A multisectoral investigation of a neonatal unit outbreak of Klebsiella pneumoniae bacteraemia at a regional hospital in Gauteng Province, South Africa

BACKGROUND. Rates of healthcare-associated infections (HAIs) among babies born in developing countries are higher than among those born in resource-rich countries, as a result of suboptimal infection prevention and control (IPC) practices. Following two reported deaths of neonates with carbapenem-resistant Klebsiella pneumoniae bloodstream infections (BSIs), we conducted an outbreak investigation in a neonatal unit of a regional hospital in Gauteng Province, South Africa. OBJECTIVES. To confirm an outbreak of K. pneumoniae BSIs and assess the IPC programme in the neonatal unit. METHODS. We calculated total and organism-specific BSI incidence risks for culture-confirmed cases in the neonatal unit for baseline and outbreak periods. We conducted a clinical record review for a subset of cases with K. pneumoniae BSI that had been reported to the investigating team by the neonatal unit. An IPC audit was performed in different areas of the neonatal unit. We confirmed species identification and antimicrobial susceptibility, and used polymerase chain reaction for confirmation of carbapenemase genes and pulsedfield gel electrophoresis (PFGE) for typing of submitted clinical isolates. RESULTS. From January 2017 to August 2018, 5 262 blood cultures were submitted, of which 11% (560/5 262) were positive. Of 560 positive blood cultures, 52% (n=292) were positive for pathogenic organisms associated with healthcare-associated BSIs. K. pneumoniae comprised the largest proportion of these cases (32%; 93/292). The total incidence risk of healthcare-associated BSI for the baseline period (January 2017 - March 2018) was 6.8 cases per 100 admissions, and that for the outbreak period (April - September 2018) was 10.1 cases per 100 admissions. The incidence risk of K. pneumoniae BSI for the baseline period was 1.6 cases per 100 admissions, compared with 5.0 cases per 100 admissions during the outbreak period. Average bed occupancy for the entire period was 118% (range 101 - 133%), that for the baseline period was 117%, and that for the outbreak period was 121%. In a subset of 12 neonates with K. pneumoniae bacteraemia, the median (interquartile range (IQR)) gestational age at birth was 27 (26 - 29) weeks, and the median (IQR) birth weight was 1 100 (880 - 1 425) g. Twelve bloodstream and 31 colonising K. pneumoniae isolates were OXA-48-positive. All isolates were genetically related by PFGE analysis (89% similarity). Inadequate IPC practices were noted, including suboptimal adherence to aseptic technique and hand hygiene (57% overall score in the neonatal intensive care unit), with poor monitoring and reporting of antimicrobial use (pharmacy score 55%). CONCLUSIONS. Overcrowding and inadequate IPC and antimicrobial stewardship contributed to a large outbreak of BSIs caused by genetically related carbapenemase-producing K. pneumoniae isolates in the neonatal unit.http://www.samj.org.zaam2021School of Health Systems and Public Health (SHSPH

Measles outbreak in South Africa: epidemiology of laboratory-confirmed measles cases and assessment of intervention, 2009-2011

