Hospital Mortality - a neglected but rich source of information supporting the transition to higher quality health systems in low and middle income countries

Intrahospital Mortality Rate after the Implementation of the Second Phase of the Health Sector Reform in Comparison with Before that in Iran

Background

The second phase of the health sector reform, called the Health Sector Evolution Plan (HSEP), has been implemented in Iran since 2014, aims to improve the equity and quality of health services. In the present study, we aimed to measure the trend of hospitalization and the crude intrahospital mortality rate from 1 year before the HSEP implementation (2013) to 5 years after the HSEP implementation (2018) in public hospitals compared with profit, nonprofit, and charity hospitals, which are affiliated with the Isfahan University of Medical Sciences (MUI).

Methods

In a prospective, cross-sectional study, the data related to the frequency of hospitalized patients and intrahospital mortality during the time of hospitalization were collected through census sampling from 39 public hospitals as the exposed hospitals and 20 profit, nonprofit, and charity hospitals as the control hospitals.

Results

After HSEP implementation, the frequency of hospitalization increased in public hospitals by 50.45% compared with the previous period. Although the crude intrahospital mortality rate increased from 12.61 to 12.93 per 1000 hospitalized patients (an increase of 2.54%) in public hospitals, the raise was not significant (P value = 0.348). The frequency of hospitalization increased in Social Security Organization's (SSO) hospitals as well as charity hospitals. However, the percent of decrease in the intrahospital mortality rates were -42.96%, -34.76%, and -18.47% in the private, charity, and SSO hospitals, respectively, but was not significant (P value > 0.05).

Conclusions

The crude intrahospital mortality rates in public hospitals affiliated with MUI did not change significantly after the implementation of the HSEP.

Scaling up Surgical Capacity in Kenya: The Kenya Hospital Assessment Tool (K-HAT)

INTRODUCTION

Although substantial progress has been achieved to bring surgical care to the forefront of global health discussions, a number of low-and middle-income countries are still in the process of developing a National Surgical, Obstetric, and Anesthesia Plan (NSOAP). This paper describes the initial step toward the development of the NSOAP through the creation of the Kenya National Hospital Assessment Tool (K-HAT).

METHODS

A study protocol was developed by a multisectoral collaborative group that represented the pillars of surgical capacity development in Kenya. The K-HAT was adapted from two World Health Organization (WHO) tools: the Service Availability and Readiness Assessment tool and the Situational Analysis Tool. The survey tool was deployed on Open Data Kit, an open-source electronic encrypted database. This new locally adapted tool was pilot tested in three hospitals in Kenya and subsequently deployed in Level 4 facilities.

RESULTS

Eighty-nine questions representing over 800 data points divided into six WHO Health Systems Strengthening sections comprised the K-HAT which was deployed to over 95% of Level 4 hospitals in Kenya. When compared to the WHO Service Availability and Readiness Assessment tool, the K-HAT collected more detailed information. The pilot test team reported that K-HAT was easy to administer, easily understood by the respondents, and that it took approximately 1 hour to collect data from each facility.

CONCLUSIONS

The K-HAT collected comprehensive information that can be used to develop Kenya's NSOAP.

General ward nurses detection and response to clinical deterioration in three hospitals at the Kenyan coast: a convergent parallel mixed methods study

BACKGROUND

In low and middle-income countries like Kenya, critical care facilities are limited, meaning acutely ill patients are managed in the general wards. Nurses in these wards are expected to detect and respond to patient deterioration to prevent cardiac arrest or death. This study examined nurses' vital signs documentation practices during clinical deterioration and explored factors influencing their ability to detect and respond to deterioration.

METHODS

This convergent parallel mixed methods study was conducted in the general medical and surgical wards of three hospitals in Kenya's coastal region. Quantitative data on the extent to which the nurses monitored and documented the vital signs 24 h before a cardiac arrest (death) occurred was retrieved from patients' medical records. In-depth, semi-structured interviews were conducted with twenty-four purposefully drawn registered nurses working in the three hospitals' adult medical and surgical wards.

RESULTS

This study reviewed 405 patient records and found most of the documentation of the vital signs was done in the nursing notes and not the vital signs observation chart. During the 24 h prior to death, respiratory rate was documented the least in only 1.2% of the records. Only a very small percentage of patients had any vital event documented for all six-time points, i.e. four hourly. Thematic analysis of the interview data identified five broad themes related to detecting and responding promptly to deterioration. These were insufficient monitoring of vital signs linked to limited availability of equipment and supplies, staffing conditions and workload, lack of training and guidelines, and communication and teamwork constraints among healthcare workers.

CONCLUSION

The study showed that nurses did not consistently monitor and record vital signs in the general wards. They also worked in suboptimal ward environments that do not support their ability to promptly detect and respond to clinical deterioration. The findings illustrate the importance of implementation of standardised systems for patient assessment and alert mechanisms for deterioration response. Furthermore, creating a supportive work environment is imperative in empowering nurses to identify and respond to patient deterioration. Addressing these issues is not only beneficial for the nurses but, more importantly, for the well-being of the patients they serve.

