits across trimesters, including consultations with a medical doctor and ultrasound examination). 2.5 District-Level Case Studies To explore local variation in child nutritional outcomes beyond broad urban–rural classifications, we conducted district-level case studies in two provinces—Central Java and South Sulawesi—selected for their contrasting geography and health system profiles. Within each province, we purposively selected a diverse set of districts representing a spectrum of settlement types, including provincial capitals, secondary cities, peri-urban areas, and rural or highland districts. In Central Java, selected districts included Kota Magelang, Kota Surakarta, Kota Semarang, Kota Pekalongan, Kota Tegal, Grobogan, and Blora. In South Sulawesi, we included Kota Makassar, Kota Palopo, Kota Pare-pare, Enrekang, Luwu Timur, Tana Toraja, and Toraja Utara. These districts were chosen to capture variation in urban scale (small vs. large), geographic accessibility (e.g., coastal, inland, or mountainous), and availability of local health infrastructure. 2.6 Statistical Analysis All statistical analyses were conducted using RStudio (version 2024.04.2–764, running R version 4.3.3). Descriptive statistics were calculated to summarize each variable by urban–rural classification at the national level. Logistic regression models were applied to estimate odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding p-values, using normal nutritional status or sufficient health service coverage as the reference group. Statistical significance was defined as a two-tailed p-value < 0.05. For district-level case studies, we examined intra-urban variation through pairwise comparisons of selected districts within Central Java and South Sulawesi. These comparisons were based on aggregated district-level data and were consistent with the ecological design of the study. For each district pair, 2x2 contingency tables were constructed for categorical nutritional outcomes, and chi-square tests were used to estimate ORs and p-values. Only districts with complete data across all four nutritional status (W/A) categories (severely underweight, underweight, normal, at-risk overweight) were included in the pairwise analysis. 3. Results 3.1 National-Level Urban–Rural Disparities Comparisons of child nutritional status, dietary practices, and maternal–child health service utilization between rural and urban populations at the national level are presented in Table 1 . Urban residence was consistently associated with lower odds of undernutrition across most anthropometric indicators. Compared to rural children, urban children had significantly lower odds of being severely underweight (OR 0.78; 95% CI: 0.75–0.81; p < 0.001) and underweight (OR 0.82; 95% CI: 0.80–0.84; p < 0.001), but higher odds of being at risk of overweight (OR 1.35; 95% CI: 1.31–1.40; p < 0.001). Similarly, urban children had lower odds of stunting (OR 0.77; 95% CI: 0.75–0.78; p < 0.001) and severe stunting (OR 0.63; 95% CI: 0.61–0.66; p < 0.001) compared to their rural counterparts. For wasting status, urban children had slightly lower odds of wasting (OR 0.95; 95% CI: 0.93–0.98; p = 0.002) and markedly lower odds of severe wasting (OR 0.79; 95% CI: 0.74–0.84; p < 0.001). The likelihood of being overweight or obese was also significantly higher among urban children (OR 1.20; 95% CI: 1.15–1.25; p < 0.001). Table 1 Distribution of child nutritional status, dietary indicators, and maternal–child health service coverage by area of residence (urban vs. rural) Variable Urban Rural OR (95%CI) p-value Nutritional status (W/A) Normal 123333 109290 Ref Ref Severely underweight 4253 4838 0.78 (0.75–0.81) < 0.001 Underweight 20319 21915 0.82 (0.80–0.84) < 0.001 Risk of overweight 9766 6404 1.35 (1.31–1.40) < 0.001 Stunting status (H/A) Normal 129064 109682 Ref Ref Stunting 22453 24831 0.77 (0.75–0.78) < 0.001 Severely stunting 5495 7378 0.63 (0.61–0.66) < 0.001 Wasting status (W/H) Normal 139207 125875 Ref Ref Wasting 9530 9031 0.95 (0.93–0.98) 0.002 Severly wasting 1719 1976 0.79 (0.74–0.84) < 0.001 Overweigth & obese 5624 4233 1.20 (1.15–1.25) < 0.001 MDD Sufficient 23388 17975 Ref Ref Insufficient 22382 22693 0.76 (0.74–0.78) < 0.001 MMFF Sufficient 12495 5801 Ref Ref Insufficient 1769 1975 0.42 (0.39–0.45) < 0.001 MAD Sufficient 17942 10306 Ref Ref Insufficient 30092 22582 0.77 (0.74–0.79) < 0.001 Protein intake (24 h) Yes 42025 27251 Ref Ref No 9794 8464 0.75 (0.73–0.78) < 0.001 Sweet-beverage intake (24 h) Yes 5617 4520 0.84 (0.81–0.88) < 0.001 No 46395 31352 Ref Ref Unhealthy food intake (24 h) Yes 25337 15721 1.22 (1.19–1.25) < 0.001 No 26689 20172 Ref Ref Vegetable intake (24 h) Yes 12356 9619 Ref Ref No 39561 26141 1.18 (1.14–1.22) < 0.001 Standard growth monitoring Received 83750 79347 Ref Ref Not received 46905 38379 1.16 (1.14–1.18) < 0.001 ANC coverage (≥ 1 visit) Received 152935 133929 Ref Ref Not received 4730 8549 0.48 (0.47–0.50) < 0.001 First-trimester ANC visit Received 134015 115265 Ref Ref Not received 23650 27213 0.75 (0.73–0.76) < 0.001 Adequate ANC coverage Received 124082 102157 Ref Ref Not received 33583 40321 0.69 (0.67–0.70) < 0.001 Comprehensive ANC Received 52187 29635 Ref Ref Not received 105478 112843 0.53 (0.52–0.54) < 0.001 Basic ANC coverage (≥ 1 visit with skilled provider) Adequate ANC coverage (4 + visits with skilled provider, appropriately spaced) Comprehensive ANC (6 + visits with trimester-based schedule and minimum 2 doctor contacts with ultrasound) Dietary indicators revealed that rural children were less likely to meet dietary adequacy. The odds of insufficient Minimum Dietary Diversity (MDD) were higher among rural children (OR 0.76; 95% CI: 0.74–0.78; p < 0.001), and they were also less likely to meet the Minimum Meal Frequency (MMFF) standard (OR 0.42; 95% CI: 0.39–0.45; p < 0.001). Similarly, the odds of receiving a Minimum Acceptable Diet (MAD) were lower for rural children (OR 0.77; 95% CI: 0.74–0.79; p < 0.001). In terms of 24-hour dietary recall, rural children had significantly lower odds of consuming protein-rich foods (OR 0.75; 95% CI: 0.73–0.78; p < 0.001). Interestingly, they were also less likely to consume sweet beverages (OR 0.84; 95% CI: 0.81–0.88; p < 0.001), but more likely to consume unhealthy foods (OR 1.22; 95% CI: 1.19–1.25; p < 0.001) and less likely to consume vegetables or fruits (OR 1.18; 95% CI: 1.14–1.22; p < 0.001). Service utilization indicators showed consistent disadvantages in rural areas. The odds of not receiving standard growth monitoring were significantly higher among rural children (OR 1.16; 95% CI: 1.14–1.18; p < 0.001). Likewise, rural mothers had lower odds of receiving any antenatal care (ANC) (OR 0.48; 95% CI: 0.47–0.50; p < 0.001), initiating ANC in the first trimester (OR 0.75; 95% CI: 0.73–0.76; p < 0.001), completing adequate ANC (OR 0.69; 95% CI: 0.67–0.70; p < 0.001), and meeting the criteria for comprehensive ANC (OR 0.53; 95% CI: 0.52–0.54; p < 0.001). 3.2 District-Level Nutritional Disparities in Central Java To complement the national-level urban–rural findings and explore whether nutrition-related advantages differ across local settings, we conducted district-level comparisons within Central Java, where the results are presented in Table 2 . Kota Magelang consistently showed more favorable nutritional outcomes compared to other urban districts. The odds of severe underweight were significantly lower in Magelang than in Surakarta (OR 0.25; 95% CI: 0.09–0.70; p = 0.008), Pekalongan (OR 0.20; 95% CI: 0.08–0.52; p < 0.001), Tegal (OR 0.15; 95% CI: 0.06–0.39; p = 0.001), and Grobogan (OR 0.17; 95% CI: 0.06–0.43; p < 0.001). A similar but statistically nonsignificant pattern was observed against Blora (OR 0.37; 95% CI: 0.13–1.06; p = 0.064). In addition, children in Magelang had significantly lower odds of being underweight compared to children in Pekalongan (OR 0.62; 95% CI: 0.46–0.85; p = 0.002), Tegal (OR 0.73; 95% CI: 0.54–1.00; p = 0.048), Grobogan (OR 0.67; 95% CI: 0.50–0.91; p = 0.009), and Blora (OR 0.63; 95% CI: 0.46–0.85; p = 0.003). Table 2 Pairwise comparisons of child nutritional status between selected urban districts in Central Java Nutritional status (District A vs B) District A District B OR (95% CI) p Magelang vs Surakarta Normal 541 410 Ref Ref Severely Underweight 5 15 0.25 (0.09–0.70) 0.008 Underweight 84 62 1.03 0.72–1.46 0.883 At-Risk Overweight 42 36 0.88 0.56–1.41 0.602 Magelang vs Surakarta Normal 541 392 Ref Ref Severely Underweight 5 5 0.72 (0.21–2.52) 0.612 Underweight 84 45 1.35 (0.92–1.99) 0.124 At-Risk Overweight 42 44 0.69 (0.44–1.08) 0.102 Surakarta vs Semarang Normal 410 392 Ref Ref Severely Underweight 15 5 2.87 (1.03–7.97) 0.043 Underweight 62 45 1.32 (0.88–1.98) 0.186 At-Risk Overweight 36 44 0.78 (0.49–1.24) 0.297 Kota Magelang vs Kota Pekalongan Normal 541 549 Ref Ref Severely Underweight 5 26 0.20 (0.08–0.52) < 0.001 Underweight 84 135 0.62 (0.46–0.85) 0.002 At-Risk Overweight 42 37 1.15 (0.73–1.82) 0.545 Kota Magelang vs Kota Tegal Normal 541 499 Ref Ref Severely Underweight 5 31 0.15 (0.06–0.39) 0.001 Underweight 84 106 0.73 (0.54–1.00) 0.048 At-Risk Overweight 42 31 1.25 (0.77–2.02) 0.363 Kota Magelang vs Grobogan Normal 541 578 Ref Ref Severely Underweight 5 32 0.17 (0.06–0.43) < 0.001 Underweight 84 133 0.67 (0.50–0.91) 0.009 At-Risk Overweight 42 28 1.60 (0.98–2.62) 0.06 Kota Magelang vs Blora Normal 541 435 Ref Ref Severely Underweight 5 11 0.37 (0.13–1.06) 0.064 Underweight 84 108 0.63 (0.46–0.85) 0.003 At-Risk Overweight 42 30 1.13 (0.69–1.83) 0.632 Interestingly, the difference between Magelang and Semarang—Central Java’s provincial capital—was not statistically tested directly in this comparison, but Semarang outperformed Surakarta in terms of severe underweight, with children in Surakarta having higher odds (OR 2.87; 95% CI: 1.03–7.97; p = 0.043). Meanwhile, pairwise comparisons involving Surakarta yielded mixed results: while no difference in underweight status was detected between Surakarta and Magelang (OR 1.03; p = 0.883), Surakarta had significantly higher odds of severe underweight compared to Semarang. No statistically significant differences were found between districts for the risk of overweight, although children in Grobogan showed a near-significant higher risk compared to those in Magelang (OR 1.60; 95% CI: 0.98–2.62; p = 0.060). 