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22,944 reports · page 2228 of 2295

Peer-reviewed articleOpenAlex5 Oct 2026

PENDAMPINGAN PENGISIAN KMS UNTUK MEMPERKUAT DETEKSI DINI GANGGUAN PERTUMBUHAN BALITA

Abstrak: Pemanfaatan Kartu Menuju Sehat (KMS) yang belum optimal dapat menghambat pemantauan pertumbuhan balita dan penyampaian informasi pertumbuhan kepada keluarga. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan kapasitas kader dalam pengisian dan pemanfaatan KMS sebagai instrumen pendukung deteksi dini gangguan pertumbuhan serta memperkuat pemahaman ibu balita terhadap hasil pemantauan pertumbuhan. Kegiatan melibatkan 24 kader melalui pelatihan, edukasi, praktik, dan pendampingan langsung pada pelayanan posyandu. Evaluasi pengetahuan dilakukan menggunakan pre-test dan post-test yang masing-masing terdiri dari 15 pertanyaan, sedangkan keterampilan diamati selama praktik pengisian dan interpretasi KMS. Rata-rata skor pengetahuan meningkat dari 60,5% pada pengukuran awal menjadi 82% setelah kegiatan, dengan peningkatan sebesar 22,5%. Observasi selama pendampingan menunjukkan antusiasme kader dan meningkatnya pemahaman mengenai fungsi KMS dalam pencatatan serta pemantauan pertumbuhan anak. Pendekatan pendampingan berbasis praktik memberikan pengalaman langsung bagi kader dalam menerapkan KMS pada pelayanan posyandu.Abstract: Suboptimal utilization of the Growth Monitoring Card (KMS) may hinder the routine monitoring of child growth and the delivery of growth-related information to families. This community service activity aimed to strengthen cadres’ capacity to complete and utilize the KMS as a supporting instrument for early detection of growth disorders and to improve mothers’ understanding of child growth monitoring. The program involved 24 community health cadres through training, health education, practical sessions, and direct mentoring during integrated health post (posyandu) services. Knowledge was assessed using pre-test and post-test measures, each consisting of 15 questions, while practical skills were observed during KMS completion and growth-chart interpretation. The mean knowledge score increased from 60.5% at baseline to 82% after the intervention, representing an increase of 22.5%. Observations during the mentoring process indicated high cadre engagement and improved understanding of the role of the KMS in documenting and monitoring child growth. Practice-based mentoring provided cadres with direct experience in applying KMS procedures during posyandu services.

Peer-reviewed articleOpenAlex5 Oct 2026

Sistem Monitoring Deteksi Risiko Stunting bagi Balita dengan IoT pada Posyandu Bougenvelle

ABSTRAK Stunting merupakan kondisi gagal tumbuh dalam balita akibat kekurangan gizi kronis, yang dapat memengaruhi perkembangan fisik dan kognitif anak. Di Posyandu Bougenvelle, pelaksanaan program “Zero New Stunting” menghadapi berbagai kendala, seperti keterlambatan pencatatan data dan kurangnya standar pemberian asupan gizi. Penelitian ini merancang sistem berbasis Internet of Things (IoT) untuk memonitoring dan pemberian rekomendasi asupan gizi balita yang berisiko stunting. Sistem terdiri dari perangkat keras berupa sensor tinggi badan (VL53L1X) dan berat badan (Load Cell serta modul HX711) yang terintegrasi dengan mikrokontroler ESP8266, serta sistem berbasis web yang menyimpan dan menimpilkan riwayat data pertumbuhan. Sistem ini juga menghitung Z index Score untuk klasifikasi status gizi dan mengirim rekomendasi asupan protein melalui WhatsApp. Metode pengembangan yang digunakan adalah prototype, yang memungkinkan penyesuaian sistem secara iteratif berdasarkan kebutuhan pengguna. Hasil implementasi menunjukan bahwa sistem dapat membantu meningkatkan akurasi data dan mempermudah proses evaluasi serta pemantauan pertumuhan balita oleh tenaga Kesehatan dan Orang tua. ABSTRACT Stunting is a condition of growth failure in toddlers caused by chronic malnutrition, which affects children's physical and cognitive development. In Posyandu Bougenvelle, the “Zero New Stunting” program faces challenges such as delayed data recording and lack of standardized nutritional intake guidelines. This research designs an Internet of Things (IoT)-based system for monitoring and recommending nutritional intake for toddlers at risk of stunting. The system consists of hardware using height (VL53L1X) and weight (Load Cell and Modul HX711) sensors integrated with an ESP8266 microcontroller, as well as a web-based platform to store and display growth history data. The system also calculates Z-scores to classify nutritional status and sends daily protein intake recommendations via WhatsApp. The prototype development method was used to allow iterative adjustments based on user feedback. Implementation results indicate the system improves data accuracy and facilitates growth monitoring and evaluation by healthcare workers and parents.

