Burden of disease and risk factors for mortality amongst hospitalized newborns in Nigeria and Kenya

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Study Justification:
The study aimed to investigate the burden of disease and risk factors for mortality among hospitalized newborns in Nigeria and Kenya. This research was conducted to address the high rates of illnesses and mortality in newborns in sub-Saharan Africa. By understanding the patient population, priority diseases, and outcomes, the study aimed to identify areas for intervention and improvement in the care of newborns.
Highlights:
– The study included data from 2280 newborns admitted to neonatal units in Nigeria and Kenya.
– The majority of infants (57.0%) were low birthweight (LBW) and 22.6% were very LBW (VLBW).
– The most common morbidities observed were jaundice, suspected sepsis, respiratory conditions, and birth asphyxia.
– The mortality rate among newborns was 18.7%, with higher rates among VLBW and very preterm infants.
– Factors associated with mortality included gestation

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it provides detailed information about the patient population, priority diseases, and outcomes in newborns admitted to neonatal units in Nigeria and Kenya. The study design is clearly described, and the results include specific data on the number of newborns admitted, birthweight, gestation, morbidities, and mortality rates. The abstract also highlights factors independently associated with mortality. To improve the evidence, the abstract could include more information about the methods used in data collection and analysis, as well as the limitations of the study.

Objective To describe the patient population, priority diseases and outcomes in newborns admitted <48 hours old to neonatal units in both Kenya and Nigeria. Study design In a network of seven secondary and tertiary level neonatal units in Nigeria and Kenya, we captured anonymised data on all admissions <48 hours of age over a 6-month period. Results 2280 newborns were admitted. Mean birthweight was 2.3 kg (SD 0.9); 57.0% (1214/2128) infants were low birthweight (LBW; <2.5kg) and 22.6% (480/2128) were very LBW (VLBW; <1.5 kg). Median gestation was 36 weeks (interquartile range 32, 39) and 21.6% (483/2236) infants were very preterm (gestation <32 weeks). The most common morbidities were jaundice (987/2262, 43.6%), suspected sepsis (955/2280, 41.9%), respiratory conditions (817/ 2280, 35.8%) and birth asphyxia (547/2280, 24.0%). 18.7% (423/2262) newborns died; mortality was very high amongst VLBW (222/472, 47%) and very preterm infants (197/483, 40.8%). Factors independently associated with mortality were gestation <28 weeks (adjusted odds ratio 11.58; 95% confidence interval 4.73–28.39), VLBW (6.92; 4.06–11.79), congenital anomaly (4.93; 2.42–10.05), abdominal condition (2.86; 1.40–5.83), birth asphyxia (2.44; 1.52–3.92), respiratory condition (1.46; 1.08–2.28) and maternal antibiotics within 24 hours before or after birth (1.91; 1.28–2.85). Mortality was reduced if mothers received a partial (0.51; 0.28–0.93) or full treatment course (0.44; 0.21–0.92) of dexamethasone before preterm delivery. Conclusion Greater efforts are needed to address the very high burden of illnesses and mortality in hospitalized newborns in sub-Saharan Africa. Interventions need to address priority issues during pregnancy and delivery as well as in the newborn.