3.3 District-Level Nutritional Disparities in South Sulawesi District-level comparisons of child nutritional status across selected areas in South Sulawesi are presented in Table 3 . These comparisons were conducted between provincial capitals, mid-tier urban centers, and highland or resource-based districts to explore local disparities beyond national trends. Kota Makassar, the provincial capital, showed significantly better outcomes in undernutrition indicators compared to Kota Pare-pare. Children in Makassar had lower odds of being severely underweight (OR 0.42; 95% CI: 0.23–0.77; p = 0.005) and underweight (OR 0.74; 95% CI: 0.55–0.98; p = 0.037), though the difference in at-risk overweight was not statistically significant (OR 1.39; p = 0.235). However, when Makassar was compared to the highland district of Tana Toraja, results were reversed. Makassar had higher odds of underweight (OR 1.79; 95% CI: 1.30–2.48; p < 0.001), and though not statistically significant, also showed a trend toward higher severe underweight (OR 2.06; p = 0.081) and at-risk overweight (OR 1.47; p = 0.13). A similar pattern was observed in the comparison between Makassar and Toraja Utara, where children in Makassar had significantly higher odds of underweight (OR 1.50; 95% CI: 1.10–2.05; p = 0.011) and at-risk overweight (OR 1.86; 95% CI: 1.08–3.20; p = 0.025), with no difference in severe underweight (OR 1.03; p = 0.924). Table 2 Pairwise comparisons of child nutritional status between selected urban districts in South Sulawesi Nutritional status (District A vs B) District A District B OR (95% CI) p-value Kota Makassar vs Kota Pare-pare Normal 541 395 Ref Ref Severely Underweight 18 31 0.42 (0.23–0.77) 0.005 Underweight 117 116 0.74 (0.55–0.98) 0.037 At-Risk Overweight 40 21 1.39 (0.81–2.40) 0.235 Kota Makassar vs Tana Toraja Normal 541 556 Ref Ref Severely Underweight 18 9 2.06 (0.92–4.62) 0.081 Underweight 117 67 1.79 (1.30–2.48) < 0.001 At-Risk Overweight 40 28 1.47 (0.89–2.41) 0.13 Kota Makassar vs Toraja Utara Normal 541 528 Ref Ref Severely Underweight 18 17 1.03 (0.53–2.03) 0.924 Underweight 117 76 1.50 (1.10–2.05) 0.011 At-Risk Overweight 40 21 1.86 (1.08–3.20) 0.025 Kota Palopo vs Enrekang Normal 497 535 Ref Ref Severely Underweight 22 24 0.99 (0.55–1.78) 0.965 Underweight 128 92 1.50 (1.12–2.01) 0.007 At-Risk Overweight 25 16 1.68 (0.89–3.19) 0.111 Kota Palopo vs Luwu Timur Normal 464 464 Ref Ref Severely Underweight 17 17 1.21 (0.63–2.30) 0.570 Underweight 105 105 1.14 (0.85–1.52) 0.380 At-Risk Overweight 21 44 0.53 (0.32–0.88) 0.014 Kota Palopo, a mid-sized city, also displayed variation in nutritional outcomes depending on the district of comparison. Compared to Enrekang, children in Palopo had significantly higher odds of underweight (OR 1.50; 95% CI: 1.12–2.01; p = 0.007), with no differences in severe underweight or at-risk overweight. Interestingly, Palopo and Luwu Timur had identical counts for normal weight, severely underweight, and underweight categories, resulting in non-significant differences for both severely underweight (OR = 1.21; 95% CI: 0.63–2.30; p = 0.570) and underweight (OR = 1.14; 95% CI: 0.85–1.52; p = 0.380). However, children in Palopo had significantly lower odds of being at risk of being overweight compared to those in Luwu Timur (OR = 0.53; 95% CI: 0.32–0.88; p = 0.014). 4. Discussion This study reveals a persistent and multifaceted urban–rural divide in nutritional and maternal health indicators among under-five children in Indonesia. Children living in rural districts had significantly higher odds of being severely underweight, underweight, stunted, and severely stunted than their urban counterparts [15]. Although wasting was only marginally higher in rural areas, the odds of severe wasting were significantly elevated. Conversely, urban children were more likely to be at risk of being overweight, confirming the early signs of a double burden of malnutrition in urban settings [16], Dietary indicators further support these patterns. Rural children were less likely to meet MDD, MMFF, and MAD. Rural children also had lower reported protein intake and vegetable consumption, with higher consumption of unhealthy foods (OR 1.22) and sugar-sweetened beverages, indicating both dietary insufficiencies and growing exposure to energy-dense foods [17, 18]. Preventive and maternal care indicators show stark rural disadvantage. Rural children were more likely to miss standard growth monitoring. Mothers in rural districts were less likely to access antenatal care, whether defined as ≥ 1 visit, first-trimester initiation (OR 0.75), adequate ANC, or comprehensive ANC. These disparities reflect systemic barriers in rural healthcare access, continuity, and quality [19, 20]. Our findings are aligned with a published systematic review revealing that food availability and accessibility in rural environments were the most consistently associated with diet quality and nutritional status [21]. Limited availability of nutritious foods and poor access to formal or informal food vendors in rural areas of LMICs are drivers for persistent undernutrition [21]. Subprovincial comparisons in Central Java underscore that urban status does not equate to uniform nutritional advantage. Kota Magelang—a relatively small city—consistently exhibited lower odds of severe underweight and underweight when compared to several larger or semi-urban districts. For instance, Magelang had significantly lower odds of severe underweight than Kota Tegal, Kota Pekalongan, Grobogan, and Surakarta. The same trend applied to underweight status versus Blora, Grobogan, and Pekalongan. Importantly, no major differences were observed between Magelang and Semarang, suggesting that smaller cities may perform comparably to provincial capitals under favorable conditions. These findings imply that local health system efficiency, service accessibility, or geographic positioning (e.g., proximity to Yogyakarta) may help explain Magelang’s favorable nutritional outcomes. The Central Java analysis reinforces the national trend of rural disadvantage but nuances it by revealing intra-urban differences—where even among urban districts, size and status do not guarantee optimal outcomes. Districts like Surakarta and Pekalongan, despite being well-known urban centers, show underperformance in key indicators, warranting targeted intervention. As for the case study in South Sulawesi, comparisons centered on two urban benchmarks—Kota Makassar and Kota Palopo—revealed unique district-level dynamics. Makassar had significantly better outcomes than Kota Pare-pare for severe underweight and underweight, in line with national urban–rural trends. However, Makassar showed significantly worse odds of underweight compared to highland districts Tana Toraja and Toraja Utara, despite having better infrastructure. This suggests that geographic location alone does not explain disparities, and cultural or programmatic factors (e.g., community feeding practices or ANC coverage) may shape outcomes [10]. Risk of overweight was higher in Toraja Utara, highlighting the early emergence of nutrition transition even in traditionally undernourished regions [22]. Palopo presented as a middle-tier city with mixed outcomes. Compared to Enrekang, Palopo had higher underweight, yet no difference in other indicators. Against Luwu Timur—a resource-rich district—Palopo had a significantly lower risk of overweight, pointing to local dietary transitions that may not track neatly with urbanization level. These district comparisons reveal three critical insights: (1) urban–rural divides persist even within provinces; (2) urbanicity does not automatically confer nutritional protection; and (3) districts experiencing economic growth (e.g., Luwu Timur) may face accelerated nutrition transitions, sometimes outpacing preventive health services. In rural areas, strengthening maternal services, expanding ANC coverage, and improving dietary diversity must be prioritized. However, these efforts will remain limited without equitable distribution of healthcare facilities and skilled personnel, particularly in remote or geographically isolated regions [4]. In urban settings—especially in major cities like Makassar—early-onset obesity and poor dietary quality demand greater attention, as sedentary lifestyles and increased access to energy-dense, nutrient-poor foods begin to shape health outcomes [23]. Notably, smaller cities such as Magelang may serve as models of localized success, possibly due to closer proximity to provincial health hubs and more adaptable primary care systems. These findings highlight how national-level averages can obscure important sub-provincial disparities [24]. As Indonesia advances toward universal health coverage and expands initiatives such as the Makan Bergizi Gratis program (MBG; Free Nutritious Meals), and the establishment of a Badan Gizi Nasional (BGN; National Nutrition Agency), nutrition planning must be decentralized and responsive to district-level realities [25]. In addition to addressing structural determinants—like healthcare infrastructure and workforce distribution—greater attention must be paid to behavioral factors, including food preferences and lifestyle patterns [26]. These findings reflect a complex interplay of behavioral, structural, and environmental determinants. In rural areas, undernutrition remains strongly linked to systemic disparities in health infrastructure, including fewer skilled personnel, longer travel distances to health facilities, and inconsistent service availability—challenges well documented in the Indonesian context [15, 27]. Lower maternal education and food insecurity may further limit caregivers’ ability to ensure adequate child nutrition [28]. In contrast, urban areas—though better served by health infrastructure—face a different set of challenges. Rapid urbanization has reshaped family diets and behaviors, with increased exposure to processed foods, limited time for home meal preparation, and easy access to sugar-sweetened beverages and fast-food outlets [29]. These patterns contribute to rising overweight and obesity even in children [30]. Furthermore, urban children often live in environments characterized by limited safe play spaces, screen-centered entertainment, and reduced physical activity—all contributing to more sedentary lifestyles [31]. Urban parents may also adopt permissive feeding styles or be more influenced by commercial food marketing [32, 33]. In cities like Makassar, this nutritional transition may be compounded by social inequalities and household income disparities, which can influence both food quality and lifestyle behaviors. In smaller cities such as Magelang, favorable nutritional outcomes may arise from closer proximity to provincial healthcare centers, smaller catchment areas that ease outreach efforts, or more cohesive community health initiatives. These cities may also be less affected by the ultra-processed food environment dominating larger metropolitan regions. However, such success may not be generalizable without attention to governance quality and frontline health service performance [34, 35]. Despite recent policy efforts such as MBG and the establishment of the BGN, the government’s approach still tends to emphasize programmatic delivery—such as food provision and service expansion—without adequately addressing the behavioral determinants and systemic poverty that sustain malnutrition [25]. These top-down initiatives often treat undernutrition as a matter of food absence or service gaps, yet fail to engage with how household behaviors, cultural norms, or parental knowledge influence child-feeding practices and health service utilization. For example, even where food is available, choices may be shaped by deeply embedded habits, misinformation, or economic constraints that push families toward calorie-dense, low-nutrient foods [36, 37]. Moreover, the current model insufficiently tackles structural poverty, which remains a root driver of both undernutrition and poor maternal health coverage. In many rural and semi-urban areas, families face intergenerational disadvantages—limited education, insecure employment, and fragile social safety nets—that cannot be resolved through food assistance alone. Without stronger integration of social protection, nutrition education, and behavior change interventions, such programs risk being palliative rather than transformative [4, 38](4,35). Critically, urban interventions often focus on supply-side measures—monitoring school meals, regulating food sales, or expanding health posts—while ignoring the social determinants of overweight and obesity, such as sedentary living, aggressive food marketing, and unequal access to recreational spaces [23, 39]. Children growing up in poor urban neighborhoods may face the paradox of food abundance but health scarcity, where cheap, unhealthy food is ubiquitous, yet safe water, green spaces, and time for physical activity are limited [40, 41]. In sum, while Indonesia’s recent nutrition programs represent important political commitments, their impact will remain limited unless they confront the behavioral, economic, and environmental complexity of malnutrition. Despite offering valuable insights, this study is not without limitations. The use of aggregate data from the 2024 SSGI precluded individual-level adjustments, limiting our ability to assess the influence of household or parental factors on nutritional outcomes. Several indicators, particularly those related to dietary intake and service utilization, relied on caregiver recall, which may introduce recall bias or overreporting. While anthropometric measurements were drawn from direct observation, inconsistencies in field procedures and equipment calibration across districts may affect data quality. Additionally, count approximations from proportion data may carry minor rounding errors, though unlikely to alter directionality of findings. Finally, the district case studies were purposively selected and may not reflect broader intra-provincial dynamics. 