David Pakpahan, Nadya Safitri

SurvivalIDRead at source

Peer-reviewed articleOpenAlex5 Oct 2026

Development and implementation of “Mazboot Bunyad”: a digital health intervention for the screening and management of undernutrition among children aged 6–59 months, at private provider clinics in urban slums of Karachi, Pakistan

Abstract Background Undernutrition remains a leading driver of child mortality and morbidity in low- and middle-income countries, yet surveillance and case management in Pakistan still rely largely on infrequent surveys and paper-based registers that cannot track nutritional status in real time. The private sector, where most Pakistani households seek care, has been largely excluded from nutrition programming. We describe the development and early implementation of Mazboot Bunyad, a digital nutrition registry embedded within an integrated nutrition–immunization programme at private provider clinics in high-risk, low-income settlements of Karachi. Methods The Android application and web dashboard used in this implementation study were designed through a participatory process using the AHEAD framework, in consultation with provincial health authorities, immunization staff, and community nutrition worker. The system used QR-linked child records, an embedded digital decision-support system that classified children by mid-upper arm circumference and has automated follow-up reminders. The tool was deployed across 33 clinics, and routinely collected programme data, a preliminary analysis of data from 1 July to 31 December 2024 were retrospectively reviewed. Results During the period of 6 months, the Mazboot Bunyad application was used to screen and enroll 8,406 children aged 6–59 months. Field teams used the embedded Digital Decision Support System (DDSS) to classify children aged 6–59 months in real-time. Of these, 71.4% were classified as normal, 22.3% as moderately acutely malnourished (MAM), and 6.2% as severely acutely malnourished (SAM). The largest share of enrollments was among children aged 13–24 months (40.5%). Male children represented a slightly larger share of total enrollments (52.2%), but SAM burden was higher among females (7.3%) compared to males (5.3%). Supervisory teams used the dashboard to identify defaulters, monitor stock availability, and follow case recovery timelines. Conclusions Early implementation of Mazboot Bunyad highlighted opportunities for enhanced nutritional surveillance using real-time tracking, but also challenges such as supply chain gaps that could affect the overall implementation. These offer valuable lessons for scaling digital tools in real world settings across Pakistan and other LMICs.

Shifa Salman Habib, Ammarah Ali, Muhammad Fazal Hussain Qureshi et al.

SurvivalPKRead at source

Peer-reviewed articleOpenAlex5 Oct 2026

Geography of child stunting and wasting in Bangladesh: a cross-sectional spatial analysis of the 2025 Multiple Indicator Cluster Survey

