Differences in clinical outcomes of bloodstream infections caused by Klebsiella aerogenes, Klebsiella pneumoniae and Enterobacter cloacae: a multicentre cohort study.

BACKGROUND: Klebsiella aerogenes has been reclassified from Enterobacter to Klebsiella genus due to its phenotypic and genotypic similarities with Klebsiella pneumoniae. It is unclear if clinical outcomes are also more similar. This study aims to assess clinical outcomes of bloodstreams infections (BSI) caused by K. aerogenes, K. pneumoniae and Enterobacter cloacae, through secondary data analysis, nested in PRO-BAC cohort study. METHODS: Hospitalized patients between October 2016 and March 2017 with monomicrobial BSI due to K. aerogenes, K. pneumoniae or E. cloacae were included. Primary outcome was a composite clinical outcome including all-cause mortality or recurrence until 30 days follow-up. Secondary outcomes were fever ≥ 72 h, persistent bacteraemia, and secondary device infection. Multilevel mixed-effect Poisson regression was used to estimate the association between microorganisms and outcome. RESULTS: Overall, 29 K. aerogenes, 77 E. cloacae and 337 K. pneumoniae BSI episodes were included. Mortality or recurrence was less frequent in K. aerogenes (6.9%) than in E. cloacae (20.8%) or K. pneumoniae (19.0%), but statistical difference was not observed (rate ratio (RR) 0.35, 95% CI 0.08 to 1.55; RR 0.42, 95% CI 0.10 to 1.71, respectively). Fever ≥ 72 h and device infection were more common in K. aerogenes group. In the multivariate analysis, adjusted for confounders (age, sex, BSI source, hospital ward, Charlson score and active antibiotic therapy), the estimates and direction of effect were similar to crude results. CONCLUSIONS: Results suggest that BSI caused by K. aerogenes may have a better prognosis than E. cloacae or K. pneumoniae BSI.

COVID-19 pandemic effects on neonatal inpatient admissions and mortality: interrupted time series analysis of facilities implementing NEST360 in Kenya, Malawi, Nigeria, and Tanzania.

BACKGROUND: The emergence of COVID-19 precipitated containment policies (e.g., lockdowns, school closures, etc.). These policies disrupted healthcare, potentially eroding gains for Sustainable Development Goals including for neonatal mortality. Our analysis aimed to evaluate indirect effects of COVID-19 containment policies on neonatal admissions and mortality in 67 neonatal units across Kenya, Malawi, Nigeria, and Tanzania between January 2019 and December 2021. METHODS: The Oxford Stringency Index was applied to quantify COVID-19 policy stringency over time for Kenya, Malawi, Nigeria, and Tanzania. Stringency increased markedly between March and April 2020 for these four countries (although less so in Tanzania), therefore defining the point of interruption. We used March as the primary interruption month, with April for sensitivity analysis. Additional sensitivity analysis excluded data for March and April 2020, modelled the index as a continuous exposure, and examined models for each country. To evaluate changes in neonatal admissions and mortality based on this interruption period, a mixed effects segmented regression was applied. The unit of analysis was the neonatal unit (n = 67), with a total of 266,741 neonatal admissions (January 2019 to December 2021). RESULTS: Admission to neonatal units decreased by 15% overall from February to March 2020, with half of the 67 neonatal units showing a decline in admissions. Of the 34 neonatal units with a decline in admissions, 19 (28%) had a significant decrease of ≥ 20%. The month-to-month decrease in admissions was approximately 2% on average from March 2020 to December 2021. Despite the decline in admissions, we found no significant changes in overall inpatient neonatal mortality. The three sensitivity analyses provided consistent findings. CONCLUSION: COVID-19 containment measures had an impact on neonatal admissions, but no significant change in overall inpatient neonatal mortality was detected. Additional qualitative research in these facilities has explored possible reasons. Strengthening healthcare systems to endure unexpected events, such as pandemics, is critical in continuing progress towards achieving Sustainable Development Goals, including reducing neonatal deaths to less than 12 per 1000 live births by 2030.

Health facility assessment of small and sick newborn care in low- and middle-income countries: systematic tool development and operationalisation with NEST360 and UNICEF.