5. Conclusion We found persistent and complex disparities in child nutrition and maternal care across Indonesia, shaped not only by rural–urban divides but also by local structural and systemic factors. At the national level, rural children consistently experience higher risks of undernutrition, limited dietary diversity, and inadequate maternal health service coverage. "Sub-provincial case studies in Central Java and South Sulawesi demonstrate that nutritional outcomes vary not only between urban and rural areas but also within urban and rural districts themselves. These intra-urban disparities suggest that factors such as local health system performance, geographic proximity to referral centers, socioeconomic conditions, and district-level governance may play a more decisive role than urbanization alone. To reduce malnutrition and promote equity in child health, Indonesia must enhance the effectiveness of its decentralized health systems and ensure interventions are tailored to specific local needs. In rural settings, this includes improving access to antenatal care and dietary diversity, while also tackling the structural poverty that underlies these gaps. In urban and transitioning districts, greater attention is needed to address the rising risk of overweight among children, driven by lifestyle-related factors such as poor diet quality and physical inactivity. Declarations Author Contribution M.I. and F.N. conceptualized the study, curated the data, and supervised the overall project. M.I. and N.F.A. designed the methodology and performed the national-level statistical analysis. D.D.C.H.R. and I.Q. conducted the sub-provincial case comparisons and contributed to data visualization. R.R. and A.S.A. assisted in data interpretation and literature review. 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Assessment of the obesogenic environment in primary schools: a multi-site case study in Jakarta. BMC Nutr. 2022;8(1):19. Vilar-Compte M, Burrola-Mendez S, Lozano-Marrufo A, Ferre-Eguiluz I, Flores D, Gaitan-Rossi P, et al. Urban poverty and nutrition challenges associated with accessibility to a healthy diet: a global systematic literature review. Int J Equity Health. 2021;20(1):40. Elshater A, Abusaada H. Poverty-free urbanism: six qualitative normative factors and 36 procedures for measuring urban poverty from a deprivation perspective. Open House International. 2025;50(2):392-414. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1OperationalDefinitionsSSGI.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7177814","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491347118,"identity":"0f4b8339-4068-48bf-ae8a-1afc7f499722","order_by":0,"name":"Muhammad Iqhrammullah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYFACxgZmCIO58QBjgw1IpPEAkVoYG4Ba0iAMQvYgazkMZuLVotvA3Hi7cI9dtO6MxIYDP3ect1vbfhhoS41NNC4tZgcYm61nPEvO3XYjseFg75nbydvOAPUyHEvLbcCtpU2a5wAzWMsB3rbbyWYHEsEuJKSlHmLL37ZzyWbnHxKl5TBYy2HetgN2ZjcI2XIY6BeeA8dzt5152HBYti05wewG0JYEfH453v7wNs+B6txtx5MPPnzbZmdvdj794YMPNTY4tYAiRQKZnwhWmYBLORSgaLEnoHgUjIJRMApGIAAAPU1v6Ecex0sAAAAASUVORK5CYII=","orcid":"","institution":"Universitas Muhammadiyah Aceh, Banda Aceh","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Iqhrammullah","suffix":""},{"id":491347119,"identity":"5b94b609-01cd-4d4b-89ba-07b051294a98","order_by":1,"name":"Nuril Farid Abshori","email":"","orcid":"","institution":"Maulana Malik Ibrahim Islamic State University Malang","correspondingAuthor":false,"prefix":"","firstName":"Nuril","middleName":"Farid","lastName":"Abshori","suffix":""},{"id":491347120,"identity":"a51853e3-7519-43e7-b66a-26ec7fb6636d","order_by":2,"name":"Derren DCH Rampengan","email":"","orcid":"","institution":"Universitas Sam Ratulangi","correspondingAuthor":false,"prefix":"","firstName":"Derren","middleName":"DCH","lastName":"Rampengan","suffix":""},{"id":491347121,"identity":"578e327e-0902-4d85-9511-420c74166912","order_by":3,"name":"Intan Qanita","email":"","orcid":"","institution":"Universitas Syiah Kuala","correspondingAuthor":false,"prefix":"","firstName":"Intan","middleName":"","lastName":"Qanita","suffix":""},{"id":491347122,"identity":"28c22f74-db96-4a52-b347-d57b034ae8cd","order_by":4,"name":"Roy Ramadhan","email":"","orcid":"","institution":"Universitas Airlangga","correspondingAuthor":false,"prefix":"","firstName":"Roy","middleName":"","lastName":"Ramadhan","suffix":""},{"id":491347123,"identity":"a971e3a1-f641-4b26-9789-c7acbf30cce5","order_by":5,"name":"Arga Setyo Adji","email":"","orcid":"","institution":"Universitas Hang Tuah","correspondingAuthor":false,"prefix":"","firstName":"Arga","middleName":"Setyo","lastName":"Adji","suffix":""},{"id":491347124,"identity":"7108ab49-ddc4-4449-8e46-b5e2539478c9","order_by":6,"name":"Fahrul Nurkolis","email":"","orcid":"","institution":"Universitas Airlangga","correspondingAuthor":false,"prefix":"","firstName":"Fahrul","middleName":"","lastName":"Nurkolis","suffix":""}],"badges":[],"createdAt":"2025-07-21 13:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7177814/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7177814/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91537491,"identity":"d07fa5cc-3dc6-4c27-b9bd-68633a7bb0f0","added_by":"auto","created_at":"2025-09-17 13:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1006785,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7177814/v1/5da7b5db-4441-41c7-87f3-467b046d4c8e.pdf"},{"id":87793868,"identity":"f0b5c29a-9038-4568-bde4-70632723effd","added_by":"auto","created_at":"2025-07-29 06:35:49","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":9897,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1OperationalDefinitionsSSGI.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7177814/v1/f02166ef83c4e9651032d254.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does urban living contribute to better nutrition? An ecological study on urban–rural disparities in Indonesia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eChild undernutrition remains a critical public health challenge in Indonesia, contributing to increased morbidity, impaired cognitive development, and long-term productivity losses. On the other hand, nutrition transitions where lower activities with increased consumption of sweet drinks and food containing too much fat causes obesity and increase the risk of non-communicable disease [1]. Evidence from a cohort study in Democratic Republic Congo has consistently linked early-life undernutrition with heightened risks of chronic diseases later in life, including cardiovascular disease, type 2 diabetes, and metabolic syndrome [2]. The Developmental Origins of Health and Disease (DOHaD) framework further reinforces that suboptimal nutrition during the first 1,000 days can lead to permanent physiological changes, predisposing individuals to non-communicable diseases across the life course [3]. In Indonesia, where stunting and wasting remain prevalent, the public health implications extend beyond childhood and into adult economic productivity and health system burden. Despite decades of national efforts to improve child nutrition, disparities persist across geographic, socioeconomic, and service-access dimensions [4]. While rural areas are often the focus of nutritional interventions due to historically lower service availability and food security, recent data suggest that these challenges are not exclusive to rural settings. In fact, substantial variation may exist within urban areas, warranting closer examination of sub-provincial patterns [4].\u003c/p\u003e\u003cp\u003eUrban environments are traditionally associated with better access to health services, improved dietary diversity, and higher socioeconomic status. These assumptions are grounded in the urban advantage hypothesis, which posits that urban children benefit from proximity to healthcare facilities, higher parental education, and greater availability of diverse foods through market integration [5]. However, emerging evidence\u0026mdash;particularly from low- and middle-income countries (LMICs)\u0026mdash;suggests that this advantage is neither uniform nor guaranteed. The urban penalty hypothesis offers a countervailing perspective, emphasizing that rapid, unregulated urbanization can lead to overcrowded living conditions, inadequate sanitation, environmental degradation, and fragmented health systems that disproportionately affect the urban poor [6]. In such contexts, urban living may expose children to nutritional risk through food insecurity, sedentary behavior, and poor dietary choices driven by the proliferation of ultra-processed foods and limited access to nutritious options\u0026mdash;especially in informal or peri-urban settlements [7, 8].\u003c/p\u003e\u003cp\u003eThe UNICEF conceptual framework of child undernutrition identifies the interplay of immediate (dietary intake, illness), underlying (household food security, maternal care, health services), and basic causes (sociopolitical context and resources) in shaping nutritional outcomes [9]. Geographic location\u0026mdash;whether urban or rural\u0026mdash;modulates these determinants through its influence on infrastructure, service delivery, and exposure to socioeconomic inequality. Additionally, the social determinants of health model underscores that structural inequities, including housing, income, and education, can differentially impact child nutrition in urban versus rural contexts [10]. Together, these frameworks justify the inclusion of urban\u0026ndash;rural stratification in nutritional analysis and highlight the need to examine intra-urban heterogeneity to avoid masking disparities with aggregate classifications.\u003c/p\u003e\u003cp\u003eUnderstanding the determinants of child undernutrition requires a multi-level framework that accounts for both structural and behavioral drivers. From a life-course and ecological perspective, child nutritional status is influenced not only by food intake but also by maternal health, healthcare access, sanitation, and social determinants such as poverty and education [11]. This study includes anthropometric indicators\u0026mdash;weight-for-age, height-for-age, and weight-for-height\u0026mdash;as they are internationally recognized proxies for acute and chronic malnutrition. We also assess dietary practices (e.g., Minimum Dietary Diversity, Minimum Meal Frequency, Minimum Acceptable Diet), which reflect both availability and caregiver behavior. Lastly, we incorporate maternal care variables, particularly antenatal care (ANC) coverage, given its well-established role in improving birth outcomes, early growth, and feeding practices.