Differences in trends in discharge location in a cohort of hospitalized patients with cancer and non-cancer diagnoses receiving specialist palliative care: A retrospective cohort study

BACKGROUND

Patients with and without cancer are frequently hospitalized, and have specialist palliative care needs. In-hospital mortality can serve as a quality indicator of acute care. Trends in acute care outcomes have not previously been evaluated in patients with confirmed specialist palliative care needs or between diagnostic groups.

AIM

To compare trends in discharge location between hospitalized patients with and without cancer who received specialist palliative care.

DESIGN

Retrospective cohort study. Association between diagnosis (cancer, non-cancer) and in-hospital mortality was assessed using multivariable logistic regression, controlling for demographic, clinical, and admission-specific information.

SETTING/PARTICIPANTS

Patients who received specialist palliative care at an academic tertiary hospital in Toronto, Canada from 2013 to 2019.

RESULTS

The cohort comprised 6846 patients, 5024 with and 1822 without cancer. A higher proportion of patients without cancer had a Palliative Performance Scale score <30%, anticipated prognosis of <1 month, and were referred for end-of-life care (all p < 0.001). The adjusted odds of dying in hospital was 1.24-times higher among patients without cancer (95% CI: 1.05-1.46; p = 0.011). Though the proportion of patients without cancer who died in hospital decreased by 8.4% from 2013 to 2019, this proportion (41.2%) remained substantially higher compared to patients with cancer (14.0%) in 2019.

CONCLUSIONS

Hospitalized patients without cancer were referred to specialist palliative care at a lower functional status, a poorer anticipated prognosis, and more likely for end-of-life care; and were more likely to die in hospital. Future studies are required to determine whether a proportion of hospital deaths in patients without cancer represent goal-discordant end-of-life care.

Mortality by Admission Diagnosis in Children 1-60 Months of Age Admitted to a Tertiary Care Government Hospital in Malawi

Diagnosis-specific mortality is a measure of pediatric healthcare quality that has been incompletely studied in sub-Saharan African hospitals. Identifying the mortality rates of multiple conditions at the same hospital may allow leaders to better target areas for intervention. In this secondary analysis of routinely collected data, we investigated hospital mortality by admission diagnosis in children aged 1-60 months admitted to a tertiary care government referral hospital in Malawi between October 2017 and June 2020. The mortality rate by diagnosis was calculated as the number of deaths among children admitted with a diagnosis divided by the number of children admitted with the same diagnosis. There were 24,452 admitted children eligible for analysis. Discharge disposition was recorded in 94.2% of patients, and 4.0% (N = 977) died in the hospital. The most frequent diagnoses among admissions and deaths were pneumonia/bronchiolitis, malaria, and sepsis. The highest mortality rates by diagnosis were found in surgical conditions (16.1%; 95% CI: 12.0-20.3), malnutrition (15.8%; 95% CI: 13.6-18.0), and congenital heart disease (14.5%; 95% CI: 9.9-19.2). Diagnoses with the highest mortality rates were alike in their need for significant human and material resources for medical care. Improving mortality in this population will require sustained capacity building in conjunction with targeted quality improvement initiatives against both common and deadly diseases.

Moving beyond audit: driving system learning using a novel mortality classification system in a tertiary training hospital in Kenya

Clinical classification systems have proliferated since the APGAR score was introduced in 1953. Numerical scores and classification systems enable qualitative clinical descriptors to be transformed into categorical data, with both clinical utility and ability to provide a common language for learning. The clarity of classification rubrics embedded in a mortality classification system provides the shared basis for discussion and comparison of results. Mortality audits have been long seen as learning tools, but have tended to be siloed within a department and driven by individual learner need. We suggest that the learning needs of the system are also important. Therefore, the ability to learn from small mistakes and problems, rather than just from serious adverse events, remains facilitated.We describe a mortality classification system developed for use in the low-resource context and how it is 'fit for purpose,' able to drive both individual trainee, departmental and system learning. The utility of this classification system is that it addresses the low-resource context, including relevant factors such as limited prehospital emergency care, delayed presentation, and resource constraints. We describe five categories: (1) anticipated death or complication following terminal illness; (2) expected death or complication given clinical situation, despite taking preventive measures; (3) unexpected death or complication, not reasonably preventable; (4) potentially preventable death or complication: quality or systems issues identified and (5) unexpected death or complication resulting from medical intervention. We document how this classification system has driven learning at the individual trainee level, the departmental level, supported cross learning between departments and is being integrated into a comprehensive system-wide learning tool.

