Aetiology of childhood pneumonia in a well vaccinated South African birth cohort: A nested case-control study of the Drakenstein Child Health Study

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Study Justification:
– Pneumonia is a leading cause of mortality and morbidity in children globally.
– The cause of pneumonia in low-income and middle-income countries after the introduction of the 13-valent pneumococcal conjugate vaccine (PCV13) has not been well studied.
– Most data on pneumonia causes are from cross-sectional studies of children admitted to the hospital.
– This study aimed to investigate the incidence and causes of childhood pneumonia in a South African birth cohort.
Study Highlights:
– The study was conducted from May 29, 2012, to Dec 1, 2014, on a well-vaccinated South African birth cohort.
– A total of 314 pneumonia cases occurred in 967 children during the study period.
– The incidence of pneumonia was 0.27 episodes per child-year.
– Severe pneumonia accounted for 21% of cases, with a case fatality ratio of 1%.
– Respiratory syncytial virus, influenza virus, and Bordetella pertussis were strongly associated with pneumonia.
– Testing of induced sputum in addition to nasopharyngeal swabs increased the yield for detection of several organisms.
– The study highlights the need for new vaccines and strategies to address the burden of childhood pneumonia.
Recommendations for Lay Reader and Policy Maker:
– Increase awareness about the continued burden of pneumonia in highly vaccinated populations.
– Emphasize the importance of respiratory syncytial virus, influenza virus, and Bordetella pertussis as key pathogens associated with pneumonia.
– Encourage the development of new vaccines and strategies to prevent childhood pneumonia.
– Promote the use of induced sputum testing in addition to nasopharyngeal swabs for improved detection of pneumonia-causing organisms.
Key Role Players:
– Researchers and scientists involved in pneumonia research and vaccine development.
– Public health officials and policymakers responsible for implementing vaccination programs.
– Healthcare providers and primary health nurses involved in pneumonia diagnosis and treatment.
– Community health workers and educators responsible for raising awareness about pneumonia prevention.
Cost Items for Planning Recommendations:
– Research and development costs for new vaccines and strategies.
– Costs for training healthcare providers and primary health nurses on pneumonia diagnosis and treatment.
– Costs for implementing pneumonia surveillance programs in local clinics and hospitals.
– Costs for community health education campaigns on pneumonia prevention and awareness.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on a nested case-control study with a large sample size and a longitudinal design. The study collected data from a well-vaccinated South African birth cohort over a period of two years. The researchers used multiplex real-time PCR to detect pathogens in nasopharyngeal swabs and induced sputum specimens. The findings show a high incidence of pneumonia in the population, with respiratory syncytial virus, influenza virus, and Bordetella pertussis being strongly associated with pneumonia. The study also suggests that testing induced sputum in addition to nasopharyngeal swabs increases the yield for detection of several organisms. To improve the evidence, future studies could consider including a control group of children without pneumonia to compare the prevalence of pathogens.

Background: Pneumonia is a leading cause of mortality and morbidity in children globally. The cause of pneumonia after introduction of the 13-valent pneumococcal conjugate vaccine (PCV13) has not been well studied in low-income and middle-income countries, and most data are from cross-sectional studies of children admitted to hospital. We aimed to longitudinally investigate the incidence and causes of childhood pneumonia in a South African birth cohort. Methods: We did a nested case-control study of children in the Drakenstein Child Health Study who developed pneumonia from May 29, 2012, to Dec 1, 2014. Children received immunisations including acellular pertussis vaccine and PCV13. A nested subgroup had nasopharyngeal swabs collected every 2 weeks throughout infancy. We identified pneumonia episodes and collected blood, nasopharyngeal swabs, and induced sputum specimens. We used multiplex real-time PCR to detect pathogens in nasopharyngeal swabs and induced sputum of pneumonia cases and in nasopharyngeal swabs of age-matched and site-matched controls. To show associations between organisms and pneumonia we used conditional logistic regression; results are presented as odds ratios (ORs) with 95% CIs. Findings: 314 pneumonia cases occurred (incidence of 0·27 episodes per child-year, 95% CI 0·24-0·31; median age 5 months [IQR 3-9]) in 967 children during 1145 child-years of follow-up. 60 (21%) cases of pneumonia were severe (incidence 0·05 episodes per child-year [95% CI 0·04-0·07]) with a case fatality ratio of 1% (three deaths). A median of five organisms (IQR 4-6) were detected in cases and controls with nasopharyngeal swabs, and a median of six organisms (4-7) recorded in induced sputum (p=0·48 compared with nasopharyngeal swabs). Bordetella pertussis (OR 11·08, 95% CI 1·33-92·54), respiratory syncytial virus (8·05, 4·21-15·38), or influenza virus (4·13, 2·06-8·26) were most strongly associated with pneumonia; bocavirus, adenovirus, parainfluenza virus, Haemophilus influenzae, and cytomegalovirus were also associated with pneumonia. In cases, testing of induced sputum in addition to nasopharyngeal swabs provided incremental yield for detection of B pertussis and several viruses. Interpretation: Pneumonia remains common in this highly vaccinated population. Respiratory syncytial virus was the most frequently detected pathogen associated with pneumonia; influenza virus and B pertussis were also strongly associated with pneumonia. Testing of induced sputum increases the yield for detection of several organisms. New vaccines and strategies are needed to address the burden of childhood pneumonia. Funding: Bill & Melinda Gates Foundation, Medical Research Council South Africa, National Research Foundation South Africa, National Institute of Health, and H3Africa.