\u003c/p\u003e\u003cp\u003eCentral Java and South Sulawesi were selected for case studies due to their contrasting geography, urbanization patterns, and health system structures. Central Java includes mature urban centers with proximity to referral hospitals and education hubs [12, 13], while South Sulawesi offers a mix of highland and coastal districts with variable infrastructure [14]. By comparing nutritional indicators across selected districts within these provinces, we aim to investigate whether urban status alone ensures nutritional advantage or whether localized determinants\u0026mdash;such as health system capacity, geographic isolation, and local governance\u0026mdash;shape outcomes more significantly. At the national level, we further examine urban\u0026ndash;rural disparities in dietary and maternal health variables using the \u003cem\u003eSurvei Status Gizi Indonesia\u003c/em\u003e 2024 (SSGI; Indonesian Nutritional Status Survey). This integration of subnational and national data allows us to explore how micro-level variation aligns or diverges from national trends, and to identify specific districts or population subgroups that require differentiated policy responses. This present study seeks to move beyond binary urban\u0026ndash;rural comparisons and contribute evidence for district-sensitive nutrition policies in Indonesia\u0026mdash;highlighting the importance of context, behavior, and service delivery in tackling child undernutrition.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Design\u003c/h2\u003e\u003cp\u003eThis ecological, cross-sectional study examined nutritional disparities among children under five in Indonesia, using data from the 2024 SSGI. The analysis focused on both national urban\u0026ndash;rural differences and district-level variation in selected areas of Central Java and South Sulawesi. These provinces were chosen as case studies to enrich our understanding of how urban\u0026ndash;rural disparities manifest at the sub-provincial level and influence child nutritional status across diverse geographic and service contexts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Data Source\u003c/h2\u003e\u003cp\u003eThe 2024 SSGI was a nationally representative survey conducted by the Indonesian Ministry of Health in collaboration with Statistics Indonesia (BPS). The target population included all households with children under five years of age across the country. A total of 345,000 under-five households were selected from 34,500 census blocks covering all 514 districts/cities, with each block contributing 10 households. Sampling followed a two-stage, one-phase stratified design. In the first stage, census blocks were selected using probability proportional to size with replacement. In the second stage, under-five households were selected systematically from updated household listings conducted prior to sampling. The survey achieved high response rates: 92.5% for households, 97.1% for children visited, and 98.7% for completed interviews. In total, 42,893 under-five children were successfully included in the final dataset.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Urban\u0026ndash;Rural Classification\u003c/h2\u003e\u003cp\u003eThe 2024 SSGI used urban\u0026ndash;rural classification criteria established by Statistics Indonesia (BPS), which are applied at the level of census blocks, not at the level of individual households or respondents. Each census block was categorized as either urban or rural based on a composite scoring system developed by BPS. This classification system considers the following indicators: (1) Population density; (2) Percentage of households working in the agricultural sector; (3) Access to urban-type facilities (such as schools or universities, health services, roads, markets, electricity, and telecommunication); and (4) Percentage of households working in agriculture. Census blocks meeting the urban threshold across these criteria were labeled as urban; all others were considered rural. This classification was applied uniformly in both national-level comparisons and sub-provincial case studies. For intra-urban comparisons in this present study, only districts composed predominantly of urban-designated census blocks were included to ensure consistency and comparability across urban settings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 National-Level Urban\u0026ndash;Rural Comparison\u003c/h2\u003e\u003cp\u003eAt the national level, this study assessed disparities in nutritional and maternal\u0026ndash;child health indicators between urban and rural populations using aggregate data from the 2024 Survei Status Gizi Indonesia (SSGI). All indicators were originally reported in percentages and were converted to absolute counts by multiplying each proportion by the number of under-five children with available data for each respective item. Nutritional status was assessed using three anthropometric indices\u0026mdash;weight-for-age (severely underweight, underweight, normal, at-risk overweight), height-for-age (stunting, severely stunting, normal), and weight-for-height (wasting, severely wasting, normal, overweight/obese)\u0026mdash;which were derived from direct measurements of children\u0026rsquo;s body weight and length/height performed by trained enumerators using standardized equipment.\u003c/p\u003e\u003cp\u003eIn contrast, dietary indicators such as Minimum Dietary Diversity (MDD), Minimum Meal Frequency (MMFF), and Minimum Acceptable Diet (MAD) were estimated based on caregiver-reported 24-hour recall of food intake. Additional dietary variables captured whether the child consumed specific food types (protein-rich foods, sweet beverages, unhealthy foods such as fried snacks or instant noodles, and vegetables or fruits) in the preceding 24 hours.\u003c/p\u003e\u003cp\u003eService-related variables reflected health service coverage and utilization, also collected through caregiver interviews. These included whether the child received routine growth monitoring and whether the mother accessed antenatal care (ANC) during pregnancy. ANC utilization was categorized into four levels: basic coverage (at least one visit with a skilled provider), first-trimester ANC, adequate ANC (four or more visits with appropriate spacing), and comprehensive ANC (six or more visits across trimesters, including consultations with a medical doctor and ultrasound examination).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 District-Level Case Studies\u003c/h2\u003e\u003cp\u003eTo explore local variation in child nutritional outcomes beyond broad urban\u0026ndash;rural classifications, we conducted district-level case studies in two provinces\u0026mdash;Central Java and South Sulawesi\u0026mdash;selected for their contrasting geography and health system profiles. Within each province, we purposively selected a diverse set of districts representing a spectrum of settlement types, including provincial capitals, secondary cities, peri-urban areas, and rural or highland districts. In Central Java, selected districts included Kota Magelang, Kota Surakarta, Kota Semarang, Kota Pekalongan, Kota Tegal, Grobogan, and Blora. In South Sulawesi, we included Kota Makassar, Kota Palopo, Kota Pare-pare, Enrekang, Luwu Timur, Tana Toraja, and Toraja Utara. These districts were chosen to capture variation in urban scale (small vs. large), geographic accessibility (e.g., coastal, inland, or mountainous), and availability of local health infrastructure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were conducted using RStudio (version 2024.04.2\u0026ndash;764, running R version 4.3.3). Descriptive statistics were calculated to summarize each variable by urban\u0026ndash;rural classification at the national level. Logistic regression models were applied to estimate odds ratios (ORs) with 95% confidence intervals (CIs) and corresponding p-values, using normal nutritional status or sufficient health service coverage as the reference group. Statistical significance was defined as a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eFor district-level case studies, we examined intra-urban variation through pairwise comparisons of selected districts within Central Java and South Sulawesi. These comparisons were based on aggregated district-level data and were consistent with the ecological design of the study. For each district pair, 2x2 contingency tables were constructed for categorical nutritional outcomes, and chi-square tests were used to estimate ORs and p-values. Only districts with complete data across all four nutritional status (W/A) categories (severely underweight, underweight, normal, at-risk overweight) were included in the pairwise analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 National-Level Urban\u0026ndash;Rural Disparities\u003c/h2\u003e\u003cp\u003eComparisons of child nutritional status, dietary practices, and maternal\u0026ndash;child health service utilization between rural and urban populations at the national level are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Urban residence was consistently associated with lower odds of undernutrition across most anthropometric indicators. Compared to rural children, urban children had significantly lower odds of being severely underweight (OR 0.78; 95% CI: 0.75\u0026ndash;0.81; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and underweight (OR 0.82; 95% CI: 0.80\u0026ndash;0.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but higher odds of being at risk of overweight (OR 1.35; 95% CI: 1.31\u0026ndash;1.40; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, urban children had lower odds of stunting (OR 0.77; 95% CI: 0.75\u0026ndash;0.78; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and severe stunting (OR 0.63; 95% CI: 0.61\u0026ndash;0.66; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to their rural counterparts. For wasting status, urban children had slightly lower odds of wasting (OR 0.95; 95% CI: 0.93\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.002) and markedly lower odds of severe wasting (OR 0.79; 95% CI: 0.74\u0026ndash;0.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The likelihood of being overweight or obese was also significantly higher among urban children (OR 1.20; 95% CI: 1.15\u0026ndash;1.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of child nutritional status, dietary indicators, and maternal\u0026ndash;child health service coverage by area of residence (urban vs. rural)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutritional status (W/A)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78 (0.75\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82 (0.80\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk of overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35 (1.31\u0026ndash;1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStunting status (H/A)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.75\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely stunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.61\u0026ndash;0.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWasting status (W/H)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e139207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9530\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.93\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverly wasting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79 (0.74\u0026ndash;0.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweigth \u0026amp; obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5624\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (1.15\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76 (0.74\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMMFF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42 (0.39\u0026ndash;0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.74\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein intake (24 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75 (0.73\u0026ndash;0.