BACKGROUND: Since 1995, measles vaccination at nine and 18 months has been routine in South Africa; however, coverage seldom reached .95%. We describe the epidemiology of laboratory-confirmed measles case-patients and assess the impact of the nationwide mass vaccination campaign during the 2009 to 2011 measles outbreak in South Africa. METHODS: Serum specimens collected from patients with suspected-measles were tested for measles-specific IgM antibodies using an enzyme-linked immunosorbent assay and genotypes of a subset were determined. To estimate the impact of the nationwide mass vaccination campaign, we compared incidence in the seven months pre- (1 September 2009–11 April 2010) and seven months post-vaccination campaign (24 May 2010–31 December 2010) periods in seven provinces of South Africa. RESULTS: A total of 18,431 laboratory-confirmed measles case-patients were reported from all nine provinces of South Africa (cumulative incidence 37 per 100,000 population). The highest cumulative incidence per 100,000 population was in children aged ,1 year (603), distributed as follows: ,6 months (302/100,000), 6 to 8 months (1083/100,000) and 9 to 11 months (724/100,000). Forty eight percent of case-patients were $5 years (cumulative incidence 54/100,000). Cumulative incidence decreased with increasing age to 2/100,000 in persons$40 years. A single strain of measles virus (genotype B3) circulated throughout the outbreak. Prior to the vaccination campaign, cumulative incidence in the targeted vs. non-targeted age group was 5.9-fold higher, decreasing to 1.7 fold following the campaign (P,0.001) and an estimated 1,380 laboratoryconfirmed measles case-patients were prevented. CONCLUSION: We observed a reduction in measles incidence following the nationwide mass vaccination campaign even though it was conducted approximately one year after the outbreak started. A booster dose at school entry may be of value given the high incidence in persons .5 years.Our acknowledgements go to the Department of Health South Africa, National, provincial and districts, the South African Field Epidemiology and Laboratory Training Programme (SAFELTP), for ongoing support in surveillance and outbreak activities; Division of Epidemiology (Tsakani Nkuna, Kelebogile Lebogang Motsepe) and Virology (Londiwe Mahlaba, Mduduzi Buthelezi, Nomfundo Radebe, Muzi Hlanzi, Wayne Howard) at the NICD-NHLS for data management and laboratory testing support respectively and Private Laboratories for their support and referring specimens to the NICD.www.plosone.orgam201

The importation and establishment of community transmission of SARSCoV- 2 during the first eight weeks of the South African COVID-19 epidemic

BACKGROUND : We describe the epidemiology of COVID-19 in South Africa following importation and during implementation of stringent lockdown measures. METHODS : Using national surveillance data including demographics, laboratory test data, clinical presentation, risk exposures (travel history, contacts and occupation) and outcomes of persons undergoing COVID-19 testing or hospitalised with COVID-19 at sentinel surveillance sites, we generated and interpreted descriptive statistics, epidemic curves, and initial reproductive numbers (Rt). FINDINGS : From 4 March to 30 April 2020, 271,670 SARS-CoV-2 PCR tests were performed (462 tests/100,000 persons). Of these, 7,892 (2.9%) persons tested positive (median age 37 years (interquartile range 28 49 years), 4,568 (58%) male, cumulative incidence of 13.4 cases/100,000 persons). Hospitalization records were found for 1,271 patients (692 females (54%)) of whom 186 (14.6%) died. Amongst 2,819 cases with data, 489/ 2819 (17.3%) travelled internationally within 14 days prior to diagnosis, mostly during March 2020 (466 (95%)). Cases diagnosed in April compared with March were younger (median age, 37 vs. 40 years), less likely female (38% vs. 53%) and resident in a more populous province (98% vs. 91%). The national initial Rt was 2.08 (95% confidence interval (CI): 1.71 2.51). INTERPRETATION : The first eight weeks following COVID-19 importation were characterised by early predominance of imported cases and relatively low mortality and transmission rates. Despite stringent lockdown measures, the second month following importation was characterised by community transmission and increasing disease burden in more populous provinces.The South African governmenthttps://www.journals.elsevier.com/eclinicalmedicineam2022School of Health Systems and Public Health (SHSPH

Epidemiology of SARS-CoV-2 infection and SARS-CoV-2 positive hospital admissions among children in South Africa

