'Quality assessment of infection control measures in delivery points in a tribal district of Maharashtra' - An observational study.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 39723019.
- Also identified by DOI 10.4103/jfmpc.jfmpc_710_24 and PMC identifier 11668461.
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Abstract
Maternal and neonatal mortality rates remain alarmingly high in low-resource settings, with infections acquired during childbirth contributing significantly to this burden. Effective infection control measures at delivery points are crucial for mitigating these risks. This observational study aimed to assess the quality of infection control practices in delivery points in a tribal district of Maharashtra, India. A community-based study was conducted at delivery points across eight talukas in Palghar district. A total of 15 delivery points were selected using convenient sampling, representing three levels of facilities (L1, L2 and L3) which are healthcare facilities divided on the basis of the provision of services by Maternal and Newborn Health Toolkit. Data was collected using the validated LaQshya programme's checklist through direct observation from June 1<sup>st</sup> to June 18<sup>th</sup>, 2021. Compliance with infection control protocols was scored based on predefined criteria. The majority of L1 delivery points showed non-compliance in criteria such as surface microbiological surveillance and monitoring of infection control practices, 20-40% of Delivery points also showed non-compliance in the availability of personal protective equipment and waste management practices. This study highlights the need for targeted interventions to improve infection control practices in delivery points, particularly in lower-level facilities. Strengthening adherence to established protocols, enhancing staff training, ensuring availability of essential supplies and implementing robust monitoring systems are imperative for reducing the burden of maternal and neonatal infections and improving healthcare outcomes in resource-constrained settings.