Predictors of quality of childcare centers in low-income settings: findings from a cross-sectional study in two Nairobi slums

Background: Rapid urbanization and increased women’s involvement in paid work have contributed to the upsurge of informal childcare centers, especially in low-income settings where quality is a major issue. However, there are limited data on the factors associated with the quality of childcare centers in informal settlements in Africa.Methods: We conducted a quantitative observation and questionnaire survey of 66 childcare centers to identify the factors associated with the quality of childcare services in two informal settlements (Korogocho and Viwandani) in Nairobi. The quality of the centers (outcome variable) was assessed using a locally developed tool. Data on center characteristics including type, size, location, length of operation, charges, and number of staff were collected. Center providers’ knowledge, attitude, and practices (KAP) in childcare were assessed through a questionnaire, focusing on nurturing care and business management. Data were described using means and standard deviation or frequencies and percentages. Associations between quality center score (outcome variable) and other variables were examined using multivariable linear regression to identify potential predictors of the quality of the center environment.Findings: A total of 129 childcare centers were identified and categorized as home-based (n = 45), center-based (n = 14), school-based (n = 61), and church-based (n = 9). The number of home-based centers was particularly high in Viwandani (n = 40; 52%). Only 9% of home-based centers reported any external support and 20% had any training on early childhood development. Of the 129 centers, 66 had complete detailed assessment of predictors of quality reported here. Unadjusted linear regressions revealed associations between quality of childcare center and center providers’ education level, type of center, support received, caregiver–child ratio, number of children in the center, and center providers’ KAP score (p < 0.05). However, in the multivariable regression, only higher levels of center provider KAP ( β = 0.51; 95% CI: 0.18, 0.84; p = 0.003) and center type ( β = 8.68; 95% CI: 2.32, 15.04; p = 0.008) were significantly associated with center quality score.Implication: Our results show that center providers’ knowledge and practices are a major driver of the quality of childcare centers in informal settlements in Nairobi. Interventions for improving the quality of childcare services in such settings should invest in equipping center providers with the necessary knowledge and skills through training and supportive supervision

The feasibility, acceptability, cost and benefits of a “communities of practice” model for improving the quality of childcare centres: a mixed-methods study in the informal settlements in Nairobi

Background: Informal childcare centres have mushroomed in the informal settlements of Nairobi, Kenya to meet the increasing demand. However, centre providers are untrained and the facilities are below standard putting children at risk of poor health and development. We aimed to co-design and test the feasibility, acceptability, cost and potential benefits of a communities of practice (CoP) model where trained community health volunteers (CHVs) provide group training sessions to build skills and improve practices in informal childcare centres.Methods: A CoP model was co-designed with sub-county health teams, centre providers and parents with inputs from Kidogo, government nutritionists and ECD experts and implemented in 68 childcare centres by trained CHVs. Its feasibility and potential benefits were measured quantitatively and qualitatively. Centre provider (n = 68) and CHV (n = 20) knowledge and practice scores before and after the intervention were assessed and compared. Intervention benefits were examined using linear regressions adjusting for potential confounding factors. We conducted in-depth interviews with 10 parents, 10 CHVs, 10 centre providers and 20 local government officials, and two focus groups with CHVs and centre providers. Qualitative data were analysed, focusing on feasibility, acceptability, potential benefits, challenges and ideas for improvement. Cost for delivering and accessing the intervention were examined.Results: The intervention was acceptable and feasible to deliver within existing government community health systems; 16 CHVs successfully facilitated CoP sessions to 58 centre providers grouped into 13 groups each with 5–6 centre providers, each group receiving four sessions representing the four modules. There were significant improvements in provider knowledge and practice (effect size = 0.40; p < 0.05) and quality of centre environment (effect size = 0.56; p < 0.01) following the intervention. CHVs’ scores showed no significant changes due to pre-existing high knowledge levels. Qualitative interviews also reported improvements in knowledge and practices and the desire among the different participants for the support to be continued. The total explicit costs were USD 22,598 and the total opportunity costs were USD 3,632 (IQR; USD 3,570, USD 4,049).Conclusion: A simple model delivered by CHVs was feasible and has potential to improve the quality of informal childcare centres. Leveraging these teams and integration of the intervention into the health system is likely to enable scale-up and sustainability in Kenya and similar contexts

HIV Prevention in a Time of COVID-19: A Report from the HIVR4P // Virtual Conference 2021.

The HIV Research for Prevention (HIVR4P) conference catalyzes knowledge sharing on biomedical HIV prevention interventions such as HIV vaccines, antibody infusions, pre-exposure prophylaxis, and microbicides in totality-from the molecular details and delivery formulations to the behavioral, social, and structural underpinnings. HIVR4P // Virtual was held over the course of 2 weeks on January 27-28 and February 3-4, 2021 as the coronavirus disease 2019 (COVID-19) pandemic continued to inflict unprecedented harm globally. The HIVR4P community came together with 1,802 researchers, care providers, policymakers, implementers, and advocates from 92 countries whose expertise spanned the breadth of the HIV prevention pipeline from preclinical to implementation. The program included 113 oral and 266 poster presentations. This article presents a brief summary of the conference highlights. Complete abstracts, webcasts, and daily rapporteur summaries may be found on the conference website (https://www.hivr4p.org/)

Rapid outbreak sequencing of Ebola virus in Sierra Leone identifies transmission chains linked to sporadic cases.