INTRODUCTION : We describe epidemiology and outcomes of confirmed SARS-CoV-2 infection and positive admissions among children <18 years in South Africa, an upper-middle income setting with high inequality. METHODS : Laboratory and hospital COVID-19 surveillance data, 28 January - 19 September 2020 was used. Testing rates were calculated as number of tested for SARS-CoV-2 divided by population at risk; test positivity rates were calculated as positive tests divided by total number of tests. In-hospital case fatality ratio (CFR) was calculated based on hospitalized positive admissions with outcome data who died in-hospital and whose death was judged SARS-CoV-2 related by attending physician. FINDINGS : 315 570 children aged <18 years were tested for SARS-CoV-2; representing 8.9% of all 3 548 738 tests and 1.6% of all children in the country. Of children tested, 46 137 (14.6%) were positive. Children made up 2.9% (n = 2007) of all SARS-CoV-2 positive admissions to sentinel hospitals. Among children, 47 died (2.6% case-fatality). In-hospital deaths were associated with male sex [adjusted odds ratio (aOR) 2.18 (95% confidence intervals [CI] 1.08–4.40)] vs female; age <1 year [aOR 4.11 (95% CI 1.08–15.54)], age 10–14 years [aOR 4.20 (95% CI1.07–16.44)], age 15–17 years [aOR 4.86 (95% 1.28–18.51)] vs age 1–4 years; admission to a public hospital [aOR 5.07(95% 2.01–12.76)] vs private hospital and ≥1 underlying conditions [aOR 12.09 (95% CI 4.19–34.89)] vs none. CONCLUSIONS : Children with underlying conditions were at greater risk of severe SARS-CoV-2 outcomes. Children > 10 years, those in certain provinces and those with underlying conditions should be considered for increased testing and vaccination.SUPPORTING INFORMATION : TABLE S1: Description of SARS-CoV-2 rRT-PCR positive children <18 years in South Africa, 1 March 2020–19 September 2020 (N = 45 609). TABLE S2: Description of SARS-CoV-2 rRT-PCR positive hospital admissions among children <18 years in South Africa by province, 1 March 2020–19 September 2020 (N = 2007). TABLE S3: Distribution of non-missing variables among children with complete follow up and included in multivariable model (N = 1817). TABLE S4: Factors associated with in-hospital death among SARS-CoV-2 rRT-PCR positive admissions in children <18 years, South Africa, 1 March 2020–19 September 2020. FIGURE S1: Number of SARS-CoV-2 rRT-PCR tests*, percent positive tests and associated- hospital admissions among children <18 years by province and epidemiology week, South Africa, 1 March 2020–19 September 2020.National Department of Health, Republic of South Africahttp://wileyonlinelibrary.com/journal/irvhj2022School of Health Systems and Public Health (SHSPH

Investigation of two suspected diarrhoeal-illness outbreaks in Northern Cape and KwaZulu-Natal provinces, South Africa, April–July 2013 : the role of rotavirus

BACKGROUND : Suspected diarrhoeal-illness outbreaks affecting mostly children < 5 years were investigated between May and July 2013 in Northern Cape province (NCP) and KwaZulu-Natal (KZN) province. This study describes the epidemiological, environmental and clinical characteristics and diarrhoeal-illnesses causative agent(s). METHODS : A descriptive cross-sectional study was conducted. Cases were patients presenting at healthcare facilities with diarrhoeal-illness between 09 April and 09 July 2013 in NCP and 01 May and 31 July 2013 in KZN. Laboratory investigations were performed on stools and water samples using microscopy, culture and sensitivity screening and molecular assays. RESULTS : A total of 953 cases including six deaths (case fatality rate [CFR]: 0.6%) were recorded in the Northern Cape province outbreak. Children < 5 years accounted for 58% of cases. Enteric viruses were detected in 51% of stools, with rotavirus detected in 43%. The predominant rotavirus strains were G3P[8] (45%) and G9P[8] (42%). Other enteric viruses were detected, with rotavirus co-infections (63%). No enteric pathogens detected in water specimens. KwaZulu-Natal outbreak: A total of 1749 cases including 26 deaths (CFR: 1.5%) were recorded. Children < 5 years accounted for 95% of cases. Rotavirus was detected in 55% of stools; other enteric viruses were detected, mostly as rotavirus co-infections. The predominant rotavirus strains were G2P[4] (54%) and G9P[8] (38%). CONCLUSION : Although source(s) of the outbreaks were not identified, the diarrhoeal-illnesses were community-acquired. It is difficult to attribute the outbreaks to one causative agent(s) because of rotavirus co-infections with other enteric pathogens. While rotavirus was predominant, the outbreaks coincided with the annual rotavirus season.http://www.sajid.co.zaam2021Medical VirologySchool of Health Systems and Public Health (SHSPH

Waterborne outbreak of gastroenteritis on the KwaZulu-Natal Coast, South Africa, December 2016/January 2017