BACKGROUND: Each year an estimated 2.3 million newborns die in the first 28 days of life. Most of these deaths are preventable, and high-quality neonatal care is fundamental for surviving and thriving. Service readiness is used to assess the capacity of hospitals to provide care, but current health facility assessment (HFA) tools do not fully evaluate inpatient small and sick newborn care (SSNC). METHODS: Health systems ingredients for SSNC were identified from international guidelines, notably World Health Organization (WHO), and other standards for SSNC. Existing global and national service readiness tools were identified and mapped against this ingredients list. A novel HFA tool was co-designed according to a priori considerations determined by policymakers from four African governments, including that the HFA be completed in one day and assess readiness across the health system. The tool was reviewed by > 150 global experts, and refined and operationalised in 64 hospitals in Kenya, Malawi, Nigeria, and Tanzania between September 2019 and March 2021. RESULTS: Eight hundred and sixty-six key health systems ingredients for service readiness for inpatient SSNC were identified and mapped against four global and eight national tools measuring SSNC service readiness. Tools revealed major content gaps particularly for devices and consumables, care guidelines, and facility infrastructure, with a mean of 13.2% (n = 866, range 2.2-34.4%) of ingredients included. Two tools covered 32.7% and 34.4% (n = 866) of ingredients and were used as inputs for the new HFA tool, which included ten modules organised by adapted WHO health system building blocks, including: infrastructure, pharmacy and laboratory, medical devices and supplies, biomedical technician workshop, human resources, information systems, leadership and governance, family-centred care, and infection prevention and control. This HFA tool can be conducted at a hospital by seven assessors in one day and has been used in 64 hospitals in Kenya, Malawi, Nigeria, and Tanzania. CONCLUSION: This HFA tool is available open-access to adapt for use to comprehensively measure service readiness for level-2 SSNC, including respiratory support. The resulting facility-level data enable comparable tracking for Every Newborn Action Plan coverage target four within and between countries, identifying facility and national-level health systems gaps for action.

Quantifying health facility service readiness for small and sick newborn care: comparing standards-based and WHO level-2 + scoring for 64 hospitals implementing with NEST360 in Kenya, Malawi, Nigeria, and Tanzania.

BACKGROUND: Service readiness tools are important for assessing hospital capacity to provide quality small and sick newborn care (SSNC). Lack of summary scoring approaches for SSNC service readiness means we are unable to track national targets such as the Every Newborn Action Plan targets. METHODS: A health facility assessment (HFA) tool was co-designed by Newborn Essential Solutions and Technologies (NEST360) and UNICEF with four African governments. Data were collected in 68 NEST360-implementing neonatal units in Kenya, Malawi, Nigeria, and Tanzania (September 2019-March 2021). Two summary scoring approaches were developed: a) standards-based, including items for SSNC service readiness by health system building block (HSBB), and scored on availability and functionality, and b) level-2 + , scoring items on readiness to provide WHO level-2 + clinical interventions. For each scoring approach, scores were aggregated and summarised as a percentage and equally weighted to obtain an overall score by hospital, HSBB, and clinical intervention. RESULTS: Of 1508 HFA items, 1043 (69%) were included in standards-based and 309 (20%) in level-2 + scoring. Sixty-eight neonatal units across four countries had median standards-based scores of 51% [IQR 48-57%] at baseline, with variation by country: 62% [IQR 59-66%] in Kenya, 49% [IQR 46-51%] in Malawi, 50% [IQR 42-58%] in Nigeria, and 55% [IQR 53-62%] in Tanzania. The lowest scoring was family-centred care [27%, IQR 18-40%] with governance highest scoring [76%, IQR 71-82%]. For level-2 + scores, the overall median score was 41% [IQR 35-51%] with variation by country: 50% [IQR 44-53%] in Kenya, 41% [IQR 35-50%] in Malawi, 33% [IQR 27-37%] in Nigeria, and 41% [IQR 32-52%] in Tanzania. Readiness to provide antibiotics by culture report was the highest-scoring intervention [58%, IQR 50-75%] and neonatal encephalopathy management was the lowest-scoring [21%, IQR 8-42%]. In both methods, overall scores were low (< 50%) for 27 neonatal units in standards-based scoring and 48 neonatal units in level-2 + scoring. No neonatal unit achieved high scores of > 75%. DISCUSSION: Two scoring approaches reveal gaps in SSNC readiness with no neonatal units achieving high scores (> 75%). Government-led quality improvement teams can use these summary scores to identify areas for health systems change. Future analyses could determine which items are most directly linked with quality SSNC and newborn outcomes.

Conducting cross-cultural, multi-lingual or multi-country scale development and validation in health care research: A 10-step framework based on a scoping review.

