Survival at older ages: are greater influenza antibody titers protective?

Antibodies are a core element of the immune system's defense against infectious diseases. We hypothesize that antibody titres might therefore be an important predictor of survival in older individuals. This is important because biomarkers that robustly measure survival have proved elusive, despite their potential utility in health care settings. We present evidence supporting the hypothesis that influenza antibody titres are associated with overall survival of older individuals, and indicate a role for biological sex in modulating this association. Since antibody titres can be modulated by vaccination, these results have important implications for public health policy on influenza control in aging populations.

Modelling interventions and contact networks to reduce the spread of carbapenem-resistant organisms between individuals in the ICU.

BACKGROUND: Contact precautions are widely used to prevent the transmission of carbapenem-resistant organisms (CROs) in hospital wards. However, evidence for their effectiveness in natural hospital environments is limited. OBJECTIVE: To determine which contact precautions, healthcare worker (HCW)-patient interactions, and patient and ward characteristics are associated with greater risk of CRO infection or colonization. DESIGN, SETTING AND PARTICIPANTS: CRO clinical and surveillance cultures from two high-acuity wards were assessed through probabilistic modelling to characterize a susceptible patient's risk of CRO infection or colonization during a ward stay. User- and time-stamped electronic health records were used to build HCW-mediated contact networks between patients. Probabilistic models were adjusted for patient (e.g. antibiotic administration) and ward (e.g. hand hygiene compliance, environmental cleaning) characteristics. The effects of risk factors were assessed by adjusted odds ratio (aOR) and 95% Bayesian credible intervals (CrI). EXPOSURES: The degree of interaction with CRO-positive patients, stratified by whether CRO-positive patients were on contact precautions. MAIN OUTCOMES AND MEASURES: The prevalence of CROs and number of new carriers (i.e. incident CRO aquisition). RESULTS: Among 2193 ward visits, 126 (5.8%) patients became colonized or infected with CROs. Susceptible patients had 4.8 daily interactions with CRO-positive individuals on contact precautions (vs 1.9 interactions with those not on contact precautions). The use of contact precautions for CRO-positive patients was associated with a reduced rate (7.4 vs 93.5 per 1000 patient-days at risk) and odds (aOR 0.03, 95% CrI 0.01-0.17) of CRO acquisition among susceptible patients, resulting in an estimated absolute risk reduction of 9.0% (95% CrI 7.6-9.2%). Also, carbapenem administration to susceptible patients was associated with increased odds of CRO acquisition (aOR 2.38, 95% CrI 1.70-3.29). CONCLUSIONS AND RELEVANCE: In this population-based cohort study, the use of contact precautions for patients colonized or infected with CROs was associated with lower risk of CRO acquisition among susceptible patients, even after adjusting for antibiotic exposure. Further studies that include organism genotyping are needed to confirm these findings.

Using Serology to Anticipate Measles Post-honeymoon Period Outbreaks.

Measles vaccination is a public health 'best buy', with the highest cost of illness averted of any vaccine-preventable disease (Ozawa et al., Bull. WHO 2017;95:629). In recent decades, substantial reductions have been made in the number of measles cases, with an estimated 20 million deaths averted from 2000 to 2017 (Dabbagh et al., MMWR 2018;67:1323). Yet, an important feature of epidemic dynamics is that large outbreaks can occur following years of apparently successful control (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Such 'post-honeymoon period' outbreaks are a result of the nonlinear dynamics of epidemics (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Anticipating post-honeymoon outbreaks could lead to substantial gains in public health, helping to guide the timing, age-range, and location of catch-up vaccination campaigns (Grais et al., J. Roy. Soc. Interface 2008003B6:67-74). Theoretical conditions for such outbreaks are well understood for measles, yet the information required to make these calculations policy-relevant is largely lacking. We propose that a major extension of serological studies to directly characterize measles susceptibility is a high priority.

Improved inference of time-varying reproduction numbers during infectious disease outbreaks.