The study was approved by the Research and Ethics Committee at the Liverpool School of Tropical Medicine (protocol number:18–0210), The Lagos University Teaching Hospital Health Research Ethics Committee (protocol number: AMD/DCST/HREC/APP/2514), The Kenya Medical Research Institute-Scientific and Ethics Review Unit (protocol number: KEMRI/SERU/CGMR-C/120/3740) and the research and ethics committees at The Jaramogi Oginga Odinga Teaching and Referral Hospital (protocol number: ERC.IB/VOL.1/510), University College Hospital Ibadan (protocol number: UI/EC/18/0446), Massey Street Children’s Hospital (protocol number: LSHSC/2222/VOL.VIB/185), Ahmadu Bello University Teaching Hospital (ABUTH/HZ/HREC/D37/2018), and Maitama District Hospital (protocol number: FHREC/2018/01/108/19-09-18). The study was conducted in five NNUs in Nigeria of which four provide tertiary level care (University College Hospital, Ibadan; Lagos University Teaching Hospital, Massey Children’s Hospital, Lagos; Ahmadu Bello University Teaching Hospital, Zaria) and one secondary level neonatal care (Maitama District Hospital, Abuja) and two in Kenya: Jaramogi Oginga Odinga Teaching and Referral Hospital, Kisumu providing tertiary level and Kilifi County Referral Hospital secondary level neonatal care. In Nigeria, the Nigeria Society of Neonatal Medicine led in the selection of these facilities and aimed to incorporate neonatal units from both the northern and southern parts of the country. In Kenya, the facilities were chosen based on previous collaborative partnerships aiming to include neonatal units that provide different levels of care i.e. tertiary and district level in different regions of the country. The basis of this selection process was prior research and clinical training collaborative partnerships between the Liverpool School of Tropical Medicine (LSTM) co-investigators and clinical researchers in Nigeria and Kenya. All neonatal units admitted both inborn and outborn infants <28 days of age. All neonatal units except one in Kenya had separate rooms/wards for admitting inborn and outborn neonates. The tertiary level units typically had 2–4 consultant neonatologists or paediatricians who supervised resident doctors/registrars, intern house officers, clinical officers and a team of 1–3 nurses per shift. In all the units, the neonates were admitted by intern house officers, medical officers or clinical officers and were reviewed by a consultant neontologist or paediatrician daily during their admission. The bed capacity ranged from 24–80 but occupancy often exceeded 100%. The district level NNUs had 1–3 consultant paediatricians who worked with medical officers, clinical officers and a team of 2–3 nurses per shift. The bed capacity ranged from 12–27. The NNUs provide care according to institutional neonatal protocols in Nigeria and the Kenya national paediatric protocol [20]. All the NNUs had access to oxygen, pulse oximetry and phototherapy but these were often limited in availability and, therefore, reserved for the sickest newborns. All the tertiary level NNUs and one of the district level NNUs used non-invasive ventilation (i.e. continuous positive airway pressure), but none used endotracheal ventilation. This was a multi-centre, prospective, observational study. During network meetings held before data collection, we established a standardised case report form (CRF) and an anonymised demographic/clinical database. Clinical criteria, laboratory analyses and imaging currently used for the diagnosis of common neonatal morbidities were reviewed and additional CRFs developed to capture episodes of suspected sepsis, respiratory problems, abdominal conditions and birth asphyxia diagnosed according to current clinical practice in each NNU. The CRFs are available from https://www.lstmed.ac.uk/nnu. All newborns aged <48 hours admitted to each NNU over a 6-month period between August 2018 and May 2019 were included in the study and had CRFs completed with the period of data collection determined by timing of ethics approval. Infants who were ≥ 48 hours of age at admission were excluded because we wanted to optimize recall of information on feeding practices from birth. In addition, infants aged 48 hours and above were generally admitted to paediatric wards rather than neonatal units. Details of maternal demography, socioeconomic status, health and the current pregnancy were recorded from ante-natal records. Details of labour and delivery were collected from hospital records. Paper CRFs were kept as part of infant case records or stored separately and updated during NNU admission. Details of clinical criteria used to diagnose infant conditions were recorded in separate forms, the analyses of which are provided in a separate manuscript that is in preparation. Data clerks in each NNU entered data into a REDcap database (http://www.project-redcap.org/), that was hosted by the Liverpool School of Tropical Medicine (LSTM). Infants were identified by a unique study number only and no personal identifiers were recorded in the database. Categorical variables were presented as frequencies and percentages. Normally distributed variables were reported using means and standard deviations (SDs) and median and interquartile ranges (IQRs) were used for non-normally distributed variables. We analysed variables according to country and level of care to provide some insights into the variability between NNUs. When evaluating differences between individual NNUs, country and level of care, we considered the clinical relevance of differences in variables as well as statistical significance. Except for five variables with ≥10% missing data (maternal HIV status, hepatitis B, syphilis, gestational diabetes, and infant length of admission), the average percentage of missing data for variables ranged from 0–6.3%. The five variables with a high percentage of missing data were not included in the multivariable logistic regression analysis. Univariate and multivariable logistic regression analyses identified factors associated with mortality with data imported into Stata version 15.0 (Stata Corp). Multivariable logistic regression odds ratio plot was performed by R V3.5.2. Kaplan-Meier survival analysis was used to estimate the independent effects of gestation and birthweight on neonatal mortality. Mothers/infants with missing data for a variable were not included in the analyses. Information about the collection of anonymised data was displayed in each NNU; no parents chose to opt-out of the study. At one of the Network sites, parents provided written informed consent; 82 parents declined consent at this site. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report.

