MATERNAL AND CHILD RISK FACTORS ASSOCIATED WITH NEONATAL MORTALITY

Suspected serial killers and unsuspected statistical blunders

A whole branch of theoretical statistics devotes itself to the analysis of clusters, the aim being to distinguish an apparent cluster arising randomly from one that is more likely to have been produced as a result of some systematic influence. There are many examples in medicine and some that involve both medicine and the legal field; criminal law in particular. Observed clusters or a series of cases in a given setting can set off alarm bells, the recent conviction of Lucy Letby in England being an example. It was an observed cluster, a series of deaths among neonates, that prompted the investigation of Letby. There have been other similar cases in the past and there will be similar cases in the future. Our purpose is not to reconsider any particular trial but, rather, to work with similar, indeed more extreme numbers of cases as a way to underline the statistical mistakes that can be made when attempting to make sense of the data. These notions are illustrated via a made-up case of 10 incidents where the anticipated count was only 2. The most common statistical analysis would associate a probability of less than 0.00005 with this outcome: A very rare event. However, a more careful analysis that avoids common pitfalls results in a probability close to 0.5, indicating that, given the circumstances, we were as likely to see 10 or more as we were to see less than 10.

The Risk Factors of Neonatal Mortality in Pekanbaru City, Indonesia

BACKGROUND: Neonatal mortality is death during the first 28 days of life, expressed per 1000 live births. In 2020, there was an increase in the number of neonatal mortality from 50 infant to 115 infant in Pekanbaru City, Indonesia. AIM: The study objective was to analyze factors associated with neonatal mortality in the Pekanbaru City, Indonesia. METHODS: The sample was 220, consisting of 110 case and 110 control samples. The total sampling technique was used for case and systematic sampling for control samples. The variables analyzed were mother’s age, mother’s education, parity, birth spacing, gestational age, birth weight, and antenatal care. Data were analyzed by multiple logistic regressions. RESULTS: The results showed that the risk factors for neonatal mortality were antenatal care < 4 times (OR = 8.2; 95% CI = 4.0−17.1), gestational age < 35 weeks (OR = 5.5; 95% CI = 2.2–14.0), mother’s < 20 years or > 35 years (OR = 3.1; 95% CI = 1.3−7.1), and birth spacing < 2 years (OR = 2.6; 95% CI = 1.2−5.7). CONCLUSION: The antenatal care, gestational age, mother’s age, and birth spacing are preventable or possibly preventable risk factors. As a recommendation, the Health Office of Pekanbaru and Community Health Service should increase promotion of antenatal care for pregnant women and family planning to preserve birth spacing for women of childbearing age.

Neonatal mortality among neonates admitted to NICU of Hiwot Fana specialized university hospital, eastern Ethiopia, 2020: a cross-sectional study design

Background

In Ethiopia, neonatal mortality is unacceptably high. Despite many efforts made by the government and other partners to reduce neonatal mortality; it has been increasing since 2014. Factors associated with neonatal mortality were explained by different researchers indifferently. There is no clear evidence to identify the magnitude of neonatal mortality and associated factors in the study area. The study aimed to assess the magnitude and factors associated with neonatal mortality.

Methods

Facility-based cross-sectional study was conducted among 834 randomly selected neonates. The study was conducted from February 20 to March 21, 2020. Data were extracted from medical records using a checklist adapted from the World Health Organization, and neonatal registration book. The data were inserted into Epi-data version 3.1 and then exported into SPSS window version 20 for analysis. Bivariate and multivariate analyses were employed to identify the association between independent variables and the outcome variable.

Results

Magnitude of neonatal mortality was 14.4% (95% CI:11.9,16.7). Being neonates of mothers whose pregnancy was complicated with antepartum hemorrhage [AOR = 4.13, 95%CI: (1.92,8.85)], born from mothers with current pregnancy complicated with pregnancy-induced hypertension [AOR = 4.41, 95%CI: (1.97,9.86)], neonates of mothers with multiple pregnancy [AOR = 2.87, 95% CI (1.08,7.61)], neonates delivered at the health center [AOR = 5.05, 95%CI: (1.72,14.79)], low birth weight [AOR = 4.01, 95%CI (1.30,12.33)], having perinatal asphyxia [AOR =3.85, 95%CI: (1.83,8.10)], and having early-onset neonatal sepsis [AOR = 3.93, 95%CI: (1.84,8.41)] were factors significantly associated with neonatal mortality.

Conclusion

The proportion of neonatal mortality was relatively in line with other studies but still needs attention. Antepartum hemorrhage, Pregnancy-induced hypertension, place of delivery, low birth weight, having perinatal asphyxia, and having neonatal sepsis were independent factors. The hospital, and health care workers should give attention to neonates admitted to intensive care units by strengthening the quality of care given at neonatal intensive care unit like infection prevention and strengthening early detection and treatment of health problems during Antenatal care visit.