Accurate estimation of the parameters characterising infectious disease transmission is vital for optimising control interventions during epidemics. A valuable metric for assessing the current threat posed by an outbreak is the time-dependent reproduction number, i.e. the expected number of secondary cases caused by each infected individual. This quantity can be estimated using data on the numbers of observed new cases at successive times during an epidemic and the distribution of the serial interval (the time between symptomatic cases in a transmission chain). Some methods for estimating the reproduction number rely on pre-existing estimates of the serial interval distribution and assume that the entire outbreak is driven by local transmission. Here we show that accurate inference of current transmissibility, and the uncertainty associated with this estimate, requires: (i) up-to-date observations of the serial interval to be included, and; (ii) cases arising from local transmission to be distinguished from those imported from elsewhere. We demonstrate how pathogen transmissibility can be inferred appropriately using datasets from outbreaks of H1N1 influenza, Ebola virus disease and Middle-East Respiratory Syndrome. We present a tool for estimating the reproduction number in real-time during infectious disease outbreaks accurately, which is available as an R software package (EpiEstim 2.2). It is also accessible as an interactive, user-friendly online interface (EpiEstim App), permitting its use by non-specialists. Our tool is easy to apply for assessing the transmission potential, and hence informing control, during future outbreaks of a wide range of invading pathogens.

A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping.

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of 'leaving no one behind' has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and 'coldspots' of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Rubella vaccination in India: identifying broad consequences of vaccine introduction and key knowledge gaps.

Rubella virus infection typically presents as a mild illness in children; however, infection during pregnancy may cause the birth of an infant with congenital rubella syndrome (CRS). As of February 2017, India began introducing rubella-containing vaccine (RCV) into the public-sector childhood vaccination programme. Low-level RCV coverage among children over several years can result in an increase in CRS incidence by increasing the average age of infection without sufficiently reducing rubella incidence. We evaluated the impact of RCV introduction on CRS incidence across India's heterogeneous demographic and epidemiological contexts. We used a deterministic age-structured model that reflects Indian states' rural and urban area-specific demography and vaccination coverage levels to simulate rubella dynamics and estimate CRS incidence with and without RCV introduction to the public sector. Our analysis suggests that current low-level private-sector vaccination has already slightly increased the burden of CRS in India. We additionally found that the effect of public-sector RCV introduction depends on the basic reproductive number, R 0, of rubella. If R 0 is five, a value empirically estimated from an array of settings, CRS incidence post-RCV introduction will likely decrease. However, if R 0 is seven or nine, some states may experience short-term or annual increases in CRS, even if a long-term total reduction in cases (30 years) is expected. Investment in population-based serological surveys and India's fever/rash surveillance system will be key to monitoring the success of the vaccination programme.

Demographics, epidemiology and the impact of vaccination campaigns in a measles-free world - Can elimination be maintained?

INTRODUCTION: All six WHO regions currently have goals for measles elimination by 2020. Measles vaccination is delivered via routine immunization programmes, which in most sub-Saharan African countries reach children around 9months of age, and supplementary immunization activities (SIAs), which target a wider age range at multi-annual intervals. In the absence of endemic measles circulation, the proportion of individuals susceptible to measles will gradually increase through accumulation of new unvaccinated individuals in each birth cohort, increasing the risk of an epidemic. The impact of SIAs and the financial investment they require, depend on coverage and target age range. MATERIALS AND METHODS: We evaluated the impact of target population age range for periodic SIAs, evaluating outcomes for two different levels of coverage, using a demographic and epidemiological model adapted to reflect populations in 4 sub-Saharan African countries. RESULTS: We found that a single SIA can maintain elimination over short time-scales, even with low routine coverage. However, maintaining elimination for more than a few years is difficult, even with large (high coverage/wide age range) recurrent SIAs, due to the build-up of susceptible individuals. Across the demographic and vaccination contexts investigated, expanding SIAs to target individuals over 10years did not significantly reduce outbreak risk. CONCLUSIONS: Elimination was not maintained in the contexts we evaluated without a second opportunity for vaccination. In the absence of an expanded routine program, SIAs provide a powerful option for providing this second dose. We show that a single high coverage SIA can deliver most key benefits in terms of maintaining elimination, with follow-up campaigns potentially requiring smaller investments. This makes post-campaign evaluation of coverage increasingly relevant to correctly assess future outbreak risk.