The Childhood Acute Illness and Nutrition (CHAIN) network nested case-cohort study protocol: a multi-omics approach to understanding mortality among children in sub-Saharan Africa and South Asia

Introduction: Many acutely ill children in low- and middle-income settings have a high risk of mortality both during and after hospitalisation despite guideline-based care. Understanding the biological mechanisms underpinning mortality may suggest optimal pathways to target for interventions to further reduce mortality. The Childhood Acute Illness and Nutrition (CHAIN) Network ( www.chainnnetwork.org) Nested Case-Cohort Study (CNCC) aims to investigate biological mechanisms leading to inpatient and post-discharge mortality through an integrated multi-omic approach. Methods and analysis; The CNCC comprises a subset of participants from the CHAIN cohort (1278/3101 hospitalised participants, including 350 children who died and 658 survivors, and 270/1140 well community children of similar age and household location) from nine sites in six countries across sub-Saharan Africa and South Asia. Systemic proteome, metabolome, lipidome, lipopolysaccharides, haemoglobin variants, toxins, pathogens, intestinal microbiome and biomarkers of enteropathy will be determined. Computational systems biology analysis will include machine learning and multivariate predictive modelling with stacked generalization approaches accounting for the different characteristics of each biological modality. This systems approach is anticipated to yield mechanistic insights, show interactions and behaviours of the components of biological entities, and help develop interventions to reduce mortality among acutely ill children. Ethics and dissemination. The CHAIN Network cohort and CNCC was approved by institutional review boards of all partner sites. Results will be published in open access, peer reviewed scientific journals and presented to academic and policy stakeholders. Data will be made publicly available, including uploading to recognised omics databases. Trial registration NCT03208725.

The Childhood Acute Illness and Nutrition (CHAIN) network nested case-cohort study protocol: a multi-omics approach to understanding mortality among children in sub-Saharan Africa and South Asia

Introduction: Many acutely ill children in low- and middle-income settings have a high risk of mortality both during and after hospitalisation despite guideline-based care. Understanding the biological mechanisms underpinning mortality may suggest optimal pathways to target for interventions to further reduce mortality. The Childhood Acute Illness and Nutrition (CHAIN) Network ( www.chainnnetwork.org) Nested Case-Cohort Study (CNCC) aims to investigate biological mechanisms leading to inpatient and post-discharge mortality through an integrated multi-omic approach. Methods and analysis; The CNCC comprises a subset of participants from the CHAIN cohort (1278/3101 hospitalised participants, including 350 children who died and 658 survivors, and 270/1140 well community children of similar age and household location) from nine sites in six countries across sub-Saharan Africa and South Asia. Systemic proteome, metabolome, lipidome, lipopolysaccharides, haemoglobin variants, toxins, pathogens, intestinal microbiome and biomarkers of enteropathy will be determined. Computational systems biology analysis will include machine learning and multivariate predictive modelling with stacked generalization approaches accounting for the different characteristics of each biological modality. This systems approach is anticipated to yield mechanistic insights, show interactions and behaviours of the components of biological entities, and help develop interventions to reduce mortality among acutely ill children. Ethics and dissemination. The CHAIN Network cohort and CNCC was approved by institutional review boards of all partner sites. Results will be published in open access, peer reviewed scientific journals and presented to academic and policy stakeholders. Data will be made publicly available, including uploading to recognised omics databases. Trial registration NCT03208725.

Construction and Clinical Application Effect of General Surgery Patient-Oriented Nursing Information Platform Using Cloud Computing

The paper aims to build a nursing information platform (NIP) for general surgery (GS) patients and explore its clinical application effect based on cloud computing (CC) technology. Specifically, the present work first analyzes and expounds on the characteristics of GS patients, the CC concept, the three-tier service mode of CC, and the cloud data center (CDC). Secondly, based on the principle of the overall system design, the evaluation indexes of medical care end, patient end, family end, and management end are constructed using Visual Studio 2010. Thirdly, the expert evaluation and user evaluation methods are selected to analyze the clinical application effect of the proposed system. Finally, SPSS is used to analyze the effect of the proposed system. The results of the first and second rounds of the expert evaluation show that the authority coefficient of experts is greater than 0.7, which indicates that the degree of expert authority is good. The proposed CC-based GS patient-oriented NIP system is universal. The evaluation results of 20 users have shown 15 doctors and nurses, 14 patients, and 18 family members, who mostly still support applying the proposed CC-based GS patient-oriented NIP system and believe that the system brings convenience and improves work efficiency. In short, more incentives should be taken to build a NIP for GS patients.

Exploring healthcare workers' perceptions on the use of morbidity and mortality audits as an avenue for learning and care improvement in Kenyan hospitals' newborn units

BACKGROUND

In many sub-Saharan African countries, including Kenya, the use of mortality and morbidity audits in maternal and perinatal/neonatal care as an avenue for learning and improving care delivery is sub-optimal due to structural, organizational, and human barriers. While attempts to address these barriers have been reported, lots of emphasis has been paid to addressing the role of tangible inputs (e.g., availing guidelines and training staff in the success of mortality and morbidity audits), while process-related factors (i.e., the role of the people, their experiences, relationships, and motivations) remain inadequately explored. We examined the processes of neonatal audits, their potential in promoting learning from gaps in care and improving care delivery, with a deliberate focus on process-related factors that generally influence mortality and morbidity (M&M) audits.

METHODS

This was an exploratory qualitative study, conducted in three hospitals, in Nairobi and Muranga counties. We employed a mix of in-depth interviews (17) and observation of 12 mortality and morbidity audit meetings. Our study participants included: nurses, doctors, trainee clinicians (i.e., junior doctors on internships), and nursing students involved in providing newborn care. These data were coded using NVivo12 employing a thematic content analysis approach.