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSweet-beverage intake (24 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.81\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnhealthy food intake (24 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22 (1.19\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetable intake (24 h)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18 (1.14\u0026ndash;1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStandard growth monitoring\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.16 (1.14\u0026ndash;1.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANC coverage (\u0026ge;\u0026thinsp;1 visit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e152935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48 (0.47\u0026ndash;0.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFirst-trimester ANC visit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e134015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75 (0.73\u0026ndash;0.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdequate ANC coverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e124082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.67\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComprehensive ANC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReceived\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot received\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53 (0.52\u0026ndash;0.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBasic ANC coverage (\u0026ge;\u0026thinsp;1 visit with skilled provider)\u003c/p\u003e\u003cp\u003eAdequate ANC coverage (4\u0026thinsp;+\u0026thinsp;visits with skilled provider, appropriately spaced)\u003c/p\u003e\u003cp\u003eComprehensive ANC (6\u0026thinsp;+\u0026thinsp;visits with trimester-based schedule and minimum 2 doctor contacts with ultrasound)\u003c/p\u003e\u003cp\u003eDietary indicators revealed that rural children were less likely to meet dietary adequacy. The odds of insufficient Minimum Dietary Diversity (MDD) were higher among rural children (OR 0.76; 95% CI: 0.74\u0026ndash;0.78; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and they were also less likely to meet the Minimum Meal Frequency (MMFF) standard (OR 0.42; 95% CI: 0.39\u0026ndash;0.45; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the odds of receiving a Minimum Acceptable Diet (MAD) were lower for rural children (OR 0.77; 95% CI: 0.74\u0026ndash;0.79; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of 24-hour dietary recall, rural children had significantly lower odds of consuming protein-rich foods (OR 0.75; 95% CI: 0.73\u0026ndash;0.78; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interestingly, they were also less likely to consume sweet beverages (OR 0.84; 95% CI: 0.81\u0026ndash;0.88; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but more likely to consume unhealthy foods (OR 1.22; 95% CI: 1.19\u0026ndash;1.25; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and less likely to consume vegetables or fruits (OR 1.18; 95% CI: 1.14\u0026ndash;1.22; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eService utilization indicators showed consistent disadvantages in rural areas. The odds of not receiving standard growth monitoring were significantly higher among rural children (OR 1.16; 95% CI: 1.14\u0026ndash;1.18; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Likewise, rural mothers had lower odds of receiving any antenatal care (ANC) (OR 0.48; 95% CI: 0.47\u0026ndash;0.50; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), initiating ANC in the first trimester (OR 0.75; 95% CI: 0.73\u0026ndash;0.76; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), completing adequate ANC (OR 0.69; 95% CI: 0.67\u0026ndash;0.70; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and meeting the criteria for comprehensive ANC (OR 0.53; 95% CI: 0.52\u0026ndash;0.54; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 District-Level Nutritional Disparities in Central Java\u003c/h2\u003e\u003cp\u003eTo complement the national-level urban\u0026ndash;rural findings and explore whether nutrition-related advantages differ across local settings, we conducted district-level comparisons within Central Java, where the results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Kota Magelang consistently showed more favorable nutritional outcomes compared to other urban districts. The odds of severe underweight were significantly lower in Magelang than in Surakarta (OR 0.25; 95% CI: 0.09\u0026ndash;0.70; p\u0026thinsp;=\u0026thinsp;0.008), Pekalongan (OR 0.20; 95% CI: 0.08\u0026ndash;0.52; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Tegal (OR 0.15; 95% CI: 0.06\u0026ndash;0.39; p\u0026thinsp;=\u0026thinsp;0.001), and Grobogan (OR 0.17; 95% CI: 0.06\u0026ndash;0.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A similar but statistically nonsignificant pattern was observed against Blora (OR 0.37; 95% CI: 0.13\u0026ndash;1.06; p\u0026thinsp;=\u0026thinsp;0.064). In addition, children in Magelang had significantly lower odds of being underweight compared to children in Pekalongan (OR 0.62; 95% CI: 0.46\u0026ndash;0.85; p\u0026thinsp;=\u0026thinsp;0.002), Tegal (OR 0.73; 95% CI: 0.54\u0026ndash;1.00; p\u0026thinsp;=\u0026thinsp;0.048), Grobogan (OR 0.67; 95% CI: 0.50\u0026ndash;0.91; p\u0026thinsp;=\u0026thinsp;0.009), and Blora (OR 0.63; 95% CI: 0.46\u0026ndash;0.85; p\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise comparisons of child nutritional status between selected urban districts in Central Java\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutritional status (District A vs B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistrict B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagelang vs Surakarta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25 (0.09\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 0.72\u0026ndash;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.883\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.88 0.56\u0026ndash;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMagelang vs Surakarta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72 (0.21\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.612\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.35 (0.92\u0026ndash;1.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.44\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurakarta vs Semarang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.87 (1.03\u0026ndash;7.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32 (0.88\u0026ndash;1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78 (0.49\u0026ndash;1.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Magelang vs Kota Pekalongan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20 (0.08\u0026ndash;0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62 (0.46\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.15 (0.73\u0026ndash;1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Magelang vs Kota Tegal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15 (0.06\u0026ndash;0.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.73 (0.54\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.25 (0.77\u0026ndash;2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Magelang vs Grobogan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.17 (0.06\u0026ndash;0.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67 (0.50\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.60 (0.98\u0026ndash;2.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Magelang vs Blora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.37 (0.13\u0026ndash;1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.46\u0026ndash;0.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13 (0.69\u0026ndash;1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eInterestingly, the difference between Magelang and Semarang\u0026mdash;Central Java\u0026rsquo;s provincial capital\u0026mdash;was not statistically tested directly in this comparison, but Semarang outperformed Surakarta in terms of severe underweight, with children in Surakarta having higher odds (OR 2.87; 95% CI: 1.03\u0026ndash;7.97; p\u0026thinsp;=\u0026thinsp;0.043). Meanwhile, pairwise comparisons involving Surakarta yielded mixed results: while no difference in underweight status was detected between Surakarta and Magelang (OR 1.03; p\u0026thinsp;=\u0026thinsp;0.883), Surakarta had significantly higher odds of severe underweight compared to Semarang. No statistically significant differences were found between districts for the risk of overweight, although children in Grobogan showed a near-significant higher risk compared to those in Magelang (OR 1.60; 95% CI: 0.98\u0026ndash;2.62; p\u0026thinsp;=\u0026thinsp;0.060).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 District-Level Nutritional Disparities in South Sulawesi\u003c/h2\u003e\u003cp\u003eDistrict-level comparisons of child nutritional status across selected areas in South Sulawesi are presented in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e. These comparisons were conducted between provincial capitals, mid-tier urban centers, and highland or resource-based districts to explore local disparities beyond national trends. Kota Makassar, the provincial capital, showed significantly better outcomes in undernutrition indicators compared to Kota Pare-pare. Children in Makassar had lower odds of being severely underweight (OR 0.42; 95% CI: 0.23\u0026ndash;0.77; p\u0026thinsp;=\u0026thinsp;0.005) and underweight (OR 0.74; 95% CI: 0.55\u0026ndash;0.98; p\u0026thinsp;=\u0026thinsp;0.037), though the difference in at-risk overweight was not statistically significant (OR 1.39; p\u0026thinsp;=\u0026thinsp;0.235).\u003c/p\u003e\u003cp\u003eHowever, when Makassar was compared to the highland district of Tana Toraja, results were reversed. Makassar had higher odds of underweight (OR 1.79; 95% CI: 1.30\u0026ndash;2.48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and though not statistically significant, also showed a trend toward higher severe underweight (OR 2.06; p\u0026thinsp;=\u0026thinsp;0.081) and at-risk overweight (OR 1.47; p\u0026thinsp;=\u0026thinsp;0.13). A similar pattern was observed in the comparison between Makassar and Toraja Utara, where children in Makassar had significantly higher odds of underweight (OR 1.50; 95% CI: 1.10\u0026ndash;2.05; p\u0026thinsp;=\u0026thinsp;0.011) and at-risk overweight (OR 1.86; 95% CI: 1.08\u0026ndash;3.20; p\u0026thinsp;=\u0026thinsp;0.025), with no difference in severe underweight (OR 1.03; p\u0026thinsp;=\u0026thinsp;0.924).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePairwise comparisons of child nutritional status between selected urban districts in South Sulawesi\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNutritional status (District A vs B)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict A\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistrict B\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Makassar vs Kota Pare-pare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42 (0.23\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.55\u0026ndash;0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39 (0.81\u0026ndash;2.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Makassar vs Tana Toraja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.06 (0.92\u0026ndash;4.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79 (1.30\u0026ndash;2.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47 (0.89\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Makassar vs Toraja Utara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.53\u0026ndash;2.