Meticillin-resistant Staphylococcus aureus (MRSA) acquisition risk in an endemic neonatal intensive care unit with an active surveillance culture and decolonization programme.

BACKGROUND: Meticillin-resistant Staphylococcus aureus (MRSA) is a leading cause of healthcare-associated infection in the neonatal intensive care unit (NICU). Decolonization may eliminate bacterial reservoirs that drive MRSA transmission. AIM: To measure the association between colonization pressure from decolonized and non-decolonized neonates and MRSA acquisition to inform use of this strategy for control of endemic MRSA. METHODS: An eight-year retrospective cohort study was conducted in a level-4 NICU that used active surveillance cultures and decolonization for MRSA control. Weekly colonization pressure exposures were defined as the number of patient-days of concurrent admission with treated (decolonized) and untreated (non-decolonized) MRSA carriers in the preceding seven days. Poisson regression was used to estimate risk of incident MRSA colonization associated with colonization pressure exposures. The population-attributable fraction was calculated to assess the proportion of overall unit MRSA incidence attributable to treated or untreated patients in this setting. FINDINGS: Every person-day increase in exposure to an untreated MRSA carrier was associated with a 6% increase in MRSA acquisition risk [relative risk (RR): 1.06; 95% confidence interval (CI): 1.01-1.11]. Risk of acquisition was not influenced by exposure to treated, isolated MRSA carriers (RR: 1.01; 95% CI: 0.98-1.04). In the context of this MRSA control programme, 22% (95% CI: 4.0-37) of MRSA acquisition could be attributed to exposures to untreated MRSA carriers. CONCLUSION: Untreated MRSA carriers were an important reservoir for transmission. Decolonized patients on contact isolation posed no detectable transmission threat, supporting the hypothesis that decolonization may reduce patient-to-patient transmission. Non-patient reservoirs may contribute to unit MRSA acquisition and require further investigation.

Seven challenges in modeling vaccine preventable diseases.

Vaccination has been one of the most successful public health measures since the introduction of basic sanitation. Substantial mortality and morbidity reductions have been achieved via vaccination against many infections, and the list of diseases that are potentially controllable by vaccines is growing steadily. We introduce key challenges for modeling in shaping our understanding and guiding policy decisions related to vaccine preventable diseases.

Seven challenges for model-driven data collection in experimental and observational studies.

Infectious disease models are both concise statements of hypotheses and powerful techniques for creating tools from hypotheses and theories. As such, they have tremendous potential for guiding data collection in experimental and observational studies, leading to more efficient testing of hypotheses and more robust study designs. In numerous instances, infectious disease models have played a key role in informing data collection, including the Garki project studying malaria, the response to the 2009 pandemic of H1N1 influenza in the United Kingdom and studies of T-cell immunodynamics in mammals. However, such synergies remain the exception rather than the rule; and a close marriage of dynamic modeling and empirical data collection is far from the norm in infectious disease research. Overcoming the challenges to using models to inform data collection has the potential to accelerate innovation and to improve practice in how we deal with infectious disease threats.

Six challenges in modelling for public health policy.

The World Health Organisation's definition of public health refers to all organized measures to prevent disease, promote health, and prolong life among the population as a whole (World Health Organization, 2014). Mathematical modelling plays an increasingly important role in helping to guide the most high impact and cost-effective means of achieving these goals. Public health programmes are usually implemented over a long period of time with broad benefits to many in the community. Clinical trials are seldom large enough to capture these effects. Observational data may be used to evaluate a programme after it is underway, but have limited value in helping to predict the future impact of a proposed policy. Furthermore, public health practitioners are often required to respond to new threats, for which there is little or no previous data on which to assess the threat. Computational and mathematical models can help to assess potential threats and impacts early in the process, and later aid in interpreting data from complex and multifactorial systems. As such, these models can be critical tools in guiding public health action. However, there are a number of challenges in achieving a successful interface between modelling and public health. Here, we discuss some of these challenges.

Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa.