RESULTS

Perceived shortcomings in the conduct of M&M audits such as unclear structure was reported to have contributed to its sub-optimal nature in promoting learning. These shortcomings, in addition to hierarchy and power dynamics, poor implementation of audit recommendations, and negative experiences, (e.g., blame) also demotivated health workers from attendance and participation in audits. Despite these, positive outcomes linked to audit recommendations, such as revision of care protocols, were reported. Overall, leadership and a blame-free culture enabled positive changes and promoted learning from audit-identified modifiable factors.

CONCLUSION

Our findings indicate that M&M audits provide a space for meaningful discussions, which may lead to learning and improvement in care delivery processes. However, a lack of participation, lack of observed positive outcomes, and negative experiences may reduce their usefulness. An enabling environment characterized by minimized effects of hierarchy and positive use of power and a blame-free culture may promote active participation, enhancing positive relationships and interactions thus promoting team learning.

Malaria hospitalisation in East Africa: age, phenotype and transmission intensity

BACKGROUND

Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity.

METHODS

Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2-10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models.

RESULTS

52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2-10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2-10 < 5%), five low-moderate transmission (PfPR2-10 5-9%), 20 moderate transmission (PfPR2-10 10-29%) and 12 high transmission (PfPR2-10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69-85%) and rare among children aged 10-14 years (0.7-5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines.

CONCLUSIONS

Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2-23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.

In-Hospital Mortality in Non-COVID-19-Related Diseases before and during the Pandemic: A Regional Retrospective Study

Italy was one of the nations most affected by SARS-CoV-2. During the pandemic period, the national government approved some restrictions to reduce diffusion of the virus. We aimed to evaluate changes in in-hospital mortality and its possible relation with patient comorbidities and different restrictive public health measures adopted during the 2020 pandemic period. We analyzed the hospital discharge records of inpatients from public and private hospitals in Apulia (Southern Italy) from 1 January 2019 to 31 December 2020. The study period was divided into four phases according to administrative restriction. The possible association between in-hospital deaths, hospitalization period, and covariates such as age group, sex, Charlson comorbidity index (CCI) class, and length of hospitalization stay (LoS) class was evaluated using a multivariable logistic regression model. The risk of death was slightly higher in men than in women (OR 1.04, 95% CI: 1.01-1.07) and was lower for every age group below the >75 years age group. The risk of in-hospital death was lower for hospitalizations with a lower CCI score. In summary, our analysis shows a possible association between in-hospital mortality in non-COVID-19-related diseases and restrictive measures of public health. The risk of hospital death increased during the lockdown period.

Trends, patterns and cause-specific neonatal mortality in Tanzania: a hospital-based retrospective survey

Background

Globally, large numbers of children die shortly after birth and many of them within the first 4 wk of life. This study aimed to determine the trends, patterns and causes of neonatal mortality in hospitals in Tanzania during 2006–2015.

Methods

This retrospective study involved 35 hospitals. Mortality data were extracted from inpatient registers, death registers and International Classification of Diseases-10 report forms. Annual specific hospital-based neonatal mortality rates were calculated and discussed. Two periods of 2006–2010 and 2011–2015 were assessed separately to account for data availability and interventions.

Results

A total of 235 689 deaths were recorded and neonatal deaths accounted for 11.3% (n=26 630) of the deaths. The majority of neonatal deaths (87.5%) occurred in the first week of life. Overall hospital-based neonatal mortality rates increased from 2.6 in 2006 to 10.4 deaths per 1000 live births in 2015, with the early neonates contributing 90% to this rate constantly over time. The neonatal mortality rate was 3.7/1000 during 2006–2010 and 10.4/1000 during 2011–2015, both periods indicating a stagnant trend in the years between. The leading causes of early neonatal death were birth asphyxia (22.3%) and respiratory distress (20.8%), while those of late neonatal death were sepsis (29.1%) and respiratory distress (20.0%).

Conclusion

The majority of neonatal deaths in Tanzania occur among the early newborns and the trend over time indicates a slow improvement. Most neonatal deaths are preventable, hence there are opportunities to reduce mortality rates with improvements in service delivery during the first 7 d and maternal care.

Measuring the efficiency of the healthcare sector in Poland – a window-DEA evaluation

Purpose

The main aim of the study was to measure and assess the efficiency of the healthcare system in Poland.

Design/methodology/approach

An output-oriented Data Envelopment Analysis model with a 2-years window analysis extension was used between 2013 and 2018. The analysis was completed with a determination of the sources of productivity changes (between the first and last year of the study period) and factors that influence efficiency.

Findings

Efficient regions have been identified and the spatial diversity in their efficiency was confirmed. The study identified individual efficiency trends together with “all-windows” best and worst performers. Using panel modeling, it was confirmed that the efficiency of health protection is influenced by, among others, accreditation certificates, the length of the waiting list or the number of medical personnel.

Research limitations/implications

Although the analysis was conducted at the voivodeship level (NUTS2), which was fully justified, it would be equally important to analyze data with a lower aggregation level. It would be extremely valuable from the perspective of difficulties faced by the healthcare system in Poland.