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.924\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50 (1.10\u0026ndash;2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.86 (1.08\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Palopo vs Enrekang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e535\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.55\u0026ndash;1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.965\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50 (1.12\u0026ndash;2.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.68 (0.89\u0026ndash;3.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Palopo vs Luwu Timur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeverely Underweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.21 (0.63\u0026ndash;2.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.570\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14 (0.85\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAt-Risk Overweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53 (0.32\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eKota Palopo, a mid-sized city, also displayed variation in nutritional outcomes depending on the district of comparison. Compared to Enrekang, children in Palopo had significantly higher odds of underweight (OR 1.50; 95% CI: 1.12\u0026ndash;2.01; p\u0026thinsp;=\u0026thinsp;0.007), with no differences in severe underweight or at-risk overweight. Interestingly, Palopo and Luwu Timur had identical counts for normal weight, severely underweight, and underweight categories, resulting in non-significant differences for both severely underweight (OR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 0.63\u0026ndash;2.30; p\u0026thinsp;=\u0026thinsp;0.570) and underweight (OR\u0026thinsp;=\u0026thinsp;1.14; 95% CI: 0.85\u0026ndash;1.52; p\u0026thinsp;=\u0026thinsp;0.380). However, children in Palopo had significantly lower odds of being at risk of being overweight compared to those in Luwu Timur (OR\u0026thinsp;=\u0026thinsp;0.53; 95% CI: 0.32\u0026ndash;0.88; p\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study reveals a persistent and multifaceted urban\u0026ndash;rural divide in nutritional and maternal health indicators among under-five children in Indonesia. Children living in rural districts had significantly higher odds of being severely underweight, underweight, stunted, and severely stunted than their urban counterparts [15]. Although wasting was only marginally higher in rural areas, the odds of severe wasting were significantly elevated. Conversely, urban children were more likely to be at risk of being overweight, confirming the early signs of a double burden of malnutrition in urban settings [16], Dietary indicators further support these patterns. Rural children were less likely to meet MDD, MMFF, and MAD. Rural children also had lower reported protein intake and vegetable consumption, with higher consumption of unhealthy foods (OR 1.22) and sugar-sweetened beverages, indicating both dietary insufficiencies and growing exposure to energy-dense foods [17, 18]. Preventive and maternal care indicators show stark rural disadvantage. Rural children were more likely to miss standard growth monitoring. Mothers in rural districts were less likely to access antenatal care, whether defined as \u0026ge;\u0026thinsp;1 visit, first-trimester initiation (OR 0.75), adequate ANC, or comprehensive ANC. These disparities reflect systemic barriers in rural healthcare access, continuity, and quality [19, 20]. Our findings are aligned with a published systematic review revealing that food availability and accessibility in rural environments were the most consistently associated with diet quality and nutritional status [21]. Limited availability of nutritious foods and poor access to formal or informal food vendors in rural areas of LMICs are drivers for persistent undernutrition [21].\u003c/p\u003e\u003cp\u003eSubprovincial comparisons in Central Java underscore that urban status does not equate to uniform nutritional advantage. Kota Magelang\u0026mdash;a relatively small city\u0026mdash;consistently exhibited lower odds of severe underweight and underweight when compared to several larger or semi-urban districts. For instance, Magelang had significantly lower odds of severe underweight than Kota Tegal, Kota Pekalongan, Grobogan, and Surakarta. The same trend applied to underweight status versus Blora, Grobogan, and Pekalongan. Importantly, no major differences were observed between Magelang and Semarang, suggesting that smaller cities may perform comparably to provincial capitals under favorable conditions. These findings imply that local health system efficiency, service accessibility, or geographic positioning (e.g., proximity to Yogyakarta) may help explain Magelang\u0026rsquo;s favorable nutritional outcomes. The Central Java analysis reinforces the national trend of rural disadvantage but nuances it by revealing intra-urban differences\u0026mdash;where even among urban districts, size and status do not guarantee optimal outcomes. Districts like Surakarta and Pekalongan, despite being well-known urban centers, show underperformance in key indicators, warranting targeted intervention.\u003c/p\u003e\u003cp\u003eAs for the case study in South Sulawesi, comparisons centered on two urban benchmarks\u0026mdash;Kota Makassar and Kota Palopo\u0026mdash;revealed unique district-level dynamics. Makassar had significantly better outcomes than Kota Pare-pare for severe underweight and underweight, in line with national urban\u0026ndash;rural trends. However, Makassar showed significantly \u003cem\u003eworse\u003c/em\u003e odds of underweight compared to highland districts Tana Toraja and Toraja Utara, despite having better infrastructure. This suggests that geographic location alone does not explain disparities, and cultural or programmatic factors (e.g., community feeding practices or ANC coverage) may shape outcomes [10]. Risk of overweight was higher in Toraja Utara, highlighting the early emergence of nutrition transition even in traditionally undernourished regions [22]. Palopo presented as a middle-tier city with mixed outcomes. Compared to Enrekang, Palopo had higher underweight, yet no difference in other indicators. Against Luwu Timur\u0026mdash;a resource-rich district\u0026mdash;Palopo had a significantly lower risk of overweight, pointing to local dietary transitions that may not track neatly with urbanization level. These district comparisons reveal three critical insights: (1) urban\u0026ndash;rural divides persist even within provinces; (2) urbanicity does not automatically confer nutritional protection; and (3) districts experiencing economic growth (e.g., Luwu Timur) may face accelerated nutrition transitions, sometimes outpacing preventive health services.\u003c/p\u003e\u003cp\u003eIn rural areas, strengthening maternal services, expanding ANC coverage, and improving dietary diversity must be prioritized. However, these efforts will remain limited without equitable distribution of healthcare facilities and skilled personnel, particularly in remote or geographically isolated regions [4]. In urban settings\u0026mdash;especially in major cities like Makassar\u0026mdash;early-onset obesity and poor dietary quality demand greater attention, as sedentary lifestyles and increased access to energy-dense, nutrient-poor foods begin to shape health outcomes [23]. Notably, smaller cities such as Magelang may serve as models of localized success, possibly due to closer proximity to provincial health hubs and more adaptable primary care systems. These findings highlight how national-level averages can obscure important sub-provincial disparities [24]. As Indonesia advances toward universal health coverage and expands initiatives such as the \u003cem\u003eMakan Bergizi Gratis program\u003c/em\u003e (MBG; Free Nutritious Meals), and the establishment of a \u003cem\u003eBadan Gizi Nasional\u003c/em\u003e (BGN; National Nutrition Agency), nutrition planning must be decentralized and responsive to district-level realities [25]. In addition to addressing structural determinants\u0026mdash;like healthcare infrastructure and workforce distribution\u0026mdash;greater attention must be paid to behavioral factors, including food preferences and lifestyle patterns [26].\u003c/p\u003e\u003cp\u003eThese findings reflect a complex interplay of behavioral, structural, and environmental determinants. In rural areas, undernutrition remains strongly linked to systemic disparities in health infrastructure, including fewer skilled personnel, longer travel distances to health facilities, and inconsistent service availability\u0026mdash;challenges well documented in the Indonesian context [15, 27]. Lower maternal education and food insecurity may further limit caregivers\u0026rsquo; ability to ensure adequate child nutrition [28]. In contrast, urban areas\u0026mdash;though better served by health infrastructure\u0026mdash;face a different set of challenges. Rapid urbanization has reshaped family diets and behaviors, with increased exposure to processed foods, limited time for home meal preparation, and easy access to sugar-sweetened beverages and fast-food outlets [29]. These patterns contribute to rising overweight and obesity even in children [30]. Furthermore, urban children often live in environments characterized by limited safe play spaces, screen-centered entertainment, and reduced physical activity\u0026mdash;all contributing to more sedentary lifestyles [31]. Urban parents may also adopt permissive feeding styles or be more influenced by commercial food marketing [32, 33]. In cities like Makassar, this nutritional transition may be compounded by social inequalities and household income disparities, which can influence both food quality and lifestyle behaviors.\u003c/p\u003e\u003cp\u003eIn smaller cities such as Magelang, favorable nutritional outcomes may arise from closer proximity to provincial healthcare centers, smaller catchment areas that ease outreach efforts, or more cohesive community health initiatives. These cities may also be less affected by the ultra-processed food environment dominating larger metropolitan regions. However, such success may not be generalizable without attention to governance quality and frontline health service performance [34, 35].\u003c/p\u003e\u003cp\u003eDespite recent policy efforts such as MBG and the establishment of the BGN, the government\u0026rsquo;s approach still tends to emphasize programmatic delivery\u0026mdash;such as food provision and service expansion\u0026mdash;without adequately addressing the behavioral determinants and systemic poverty that sustain malnutrition [25]. These top-down initiatives often treat undernutrition as a matter of food absence or service gaps, yet fail to engage with how household behaviors, cultural norms, or parental knowledge influence child-feeding practices and health service utilization. For example, even where food is available, choices may be shaped by deeply embedded habits, misinformation, or economic constraints that push families toward calorie-dense, low-nutrient foods [36, 37]. Moreover, the current model insufficiently tackles structural poverty, which remains a root driver of both undernutrition and poor maternal health coverage. In many rural and semi-urban areas, families face intergenerational disadvantages\u0026mdash;limited education, insecure employment, and fragile social safety nets\u0026mdash;that cannot be resolved through food assistance alone. Without stronger integration of social protection, nutrition education, and behavior change interventions, such programs risk being palliative rather than transformative [4, 38](4,35).