Measles vaccination is estimated to have averted 13·8 million deaths between 2000 and 2012. Persisting heterogeneity in coverage is a major contributor to continued measles mortality, and a barrier to measles elimination and introduction of rubella-containing vaccine. Our objective is to identify determinants of inequities in coverage, and how vaccine delivery must change to achieve elimination goals, which is a focus of the WHO Decade of Vaccines. We combined estimates of travel time to the nearest urban centre (⩾50 000 people) with vaccination data from Demographic Health Surveys to assess how remoteness affects coverage in 26 African countries. Building on a statistical mapping of coverage against age and geographical isolation, we quantified how modifying the rate and age range of vaccine delivery affects national coverage. Our scenario analysis considers increasing the rate of delivery of routine vaccination, increasing the target age range of routine vaccination, and enhanced delivery to remote areas. Geographical isolation plays a key role in defining vaccine inequity, with greater inequity in countries with lower measles vaccine coverage. Eliminating geographical inequities alone will not achieve thresholds for herd immunity, indicating that changes in delivery rate or age range of routine vaccination will be required. Measles vaccine coverage remains far below targets for herd immunity in many countries on the African continent and is likely to be inadequate for achieving rubella elimination. The impact of strategies such as increasing the upper age range eligible for routine vaccination should be considered.

Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South Africa.

Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Since vaccination at levels short of those necessary to achieve eradication may increase the average age of infection, and thus potentially the CRS burden, introduction of the vaccine has been limited to contexts where coverage is high. Recent work suggests that spatial heterogeneity in coverage should also be a focus of concern. Here, we use a detailed dataset from South Africa to explore the implications of heterogeneous vaccination for the burden of CRS, introducing realistic vaccination scenarios based on reported levels of measles vaccine coverage. Our results highlight the potential impact of country-wide reductions of incidence of rubella on the local CRS burdens in districts with small population sizes. However, simulations indicate that if rubella vaccination is introduced with coverage reflecting current estimates for measles coverage in South Africa, the burden of CRS is likely to be reduced overall over a 30 year time horizon by a factor of 3, despite the fact that this coverage is lower than the traditional 80 per cent rule of thumb for vaccine introduction, probably owing to a combination of relatively low birth and transmission rates. We conclude by discussing the likely impact of private-sector vaccination.

Close encounters of the infectious kind: methods to measure social mixing behaviour.

A central tenet of close-contact or respiratory infection epidemiology is that infection patterns within human populations are related to underlying patterns of social interaction. Until recently, few researchers had attempted to quantify potentially infectious encounters made between people. Now, however, several studies have quantified social mixing behaviour, using a variety of methods. Here, we review the methodologies employed, suggest other appropriate methods and technologies, and outline future research challenges for this rapidly advancing field of research.

Impact of birth rate, seasonality and transmission rate on minimum levels of coverage needed for rubella vaccination.

Childhood rubella infection in early pregnancy can lead to fetal death or congenital rubella syndrome (CRS) with multiple disabilities. Reduction of transmission via universal vaccination can prevent CRS, but inadequate coverage may increase CRS numbers by increasing the average age at infection. Consequently, many countries do not vaccinate against rubella. The World Health Organization recommends that for safe rubella vaccination, at least 80% coverage of each birth cohort should be sustained. The nonlinear relationship between CRS burden and infection dynamics has been much studied; however, how the complex interaction between epidemic and demographic dynamics affects minimum safe levels of coverage has not been quantitatively evaluated across scales necessary for a global assessment. We modelled 30-year CRS burdens across epidemiological and demographic settings, including the effect of local interruption of transmission via stochastic fadeout. Necessary minimum vaccination coverage increases markedly with birth and transmission rates, independent of amplitude of seasonal fluctuations in transmission. Susceptible build-up in older age groups following local stochastic extinction of rubella increased CRS burden, indicating that spatial context is important. In low birth-rate settings, 80% routine coverage is a conservative guideline, particularly if supplemented with campaigns and vaccination of women of childbearing age. Where birth and transmission rates are high, immunization coverage must be well above 80% and campaigns may be needed. Policy-makers should be aware of the potential negative effect of local extinction of rubella, since heterogeneity in vaccination coverage will shape extinction patterns, potentially increasing CRS burdens.

Structured models of infectious disease: inference with discrete data.