Background. Stunting and wasting drive child mortality and are central to Sustainable Development Goal target 2.2. In 2025, 24.0% of Bangladeshi children under five were stunted and 12.9% wasted, meeting the national stunting target but missing the 8% wasting target. We compared where the two occur and tested whether environmental factors explain the patterns. Methods. We linked the 2025 Bangladesh Multiple Indicator Cluster Survey to cluster GPS locations and environmental data. Moran's I tested clustering and Getis-Ord Gi* found hotspots across 64 districts and 3,144 clusters. We tested whether six environmental and access factors explained the hotspots, estimated what share of affected children each hotspot list reached, and used a joint model to compare factor associations between outcomes. Results. Stunting clustered by region (district Moran's I 0.21, P=0.002), with a north-eastern hotspot centred on Sylhet and Sunamganj. Six environmental factors together removed this clustering (I −0.01, P=0.45) and about 60% of between-district variation (95% CI 28 to 84), more than rainfall alone, with the gain over generic regional terms uncertain. Wasting showed no clear regional clustering (I 0.05, P=0.17), and district stunting and wasting rates were unrelated (r −0.01). Stunting hotspot districts held 5.9% of wasted children, the same as their 5.9% population share. Children with both conditions (902 cases) did not cluster. Altitude and rainfall related differently to stunting than to wasting (all P<0.004), but altitude associations were sensitive to how altitude was modelled. Conclusion. Stunting in Bangladesh is concentrated in the north-east, and six environmental and access factors together account for much of this, though no single factor, including rainfall, does. Wasting shows no clear regional pattern. A stunting hotspot list reached no more wasted children than population share, so surveillance may need both measures, with finer-scale data for wasting.

S.M. Ashraf Ul Hoque, Khairul Islam, Mohammad Khalid Hasan et al.

SurvivalBDRead at source

Peer-reviewed articleOpenAlex5 Oct 2026

Beyond therapeutic feeding: a call for integrated strategies to end child malnutrition in Somalia

Somalia records one of the highest global burdens of child wasting and stunting, with approximately 1.7–1.85 million children under five estimated to be affected by acute malnutrition annually during 2022–2025, including approximately 421,000–513,550 children with severe acute malnutrition. For over two decades, the dominant policy response has been community-based management of acute malnutrition (CMAM), a curative intervention that treats clinical endpoints rather than underlying drivers. Despite meeting international cure-rate standards, CMAM alone cannot address the food insecurity, water, sanitation and hygiene (WASH), microbial and parasitic infectious diseases, health-system, and climatic drivers that perpetuate malnutrition. This paper integrates recent Somali survey, programme-evaluation, and administrative data to show that the national burden of acute malnutrition has remained high despite continued treatment scale-up, with stunting falling only 9.7 percentage points over 24 years, relapse after discharge remaining a documented challenge, and nationally representative Somalia-specific estimates of CMAM treatment coverage remaining unavailable. Diagnostic discordance between screening tools leaves an unknown share of wasted children undetected, and persistent sex- and region-based disparities highlight structural gaps. Ending child malnutrition in Somalia requires rebalancing investment from an emergency, treatment-centred paradigm toward integrated, multisectoral strategies combining therapeutic feeding with WASH investment, maternal and infant feeding support, primary health-care strengthening, climate-resilient livelihoods, and shock-responsive social protection. We propose four policy shifts: sustaining CMAM while addressing diagnostic gaps, mandating nutrition-sensitive financing, institutionalizing relapse prevention, and expanding anticipatory financing. These shifts require governance reforms and multi-year donor commitment to address the determinants of child nutrition in Somalia comprehensively.

Abdifatah Mohamed Tahlil, Mohamed Jama Hashi, Hafsa Mohamed Hassan et al.

SurvivalSORead at source

Peer-reviewed articleOpenAlex5 Oct 2026

A copula-based geo-additive approach to childhood health outcomes in five West African nations