\u003c/p\u003e\u003cp\u003eCritically, urban interventions often focus on supply-side measures\u0026mdash;monitoring school meals, regulating food sales, or expanding health posts\u0026mdash;while ignoring the social determinants of overweight and obesity, such as sedentary living, aggressive food marketing, and unequal access to recreational spaces [23, 39]. Children growing up in poor urban neighborhoods may face the paradox of food abundance but health scarcity, where cheap, unhealthy food is ubiquitous, yet safe water, green spaces, and time for physical activity are limited [40, 41]. In sum, while Indonesia\u0026rsquo;s recent nutrition programs represent important political commitments, their impact will remain limited unless they confront the behavioral, economic, and environmental complexity of malnutrition.\u003c/p\u003e\u003cp\u003eDespite offering valuable insights, this study is not without limitations. The use of aggregate data from the 2024 SSGI precluded individual-level adjustments, limiting our ability to assess the influence of household or parental factors on nutritional outcomes. Several indicators, particularly those related to dietary intake and service utilization, relied on caregiver recall, which may introduce recall bias or overreporting. While anthropometric measurements were drawn from direct observation, inconsistencies in field procedures and equipment calibration across districts may affect data quality. Additionally, count approximations from proportion data may carry minor rounding errors, though unlikely to alter directionality of findings. Finally, the district case studies were purposively selected and may not reflect broader intra-provincial dynamics.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe found persistent and complex disparities in child nutrition and maternal care across Indonesia, shaped not only by rural\u0026ndash;urban divides but also by local structural and systemic factors. At the national level, rural children consistently experience higher risks of undernutrition, limited dietary diversity, and inadequate maternal health service coverage. \"Sub-provincial case studies in Central Java and South Sulawesi demonstrate that nutritional outcomes vary not only between urban and rural areas but also within urban and rural districts themselves. These intra-urban disparities suggest that factors such as local health system performance, geographic proximity to referral centers, socioeconomic conditions, and district-level governance may play a more decisive role than urbanization alone. To reduce malnutrition and promote equity in child health, Indonesia must enhance the effectiveness of its decentralized health systems and ensure interventions are tailored to specific local needs. In rural settings, this includes improving access to antenatal care and dietary diversity, while also tackling the structural poverty that underlies these gaps. In urban and transitioning districts, greater attention is needed to address the rising risk of overweight among children, driven by lifestyle-related factors such as poor diet quality and physical inactivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.I. and F.N. conceptualized the study, curated the data, and supervised the overall project. M.I. and N.F.A. designed the methodology and performed the national-level statistical analysis. D.D.C.H.R. and I.Q. conducted the sub-provincial case comparisons and contributed to data visualization. R.R. and A.S.A. assisted in data interpretation and literature review. M.I. and D.D.C.H.R. drafted the initial manuscript. All authors contributed to critical revisions, reviewed the manuscript thoroughly, and approved the final version for submission.\u003c/p\u003e"},{"header":"First Reference List","content":"\u003col\u003e\n\u003cli\u003eLowe C, Kelly M, Sarma H, Richardson A, Kurscheid JM, Laksono B, Amaral S, Stewart D, Gray DJ. The double burden of malnutrition and dietary patterns in rural Central Java, Indonesia. Lancet Reg Health West Pac. 2021;14:100205. doi:10.1016/j.lanwpc.2021.100205.\u003c/li\u003e\n\u003cli\u003eMwene-Batu P, Bisimwa G, Ngaboyeka G, Dramaix M, Macq J, Hermans MP, Lemogoum D, Donnen P. Severe acute malnutrition in childhood, chronic diseases, and human capital in adulthood in the Democratic Republic of Congo: the Lwiro Cohort Study. Am J Clin Nutr. 2021 Jul 1;114(1):70-79. doi: 10.1093/ajcn/nqab034. PMID: 33826712; PMCID: PMC8246611.\u003c/li\u003e\n\u003cli\u003eStein AD, Obrutu OE, Behere RV, Yajnik CS. Developmental undernutrition, offspring obesity and type 2 diabetes. Diabetologia. 2019 Oct;62(10):1773\u0026ndash;1778. doi:10.1007/s00125-019-4930-1.\u003c/li\u003e\n\u003cli\u003eAyuningtyas D, Hapsari D, Rachmalina R, Amir V, Rachmawati R, Kusuma D. Geographic and Socioeconomic Disparity in Child Undernutrition across 514 Districts in Indonesia. Nutrients. 2022 Feb 17;14(4):843. doi: 10.3390/nu14040843. PMID: 35215492; PMCID: PMC8874971.\u003c/li\u003e\n\u003cli\u003eAlvarez-Galvez J, Ortega-Martin E, Carretero-Bravo J et al (2023) Social determinants of multimorbidity patterns: a systematic review. Front Public Health 11:1081518. 10.3389/fpubh.2023.1081518 \u003c/li\u003e\n\u003cli\u003eCacciatore S, Mao S, Nu\u0026ntilde;ez MV, Massaro C, Spadafora L, Bernardi M, Perone F, Sabouret P, Biondi-Zoccai G, Banach M, Calvani R, Tosato M, Marzetti E, Landi F. Urban health inequities and healthy longevity: traditional and emerging risk factors across the cities and policy implications. Aging Clin Exp Res. 2025 May 7;37(1):143. doi: 10.1007/s40520-025-03052-1. PMID: 40332678; PMCID: PMC12058932.\u003c/li\u003e\n\u003cli\u003eUnited Nations Children\u0026rsquo;s Fund (UNICEF). Conceptual Framework on Maternal and Child Nutrition. New York: UNICEF; 2020. Available from: https://www.unicef.org/media/113291/file/UNICEFConceptualFramework.pdf\u003c/li\u003e\n\u003cli\u003eChelak K, Chakole S. The Role of Social Determinants of Health in Promoting Health Equality: A Narrative Review. Cureus. 2023 Jan 5;15(1):e33425. doi: 10.7759/cureus.33425. PMID: 36751221; PMCID: PMC9899154.\u003c/li\u003e\n\u003cli\u003eSiramaneerat, I., Astutik, E., Agushybana, F. et al. Examining determinants of stunting in Urban and Rural Indonesian: a multilevel analysis using the population-based Indonesian family life survey (IFLS). BMC Public Health 2021. 24, 1371 https://doi.org/10.1186/s12889-024-18824-z\u003c/li\u003e\n\u003cli\u003eMarkovic, M., Vasic, G., Bjelica, B. 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Cureus, 15(1), e33425. https://doi.org/10.7759/cureus.33425\u003c/li\u003e\n\u003cli\u003eYetti, EY., Safar, M., Zulkifli, A., Indriasari, R., Tombeg, Z. The Association between Eat Culture and Obesity among Adolescents in Tana Toraja. Indian Journal of Public Health Research \u0026amp; Development. (2018). 9(11): 542-547. DOI:10.5958/0976-5506.2018.01506.1\u003c/li\u003e\n\u003cli\u003eNurwanti, E., Hadi, H., Chang, J.-S., Chao, J. C.-J., Paramashanti, B. A., Gittelsohn, J., \u0026amp; Bai, C.-H. (2019). Rural\u0026ndash;Urban Differences in Dietary Behavior and Obesity: Results of the Riskesdas Study in 10\u0026ndash;18-Year-Old Indonesian Children and Adolescents. Nutrients, 11(11), 2813. https://doi.org/10.3390/nu11112813\u003c/li\u003e\n\u003cli\u003eYasinta, F., \u0026amp; Hidayah, U. (2024). Analysis of Effectiveness of Health Facilities Services in Magelang Regency, Indonesia. Planning Malaysia, 22(33). https://doi.org/10.21837/pm.v22i33.1552\u003c/li\u003e\n\u003cli\u003eManggi Habir \u0026amp; Siwage Dharma Negara. (2025) Prabowo\u0026rsquo;s First 100 Days and beyond as President: A Security-Focused Economic Agenda. Bulletin of Indonesian Economic Studies 61:1, pages 3-37.\u003c/li\u003e\n\u003cli\u003eArdianti, R. Dhenok , Salimo, H. and Cilmiaty, R. (2021). The Effect of Dietary Diversity on Nutritional Status in Indonesian Children: A Review. International Journal of Nutrition Sciences, 6(3), 119-125. doi: 10.30476/ijns.2021.90861.1130\u003c/li\u003e\n\u003cli\u003eAtamou, L., Rahmadiyah, D. C., Hassan, H., \u0026amp; Setiawan, A. (2023). Analysis of the Determinants of Stunting among Children Aged below Five Years in Stunting Locus Villages in Indonesia. Healthcare, 11(6), 810. https://doi.org/10.3390/healthcare11060810\u003c/li\u003e\n\u003cli\u003ePrasetyo, Y.B., Permatasari, P. \u0026amp; Susanti, H.D. 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A qualitative exploration of ultra-processed foods consumption and eating out behaviours in an Indonesian urban food environment. Nutrition and health, 30(3), 613\u0026ndash;623. https://doi.org/10.1177/02601060221133897\u003c/li\u003e\n\u003cli\u003eColozza, D., Wang, Y.-C., \u0026amp; Avendano, M. (2023). Does urbanisation lead to unhealthy diets? Longitudinal evidence from Indonesia. Health \u0026amp; Place, 83, 103091.\u003c/li\u003e\n\u003cli\u003eISEAS. (2024, November 20). Indonesia\u0026rsquo;s Free Nutritious Meal (Makan Bergizi Gratis) Programme Offers Policy Lessons for Other Middle-Income Countries (ISEAS Perspective No. 2024/96). ISEAS \u0026ndash; Yusof Ishak Institute. https://www.iseas.edu.sg/wp-content/uploads/2024/10/ISEAS_Perspective_2024_96.pdf\u003c/li\u003e\n\u003cli\u003eCahyati Teguh Pancani, P., \u0026amp; Ningsih, N. (2025). 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Atlantis Press. https://doi.org/10.2991/978-94-6463-494-5_12\u003c/li\u003e\n\u003cli\u003eBelbase, K., Morgan, R., Mittal, S., Masri, R., \u0026amp; Zahr, S. (2024, January). Making social protection work for improved nutrition: A scoping review of state \u0026amp; opportunities in the Asia region [Technical report]. Nutrition International. https://www.nutritionintl.org/learning-resource/making-social-protection-work-for-improved-nutrition-as\u003c/li\u003e\n\u003cli\u003eKhoe, L.C., Widyahening, I.S., Ali, S. et al. Assessment of the obesogenic environment in primary schools: a multi-site case study in Jakarta. BMC Nutr 8, 19 (2022). https://doi.org/10.1186/s40795-022-00513-y\u003c/li\u003e\n\u003cli\u003eVilar-Compte, M., Burrola-M\u0026eacute;ndez, S., Lozano-Marrufo, A. et al. Urban poverty and nutrition challenges associated with accessibility to a healthy diet: a global systematic literature review. Int J Equity Health 20, 40 (2021). https://doi.org/10.1186/s12939-020-01330-0\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization of the United Nations (FAO). (2023). Urban food systems and nutrition: Integrating actions across sectors and stakeholders. FAO. https://openknowledge.fao.org/server/.api/core/bitstreams/1f66b67b-1e45-45d1-b003-86162fd35dab/content\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Second Reference List","content":"\u003col\u003e\n \u003cli\u003eLowe C, Kelly M, Sarma H, Richardson A, Kurscheid JM, Laksono B, et al. The double burden of malnutrition and dietary patterns in rural Central Java, Indonesia. The Lancet Regional Health\u0026ndash;Western Pacific. 2021;14.\u003c/li\u003e\n \u003cli\u003eMwene-Batu P, Bisimwa G, Ngaboyeka G, Dramaix M, Macq J, Hermans MP, et al. Severe acute malnutrition in childhood, chronic diseases, and human capital in adulthood in the Democratic Republic of Congo: the Lwiro Cohort Study. The American journal of clinical nutrition. 2021;114(1):70-9.\u003c/li\u003e\n \u003cli\u003eStein AD, Obrutu OE, Behere RV, Yajnik CS. Developmental undernutrition, offspring obesity and type 2 diabetes. Diabetologia. 2019;62(10):1773-8.\u003c/li\u003e\n \u003cli\u003eAyuningtyas D, Hapsari D, Rachmalina R, Amir V, Rachmawati R, Kusuma D. Geographic and socioeconomic disparity in child undernutrition across 514 districts in Indonesia. Nutrients. 2022;14(4):843.\u003c/li\u003e\n \u003cli\u003eAlvarez-Galvez J, Ortega-Martin E, Carretero-Bravo J, Perez-Munoz C, Suarez-Lledo V, Ramos-Fiol B. Social determinants of multimorbidity patterns: A systematic review. Frontiers in Public Health. 