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Background: Afghan refugees in Pakistan, particularly in Quetta, Balochistan, encounter formidable barriers in accessing maternal, newborn, and child health (MNCH) services. These challenges have been intensified by the COVID-19 pandemic and entrenched systemic health inequities. Methods: This qualitative study, conducted from February to April 2023, aimed to assess the obstacles within health systems and community environments that hinder MNCH service access among Afghan refugees. The study involved 20 key informants through in-depth interviews and focus group discussions, including Afghan refugee women, community elders, health workers, and representatives from non-governmental organizations and government agencies. The research focused on experiences during the initial four waves of the COVID-19 pandemic (2020-2021), utilizing a conceptual framework integrating Health Emergency Disaster Risk Management (Health-EDRM) with primary health care. Findings: The study identified significant systemic barriers to accessing MNCH services, such as insufficient funding, inadequate health infrastructure, and discriminatory practices within the healthcare workforce. Additionally, community-level obstacles were prominent, including cultural and language differences, geographical isolation, and economic constraints. The integration of Health-EDRM into local health systems was minimal, with many stakeholders either needing to be made aware of or unengaged with the framework. Conclusion: The findings highlight a critical need for comprehensive policy reforms, infrastructure enhancement, and community-centered approaches to address Afghan refugees' health needs effectively. Strengthening the integration of health-EDRM into health systems is crucial for enhancing resilience and ensuring continuous care during health emergencies. The study calls for concerted efforts to implement culturally sensitive health interventions that include disaster risk management components to improve MNCH outcomes among Afghan refugees in crisis-affected settings. Addressing systemic and community-level barriers makes creating a more resilient and equitable health system for vulnerable populations possible. [ABSTRACT FROM AUTHOR]

CONTEXT: Clinical sign algorithms are a key strategy to identify young infants at risk of mortality. OBJECTIVE: Synthesize the evidence on the accuracy of clinical sign algorithms to predict all-cause mortality in young infants 0--59 days. DATA SOURCES: MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION: Studies evaluating the accuracy of infant clinical sign algorithms to predict mortality. DATA EXTRACTION: We used Cochrane methods for study screening, data extraction, and risk of bias assessment. We determined certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS: We included 11 studies examining 26 algorithms. Three studies from non-hospital/ communitysettingsexaminedsign-basedchecklists(n 5 13). Eight hospital-based studies validated regression models (n 5 13), which were administered as weighted scores (n 5 8), regression formulas (n 5 4), and a nomogram (n 5 1). One checklist from India had a sensitivity of 98% (95% CI: 88%--100%) and specificity of 94% (93%--95%) for predicting sepsis-related deaths. However, external validation in Bangladesh showed very low sensitivity of 3% (0%--10%) with specificity of 99% (99%--99%) for all-cause mortality (ages 0--9 days). For hospital-based prediction models, area under the curve (AUC) ranged from 0.76--0.93 (n 5 13). The Score for Essential Neonatal Symptoms and Signs had an AUC of 0.89 (0.84--0.93) in the derivation cohort for mortality, and external validation showed an AUC of 0.83 (0.83--0.84). LIMITATIONS: Heterogeneity of algorithms and lack of external validation limited the evidence. CONCLUSIONS: Clinical sign algorithms may help identify at-risk young infants, particularly in hospital settings; however, overall certainty of evidence is low with limited external validation. [ABSTRACT FROM AUTHOR]

CONTEXT: Accurate identification of possible sepsis in young infants is needed to effectively manage and reduce sepsis-related morbidity and mortality. OBJECTIVE: Synthesize evidence on the diagnostic accuracy of clinical sign algorithms to identify young infants (aged 0--59 days) with suspected sepsis. DATA SOURCES: MEDLINE, Embase, CINAHL, Global Index Medicus, and Cochrane CENTRAL Registry of Trials. STUDY SELECTION: Studies reporting diagnostic accuracy measures of algorithms including infant clinical signs to identify young infants with suspected sepsis. DATA EXTRACTION: We used Cochrane methods for study screening, data extraction, risk of bias assessment, and determining certainty of evidence using Grading of Recommendations Assessment Development and Evaluation. RESULTS: We included 19 studies (12 Integrated Management of Childhood Illness [IMCI] and 7 non-IMCI studies). The current World Health Organization (WHO) 7-sign IMCI algorithm had a sensitivity of 79% (95% CI 77%--82%) and specificity of 77% (95% CI 76%--78%) for identifying sick infants aged 0--59 days requiring hospitalization/antibiotics (1 study, N 5 8889). Any IMCI algorithm had a pooled sensitivity of 84% (95% CI 75%--90%) and specificity of 80% (95% CI 64%--90%) for identifying suspected sepsis (11 studies, N 5 15523). When restricting the reference standard to laboratory-supported sepsis, any IMCI algorithm had a pooled sensitivity of 86% (95% CI 82%--90%) and lower specificity of 61% (95% CI 49%--72%) (6 studies, N 5 14278). LIMITATIONS: Heterogeneity of algorithms and reference standards limited the evidence. CONCLUSIONS: IMCI algorithms had acceptable sensitivity for identifying young infants with suspected sepsis. Specificity was lower using a reference standard of laboratory-supported sepsis diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: Neonatal deaths and stillbirths are significant public health concerns in Pakistan, with an estimated stillbirth rate of 43 per 1,000 births and a neonatal mortality rate of 46 deaths per 1,000 live births. Limited access to obstetric care, poor health seeking behaviors and lack of quality healthcare are the leading root causes for stillbirths and neonatal deaths. Rehri Goth, a coastal slum in Karachi, faces even greater challenges due to extreme poverty, and inadequate infrastructure. This study aims to investigate the causes and pathways leading to stillbirths and neonatal deaths in Rehri Goth to develop effective maternal and child health interventions. Methods: A mixed-method cohort study was nested with the implementation of large maternal, neonatal and child health program, captured all stillbirths and neonatal death during the period of May 2014 till June 2018. The Verbal and Social Autopsy (VASA) tool (WHO 2016) was used to collect primary data from all death events to determine the causes as well as the pathways. Interviews were conducted both retrospectively and prospectively with mothers and caregivers. Two trained physicians reviewed the VASA form and the medical records (if available) and coded the cause of death blinded to each other. Descriptive analysis was used to categorize stillbirth and neonatal mortality data into high- and low-mortality clusters, followed by chi-square tests to explore associations between categories, and concluded with a qualitative analysis. Results: Out of 421 events captured, complete VASA interviews were conducted for 317 cases. The leading causes of antepartum stillbirths were pregnancy-induced hypertension (22.4%) and maternal infections (13.4%), while obstructed labor was the primary cause of intrapartum stillbirths (38.3%). Neonatal deaths were primarily caused by perinatal asphyxia (36.1%) and preterm birth complications (27.8%). The qualitative analysis on a subset of 40 death events showed that health system (62.5%) and community factors (37.5%) contributing to adverse outcomes, such as delayed referrals, poor triage systems, suboptimal quality of care, and delayed care-seeking behaviors. Conclusion: The study provides an opportunity to understand the causes of stillbirths and neonatal deaths in one of the impoverished slums of Karachi. The data segregation by clusters as well as triangulation with qualitative analysis highlight the needs of evidence-based strategies for maternal and child health interventions in disadvantaged communities. [ABSTRACT FROM AUTHOR]

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