Based on the provided description, here are some potential innovations that could improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can allow healthcare providers to remotely monitor and provide consultations to pregnant women, especially in remote or underserved areas. This can help improve access to prenatal care and reduce the need for travel.

2. Mobile health (mHealth) applications: Developing mobile applications that provide educational resources, appointment reminders, and personalized health information can empower pregnant women to take control of their own health. These apps can also facilitate communication between healthcare providers and patients.

3. Community health workers: Training and deploying community health workers who can provide basic prenatal care, health education, and referrals to pregnant women in their communities can help improve access to maternal health services, especially in areas with limited healthcare infrastructure.

4. Maternal health clinics: Establishing dedicated maternal health clinics that provide comprehensive prenatal care, including regular check-ups, screenings, and counseling, can ensure that pregnant women receive the necessary care in a timely manner.

5. Mobile clinics: Setting up mobile clinics that travel to remote or underserved areas can bring maternal health services closer to the communities that need them. These clinics can provide prenatal care, screenings, vaccinations, and other essential services.

6. Health financing schemes: Implementing health financing schemes, such as health insurance or subsidized healthcare for pregnant women, can reduce financial barriers to accessing maternal health services.

7. Public-private partnerships: Collaborating with private healthcare providers and organizations can help expand access to maternal health services, especially in areas where public healthcare facilities are limited.

8. Maternal health awareness campaigns: Conducting awareness campaigns to educate communities about the importance of maternal health, early prenatal care, and the availability of services can help increase utilization of maternal health services.

9. Strengthening healthcare infrastructure: Investing in improving healthcare infrastructure, including the availability of well-equipped neonatal units, trained healthcare professionals, and essential medical supplies, can ensure that pregnant women and newborns receive the necessary care.

10. Research and data-driven interventions: Conducting research studies, like the one described in the provided description, can help identify specific risk factors and priority areas for intervention. Using this data to inform targeted interventions can lead to improved access to maternal health services.

It is important to note that the specific context and needs of the communities in Nigeria and Kenya should be taken into consideration when implementing these innovations.
AI Innovations Description
The study titled “Burden of disease and risk factors for mortality amongst hospitalized newborns in Nigeria and Kenya” provides important insights into the patient population, priority diseases, and outcomes in newborns admitted to neonatal units in both countries. The study found that a significant proportion of newborns admitted were low birthweight (LBW) and very low birthweight (VLBW), with high rates of mortality among VLBW and very preterm infants. The most common morbidities observed were jaundice, suspected sepsis, respiratory conditions, and birth asphyxia.

Based on the findings of this study, a recommendation to improve access to maternal health and reduce the burden of illnesses and mortality in hospitalized newborns in sub-Saharan Africa could be the implementation of comprehensive maternal and neonatal care interventions. These interventions should focus on addressing priority issues during pregnancy and delivery, as well as in the newborn period.

Specific recommendations could include:

1. Strengthening antenatal care: Enhance access to quality antenatal care services, including regular check-ups, screenings, and health education for pregnant women. This can help identify and manage risk factors that contribute to LBW and preterm births.

