Dataset Information

Modeling Optimal Laboratory Testing Strategies for Bacterial Meningitis Surveillance in Africa.

ABSTRACT: Since 2010, the introduction of an effective serogroup A meningococcal conjugate vaccine has led to the near-elimination of invasive Neisseria meningitidis serogroup A disease in Africa's meningitis belt. However, a significant burden of disease and epidemics due to other bacterial meningitis pathogens remain in the region. High-quality surveillance data with laboratory confirmation is important to monitor circulating bacterial meningitis pathogens and design appropriate interventions, but complete testing of all reported cases is often infeasible. Here, we use case-based surveillance data from 5 countries in the meningitis belt to determine how accurately estimates of the distribution of causative pathogens would represent the true distribution under different laboratory testing strategies. Detailed case-based surveillance data was collected by the MenAfriNet surveillance consortium in up to 3 seasons from participating districts in 5 countries. For each unique country-season pair, we simulated the accuracy of laboratory surveillance by repeatedly drawing subsets of tested cases and calculating the margin of error of the estimated proportion of cases caused by each pathogen (the greatest pathogen-specific absolute error in proportions between the subset and the full set of cases). Across the 12 country-season pairs analyzed, the 95% credible intervals around estimates of the proportion of cases caused by each pathogen had median widths of ±0.13, ±0.07, and ±0.05, respectively, when random samples of 25%, 50%, and 75% of cases were selected for testing. The level of geographic stratification in the sampling process did not meaningfully affect accuracy estimates. These findings can inform testing thresholds for laboratory surveillance programs in the meningitis belt.

SUBMITTER: Walker J

PROVIDER: S-EPMC8409536 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Modeling Optimal Laboratory Testing Strategies for Bacterial Meningitis Surveillance in Africa.

Walker Joseph J Soeters Heidi M HM Novak Ryan R Diallo Alpha Oumar AO Vuong Jeni J Bicaba Brice Wilfried BW Medah Isaie I Yaméogo Issaka I Ouédraogo-Traoré Rasmata R Gamougame Kadidja K Moto Daugla Doumagoum DD Dembélé Assétou Y AY Guindo Ibrehima I Coulibaly Souleymane S Issifou Djibo D Zaneidou Maman M Assane Hamadi H Nikiema Christelle C Sadji Adodo A Fernandez Katya K Mwenda Jason M JM Bita Andre A Lingani Clément C Tall Haoua H Tarbangdo Félix F Sawadogo Guetwende G Paye Marietou F MF Wang Xin X McNamara Lucy A LA

The Journal of infectious diseases 20210901 12 Suppl 2

Since 2010, the introduction of an effective serogroup A meningococcal conjugate vaccine has led to the near-elimination of invasive Neisseria meningitidis serogroup A disease in Africa's meningitis belt. However, a significant burden of disease and epidemics due to other bacterial meningitis pathogens remain in the region. High-quality surveillance data with laboratory confirmation is important to monitor circulating bacterial meningitis pathogens and design appropriate interventions, but compl ...[more]

PMID: 34469549

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Project description:BackgroundThe meningitis belt of sub-Saharan Africa has traditionally experienced large outbreaks of meningitis mainly caused by Neisseria meningitidis. More recently, Streptococcus pneumoniae has been recognized as a cause of meningitis outbreaks in the region. Little is known about the natural history and epidemiology of these outbreaks, and, in contrast to meningococcal meningitis, there is no agreed definition for a pneumococcal meningitis epidemic. The aim of this analysis was to systematically review and understand pneumococcal meningitis outbreaks in Africa between 2000 and 2018.MethodsMeningitis outbreaks were identified using a systematic literature review and analyses of meningitis surveillance databases. Potential outbreaks were included in the final analysis if they reported at least 10 laboratory-confirmed meningitis cases above baseline per week with ≥50% of cases confirmed as pneumococcus.ResultsA total of 10 potential pneumococcal meningitis outbreaks were identified in Africa between 2000 and 2018. Of these, 2 were classified as confirmed, 7 were classified as possible, and 1 was classified as unlikely. Three outbreaks spanned more than 1 year. In general, the outbreaks demonstrated lower peak attack rates than meningococcal meningitis outbreaks and had a predominance of serotype 1. Patients with pneumococcal meningitis tended to be older and had higher case fatality rates than meningococcal meningitis cases. An outbreak definition, which includes a weekly district-level incidence of at least 10 suspected cases per 100 000 population per week, with >10 cumulative confirmed cases of pneumococcus per year, would have identified all 10 potential outbreaks.ConclusionsGiven the frequency of and high case fatality from pneumococcal meningitis outbreaks, public health recommendations on vaccination strategies and the management of outbreaks are needed. Improved laboratory testing for S. pneumoniae is critical for early outbreak identification.