To end the largest known outbreak of Ebola virus disease (EVD) in West Africa and to prevent new transmissions, rapid epidemiological tracing of cases and contacts was required. The ability to quickly identify unknown sources and chains of transmission is key to ending the EVD epidemic and of even greater importance in the context of recent reports of Ebola virus (EBOV) persistence in survivors. Phylogenetic analysis of complete EBOV genomes can provide important information on the source of any new infection. A local deep sequencing facility was established at the Mateneh Ebola Treatment Centre in central Sierra Leone. The facility included all wetlab and computational resources to rapidly process EBOV diagnostic samples into full genome sequences. We produced 554 EBOV genomes from EVD cases across Sierra Leone. These genomes provided a detailed description of EBOV evolution and facilitated phylogenetic tracking of new EVD cases. Importantly, we show that linked genomic and epidemiological data can not only support contact tracing but also identify unconventional transmission chains involving body fluids, including semen. Rapid EBOV genome sequencing, when linked to epidemiological information and a comprehensive database of virus sequences across the outbreak, provided a powerful tool for public health epidemic control efforts

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

A structured approach to recording AIDS-defining illnesses in Kenya: A SNOMED CT based solution

Several studies conducted in sub-Saharan Africa (SSA) have shown that routine clinical data in HIV clinics often have errors. Lack of structured and coded documentation of diagnosis of AIDS defining illnesses (ADIs) can compromise data quality and decisions made on clinical care. We used a structured framework to derive a reference set of concepts and terms used to describe ADIs. The four sources used were: (i) CDC/Accenture list of opportunistic infections, (ii) SNOMED Clinical Terms (SNOMED CT), (iii) Focus Group Discussion (FGD) among clinicians and nurses attending to patients at a referral provincial hospital in western Kenya, and (iv) chart abstraction from the Maternal Child Health (MCH) and HIV clinics at the same hospital. Using the January 2014 release of SNOMED CT, concepts were retrieved that matched terms abstracted from approach iii & iv, and the content coverage assessed. Post-coordination matching was applied when needed. The final reference set had 1054 unique ADI concepts which were described by 1860 unique terms. Content coverage of SNOMED CT was high (99.9% with pre-coordinated concepts; 100% with post-coordination). The resulting reference set for ADIs was implemented as the interface terminology on OpenMRS data entry forms. Different sources demonstrate complementarity in the collection of concepts and terms for an interface terminology. SNOMED CT provides a high coverage in the domain of ADIs. Further work is needed to evaluate the effect of the interface terminology on data quality and quality of car

Better adherence to pre-antiretroviral therapy guidelines after implementing an electronic medical record system in rural Kenyan HIV clinics: a multicenter pre–post study

Introduction: The monitoring of pre-antiretroviral therapy (pre-ART) is a key indicator of HIV quality of care. This study investigated the association of an electronic medical record system (EMR) with adherence to pre-ART guidelines in rural HIV clinics in Kenya. Methods: A retrospective study was carried out to assess the quality of pre-ART care using three indicators: (1) the performance of a baseline CD4 test, (2) time from enrollment in care to first CD4 test, and (3) time from baseline CD4 to second CD4 test. A comparison of these indicators was made pre and post the introduction of an EMR system in 17 rural HIV clinics. Results: A total of 18 523 patients were receiving pre-ART care, of whom 38.8% in the paper group had had at least one CD4 test compared to 53.4% in the EMR group (p < 0.001). The adjusted odds of performing a CD4 test in clinics using an EMR was 1.59 (95% confidence interval 1.49–1.69). The median time from enrolment into HIV care to first CD4 test was 1.40 months (interquartile range (IQR) 0.47–4.87) for paper vs. 0.93 months (IQR 0.43–3.37) for EMR. The median time from baseline to first CD4 follow-up was 7.5 months (IQR 5.97–10.73) for paper and 6.53 months (IQR 5.57–7.87) for EMR. Conclusion: The use of the EMR system was associated with better compliance to HIV guidelines for pre-ART care. EMRs have a potential positive impact on quality of care for HIV patients in resource-constrained settings

Electronic medical record systems are associated with appropriate placement of HIV patients on antiretroviral therapy in rural health facilities in Kenya: a retrospective pre-post study