An unexpected increase in gastroenteritis cases was reported by healthcare workers on the KwaZulu-Natal Coast, South Africa, January 2017 with >600 cases seen over a 3-week period. A case–control study was conducted to identify the source and risk factors associated with the outbreak so as to recommend control and prevention measures. Record review identified cases and controls and structured-telephonic interviews were conducted to obtain exposure history. Stool specimens were collected from 20 cases along with environmental samples and both screened for enteric pathogens. A total of 126 cases and 62 controls were included in the analysis. The odds of developing gastroenteritis were 6.0 times greater among holiday makers than residents (95% confidence interval (CI) 2.0–17.7). Swimming in the lagoon increased the odds of developing gastroenteritis by 3.3 times (95% CI 1.06–10.38). Lagoon water samples tested positive for norovirus (NoV) GI.6, GII.3 and GII.6, astrovirus and rotavirus. Eleven (55%) stool specimens were positive for NoV with eight genotyped as GI.1 (n = 2), GI.5 (n = 3), GI.6 (n = 2), and GI.7 (n = 1). A reported sewage contamination event impacting the lagoon was the likely source with person-to-person spread perpetuating the outbreak. Restriction to swimming in the lagoon was apparently ineffective at preventing the outbreak, possibly due to inadequate enforcement, communication and signage strategies.https://www.cambridge.org/core/journals/epidemiology-and-infection2019-01-01hj2018Medical Virolog

Influenza epidemiology and vaccine effectiveness among patients with influenza-like illness, viral watch sentinel sites, South Africa, 2005-2009.

There is limited data on the epidemiology of influenza and few published estimates of influenza vaccine effectiveness (VE) from Africa. In April 2009, a new influenza virus strain infecting humans was identified and rapidly spread globally. We compared the characteristics of patients ill with influenza A(H1N1)pdm09 virus to those ill with seasonal influenza and estimated influenza vaccine effectiveness during five influenza seasons (2005-2009) in South Africa.Epidemiological data and throat and/or nasal swabs were collected from patients with influenza-like illness (ILI) at sentinel sites. Samples were tested for seasonal influenza viruses using culture, haemagglutination inhibition tests and/or polymerase chain reaction (PCR) and for influenza A(H1N1)pdm09 by real-time PCR. For the vaccine effectiveness (VE) analysis we considered patients testing positive for influenza A and/or B as cases and those testing negative for influenza as controls. Age-adjusted VE was calculated as 1-odds ratio for influenza in vaccinated and non-vaccinated individuals.From 2005 through 2009 we identified 3,717 influenza case-patients. The median age was significantly lower among patients infected with influenza A(H1N1)pdm09 virus than those with seasonal influenza, 17 and 27 years respectively (p<0.001). The vaccine coverage during the influenza season ranged from 3.4% in 2009 to 5.1% in 2006 and was higher in the ≥50 years (range 6.9% in 2008 to 13.2% in 2006) than in the <50 years age group (range 2.2% in 2007 to 3.7% in 2006). The age-adjusted VE estimates for seasonal influenza were 48.6% (4.9%, 73.2%); -14.2% (-9.7%, 34.8%); 12.0% (-70.4%, 55.4%); 67.4% (12.4%, 90.3%) and 29.6% (-21.5%, 60.1%) from 2005 to 2009 respectively. For the A(H1N1)pdm09 season, the efficacy of seasonal vaccine was -6.4% (-93.5%, 43.3%).Influenza vaccine demonstrated a significant protective effect in two of the five years evaluated. Low vaccine coverage may have reduced power to estimate vaccine effectiveness

Molecular characterisation and epidemiology of enterovirus-associated aseptic meningitis in the Western and Eastern Cape Provinces, South Africa 2018–2019