Practical implications

The identification of areas and problems affecting the efficiency of the healthcare system in Poland may also be a hint for other countries with similar system solutions that also struggle with the same problems.

Originality/value

The paper explains the efficiency of the country's healthcare system while also paying attention to changes in its level, factors influencing it, spatial diversity and impact on the sector functioning.

Surgical service monitoring and quality control systems at district hospitals in Malawi, Tanzania and Zambia: a mixed-methods study

BACKGROUND

In low-income and middle-income countries, an estimated one in three clinical adverse events happens in non-complex situations and 83% are preventable. Poor quality of care also leads to inefficient use of human, material and financial resources for health. Improving outcomes and mitigating the risk of adverse events require effective monitoring and quality control systems.

AIM

To assess the state of surgical monitoring and quality control systems at district hospitals (DHs) in Malawi, Tanzania and Zambia.

METHODS

A mixed-methods cross-sectional study of 75 DHs: Malawi (22), Tanzania (30) and Zambia (23). This included a questionnaire, interviews and visual inspection of operating theatre (OT) registers. Data were collected on monitoring and quality systems for surgical activity, processes and outcomes, as well as perceived barriers.

RESULTS

53% (n=40/75) of DHs use more than one OT register to record surgical operations. With the exception of standardised printed OT registers in Zambia, the register format (often handwritten books) and type of data collected varied between DHs. Monthly reports were seldom analysed by surgical teams. Less than 30% of all surveyed DHs used surgical safety checklists (n=22/75), and <15% (n=11/75) performed surgical audits. 73% (n=22/30) of DHs in Tanzania and less than half of DHs in Malawi (n=11/22) and Zambia (n=10/23) conducted surgical case reviews. Reports of surgical morbidity and mortality were compiled in 65% (n=15/23) of Zambian DHs, and in less than one-third of DHs in Tanzania (n=9/30) and Malawi (n=4/22). Reported barriers to monitoring and quality systems included an absence of formalised guidelines, continuous training opportunities as well as inadequate accountability mechanisms.

CONCLUSIONS

Surgical monitoring and quality control systems were not standard among sampled DHs. Improvements are needed in standardisation of quality measures used; and in ensuring data completeness, analysis and utilisation for improving patient outcomes.

Employing learning health system principles to advance research on severe neonatal and paediatric illness in Kenya

We have worked to develop a Clinical Information Network (CIN) in Kenya as an early form of learning health systems (LHS) focused on paediatric and neonatal care that now spans 22 hospitals. CIN's aim was to examine important outcomes of hospitalisation at scale, identify and ultimately solve practical problems of service delivery, drive improvements in quality and test interventions. By including multiple routine settings in research, we aimed to promote generalisability of findings and demonstrate potential efficiencies derived from LHS. We illustrate the nature and range of research CIN has supported over the past 7 years as a form of LHS. Clinically, this has largely focused on common, serious paediatric illnesses such as pneumonia, malaria and diarrhoea with dehydration with recent extensions to neonatal illnesses. CIN also enables examination of the quality of care, for example that provided to children with severe malnutrition and the challenges encountered in routine settings in adopting simple technologies (pulse oximetry) and more advanced diagnostics (eg, Xpert MTB/RIF). Although regular feedback to hospitals has been associated with some improvements in quality data continue to highlight system challenges that undermine provision of basic, quality care (eg, poor access to blood glucose testing and routine microbiology). These challenges include those associated with increased mortality risk (eg, delays in blood transfusion). Using the same data the CIN platform has enabled conduct of randomised trials and supports malaria vaccine and most recently COVID-19 surveillance. Employing LHS principles has meant engaging front-line workers, clinical managers and national stakeholders throughout. Our experience suggests LHS can be developed in low and middle-income countries that efficiently enable contextually appropriate research and contribute to strengthening of health services and research systems.

Making every death count: institutional mortality accuracy at Ola During Children's Hospital, Sierra Leone

Introduction

health care data accuracy feeds the development of sound healthcare policy and the prioritisation of interventions in scarce resource environments. We designed a retrospective study at the sole paediatric government hospital in Sierra Leone to examine mortality statistics, specifically: the accuracy of mortality data collected in 2017; and the quality of cause of death (CoD) reporting for 2017.

Methods

the retrospective audit included all available mortality statistics collected at the hospital during the 2017 calendar year. For the purpose of calculating a mortality rate, admission data was additionally gathered. Four different hospital entities were identified that collected mortality data (the Monitoring and Evaluation (M&E) office; the nurse ledgers; the office of births and deaths; and the mortuary). Data from each hospital entity were used for the comparative analysis.

Results

striking differences were found in the rate of hospital mortality reported by different entities. The M&E office (responsible for providing data to the ministry of health and sanitation) reported a hospital mortality rate of 2.94% in 2017. Mortuary and nursing admissions records showed a hospital mortality rate of 18.7%. Discrepancies and issues of quality in CoD reporting between hospital entities were identified.

Conclusion

significant variations were found in the generation of official hospital mortality data. Mortality data informs health service prioritisation, resource distribution, outcome measures and epidemiological surveillance. Resources to support quality improvement initiatives are needed in the creation of an in-hospital system that reports accurate data with a process for real-time institutional data feedback.

