Determinants and Outcome of Community-Acquired Late-Onset Neonatal Sepsis in Rural Bangladesh.
Kazi Nazmus SaqeebMohammod Jobayer ChistiMd Alfazal KhanTahmeed AhmedMohammod Jobayer ChistiPublished in: Global pediatric health (2019)
Background. This study examined the sociodemographic as well as other determinants and outcome of community-acquired late-onset neonatal sepsis (LONS) in rural Bangladesh at Matlab. Methods. In this retrospective chart review, we used an unmatched case-control design (1:2 ratio) to evaluate the factors associated with LONS and their outcomes among babies admitted to the neonatal ward of Matlab Hospital of icddr,b, from January 2012 to December 2014. Neonates presenting with any of the clinical signs of serious bacterial infection during 3 to 28 days of life constituted the cases (LONS), and those without LONS constituted the controls. All the data were retrieved from the electronic databases of Matlab Hospital and Matlab Health and Demographic Surveillance System. Results. Among 1482 admitted neonates, 202 were cases and 404 were randomly selected controls. In babies with LONS, case fatality rate (1% vs 0%, P = .037), duration of inpatient stay (4 days vs 2 days, P < .001), and referral to higher center (9% vs 5%, P = .020) were higher. In an adjusted model, undernutrition (weight for length Z score < -2; odds ratio [OR] = 1.8, 95% confidence interval [CI] = 1.2-2.94), admission in winter season (OR = 1.62, 95% CI = 1.09-2.41), mother's schooling <10 years (OR = 1.76, 95% CI = 1.09-2.85), primiparity (OR = 1.55, 95% CI = 1.05-2.29), home delivery (OR = 1.87, 95% CI = 1.07-3.26), and household food insecurity (OR = 2.78, 95% CI = 1.31-5.88) were found to be independently associated with LONS. Conclusion. LONS posed considerable socioeconomic burden to the rural community. Further studies are required to consolidate our findings.
Keyphrases
- late onset
- healthcare
- mental health
- early onset
- case control
- south africa
- public health
- acute kidney injury
- intensive care unit
- emergency department
- body mass index
- low birth weight
- big data
- septic shock
- physical activity
- primary care
- palliative care
- electronic health record
- preterm infants
- risk factors
- machine learning
- weight gain
- health information
- body weight
- social media