2023;11:1081518.\u003c/li\u003e\n \u003cli\u003eCacciatore S, Mao S, Nu\u0026ntilde;ez MV, Massaro C, Spadafora L, Bernardi M, et al. Urban health inequities and healthy longevity: traditional and emerging risk factors across the cities and policy implications. Aging Clinical and Experimental Research. 2025;37(1):143.\u003c/li\u003e\n \u003cli\u003ePopkin BM, Corvalan C, Grummer-Strawn LM. Dynamics of the double burden of malnutrition and the changing nutrition reality. The Lancet. 2020;395(10217):65-74.\u003c/li\u003e\n \u003cli\u003eKenney E, Rampalli KK, Samin S, Frongillo EA, Reyes LI, Bhandari S, et al. How livelihood change affects food choice behaviors in low-and middle-income countries: A scoping review. Advances in Nutrition. 2024;15(5):100203.\u003c/li\u003e\n \u003cli\u003eUnited Nations Children\u0026rsquo;s Fund. Conceptual Framework on Maternal and Child Nutrition 2020 [Available from: https://www.unicef.org/media/113291/file/UNICEFConceptualFramework.pdf.\u003c/li\u003e\n \u003cli\u003eChelak K, Chakole S. The role of social determinants of health in promoting health equality: a narrative review. Cureus. 2023;15(1).\u003c/li\u003e\n \u003cli\u003eKatoch OR. Determinants of malnutrition among children: A systematic review. Nutrition. 2022;96:111565.\u003c/li\u003e\n \u003cli\u003ePravitasari AE, Indraprahasta GS, Rustiadi E, Rosandi VB, Stanny YA, Wulandari S, et al. Dynamics and Predictions of Urban Expansion in Java, Indonesia: Continuity and Change in Mega-Urbanization. ISPRS International Journal of Geo-Information. 2024;13(3).\u003c/li\u003e\n \u003cli\u003eWulandari RD, Laksono AD, Rohmah N, Ashar H. Regional differences in primary healthcare utilization in Java Region-Indonesia. PLoS One. 2023;18(3):e0283709.\u003c/li\u003e\n \u003cli\u003ePalutturi S, Wahyu A, Indar MS, Moedjino AI, Birawida AB, Hidayanti H, et al. Expert Needs of Healthy Public Health Centre Development in the Archipelago Area of South Sulawesi. significance. 2022;14:15.\u003c/li\u003e\n \u003cli\u003eSiramaneerat I, Astutik E, Agushybana F, Bhumkittipich P, Lamprom W. Examining determinants of stunting in Urban and Rural Indonesian: a multilevel analysis using the population-based Indonesian family life survey (IFLS). BMC Public Health. 2024;24(1):1371.\u003c/li\u003e\n \u003cli\u003eMarković M, Vasić G, Bjelica B, Zelenović M. Differences in the nutritional status of urban and rural children. Differences. 2022;6(11):5-9.\u003c/li\u003e\n \u003cli\u003eCacciatore S, Mao S, Nunez MV, Massaro C, Spadafora L, Bernardi M, et al. Urban health inequities and healthy longevity: traditional and emerging risk factors across the cities and policy implications. Aging Clin Exp Res. 2025;37(1):143.\u003c/li\u003e\n \u003cli\u003eGustavia Yolanda S, Ismarwati I. The Influence of Feeding Practice on the Risk of Stunting in Toddler: A Scoping Review. Jurnal Ilmu Kesehatan Masyarakat. 2024;15(2):149-66.\u003c/li\u003e\n \u003cli\u003eIdris H, Sari I. Factors associated with the completion of antenatal care in Indonesia: A cross-sectional data analysis based on the 2018 Indonesian Basic Health Survey. Belitung Nurs J. 2023;9(1):79-85.\u003c/li\u003e\n \u003cli\u003eChoudhury S, Bi AZ, Medina-Lara A, Morrish N, Veettil PC. The rural food environment and its association with diet, nutrition status, and health outcomes in low-income and middle-income countries (LMICs): a systematic review. BMC Public Health. 2025;25(1):994.\u003c/li\u003e\n \u003cli\u003eYetti RE, Safar M, Zulkifli A, Indriasari R, Tombeg Z, Manggabarani S, et al. The association between eat culture and obesity among adolescents in tana toraja. Indian Journal of Public Health Research \u0026amp; Development. 2018;9(11).\u003c/li\u003e\n \u003cli\u003eNurwanti E, Hadi H, Chang JS, Chao JC, Paramashanti BA, Gittelsohn J, et al. Rural-Urban Differences in Dietary Behavior and Obesity: Results of the Riskesdas Study in 10-18-Year-Old Indonesian Children and Adolescents. Nutrients. 2019;11(11).\u003c/li\u003e\n \u003cli\u003eYasinta F, Hidayah U. Analysis of Effectiveness of Health Facilities Services in Magelang Regency, Indonesia. Planning Malaysia. 2024;22.\u003c/li\u003e\n \u003cli\u003eHabir M, Negara SD. Prabowo\u0026rsquo;s First 100 Days and beyond as President: A Security-Focused Economic Agenda. Bulletin of Indonesian Economic Studies. 2025;61(1):3-37.\u003c/li\u003e\n \u003cli\u003eArdianti RD, Salimo H, Cilmiaty R. The Effect of Dietary Diversity on Nutritional Status in Indonesian Children: A Review. International Journal of Nutrition Sciences. 2021;6(3):119-25.\u003c/li\u003e\n \u003cli\u003eAtamou L, Rahmadiyah DC, Hassan H, Setiawan A. Analysis of the Determinants of Stunting among Children Aged below Five Years in Stunting Locus Villages in Indonesia. Healthcare (Basel). 2023;11(6).\u003c/li\u003e\n \u003cli\u003ePrasetyo YB, Permatasari P, Susanti HD. The effect of mothers\u0026rsquo; nutritional education and knowledge on children\u0026rsquo;s nutritional status: a systematic review. International Journal of Child Care and Education Policy. 2023;17(1).\u003c/li\u003e\n \u003cli\u003eKumar GS, Kulkarni M, Rathi N. Evolving Food Choices Among the Urban Indian Middle-Class: A Qualitative Study. Front Nutr. 2022;9:844413.\u003c/li\u003e\n \u003cli\u003eKerr JA, Patton GC, Cini KI, Abate YH, Abbas N, Abd Al Magied AH, et al. Global, regional, and national prevalence of child and adolescent overweight and obesity, 1990\u0026ndash;2021, with forecasts to 2050: a forecasting study for the Global Burden of Disease Study 2021. The Lancet. 2025;405(10481):785-812.\u003c/li\u003e\n \u003cli\u003eFadhli NR, Yudasmara DS, Ludyana E, I\u0026rsquo;tamada EZ, editors. Sedentary Screen Time and Gross Motor Skills of Indonesian Preschoolers in Urban Areas. 5th International Conference on Sport Science and Health (ICSSH 2021); 2022: Atlantis Press.\u003c/li\u003e\n \u003cli\u003eAgung FH, Sekartini R, Sudarsono NC, Hendarto A, Dhamayanti M, Werdhani RA, et al. The barriers of home environments for obesity prevention in Indonesian adolescents. BMC Public Health. 2022;22(1):2348.\u003c/li\u003e\n \u003cli\u003eSelma A, Helda K. Exposure and approval of food marketing strategies: a mixed methods study among household food providers in Jakarta. Malaysian Journal of Nutrition. 2019:47-62.\u003c/li\u003e\n \u003cli\u003eColozza D. A qualitative exploration of ultra-processed foods consumption and eating out behaviours in an Indonesian urban food environment. Nutr Health. 2024;30(3):613-23.\u003c/li\u003e\n \u003cli\u003eColozza D, Wang Y-C, Avendano M. Does urbanisation lead to unhealthy diets? Longitudinal evidence from Indonesia. Health \u0026amp; Place. 2023;83:103091.\u003c/li\u003e\n \u003cli\u003eItsar Bolo R, Nur H, Fattah H, Nur E, editors. Assessing of Parental Feeding Practice for Childhood in Indonesia: A Rasch Insight. Proceedings of the Pacific-Rim Objective Measurement Symposium (PROMS 2023); 2024 2024/08/22: Atlantis Press.\u003c/li\u003e\n \u003cli\u003eWanda D, Astuti A, Utami AR, Lita BFF. Community lifestyle influences feeding practices among Indonesian infants and young children. Enferm Clin (Engl Ed). 2022;32 Suppl 1:S46-S53.\u003c/li\u003e\n \u003cli\u003eTeklewold H, Gebrehiwot T, Bezabih M. Social protection and vulnerability to nutrition security: empirical evidence from Ethiopia. Food Security. 2022;14(5):1191-205.\u003c/li\u003e\n \u003cli\u003eKhoe LC, Widyahening IS, Ali S, Khusun H. Assessment of the obesogenic environment in primary schools: a multi-site case study in Jakarta. BMC Nutr. 2022;8(1):19.\u003c/li\u003e\n \u003cli\u003eVilar-Compte M, Burrola-Mendez S, Lozano-Marrufo A, Ferre-Eguiluz I, Flores D, Gaitan-Rossi P, et al. Urban poverty and nutrition challenges associated with accessibility to a healthy diet: a global systematic literature review. Int J Equity Health. 2021;20(1):40.\u003c/li\u003e\n \u003cli\u003eElshater A, Abusaada H. Poverty-free urbanism: six qualitative normative factors and 36 procedures for measuring urban poverty from a deprivation perspective. Open House International. 2025;50(2):392-414.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Child malnutrition, dietary diversity, antenatal care, Indonesia, health inequity","lastPublishedDoi":"10.21203/rs.3.rs-7177814/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7177814/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eIndonesia faces a double burden of malnutrition, with urban children generally less affected by undernutrition but increasingly prone to being overweight. However, national trends may mask sub-provincial disparities driven by uneven access to health services, food quality, and socioeconomic conditions\u0026mdash;patterns that remain underexplored.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eTo assess disparities in child nutritional and maternal care indicators between urban and rural areas at the national level and to conduct sub-provincial analyses in selected districts to uncover patterns masked by aggregated national data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed an ecological analysis using data from the 2024 Indonesian Nutritional Status Survey. First, we assessed national-level disparities in child nutritional and maternal care indicators between urban and rural areas using odds ratios (OR) and chi-square tests. To capture localized patterns hidden by national aggregates, we then conducted sub-provincial case studies in selected districts of Central Java and South Sulawesi, comparing outcomes across different urban and rural settings.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eNationally, urban children had lower odds of undernutrition\u0026mdash;including severely underweight (OR 0.78; 95% CI: 0.75\u0026ndash;0.81), underweight (OR 0.82; 95% CI: 0.80\u0026ndash;0.84), and stunting (OR 0.77; 95% CI: 0.75\u0026ndash;0.78)\u0026mdash;but higher odds of being at risk of overweight (OR 1.35; 95% CI: 1.31\u0026ndash;1.40) and consuming unhealthy foods (OR 1.22; 95% CI: 1.19\u0026ndash;1.25). Rural areas consistently showed worse access to dietary diversity and antenatal care. In Central Java, Kota Magelang showed lower risk of severe underweight compared to Kota Surakarta (OR 0.25; 95% CI: 0.09\u0026ndash;0.70; p\u0026thinsp;=\u0026thinsp;0.008) and Kota Tegal (OR 0.15; 95% CI: 0.06\u0026ndash;0.39; p\u0026thinsp;=\u0026thinsp;0.001). In South Sulawesi, Kota Makassar had lower odds of severe underweight than Kota Pare-pare (OR 0.42; 95% CI: 0.23\u0026ndash;0.77; p\u0026thinsp;=\u0026thinsp;0.005), but higher underweight risk than Tana Toraja (OR 1.79; 95% CI: 1.30\u0026ndash;2.48; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWhile urban areas generally have lower undernutrition, this study reveals that substantial disparities also exist between and within urban and rural districts. Kota Magelang, a small urban city, shows more favorable outcomes\u0026mdash;possibly due to proximity to referral centers\u0026mdash;while cities like Palopo and surrounding rural areas remain vulnerable.\u003c/p\u003e","manuscriptTitle":"Does urban living contribute to better nutrition? An ecological study on urban–rural disparities in Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 06:27:45","doi":"10.21203/rs.3.rs-7177814/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8984cb7d-8c11-4522-bde5-8c10ff2a5838","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-17T13:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 06:27:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7177814","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7177814","identity":"rs-7177814","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}