The usage of structured population models can make substantial contributions to public health, particularly for infections where clinical outcomes vary over age. There are three theoretical challenges in implementing such analyses: (i) developing an appropriate framework that models both demographic and epidemiological transitions; (ii) parameterizing the framework, where parameters may be based on data ranging from the biological course of infection, basic patterns of human demography, specific characteristics of population growth, and details of vaccination regimes implemented; (iii) evaluating public health strategies in the face of changing human demography. We illustrate the general approach by developing a model of rubella in Costa Rica. The demographic profile of this infection is a crucial aspect of its public health impact, and we use a transient perturbation analysis to explore the impact of changing human demography on immunization strategies implemented.

Maintaining high rates of measles immunization in Africa.

Supplementary immunization activities (SIAs) are important in achieving high levels of population immunity to measles virus. Using data from a 2006 survey of measles vaccination in Lusaka, Zambia, we developed a model to predict measles immunity following routine vaccination and SIAs, and absent natural infection. Projected population immunity was compared between the current programme and alternatives, including supplementing routine vaccination with a second dose, or SIAs at 1-, 2-, 3-, 4- and 5-year intervals. Current routine vaccination plus frequent SIAs could maintain high levels of population immunity in children aged <5 years, even if each frequent SIA has low coverage (e.g. ≥ 72% for bi-annual 60% coverage SIAs vs. ≥ 69% for quadrennial 95% coverage SIAs). A second dose at 12 months with current coverage could achieve 81% immunity. Circulating measles virus will only increase population immunity. Public health officials should consider frequent SIAs when resources for a two-dose strategy are unavailable.

Effectiveness of Non-pharmaceutical Interventions in Controlling an Influenza A Outbreak in a School, Thailand, November 2007.

Non-pharmaceutical interventions are often recommended as a component of integrated control measures for pandemic influenza, but the effectiveness needs to be evaluated. An outbreak of influenza A (H1N1) in northern Thailand in November 2007 offered opportunity to evaluate these interventions. An investigation was conducted to describe the outbreak, evaluate effectiveness of non-pharmaceutical interventions and assess surge capacity of health agencies. A descriptive study was conducted by interviewing students and personnel in a school. We characterized transmission of the virus in this outbreak and explored effects of control measures. We identified that 44% of the students and teachers developed influenza during the 19-day outbreak. Non-pharmaceutical interventions including school closure, setting up a field hospital and community health education were implemented. These measures possibly limited the outbreak spreading to other schools nearby. Surveillance and preparedness plans could be strengthened to respond to pandemic and inter-pandemic influenza by using non-pharmaceutical interventions.

Monitoring the health status and impact of treatment on Americans: the Medicare Beneficiary Health Status Registry.

OBJECTIVE: A major new survey program, the Medicare Beneficiary Health Status Registry (MBHSR), has been proposed to improve the monitoring of the health status of Medicare beneficiaries. The MBHSR would collect data by mail with telephone follow up of nonrespondents to permit economical assessment of a total Registry of approximately 200,000 Medicare beneficiaries, approximately 54,000 of whom would be surveyed in any given year. (Surveys would be conducted of samples of new enrollees who would be reinterviewed every five years.) METHOD: To assess the feasibility of that approach, a field test was conducted with a probability sample (n = 1,922) that comprised approximately equal numbers of new Medicare enrollees (aged, 65) and current beneficiaries (age range, 76-80). The field test was designed to assess the quality of the data that this design would produce. FINDINGS: Results indicate that the proposed design of the MBHSR could achieve response rates of approximately 80% among both age cohorts using a survey instrument that took 30 minutes to complete. Internal reliability of Activities of Daily Living, Instrumental Activities of Daily Living, Mobility, Mental Health Index, General Health, and Prostate Symptomatology scales ranged from 0.77 to 0.93. When measurements were repeated approximately 30 days after the initial survey, moderate to high levels of cross temporal correlation (range, 0.64-0.96) were found for most indexes, with the exception of prostate symptomatology. In addition, an earlier comparison of survey responses in the MBHSR field test to Medicare payment records indicated that the MBHSR field test obtained highly accurate reports of most of the major surgeries that were recorded in Medicare claims files. CONCLUSION: The design proposed for the MBHSR is feasible. If implemented, it should produce acceptably high rates of response and data quality.