Digital Health Innovations, Tools, and Resources to Support Helping Babies Survive Programs

The Helping Babies Survive (HBS) initiative features a suite of evidence-based curricula and simulation-based training programs designed to provide health workers in low- and middle-income countries (LMICs) with the knowledge, skills, and competencies to prevent, recognize, and manage leading causes of newborn morbidity and mortality. Global scale-up of HBS initiatives has been rapid. As HBS initiatives rolled out across LMIC settings, numerous bottlenecks, gaps, and barriers to the effective, consistent dissemination and implementation of the programs, across both the pre- and in-service continuums, emerged. Within the first decade of expansive scale-up of HBS programs, mobile phone ownership and access to cellular networks have also concomitantly surged in LMICs. In this article, we describe a number of HBS digital health innovations and resources that have been developed from 2010 to 2020 to support education and training, data collection for monitoring and evaluation, clinical decision support, and quality improvement. Helping Babies Survive partners and stakeholders can potentially integrate the described digital tools with HBS dissemination and implementation efforts in a myriad of ways to support low-dose high-frequency skills practice, in-person refresher courses, continuing medical and nursing education, on-the-job training, or peer-to-peer learning, and strengthen data collection for key newborn care and quality improvement indicators and outcomes. Thoughtful integration of purpose-built digital health tools, innovations, and resources may assist HBS practitioners to more effectively disseminate and implement newborn care programs in LMICs, and facilitate progress toward the achievement of Sustainable Development Goal health goals, targets, and objectives.

The age-specific incidence of hospitalized paediatric malaria in Uganda

BACKGROUND

Understanding the relationship between malaria infection risk and disease outcomes represents a fundamental component of morbidity and mortality burden estimations. Contemporary data on severe malaria risks among populations of different parasite exposures are scarce. Using surveillance data, we compared rates of paediatric malaria hospitalisation in areas of varying parasite exposure levels.

METHODS

Surveillance data at five public hospitals; Jinja, Mubende, Kabale, Tororo, and Apac were assembled among admissions aged 1 month to 14 years between 2017 and 2018. The address of each admission was used to define a local catchment population where national census data was used to define person-year-exposure to risk. Within each catchment, historical infection prevalence was assembled from previously published data and current infection prevalence defined using 33 population-based school surveys among 3400 children. Poisson regression was used to compute the overall and site-specific incidences with 95% confidence intervals.

RESULTS

Both current and historical Plasmodium falciparum prevalence varied across the five sites. Current prevalence ranged from < 1% in Kabale to 54% in Apac. Overall, the malaria admission incidence rate (IR) was 7.3 per 1000 person years among children aged 1 month to 14 years of age (95% CI: 7.0, 7.7). The lowest rate was described at Kabale (IR = 0.3; 95 CI: 0.1, 0.6) and highest at Apac (IR = 20.3; 95 CI: 18.9, 21.8). There was a correlation between IR across the five sites and the current parasite prevalence in school children, though findings were not statistically significant. Across all sites, except Kabale, malaria admissions were concentrated among young children, 74% were under 5 years. The median age of malaria admissions at Kabale hospital was 40 months (IQR 20, 72), and at Apac hospital was 36 months (IQR 18, 69). Overall, severe anaemia (7.6%) was the most common presentation and unconsciousness (1.8%) the least common.

CONCLUSION

Malaria hospitalisation rates remain high in Uganda particularly among young children. The incidence of hospitalized malaria in different locations in Uganda appears to be influenced by past parasite exposure, immune acquisition, and current risks of infection. Interruption of transmission through vector control could influence age-specific severe malaria risk.

The Clinical Profile of Severe Pediatric Malaria in an Area Targeted for Routine RTS,S/AS01 Malaria Vaccination in Western Kenya

BACKGROUND

The malaria prevalence has declined in western Kenya, resulting in the risk of neurological phenotypes in older children. This study investigates the clinical profile of pediatric malaria admissions ahead of the introduction of the RTS,S/AS01 vaccine.

METHODS

Malaria admissions in children aged 1 month to 15 years were identified from routine, standardized, inpatient clinical surveillance data collected between 2015 and 2018 from 4 hospitals in western Kenya. Malaria phenotypes were defined based on available data.

RESULTS

There were 5766 malaria admissions documented. The median age was 36 months (interquartile range, 18-60): 15% were aged between 1-11 months of age, 33% were aged 1-23 months of age, and 70% were aged 1 month to 5 years. At admission, 2340 (40.6%) children had severe malaria: 421/2208 (19.1%) had impaired consciousness, 665/2240 (29.7%) had an inability to drink or breastfeed, 317/2340 (13.6%) had experienced 2 or more convulsions, 1057/2340 (45.2%) had severe anemia, and 441/2239 (19.7%) had severe respiratory distress. Overall, 211 (3.7%) children admitted with malaria died; 163/211 (77% deaths, case fatality rate 7.0%) and 48/211 (23% deaths, case fatality rate 1.4%) met the criteria for severe malaria and nonsevere malaria at admission, respectively. The median age for fatal cases was 33 months (interquartile range, 12-72) and the case fatality rate was highest in those unconscious (44.4%).

