Antibiotic use for typhoid in India: Estimation from private sector prescription data during 2013-15

Background India's typhoid burden estimates are based on a limited number of population-based studies and data from a grossly incomplete disease surveillance system. In this study, we estimated the total and sex-and age-specific antibiotic prescription rates for typhoid. Methods We used systematic antibiotic prescription by private sector primary care physicians in India. We categorized antibiotics using the WHO classification system and calculated the prescription for various classes of antibiotics. Results We analyzed 671 million prescriptions for the three-year period (2013-2015), of which an average of 8.98 million antibiotic prescriptions per year was for typhoid, accounting for 714 prescriptions per 100,000 population. Combination antibiotics are the preferred choice of prescribers in the adult age group, while cephalosporins are the preferred choice in children and young age. The prescription rate decreased from 792/100,000 in 2013 to 635 in 2015. Conclusion We report a higher rate of antibiotic prescription for typhoid using prescription data, indicating a higher disease burden than previously estimated. Quinolones are still widely used in monotherapy, and children less than 10 years account for more than a million cases annually, which calls for a routine vaccination program.

Retrospective cohort study of COVID-19 among children in Fulton County, Georgia, March 2020–June 2021

ObjectiveTo describe case rates, testing rates and percent positivity of COVID-19 among children aged 0–18 years by school-age grouping.DesignWe abstracted data from Georgia’s State Electronic Notifiable Disease Surveillance System on all 10 437 laboratory-confirmed COVID-19 cases among children aged 0–18 years during 30 March 2020 to 6 June 2021. We examined case rates, testing rates and percent positivity by school-aged groupings, namely: preschool (0–4 years), elementary school (5–10 years), middle school (11–13 years), and high school (14–18 years) and compared these data among school-aged children with those in the adult population (19 years and older).SettingFulton County, Georgia.Main outcome measuresCOVID-19 case rates, testing rates and percent positivity.ResultsOver time, the proportion of paediatric cases rose substantially from 1.1% (April 2020) to 21.6% (April 2021) of all cases in the county. Age-specific case rates and test rates were consistently highest among high-school aged children. Test positivity was similar across school-age groups, with periods of higher positivity among high-school aged children.ConclusionsLow COVID-19 testing rates among children, especially early in the pandemic, likely underestimated the true burden of disease in this age group. Despite children having lower measured incidence of COVID-19, we found when broader community incidence increased, incidence also increased among all paediatric age groups. As the COVID-19 pandemic continues to evolve, it remains critical to continue learning about the incidence and transmissibility of COVID-19 in children.

A Retrospective Cohort Study of COVID-19 among Children in Fulton County, Georgia, March 2020 - June 2021

Objective: To describe case rates, testing rates, and percent positivity of coronavirus disease 2019 (COVID-19) among children aged 0-18 years by school-age grouping. Design: We abstracted data from Georgia's State Electronic Notifiable Disease Surveillance System on all 10,437 laboratory-confirmed COVID-19 cases among children aged 0-18 years during March 30, 2020 to June 6, 2021. We examined case rates, testing rates, and percent positivity by school-aged groupings, namely: preschool (0-4 years), elementary school (5-10 years), middle school (11-13 years), and high school (14-18 years) and compared these data among school aged children to those in the adult population (19 years and older). Setting: Fulton County, Georgia. Main outcome measures: COVID-19 case rates, testing rates, and percent positivity. Results: Over time, the proportion of pediatric cases rose substantially from 1.1% (April 2020) to 21.6% (April 2021) of all cases in the county. Age-specific case rates and test rates were consistently highest among high-school aged children. Test positivity was similar across school-age groups, with periods of higher positivity among high-school aged children. Conclusions: Low COVID-19 testing rates among children, especially early in the pandemic, likely underestimate the true burden of disease in this age group. Despite children having lower measured incidence of COVID-19, we found when broader community incidence increased, incidence also increased among all pediatric age groups. As the COVID-19 pandemic continues to evolve, it remains critical to continue learning about the incidence and transmissibility of COVID-19 in children.

Australian National Enterovirus Reference Laboratory annual report, 2020

Australia monitors its polio-free status by conducting surveillance for cases of acute flaccid paralysis (AFP) in children less than 15 years of age, as recommended by the World Health Organization (WHO). Cases of AFP in children are notified to the Australian Paediatric Surveillance Unit or the Paediatric Active Enhanced Disease Surveillance System and faecal specimens are referred for virological investigation to the National Enterovirus Reference Laboratory. In 2020, no cases of poliomyelitis were reported from clinical surveillance; Australia reported 1.09 non-polio AFP cases per 100,000 children, thereby meeting the WHO’s performance criterion for a sensitive surveillance system. The non-polio enteroviruses coxsackievirus A10 and coxsackievirus A16 were identified from clinical specimens collected from AFP cases. Australia also performs enterovirus surveillance and environmental surveillance to complement the clinical system focussed on children. In 2020, there were 140 cases of wild poliovirus reported from the two remaining endemic countries: Afghanistan and Pakistan. Another 28 countries reported cases of circulating vaccine-derived poliovirus.

Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK

ObjectivesTo evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.DesignA geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.ParticipantsAll hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020.Outcome measuresPredictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.ResultsPeak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%–20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.ConclusionsLocal demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.

A five-year retrospective study on the epidemiology of hand, foot and mouth disease in Sabah, Malaysia

Hand, foot, and mouth disease (HFMD) is endemic in Malaysia, with the number of cases increasing. Sabah has experienced several HFMD outbreaks, but information on the epidemiology and molecular characteristics of responsible viruses is scarce. In this study, data of 17,574 reports of HFMD cases in Sabah from 2015 to 2019 were extracted from a public health disease surveillance system and analyzed. Twenty-one swab samples from 13 children were collected from Beaufort, Sabah, during an outbreak in August 2018 for detection and serotyping of causative viruses by semi-nested reverse transcription-polymerase chain reaction (snRT-PCR) of the VP4–VP2 region and consensus degenerate hybrid oligonucleotide primer PCR of the VP1 region, respectively. Nucleotide sequencing and phylogenetic analysis were conducted by the neighbor-joining method. The average annual incidence of HFMD was 94.3 per 100,000 people, with the greatest yearly increase between 2017 and 2018. Swabs from six children were tested positive for enterovirus, of which five were positive for CVA16 and one for EV71. All CVA16 strains belonged to sub-genotype B1a, and the EV71 strain belonged to sub-genotype B5. Phylogenetic analyses indicate that enterovirus genotype shift might be responsible for the increasing trend of HFMD in Sabah, however, further study is needed.

Early transmission dynamics of SARS-CoV-2 in Indonesia

The objective of this study was to determine the transmission dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and to evaluate the vigilance of the health system during the early phase of coronavirus disease 2019 (COVID-19) outbreak in Indonesia. The early epidemiology and transmission chains of COVID-19 were analyzed based on data from the Directorate General of Disease Prevention and Control of the Indonesian Ministry of Health. The results of this study shown although Indonesia is a country with a high relative importation risk of SARS-CoV-2, the first two cases of COVID-19 were identified on March 2, 2020. This relatively late date by regional standards raises the possibility of undetected cases beforehand. The first case was a foreigner citizen who visited the capital city of Jakarta and later was diagnosed COVID-19 after returning from Indonesia. One week later after the first case, 27 confirmed COVID-19 cases had been reported in Indonesia, and the majority of the cases were clustered together. Apart from the possibility of underdetection of COVID-19 cases in the country, the government has strengthened the disease surveillance system and established an outbreak preparedness system to diagnose and control COVID-19.