2. Promoting maternal nutrition: Implement programs that educate and support pregnant women in adopting a healthy and balanced diet to improve maternal and fetal health. Adequate nutrition during pregnancy can contribute to optimal birthweight and reduce the risk of complications.

3. Enhancing infection prevention and control: Implement strategies to prevent and manage infections during pregnancy and childbirth, such as promoting hand hygiene, proper sterilization of equipment, and appropriate use of antibiotics. This can help reduce the incidence of suspected sepsis and other infectious morbidities in newborns.

4. Improving access to skilled birth attendants: Ensure that all pregnant women have access to skilled birth attendants, such as midwives or obstetricians, who can provide appropriate care during labor and delivery. Skilled birth attendants can help identify and manage complications promptly, reducing the risk of birth asphyxia and other adverse outcomes.

5. Strengthening neonatal care services: Invest in the improvement of neonatal units, including increasing bed capacity, ensuring availability of essential equipment (such as oxygen and phototherapy devices), and training healthcare providers in neonatal resuscitation and care. This can help improve outcomes for newborns with morbidities such as jaundice, respiratory conditions, and birth asphyxia.

6. Implementing antenatal corticosteroid therapy: Promote the use of antenatal corticosteroid therapy, such as dexamethasone, in cases of threatened preterm delivery. This treatment has been shown to reduce mortality and improve outcomes in preterm infants.

By implementing these recommendations, it is possible to improve access to maternal health and reduce the burden of illnesses and mortality among hospitalized newborns in sub-Saharan Africa. However, it is important to note that these recommendations should be tailored to the specific context and resources available in each country or region.
AI Innovations Methodology
Based on the provided study, here are some potential recommendations to improve access to maternal health:

1. Strengthen antenatal care: Enhance antenatal care services to ensure early detection and management of high-risk pregnancies, including regular check-ups, screenings, and education on healthy pregnancy practices.

2. Improve access to skilled birth attendants: Increase the availability and accessibility of skilled birth attendants, such as midwives or obstetricians, especially in rural and underserved areas.

3. Enhance emergency obstetric care: Ensure that healthcare facilities have the necessary resources, equipment, and trained staff to provide emergency obstetric care, including timely interventions for complications during childbirth.

4. Promote community-based interventions: Implement community-based programs that focus on educating and empowering women and their families about maternal health, including the importance of prenatal and postnatal care, nutrition, and hygiene practices.

5. Strengthen referral systems: Establish and strengthen referral systems between primary healthcare centers, hospitals, and specialized maternal health facilities to ensure timely and appropriate care for high-risk pregnancies and complications.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Data collection: Gather baseline data on key indicators related to maternal health, such as maternal mortality rates, access to antenatal care, skilled birth attendance, and availability of emergency obstetric care.

2. Define simulation parameters: Determine the specific variables and parameters to be simulated, such as the increase in the number of skilled birth attendants, the improvement in access to emergency obstetric care, or the coverage of community-based interventions.

3. Model development: Develop a simulation model that incorporates the baseline data and the defined parameters. This model should simulate the impact of the recommendations on the selected indicators over a specific time period.

4. Sensitivity analysis: Conduct sensitivity analysis to assess the robustness of the simulation model by varying the input parameters and evaluating the resulting changes in the simulated outcomes.

5. Scenario analysis: Run different scenarios within the simulation model to explore the potential impact of various combinations of recommendations on improving access to maternal health. This can help identify the most effective and cost-efficient interventions.

6. Interpretation of results: Analyze the simulation results to determine the projected changes in the selected indicators, such as reductions in maternal mortality rates or improvements in access to antenatal care. Assess the feasibility and potential challenges of implementing the recommended interventions based on the simulation outcomes.

7. Policy and program development: Use the simulation results to inform policy and program development, prioritizing the most effective and feasible interventions to improve access to maternal health.

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