CONCLUSIONS

Severe malaria in western Kenya is still predominantly seen among the younger pediatric age group and current interventions targeted for those <5 years are appropriate. However, there are increasing numbers of children older than 5 years admitted with malaria, and ongoing hospital surveillance would identify when interventions should target older children.

A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: comparing model predictions and empirical observations

BACKGROUND

The most widely used measures of declining burden of malaria across sub-Saharan Africa are predictions from geospatial models. These models apply spatiotemporal autocorrelations and covariates to parasite prevalence data and then use a function of parasite prevalence to predict clinical malaria incidence. We attempted to assess whether trends in malaria cases, based on local surveillance, were similar to those captured by Malaria Atlas Project (MAP) incidence surfaces.

METHODS

We undertook a systematic review (PROSPERO International Prospective Register of Systematic Reviews; ID = CRD42019116834) to identify empirical data on clinical malaria in Africa since 2000, where reports covered at least 5 continuous years. The trends in empirical data were then compared with the trends of time-space matched clinical malaria incidence from MAP using the Spearman rank correlation. The correlations (rho) between changes in empirically observed and modelled estimates of clinical malaria were displayed by forest plots and examined by meta-regression.

RESULTS

Sixty-seven articles met our inclusion criteria representing 124 sites from 24 African countries. The single most important factor explaining the correlation between empirical observations and modelled predictions was the slope of empirically observed data over time (rho = - 0.989; 95% CI - 0.998, - 0.939; p < 0.001), i.e. steeper declines were associated with a stronger correlation between empirical observations and modelled predictions. Factors such as quality of study, reported measure of malaria and endemicity were only slightly predictive of such correlations.

CONCLUSIONS

In many locations, both local surveillance data and modelled estimates showed declines in malaria burden and hence similar trends. However, there was a weak association between individual surveillance datasets and the modelled predictions where stalling in progress or resurgence of malaria burden was empirically observed. Surveillance data were patchy, indicating a need for improved surveillance to strengthen both empiric reporting and modelled predictions.

Evaluating the impact of a hospital scale-up phase of a quality improvement intervention in Ghana on mortality for children under five

OBJECTIVE

To evaluate the scale-up phase of a national quality improvement initiative across hospitals in Southern Ghana.

DESIGN

This evaluation used a comparison of pre- and post-intervention means to assess changes in outcomes over time. Multivariable interrupted time series analyses were performed to determine whether change categories (interventions) tested were associated with improvements in the outcomes.

SETTING

Hospitals in Southern Ghana.

PARTICIPANTS

The data sources were monthly outcome data from intervention hospitals along with program records.

INTERVENTION

The project used a quality improvement approach whereby process failures were identified by health staff and process changes were implemented in hospitals and their corresponding communities. The three change categories were: timely care-seeking, prompt provision of care and adherence to protocols.

MAIN OUTCOME MEASURES

Facility-level neonatal mortality, facility-level postneonatal infant mortality and facility-level postneonatal under-five mortality.

RESULTS

There were significant improvements for two outcomes from the pre-intervention to the post-intervention phase. Postneonatal infant mortality dropped from 44.3 to 21.1 postneonatal infant deaths per 1000 admissions, while postneonatal under-five mortality fell from 23.1 to 11.8 postneonatal under-five deaths per 1000 admissions. The multivariable interrupted time series analysis indicated that over the long-term the prompt provision of care change category was significantly associated with reduced postneonatal under five mortality (β = -0.0024, 95% CI -0.0051, 0.0003, P < 0.10).

CONCLUSIONS

The reduced postneonatal under-five mortality achieved in this project gives support to the promotion of quality improvement as a means to achieve health impacts at scale.

Care practices and neonatal survival in 52 neonatal intensive care units in Telangana and Andhra Pradesh, India: A cross-sectional study

BACKGROUND

The Indian government supports both public- and private-sector provision of hospital care for neonates: neonatal intensive care is offered in public facilities alongside a rising number of private-for-profit providers. However, there are few published reports about mortality levels and care practices in these facilities. We aimed to assess care practices, causes of admission, and outcomes from neonatal intensive care units (NICUs) in public secondary and private tertiary hospitals and both public and private medical colleges enrolled in a quality improvement collaborative in Telangana and Andhra Pradesh-2 Indian states with a respective population of 35 and 50 million.