EARLY NEONATAL NEAR MISS IN A UNIVERSITY HOSPITAL: COMPARATIVE CROSS-SECTIONAL STUDY

OBJECTIVE

To compare 2012 and 2016 data on early neonatal near miss indicators from Health Information Systems at a university hospital.

METHODS

This is a cross-sectional study conducted in 2012 and 2016. We considered early neonatal near misses the live births that presented one of the following risk conditions at birth: gestational age <33 weeks, birth weight <1,750g or 5-minute Apgar score <7, or Neonatal Intensive Care Unit (NICU) admission, and were alive until the 7th day of life. Data were collected from the Live Birth Information System, Hospital Information System, and Mortality Information System. We calculated the early neonatal mortality rate, neonatal near miss rate, severe neonatal outcome rate, early neonatal survival index, and early neonatal mortality index, compared by year of birth.

RESULTS

In 2012, 304 early neonatal near misses were registered, with a higher proportion of cases with very low birth weight and mothers who had zero to three prenatal visits. In 2016, the number of cases was 243, with a predominance of more NICU admissions. The incidence of early neonatal deaths and early neonatal near misses was higher in 2012 than in 2016.

CONCLUSIONS

Neonatal near miss indicators identified difference between years. The cases were more severe in 2012 and there were more NICU admissions in 2016.

Determinants of neonatal mortality at neonatal intensive care unit in Northeast Ethiopia: unmatched case-control study

Background

Globally, in 2016, about 38% and 3% of all neonatal death were recorded in sub-Saharan Africa and Ethiopia, respectively. In the same year, 47 neonates out of 1000 live births were not surviving in the first 28 days of age in the Amhara region, Ethiopia. Despite the highest burden of neonatal death in the region, specific or the proximate determinants of neonatal death in the neonatal intensive care unit were not well identified.

Objective

This study aimed to identify the determinants of neonatal mortality at neonatal intensive care unit in Dessie Referral Hospital, Northeast Ethiopia.

Methods

An institution-based unmatched case-control study was conducted on neonates admitted to the neonatal intensive care unit of Dessie Referral Hospital, from January 1, 2016, to December 30, 2017. A total of 390 charts (130 cases and 260 controls) were selected by simple random sampling technique. The data were abstracted from the facility-based data abstraction form. A binary logistic regression analysis was fitted to identify the determinants of neonatal mortality.

Results

Pregnancy-induced hypertension (AOR = 4.57; 95% CI 1.45–14.43), prolonged rupture of membrane (AOR = 2.04; 95% CI 1.13–3.68), very low birth weight (AOR = 7.00; 95% CI 2.10–23.35), and low birth weight (AOR = 2.12; 95% CI 1.10–4.20) were identified factors. Moreover, respiratory distress syndrome (AOR = 3.61; 95% CI 1.10–12.04), perinatal asphyxia (AOR = 2.27; 95% CI 1.18–4.39), meconium aspiration syndrome (AOR = 2.35; 95% CI 1.12–4.97), and infection (AOR = 2.26; 95% CI 1.34–3.82) were also significantly associated with neonatal death.

Conclusions

Pregnancy-induced hypertension, prolonged rupture of membrane, low birth weight, respiratory distress syndrome, perinatal asphyxia, meconium aspiration syndrome, and infections were the major determinants of neonatal mortality. Therefore, special attention will be given to small and sick babies. Moreover, early anticipation of complications and management of mothers who had pregnancy-induced hypertension and prolonged rupture of the membrane would save neonates.

Risk factors for neonatal death in the capital city with the lowest infant mortality rate in Brazil

OBJECTIVES

To analyze the risk factors for neonatal death in Florianópolis, the Brazilian city capital with the lowest infant mortality rate.

METHOD

Data were extracted from a historical cohort with 15,879 live births. A model was used that included socioeconomic, behavioral, and health service use risk factors, as well as the Apgar score and biological factors. Risk factors were analyzed by hierarchical logistic regression.

RESULTS

Based on the multivariate analysis, socioeconomic factors showed no association with death. Insufficient prenatal consultations showed an OR of 3.25 (95% CI: 1.70-6.48) for death. Low birth weight (OR 8.42; 95% CI: 3.45-21.93); prematurity (OR 5.40; 95% CI: 2.22-13.88); malformations (OR 4.42; 95% CI: 1.37-12.43); and low Apgar score at the first (OR 6.65; 95% CI: 3.36-12.94) and at the fifth (OR 19.78; 95% CI: 9.12-44.50) minutes, were associated with death.

CONCLUSION

Differing from other studies, socioeconomic conditions were not associated with neonatal death. Insufficient prenatal consultations, low Apgar score, prematurity, low birth weight, and malformations showed an association, reinforcing the importance of prenatal access universalization and its integration with medium and high-complexity neonatal care services.