BACKGROUND : Enteroviruses are amongst the most common causes of aseptic meningitis. Between November 2018 and May 2019, an outbreak of enterovirus-associated aseptic meningitis cases was noted in the Western and Eastern Cape Provinces, South Africa. OBJECTIVES : To describe the epidemiology and phylogeography of enterovirus infections during an aseptic meningitis outbreak in the Western and Eastern Cape Provinces of South Africa. METHODS : Cerebrospinal fluid samples from suspected cases were screened using a polymerase chain reaction targeting the 5’UTR. Confirmed enterovirus-associated meningitis samples underwent molecular typing through species–specific VP1/VP2 primers and pan-species VP1 primers. RESULTS : Between November 2018 and May 2019, 3497 suspected cases of aseptic meningitis were documented in the Western and Eastern Cape Provinces. Median age was 8 years (range 0–61), interquartile range (IQR=4–13 years), 405/735 (55%) male. 742/3497 (21%) cases were laboratory – confirmed enterovirus positive by routine diagnostic PCR targeting the 5’UTR. 128/742 (17%) underwent molecular typing by VP1 gene sequencing. Echovirus 4 (E4) was detected in 102/128 (80%) cases. Echovirus 9 was found in 7%, Coxsackievirus A13 in 3%. 10 genotypes contributed to the remaining 10% of cases. Synonymous mutations were found in most cases, with sporadic amino acid changes in 13 (12.7%) cases. CONCLUSION : The aseptic meningitis outbreak was associated with echovirus 4. Stool samples are valuable for molecular typing in CSF confirmed EV-associated aseptic meningitis.The National Health Laboratory Service Research Trust Development Grant and the Poliomyelitis Research Foundation.https://www.elsevier.com/locate/jcvam2022School of Health Systems and Public Health (SHSPH

Effectiveness of seasonal influenza vaccination in community-dwelling elderly people:An individual participant data meta-analysis of test-negative design case-control studies

Background Several aggregate data meta-analyses have provided estimates of the effectiveness of influenza vaccination in community-dwelling elderly people. However, these studies ignored the effects of patient-level confounders such as sex, age, and chronic diseases that could bias effectiveness estimates. We aimed to assess the confounder-adjusted effectiveness of influenza vaccines on laboratory-confirmed influenza among elderly people by conducting a global individual participant data meta-analysis. Methods In this individual participant data meta-analysis, we considered studies included in a previously conducted aggregate data meta-analysis that included test-negative design case-control studies published up to July 13, 2014. We contacted all authors of the included studies on Dec 1, 2014, to request individual participant data. Patients were excluded if their unique identifier was missing, their vaccination status was unknown, their outcome status was unknown, or they had had suspected influenza infection more than once in the same influenza season. Cases were patients with influenza-like illness symptoms who tested positive for at least one of A H1N1, A H1N1 pdm09, A H3N2, or B viruses; controls were patients with infl uenza-like illness symptoms who tested negative for these virus types or subtypes. Influenza vaccine effectiveness against overall and subtype-specific laboratory-confirmed influenza were the primary and secondary outcomes. We used a generalised linear mixed model to calculate adjusted vaccine effectiveness according to vaccine match to the circulating strains of influenza virus and intensity of the virus activity (epidemic or non-epidemic). Vaccine effectiveness was defined as the relative reduction in risk of laboratory-confirmed influenza in vaccinated patients compared with unvaccinated patients. We did subgroup analyses to estimate vaccine effectiveness according to hemisphere, age category, and health status. Findings We received 23 of the 53 datasets included in the aggregate data meta-analysis. Furthermore, six additional datasets were provided by data collaborators, which resulted in individual participant data for a total of 5210 participants. A total of 4975 patients had the required data for analysis. Of these, 3146 (63%) were controls and 1829 (37%) were cases. Influenza vaccination was significantly effective during epidemic seasons irrespective of vaccine match status (matched adjusted vaccine effectiveness 44.38%, 95% CI 22.63-60.01; mismatched adjusted vaccine effectiveness 20.00%, 95% CI 3.46-33.68; analyses in the imputed dataset). Seasonal influenza vaccination did not show significant effectiveness during non-epidemic seasons. We found substantial variation in vaccine effectiveness across virus types and subtypes, with the highest estimate for A H1N1 pdm09 (53.19%, 10.25-75.58) and the lowest estimate for B virus types (-1.52%, -39.58 to 26.16). Although we observed no significant differences between subgroups in each category (hemisphere, age, and health status), influenza vaccination showed a protective effect among elderly people with cardiovascular disease, lung disease, or aged 75 years and younger. Interpretation Influenza vaccination is moderately effective against laboratory-confirmed influenza in elderly people during epidemic seasons. More research is needed to investigate factors affecting vaccine protection (eg, brand-specific or type-specific vaccine effectiveness and repeated annual vaccination) in elderly people

Comparison of vaccine composition to circulating viruses, South Africa, 2005–2009.

<p><b>Predominating circulating strains in bold.</b></p><p><b>Predominating B strains indicated.</b></p