METHODS AND FINDINGS

We conducted a cross-sectional study between 30 May and 26 August 2016 as part of a baseline evaluation in 52 consenting hospitals (26 public secondary hospitals, 5 public medical colleges, 15 private tertiary hospitals, and 6 private medical colleges) offering neonatal intensive care. We assessed the availability of staff and services, adherence to evidence-based practices at admission, and case fatality after admission to the NICU using a range of tools, including facility assessment, observations of admission, and abstraction of registers and telephone interviews after discharge. Our analysis is adjusted for clustering and weighted for caseload at the hospital level and presents findings stratified by type and ownership of hospitals. In total, the NICUs included just over 3,000 admissions per month. Staffing and infrastructure provision were largely according to government guidelines, except that only a mean of 1 but not the recommended 4 paediatricians were working in public secondary NICUs per 10 beds. On admission, all neonates admitted to private hospitals had auscultation (100%, 19 of 19 observations) but only 42% (95% confidence interval [CI] 25%-62%, p-value for difference is 0.361) in public secondary hospitals. The most common single cause of admission was preterm birth (25%) followed by jaundice (23%). Case-fatality rates at age 28 days after admission to a NICU were 4% (95% CI 2%-8%), 15% (9%-24%), 4% (2%-8%) and 2% (1%-5%) (Chi-squared p = 0.001) in public secondary hospitals, public medical colleges, private tertiary hospitals, and private medical colleges, respectively, according to facility registers. Case fatality according to postdischarge telephone interviews found rates of 12% (95% CI 7%-18%) for public secondary hospitals. Roughly 6% of admitted neonates were referred to another facility. Outcome data were missing for 27% and 8% of admissions to private tertiary hospitals and private medical colleges. Our study faced the limitation of missing data due to incomplete documentation. Further generalizability was limited due to the small sample size among private facilities.

CONCLUSIONS

Our findings suggest differences in quality of neonatal intensive care and 28-day survival between the different types of hospitals, although comparison of outcomes is complicated by differences in the case mix and referral practices between hospitals. Uniform reporting of outcomes and risk factors across the private and public sectors is required to assess the benefits for the population of mixed-care provision.

Competing risk survival analysis of time to in-hospital death or discharge in a large urban neonatal unit in Kenya

Background: Clinical outcomes data are a crucial component of efforts to improve health systems globally. Strengthening of these health systems is essential if the Sustainable Development Goals (SDG) are to be achieved. Target 3.2 of SDG Goal 3 is to end preventable deaths and reduce neonatal mortality to 12 per 1,000 or lower by 2030. There is a paucity of data on neonatal in-hospital mortality in Kenya that is poorly captured in the existing health information system. Better measurement of neonatal mortality in facilities may help promote improvements in the quality of health care that will be important to achieving SDG 3 in countries such as Kenya. Methods: This was a cohort study using routinely collected data from a large urban neonatal unit in Nairobi, Kenya. All the patients admitted to the unit between April 2014 to December 2015 were included. Clinical characteristics are summarised descriptively, while the competing risk method was used to estimate the probability of in-hospital mortality considering discharge alive as the competing risk. Results: A total of 9,115 patients were included. Most were males (966/9115, 55%) and the majority (6287/9115, 69%) had normal birthweight (2.5 to 4 kg). Median length of stay was 2 days (range, 0 to 98 days) while crude mortality was 9.2% (839/9115). The probability of in-hospital death was higher than discharge alive for birthweight less than 1.5 kg with the transition to higher probability of discharge alive observed after the first week in birthweight 1.5 to <2 kg. Conclusions: These prognostic data may inform decision making, e.g. in the organisation of neonatal in-patient service delivery to improve the quality of care. More of such data are therefore required from neonatal units in Kenya and other low resources settings especially as more advanced neonatal care is scaled up.

On the estimation of population cause-specific mortality fractions from in-hospital deaths

BACKGROUND

Almost all countries without complete vital registration systems have data on deaths collected by hospitals. However, these data have not been widely used to estimate cause of death (COD) patterns in populations because only a non-representative fraction of people in these countries die in health facilities. Methods that can exploit hospital mortality statistics to reliably estimate community COD patterns are required to strengthen the evidence base for disease and injury control programs. We propose a method that weights hospital-certified causes by the probability of death to estimate population cause-specific mortality fractions (CSMFs).

METHODS

We used an established verbal autopsy instrument (VAI) to collect data from hospital catchment areas in Chandpur and Comilla Districts, Bangladesh, and Bohol province, the Philippines, between 2011 and 2014, along with demographic covariates for each death. Hospital medical certificates of cause of death (death certificates) were collected and mapped to the corresponding cause categories of the VAI. Tariff 2.0 was used to assign a COD for community deaths. Logistic regression models were created for broad causes in each country to calculate the probability of in-hospital death, given a set of covariate values. The reweighted CSMFs for deaths in the hospital catchment population, represented by each hospital death, were calculated from the corresponding regression models.

RESULTS

We collected data on 4228 adult deaths in the Philippines and 3725 deaths in Bangladesh. Short time to hospital and education were consistently associated with in-hospital death in the Philippines and absence of a disability was consistently associated with in-hospital death in Bangladesh. Non-communicable diseases (excluding stroke) and stroke were the leading causes of death in both the Philippines (33.9%, 19.1%) and Bangladesh (46.1%, 21.1%) according to the reweighted method. The reweighted method generally estimated CSMFs that fell between those derived from hospitals and those diagnosed by Tariff 2.0.

CONCLUSIONS

Statistical methods can be used to derive estimates of cause-specific probability of death in-hospital for Bangladesh and the Philippines to generate population CSMFs. In regions where hospital death certification is of reasonable quality and routine verbal autopsy is not applied, these estimates could be applied to generate cost-effective and robust CSMFs for the population.