We did a nested case-control study of children included in the Drakenstein Child Health Study12 who developed pneumonia from May 29, 2012, to Dec 1, 2014. The Drakenstein Child Health Study12 was undertaken at two public, primary health-care clinics located about 2 km apart in Paarl, a periurban area in South Africa. One clinic (TC Newman) served a mixed-race population and the second clinic (Mbekweni) a black African population. Pregnant women aged 18 years or older, at 20–28 weeks’ gestation, attending one of the two clinics for antenatal care, and remaining in the area for at least 1 year were enrolled. Ethics approval was obtained from the University of Cape Town Faculty of Health Sciences Research Ethics Committee, and the Provincial Research committee approved the study. Mothers provided written informed consent at enrolment and provided consent after the first year. All births occurred at Paarl hospital. Follow-up of children was done from May 29, 2012, through early childhood13 and paralleled routine child health visits at 6, 10, and 14 weeks and 6, 9, 12, 18, 30, and 42 months. An additional study visit was done at 6–10 weeks at Paarl hospital. All children were given primary health care and immunisations at the two clinics including four doses of a five vaccine combination (diphtheria, tetanus, acellular pertussis, H influenzae type b, and inactivated polio vaccine) at 6, 10, and 14 weeks and 18 months; the measles vaccine at 9 months and 18 months; and the PCV13 at 6 weeks, 14 weeks, and 9 months. Continuous pneumonia surveillance was implemented at all local clinics and at Paarl hospital. Mothers were given a mobile phone number if they needed to contact the study team at any time. Mothers were counselled regarding key respiratory symptoms and advised to attend or contact study staff whenever a child developed cough or difficulty breathing. Primary health nurses and study staff were trained to recognise WHO-defined pneumonia or severe pneumonia.14 Study staff reviewed patient records at catchment clinics (Phola Park, Thokoza; JJ du Preez, Paarl; Klein Nederburg, Paarl, South Africa) and Paarl hospital, and performed surveillance for any missed pneumonia episode. All admissions to hospital were at Paarl hospital, the only hospital serving this population. Children were followed throughout their duration in hospital, and at 2 days and 6 weeks after discharge or after an ambulatory episode. Longitudinal measurement of risk factors (nutrition, environment, vaccinations received, and child and maternal factors) was done at study visits and at case presentation. Infant anthropometry and maternal smoking or passive smoke exposure were measured by urine cotinine longitudinally and at case presentation.15 Nasopharyngeal swabs were collected every 2 weeks for the first year in a subgroup (intensive cohort); enrolment in the intensive cohort was at the participant’s discretion. All children had nasopharyngeal swabs taken every 6 months. A chest radiograph was done in infants with pneumonia who had been admitted to hospital. Laboratory staff were masked to case-control status. Cases were any episode of pneumonia, irrespective of severity, excluding congenital pneumonia (defined as presentation before postnatal discharge). Controls were incidence-density matched to cases (1–2:1) by birth date (to within 2 weeks), age of presentation (to within 2 weeks), and site of enrolment. By design, controls could be sampled more than once, but this occurred only infrequently. We did separate analyses of cases compared with asymptomatic controls and with controls with symptoms of upper respiratory tract infection (ie, cough, runny or blocked nose, or sore throat). For every case, two nasopharyngeal swabs (FLOQSwabs, Copan Diagnostics, Murrieta, CA, USA) and an induced sputum specimen were obtained.10 The first nasopharyngeal swab taken was immediately transferred into nucleic acid preservation medium (PrimeStore, Longhorn Vaccines and Diagnostics, San Antonio, TX, USA), the second swab was placed into 1 mL of skimmed milk-tryptone-glucose-glycerol (STGG) transport