Background: Valid, reliable and cross-cultural equivalent scales and measurement instruments that enable comparisons across diverse populations in different countries are important for global health research and practice. We developed a 10-step framework through a scoping review of the common strategies and techniques used for scale development and validation in a cross-cultural, multi-lingual, or multi-country setting, especially in health care research. Methods: We searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies that collected data from two or more countries or in two or more languages at any stages of scale development or validation and published between 2010-22. We categorised the techniques into three commonly used scale development and validation stages (item generation, scale development, and scale evaluation) as well as during the translation stage. We described the most commonly used techniques at each stage. Results: We identified 141 studies that were included in the analysis. We summarised 14 common techniques and strategies, including focus groups or interviews with diverse target populations, and involvement of measurement experts and linguists for item content validity expert panel at the item generation stage; back-and-forth translation, collaborative team approach for the translation stage; cognitive interviews and different recruitment strategies and incentives in different settings for scale development stage; and three approaches for measurement invariance (multigroup confirmatory factor analysis, differential item functioning and multiple indicator multiple causes) for scale evaluation stage. Conclusions: We provided a 10-step framework for cross-cultural, multi-lingual or multi-country scale development and validation based on these techniques and strategies. More research and synthesis are needed to make scale development more culturally competent and enable scale application to better meet local health and development needs.

Experiences of core outcome set developers on including stakeholders from low- and middle-income countries: An online survey.

Core outcome set (COS) development and use enhances comparability of research findings. It may also enhance the translation of research into practice and reduce research waste. However, there is limited involvement of stakeholders from low- and middle-income countries (LMICs) in COS development and use. In this study, we explored the experiences of researchers in COS development projects who included stakeholders from LMICs. Online survey conducted in English of 70 COS developers from HICs who had included LMIC stakeholders in the process of developing a COS, published before the end of 2019. Respondents were identified from the COMET database and sent a link to the survey via a personalised email. Quantitative data were analysed using simple descriptive statistics. Qualitative data analysis was based on qualitative content analysis. There were 37 respondents yielding a 53% overall response rate. Analysis was limited to the responses related to 29 COS developed in the years 2015 to 2019, to reduce the potential for recall bias for earlier COS. Most respondents 20/29 (69%) were researchers. Determining 'what to measure' was reported as the most common stage of inclusion of LMIC stakeholders. Respondents cited (24/29, 83%) their ongoing collaborations with LMIC stakeholders such as clinicians or researchers as their main rationale for including LMICs stakeholders and reported that translation of the Delphi into languages other than English may be useful to enhance wider stakeholder participation. Involvement of LMIC stakeholders only in the later stages of COS development, lack of adequate resources to support their involvement, and lack of networks and contacts were thought to limit fuller participation of stakeholders from LMICs. To improve the involvement of LMIC stakeholders in COS development and use, COS developers need to raise awareness on the utility of COS. The need for and feasibility of translation into multiple languages warrants further discussion.

To adopt or adapt an existing neonatal core outcome set in Kenya: a study protocol.

BACKGROUND: Development and use of core outcome set(s) (COS) in research can reduce research wastage by ensuring that a minimum set of outcomes are always reported on. Neonatal morbidity and mortality are a big burden in low- and middle-income countries (LMICs). Research is continuously being undertaken to reduce this burden. Currently, there is no COS for neonatal research in LMICs but there exists one for neonatal research in high-income countries (HICs). OBJECTIVES: To determine outcomes that are useful for neonatal care in Kenya to inform whether an existing COS should be adopted or adapted. To assess the feasibility of a routine data collection system to collect data of the agreed-on COS. METHODS: A review of existing literature on neonatal research in LMICs followed by a qualitative study of key stakeholders in neonatal care. To explore whether to adapt or adopt, in two hospitals, two focus group discussions with 6-8 parents/caregivers will be undertaken (one each in two hospitals). Key informant interviews will also be conducted with 6 health care providers in each of the hospitals. At the policy-making level, we will conduct 10 key informant interviews. Qualitative data will be analyzed thematically. A consensus meeting will be undertaken with key stakeholders, who will be presented with an overview of the COS developed for HICs, key findings from the literature, and the qualitative study to determine context-appropriate COS. The agreed-on outcomes will be counterchecked against the case records in the two hospitals. The feasibility of collecting the outcomes on a routine electronic research database, the Clinical Information Network that collects standardized data at admission and discharge, will be explored. The congruence (or not) of the outcomes will be documented and be used to enrich the discussion and provide a snapshot of the feasibility of the health information system to collect routine data on the COS. CONCLUSIONS: A COS for use in neonatal care in Kenya will help enhance outcome measurements and reporting not just in research but also in routine practice. This will enhance the comparability of interventions in trials and routine settings leading to reduced research wastage and likely improved quality of care. Additionally, the methodology used for this work can be adopted in other settings as a means of adopting or adapting an existing COS.