There is little evidence that electronic medical record (EMR) use is associated with better compliance with clinical guidelines on initiation of antiretroviral therapy (ART) among ART-eligible HIV patients. We assessed the effect of transitioning from paper-based to an EMR-based system on appropriate placement on ART among eligible patients. We conducted a retrospective, pre-post EMR study among patients enrolled in HIV care and eligible for ART at 17 rural Kenyan clinics and compared the: (1) proportion of patients eligible for ART based on CD4 count or WHO staging who initiate therapy; (2) time from eligibility for ART to ART initiation; (3) time from ART initiation to first CD4 test. 7298 patients were eligible for ART; 54.8% (n=3998) were enrolled in HIV care using a paper-based system while 45.2% (n=3300) were enrolled after the implementation of the EMR. EMR was independently associated with a 22% increase in the odds of initiating ART among eligible patients (adjusted OR (aOR) 1.22, 95% CI 1.12 to 1.33). The proportion of ART-eligible patients not receiving ART was 20.3% and 15.1% for paper and EMR, respectively (χ(2)=33.5, p <0.01). Median time from ART eligibility to ART initiation was 29.1 days (IQR: 14.1-62.1) for paper compared to 27 days (IQR: 12.9-50.1) for EMR. EMRs can improve quality of HIV care through appropriate placement of ART-eligible patients on treatment in resource limited settings. However, other non-EMR factors influence timely initiation of AR

Prioritizing interventions for cholera control in Kenya, 2015-2020.

Kenya has experienced cholera outbreaks since 1971, with the most recent wave beginning in late 2014. Between 2015-2020, 32 of 47 counties reported 30,431 suspected cholera cases. The Global Task Force for Cholera Control (GTFCC) developed a Global Roadmap for Ending Cholera by 2030, which emphasizes the need to target multi-sectoral interventions in priority cholera burden hotspots. This study utilizes the GTFCC's hotspot method to identify hotspots in Kenya at the county and sub-county administrative levels from 2015 through 2020. 32 of 47 (68.1%) counties reported cholera cases during this time while only 149 of 301 (49.5%) sub-counties reported cholera cases. The analysis identifies hotspots based on the mean annual incidence (MAI) over the past five-year period and cholera's persistence in the area. Applying a MAI threshold of 90th percentile and the median persistence at both the county and sub-county levels, we identified 13 high risk sub-counties from 8 counties, including the 3 high risk counties of Garissa, Tana River and Wajir. This demonstrates that several sub-counties are high level hotspots while their counties are not. In addition, when cases reported by county versus sub-county hotspot risk are compared, 1.4 million people overlapped in the areas identified as both high-risk county and high-risk sub-county. However, assuming that finer scale data is more accurate, 1.6 million high risk sub-county people would have been misclassified as medium risk with a county-level analysis. Furthermore, an additional 1.6 million people would have been classified as living in high-risk in a county-level analysis when at the sub-county level, they were medium, low or no-risk sub-counties. This results in 3.2 million people being misclassified when county level analysis is utilized rather than a more-focused sub-county level analysis. This analysis highlights the need for more localized risk analyses to target cholera intervention and prevention efforts towards the populations most vulnerable

Effect of a clinical decision support system on early action on immunological treatment failure in patients with HIV in Kenya: a cluster randomised controlled trial

A clinical decision support system (CDSS) is a computer program that applies a set of rules to data stored in electronic health records to offer actionable recommendations. We aimed to establish whether a CDSS that supports detection of immunological treatment failure among patients with HIV taking antiretroviral therapy (ART) would improve appropriate and timely action. We did this prospective, cluster randomised controlled trial in adults and children (aged ≥18 months) who were eligible for, and receiving, ART at HIV clinics in Siaya County, western Kenya. Health facilities were randomly assigned (1:1), via block randomisation (block size of two) with a computer-generated random number sequence, to use electronic health records either alone (control) or with CDSS (intervention). Facilities were matched by type and by number of patients enrolled in HIV care. The primary outcome measure was the difference between groups in the proportion of patients who experienced immunological treatment failure and had a documented clinical action. We used generalised linear mixed models with random effects to analyse clustered data. This trial is registered with ClinicalTrials.gov, number NCT01634802. Between Sept 1, 2012, and Jan 31, 2014, 13 clinics, comprising 41,062 patients, were randomly assigned to the control group (n=6) or the intervention group (n=7). Data collection at each site took 12 months. Among patients eligible for ART, 10,358 (99%) of 10,478 patients were receiving ART at control sites and 10,991 (99%) of 11,028 patients were receiving ART at intervention sites. Of these patients, 1125 (11%) in the control group and 1342 (12%) in the intervention group had immunological treatment failure, of whom 332 (30%) and 727 (54%), respectively, received appropriate action. The likelihood of clinicians taking appropriate action on treatment failure was higher with CDSS alerts than with no decision support system (adjusted odds ratio 3·18, 95% CI 1·02-9·87). CDSS significantly improved the likelihood of appropriate and timely action on immunological treatment failure. We expect our findings will be generalisable to virological monitoring of patients with HIV receiving ART once countries implement the 2015 WHO recommendation to scale up viral load monitoring. US President's Emergency Plan for AIDS Relief (PEPFAR), through the US Centers for Disease Control and Preventio