medium. Swabs were transported on ice to the laboratory and frozen at −80°C for batch testing. The swab in STGG was cultured for bacteria; total nucleic acid was extracted from the swab in nucleic acid preservation medium with mechanical lysis on a Tissuelyzer LT (Qiagen, Hilden, Germany) followed by extraction with the QIAsymphony Virus/Bacteria Mini Kit (Qiagen, Hilden, Germany). We did quantitative, multiplex, real-time PCR (qPCR) with FTDResp33 (Fast-Track Diagnostics, Esch-sur-Alzet, Luxembourg) to identify up to 33 potential organisms of respiratory viruses (influenza A, B, and C; parainfluenza 1, 2, 3, and 4; coronaviruses NL63, 229E, OC43, HKU1; human metapneumoviruses A and B; rhinovirus; respiratory syncytial viruses A and B; adenovirus; enterovirus; parechovirus; bocavirus; and cytomegalovirus), fungi (Pneumocystis jirovecii), and bacteria (Mycoplasma pneumoniae, Chlamydophila pneumoniae, Streptococcus pneumoniae, H influenzae type b, Staphylococcus aureus, Moraxella catarrhalis, Bordetella pertussis, Klebsiella pneumoniae, Legionella species, salmonella species, and H influenzae). K pneumoniae and Legionella spp were omitted from this analysis because of difficulties with assay specificity. Standard curves were derived with plasmid standards supplied by the manufacturer for each organism. Every induced sputum specimen was transported to the laboratory on ice and split into two aliquots. The first underwent nucleic acid extraction and testing with FTDResp33, as previously stated; the second was cultured for bacteria. A blood culture for bacteria was obtained in cases admitted to hospital. For control children, two nasopharyngeal swabs were collected at each visit and stored for later retrieval and processing as described for case swabs. Laboratory staff were masked to case-control status. Weight-for-age and height-for-age Z scores were derived from WHO child growth standards.16 Socioeconomic status comprised a composite of asset ownership, household income, employment, and education.13 We compared data from cases and controls with conditional logistic regression. Dependent variables of interest were organisms from FTDResp33, analysed as binary (present or absent) and continuous (log copies or specimen) values. Model building examined potential confounding factors identified a priori from demographic and clinical measures of child’s sex, in-utero HIV exposure, maternal age, maternal smoking, and socioeconomic status; because no clear confounding factors were consistently identified based on appreciable changes in the point estimate for pathogen–pneumonia associations, models presented account for matching factors only. Because children might have had more than one episode of pneumonia (and thus participate in more than one case-control set), we also examined mixed-effects models with children and case-control sets as random or fixed effects, and also conditional logistic regression models restricted to first case episodes. We based co-organism analysis on matched case-control pairs, investigating the presence or absence of two organisms at a time. Of 16 possible response patterns, seven involved co-occurrence, with the less than one, one, or more than one pattern of co-occurrence in both cases and controls not adding information that differentiated cases from controls. The observed frequencies of the remaining six response patterns were compared with their expected values based on the hypothesis of random co-occurrence with a Pearson χ2 test. Multiple p values for different organism pairs were corrected with use of the Benjamini-Hochberg correction for false discovery rate. We did subsidiary analyses to stratify case-control comparisons on pneumonia severity and control children’s symptoms. Throughout, regression diagnostics followed standard procedures, and all statistical tests were two-sided at α=0·05. We used Stata (version 13.0) and R (version 3.2.2) for data analyses. The sponsors of the study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data and had final responsibility for the decision to submit for publication.