Project description:As economic woes of the COVID-19 pandemic deepen, strategies are being formulated to avoid the need for prolonged stay-at-home orders, while implementing risk-based quarantine, testing, contact tracing and surveillance protocols. Given limited resources and the significant economic, public health, and operational challenges of the current 14-day quarantine recommendation, it is vital to understand if shorter but equally effective quarantine and testing strategies can be deployed. To quantify the probability of post-quarantine transmission upon isolation of a positive test, we developed a mathematical model in which we varied quarantine duration and the timing of molecular tests for three scenarios of entry into quarantine. Specifically, we consider travel quarantine, quarantine of traced contacts with an unknown time if infection, and quarantine of cases with a known time of exposure. With a one-day delay between test and result, we found that testing on exit (or entry and exit) can reduce the duration of a 14-day quarantine by 50%, while testing on entry shortened quarantine by at most one day. Testing on exit more effectively reduces post-quarantine transmission than testing upon entry. Furthermore, we identified the optimal testing date within quarantines of varying duration, finding that testing on exit was most effective for quarantines lasting up to seven days. As a real-world validation of these principles, we analyzed the results of 4,040 SARS CoV-2 RT-PCR tests administered to offshore oil rig employees. Among the 47 positives obtained with a testing on entry and exit strategy, 16 cases that previously tested negative at entry were identified, with no further cases detected among employees following quarantine exit. Moreover, this strategy successfully prevented an expected nine offshore transmission events stemming from cases who had tested negative on the entry test, each one a serious concern for initiating rapid spread and a disabling outbreak in the close quarters of an offshore rig. This successful outcome highlights that appropriately timed testing can make shorter quarantines more effective, thereby minimizing economic impacts, disruptions to operational integrity, and COVID-related public health risks.

Project description:BackgroundThis study aims to assess the most efficient combinations of vaccination and screening coverage for the prevention of cervical cancer (CC) at different levels of expenditure in Nigeria.MethodsAn optimization procedure, using a linear programming approach and requiring the use of two models (an evaluation and an optimization model), was developed. The evaluation model, a Markov model, estimated the annual number of CC cases at steady state in a population of 100,000 women for four alternative strategies: screening only; vaccination only; screening and vaccination; and no prevention. The results of the Markov model for each scenario were used as inputs to the optimization model determining the optimal proportion of the population to receive screening and/or vaccination under different scenarios. The scenarios varied by available budget, maximum screening and vaccination coverage, and overall reachable population.ResultsIn the base-case optimization model analyses, with a coverage constraint of 20% for one lifetime screening, 95% for vaccination and a budget constraint of $1 per woman per year to minimize CC incidence, the optimal mix of prevention strategies would result in a reduction of CC incidence of 31% (3-dose vaccination available) or 46% (2-dose vaccination available) compared with CC incidence pre-vaccination. With a 3-dose vaccination schedule, the optimal combination of the different strategies across the population would be 20% screening alone, 39% vaccination alone and 41% with no prevention, while with a 2-dose vaccination schedule the optimal combination would be 71% vaccination alone, and 29% with no prevention. Sensitivity analyses indicated that the results are sensitive to the constraints included in the optimization model as well as the cervical intraepithelial neoplasia (CIN) and CC treatment cost.ConclusionsThe results of the optimization model indicate that, in Nigeria, the most efficient allocation of a limited budget would be to invest in both vaccination and screening with a 3-dose vaccination schedule, and in vaccination alone before implementing a screening program with a 2-dose vaccination schedule.