Background Anemia and malnutrition are major contributors to child morbidity and mortality in Sub-Saharan Africa, particularly in West Africa where their prevalence remains high due to multifaceted socio-economic and environmental factors. Malnutrition, as defined by WHO, includes both undernutrition and overnutrition and is linked to increased risk of non-communicable diseases. Anemia, characterized by low hemoglobin levels, impairs oxygen transport and is associated with developmental delays and increased mortality in children. Given their interrelated nature, a joint modeling approach is essential to understand shared risk factors and inform targeted interventions. Methods This study analyses pooled DHS data from Gabon, Gambia, Liberia, Mauritania, and Nigeria to identify determinants of anemia and malnutrition. Malnutrition is measured using the Composite Index of Anthropometric Failure (CIAF), which integrates stunting, underweight, and wasting for a comprehensive assessment. A copula-based geo-additive joint model is employed to simultaneously examine both conditions, accounting for spatial variation and interdependence. This approach offers a novel framework for informing policy and improving child health outcomes in West Africa. Results Key predictors of malnutrition and anemia were identified across five West African countries. Malnutrition risk was significantly associated with maternal education, water source, household wealth, child age, gender, and feeding practices. Notably, children from households using non-bottled water sources and those given milk were at higher risk. Several water-source categories were positively associated with malnutrition relative to bottled/sachet water; however, these associations may reflect socioeconomic and residential differences in the reference group and warrant cautious, non-causal interpretation. Anemia risk was significantly lower among urban children and those whose mothers had higher education. Fortified food and egg consumption were protective against anemia, while poverty and poor water sources increased risk. These findings highlight distinct and overlapping determinants of both conditions. Conclusion This study highlights the complex interplay of socioeconomic, environmental, and dietary factors influencing malnutrition and anemia among children in West Africa. Key predictors include maternal education, household wealth, water source, and feeding practices, with notable geographic variation. Addressing these challenges requires integrated, multisectoral strategies focused on improving maternal education, access to safe water, dietary diversity, and poverty reduction. Geographically targeted interventions and strengthened health systems are essential.

Reshav Beni, Shaun Ramroop, Faustin Habyarimana

SurvivalNGLRGMMRRead at source

Peer-reviewed articleOpenAlex6 Oct 2026

Macro-micro dynamics of child undernutrition in Pakistan: a structural learning-based Bayesian network analysis of household and policy drivers

Background Child undernutrition remains a major public health concern and creates health-related inequalities in developing countries, with long-term consequences for human development deficits. Most of these early studies were conducted using conventional econometric techniques; however, understanding the complex, multi-level determinants of undernutrition may require more advanced analytical methods. Methods This study, PC algorithm is used for Structural Learning-Based Bayesian Networks [SL-BN] (a graphical model based on directed acyclic graph with joint and conditional associations)—which is conceptually different than the weight-based networks, to examine the macro- and micro-level determinants of child undernutrition in Pakistan. Macro-level analysis combines historical nutrition policy evaluation (critical discourse) with WDI and UNDP data (2000–2022), spanning the MDG and SDG eras. Micro-level analysis draws on the Pakistan Demographic and Health Survey (PDHS 2017–18) to identify socioeconomic drivers of child malnutrition and nutritional inequalities. Results In micro analysis, the SL-BN network structure identifies household-level determinants—such as poor wealth status, rural residence, poor environmental conditions, low maternal education, and diarrhea—as key drivers of child undernutrition. Inequality is stark, with malnutrition disproportionately concentrated among the poorest. However, the SL-BN findings and critical assessment of nutritional policies and programs found that low investment in human development, foreign dependency on health, poverty, inflation, and a history of inconsistent nutritional policies to combat hunger and undernutrition are the fundamental issues at the macro level in Pakistan. Conclusion The study concluded that the underlying micro level determinants of child undernutrition in Pakistan cannot overcome without long-term targeted policies at macro level.

Muhammad Shahid, Jiayi Song, Zaiba Ali et al.

SurvivalPKRead at source

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PENDAMPINGAN PENGISIAN KMS UNTUK MEMPERKUAT DETEKSI DINI GANGGUAN PERTUMBUHAN BALITA

Sistem Monitoring Deteksi Risiko Stunting bagi Balita dengan IoT pada Posyandu Bougenvelle

Development and implementation of “Mazboot Bunyad”: a digital health intervention for the screening and management of undernutrition among children aged 6–59 months, at private provider clinics in urban slums of Karachi, Pakistan

Geography of child stunting and wasting in Bangladesh: a cross-sectional spatial analysis of the 2025 Multiple Indicator Cluster Survey

Advancing paediatric nutrition: interdisciplinary perspectives from the clinic to the community – symposium proceedings

Beyond therapeutic feeding: a call for integrated strategies to end child malnutrition in Somalia

A copula-based geo-additive approach to childhood health outcomes in five West African nations

Reconstructing Lived Experiences

Unequal from the start? Children’s life histories and their employment outcomes in India

Macro-micro dynamics of child undernutrition in Pakistan: a structural learning-based Bayesian network analysis of household and policy drivers

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