Based on the provided information, it seems that the study focused on identifying the causes of childhood pneumonia in a South African birth cohort. The study utilized various methods, including longitudinal investigation, multiplex real-time PCR, and collection of nasopharyngeal swabs and induced sputum specimens. The findings highlighted the association of certain pathogens, such as Bordetella pertussis, respiratory syncytial virus, and influenza virus, with pneumonia. The study also suggested that testing induced sputum in addition to nasopharyngeal swabs increased the yield for detection of several organisms.

Based on this study, potential innovations to improve access to maternal health and address the burden of childhood pneumonia could include:

1. Improved Vaccination Strategies: Develop new vaccines or enhance existing ones to target the pathogens strongly associated with pneumonia, such as Bordetella pertussis, respiratory syncytial virus, and influenza virus. This could help prevent pneumonia cases in children and reduce the burden on maternal health services.

2. Enhanced Diagnostic Methods: Invest in research and development of more efficient and accurate diagnostic methods for identifying the pathogens causing pneumonia. This could include advancements in multiplex real-time PCR technology or the development of point-of-care diagnostic tools that can be easily used in resource-limited settings.

3. Strengthened Pneumonia Surveillance: Implement continuous pneumonia surveillance programs in primary health-care clinics and hospitals to ensure early detection and prompt treatment of pneumonia cases in children. This could involve training healthcare providers to recognize pneumonia symptoms and providing them with the necessary tools and resources for effective surveillance.

4. Increased Awareness and Education: Conduct community-based awareness campaigns to educate mothers and caregivers about the signs and symptoms of pneumonia in children. This could help improve early recognition of pneumonia cases and encourage timely seeking of healthcare services.

5. Integrated Maternal and Child Health Services: Promote integration of maternal and child health services to ensure comprehensive care for both mothers and children. This could involve establishing multidisciplinary teams that provide coordinated care and support throughout the pregnancy and early childhood period, including immunizations, nutrition counseling, and respiratory health monitoring.

It is important to note that these recommendations are based on the information provided and may need to be further evaluated and tailored to the specific context and needs of the target population.
AI Innovations Description
The study mentioned is focused on investigating the causes of childhood pneumonia in a South African birth cohort. The researchers conducted a nested case-control study, collecting data from children who developed pneumonia between May 29, 2012, and Dec 1, 2014. The study aimed to identify the pathogens associated with pneumonia in a population that had received the 13-valent pneumococcal conjugate vaccine (PCV13).

The findings of the study showed that pneumonia remained common in this highly vaccinated population. The most frequently detected pathogens associated with pneumonia were respiratory syncytial virus, influenza virus, and Bordetella pertussis. Testing of induced sputum in addition to nasopharyngeal swabs increased the yield for detection of several organisms.

Based on these findings, the study suggests that new vaccines and strategies are needed to address the burden of childhood pneumonia. This recommendation highlights the importance of developing innovative approaches to improve access to maternal health, such as the development of new vaccines and strategies to prevent and treat pneumonia in children.
AI Innovations Methodology
The provided text describes a nested case-control study conducted to investigate the incidence and causes of childhood pneumonia in a South African birth cohort. The study aimed to identify the pathogens associated with pneumonia in a population that had received the 13-valent pneumococcal conjugate vaccine (PCV13). The methodology involved collecting data from children who developed pneumonia between May 29, 2012, and Dec 1, 2014, and comparing them to matched controls. Nasopharyngeal swabs and induced sputum specimens were collected and tested using multiplex real-time PCR to detect pathogens. Associations between organisms and pneumonia were analyzed using conditional logistic regression.

To improve access to maternal health, several innovations can be considered:

1. Telemedicine: Implementing telemedicine platforms that allow pregnant women to consult with healthcare providers remotely can improve access to prenatal care, especially for those in remote or underserved areas.

2. Mobile health (mHealth) applications: Developing mobile applications that provide pregnant women with information, reminders for appointments, and access to educational resources can help improve maternal health outcomes.

3. Community health workers: Training and deploying community health workers who can provide prenatal care, education, and support to pregnant women in their communities can help improve access to maternal health services.

4. Transport services: Establishing transportation services specifically for pregnant women to ensure they can easily access healthcare facilities for prenatal care, delivery, and postnatal care.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Identify the specific group of pregnant women who would benefit from improved access to maternal health services, such as those in rural areas or low-income communities.

2. Collect baseline data: Gather information on the current state of access to maternal health services in the target population, including factors such as distance to healthcare facilities, availability of transportation, and utilization of prenatal care.

3. Implement the recommended innovations: Introduce the proposed innovations, such as telemedicine platforms, mHealth applications, community health workers, or transport services, in the target population.

4. Monitor and evaluate: Track the implementation of the innovations and collect data on their utilization and impact. This could include measuring the number of pregnant women using telemedicine services, the frequency of mHealth application usage, the number of community health worker visits, or the utilization of transport services.

5. Analyze the data: Analyze the collected data to assess the impact of the innovations on improving access to maternal health services. This could involve comparing the utilization of maternal health services before and after the implementation of the innovations, as well as assessing any changes in health outcomes or patient satisfaction.

6. Adjust and refine: Based on the analysis of the data, make any necessary adjustments or refinements to the implemented innovations to further improve access to maternal health services.

By following this methodology, it would be possible to simulate the impact of the recommended innovations on improving access to maternal health and make evidence-based decisions on their implementation.

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