Project description:BackgroundBacterial meningitis is associated with significant morbidity and mortality worldwide. We aimed to describe the epidemiology, aetiology, trends over time and outcomes of laboratory-confirmed bacterial meningitis in England during 2012-2019.MethodsUK Health Security Agency routinely receives electronic notifications of confirmed infections from National Health Service hospital laboratories in England. Data were extracted for positive bacterial cultures, PCR-positive results for Neisseria meningitidis or Streptococcus pneumoniae from cerebrospinal fluid and positive blood cultures in patients with clinical meningitis.FindingsDuring 2012-19, there were 6554 laboratory-confirmed cases. Mean annual incidence was 1.49/100,000, which remained stable throughout the surveillance period (p = 0.745). There were 155 different bacterial species identified, including 68.4% (106/1550) Gram-negative and 31.6% (49/155) Gram-positive bacteria. After excluding coagulase-negative staphylococci (2481/6554, 37.9%), the main pathogens causing meningitis were Streptococcus pneumoniae (811/4073, 19.9%), Neisseria meningitidis (497/4073, 12.2%), Staphylococcus aureus (467/4073, 11.5%), Escherichia coli (314/4073, 7.7%) and group B streptococcus (268/4073, 6.6%). Pneumococcal meningitis incidence increased significantly during 2012-9, while meningococcal, group A streptococcal and tuberculous meningitis declined. Infants aged <3 months had the highest mean incidence (55.6/100,000; 95% CI, 47.7-63.5) driven mainly by group B streptococci, followed by 3-11 month-olds (8.1/100,000; 95% CI 7.1-9.0), where pneumococcal and meningitis predominated. The 30-day case-fatality rate (CFR) was 10.0% (71/6554). Group A streptococcal meningitis had the highest CFR (47/85, 55.3%). The probability of surviving at 30 days was 95.3% (95% CI, 93.4-97.3%) for infants and 80.0% for older adults (77-84%).InterpretationThe incidence of bacterial meningitis has remained stable. The high CFR highlights a need for prevention through vaccination.FundingPHE.

Project description:Since early March 2020, government agencies have utilized a wide variety of non-pharmaceutical interventions to mitigate the spread of COVID-19 and have struggled to determine when it is appropriate to return to in-person activities after an outbreak is detected. At many universities, fundamental issues related to understanding the spread of the disease (e.g. the transmission rate), the ability of administrators to respond quickly enough by closing when there is a sudden rise in cases, and how to make a decision on when to reopen remains a concern. Surveillance testing strategies have been implemented in some places, and those test outcomes have dictated whether to reopen, to simultaneously monitor community spread, and/or to isolate discovered cases. However, the question remains as to when it is safe to reopen and how much testing is required to remain safely open while keeping infection numbers low. Here, we propose an extension of the classic SIR model to investigate reopening strategies for a fixed testing strategy, based on feedback from testing results. Specifically, we close when a predefined proportion of the population becomes infected, and later reopen when that infected proportion decreases below a predefined threshold. A valuable outcome of our approach is that our reopening strategies are robust to variation in almost all model parameters, including transmission rates, which can be extremely difficult to determine as they typically differ between variants, location, vaccination status, etc. Thus, these strategies can be, in theory, translated over to new variants in different regions of the world. Examples of robust feedback strategies for high disease transmission and a fixed testing capacity include (1) a single long lock down followed by a single long in-person period, and (2) multiple shorter lock downs followed by multiple shorter in-person periods. The utility of this approach of having multiple strategies is that administrators of universities, schools, business, etc. can use a strategy that is best adapted for their own functionality.

Dataset Information

Modeling Optimal Laboratory Testing Strategies for Bacterial Meningitis Surveillance in Africa.

Publications

Modeling Optimal Laboratory Testing Strategies for Bacterial Meningitis Surveillance in Africa.

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