Over time, adaptive Gaussian Hermite quadrature (QUAD) has become the preferred method for estimating generalized linear mixed models with binary outcomes. However, penalized quasi-likelihood (PQL) is still used frequently. In this work, we systematically evaluated whether matching results from PQL and QUAD indicate less bias in estimated regression coefficients and variance parameters via simulation.
We performed a simulation study in which we varied the size of the data set, probability of the outcome, variance of the random effect, number of clusters and number of subjects per cluster, etc. We estimated bias in the regression coefficients, odds ratios and variance parameters as estimated via PQL and QUAD. We ascertained if similarity of estimated regression coefficients, odds ratios and variance parameters predicted less bias.
Overall, we found that the absolute percent bias of the odds ratio estimated via PQL or QUAD increased as the PQL- and QUAD-estimated odds ratios became more discrepant, though results varied markedly depending on the characteristics of the dataset
Given how markedly results varied depending on data set characteristics, specifying a rule above which indicated biased results proved impossible.
This work suggests that comparing results from generalized linear mixed models estimated via PQL and QUAD is a worthwhile exercise for regression coefficients and variance components obtained via QUAD, in situations where PQL is known to give reasonable results.
Malaria is a significant public health threat in the Brazilian Amazon. Previous research has shown that deforestation creates breeding sites for the main malaria vector in Brazil, Anopheles darlingi, but the influence of selective logging, forest fires, and road construction on malaria risk has not been assessed. To understand these impacts, we constructed a negative binomial model of malaria counts at the municipality level controlling for human population and social and environmental risk factors. Both paved and unpaved roadways and fire zones in a municipality increased malaria risk. Within the timber production states where 90% of deforestation has occurred, compared with areas without selective logging, municipalities where 0–7% of the remaining forests were selectively logged had the highest malaria risk (1.72, 95% CI 1.18–2.51), and areas with higher rates of selective logging had the lowest risk (0.39, 95% CI 0.23–0.67). We show that roads, forest fires, and selective logging are previously unrecognized risk factors for malaria in the Brazilian Amazon and highlight the need for regulation and monitoring of sub-canopy forest disturbance.
Despite highly successful vaccination programs and high vaccine uptake, both endemic pertussis and periodic pertussis outbreaks continue to occur. The under-recognized role of adolescents and adults in disease transmission, due to waning immunity following natural infection and vaccination, and reduced likelihood of correct diagnosis, may contribute to pertussis persistence. We constructed a mathematical model to describe the transmission of pertussis in Southern Ontario in both pre-vaccine and vaccine eras, to estimate the underlying burden of pertussis in the population. The model was well calibrated using the best available data on pertussis in the pre-vaccination (1880–1929) and vaccination (1993–2004) eras in Ontario. Pertussis under-reporting by age group was estimated by comparing model-projected incidence to reported laboratory-confirmed cases for Greater Toronto. Best-fit model estimates gave a basic reproductive number of approximately 10.6, (seasonal range 9.9 to 11.5). Under-reporting increased with age, and approximately >95% of infections in children were caused by infections in persons with waning immunity to pertussis following prior infection or vaccination. A well-calibrated model suggests that under-recognized cases of pertussis in older individuals are likely to be an important driver of ongoing pertussis outbreaks in children. Model projections strongly support enhancement of booster vaccination efforts in adults.
China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.
Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran’s I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011.
Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran’s I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.
The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized.
Population trends, defined as interval-specific proportional changes in population size, are often used to help identify species of conservation interest. Efficient modeling of such trends depends on the consideration of the correlation of population changes with key spatial and environmental covariates. This can provide insights into causal mechanisms and allow spatially explicit summaries at scales that are of interest to management agencies. We expand the hierarchical modeling framework used in the North American Breeding Bird Survey (BBS) by developing a spatially explicit model of temporal trend using a conditional autoregressive (CAR) model. By adopting a formal spatial model for abundance, we produce spatially explicit abundance and trend estimates. Analyses based on large-scale geographic strata such as Bird Conservation Regions (BCR) can suffer from basic imbalances in spatial sampling. Our approach addresses this issue by providing an explicit weighting based on the fundamental sample allocation unit of the BBS. We applied the spatial model to three species from the BBS. Species have been chosen based upon their well-known population change patterns, which allows us to evaluate the quality of our model and the biological meaning of our estimates. We also compare our results with the ones obtained for BCRs using a nonspatial hierarchical model (Sauer and Link 2011). Globally, estimates for mean trends are consistent between the two approaches but spatial estimates provide much more precise trend estimates in regions on the edges of species ranges that were poorly estimated in non-spatial analyses. Incorporating a spatial component in the analysis not only allows us to obtain relevant and biologically meaningful estimates for population trends, but also enables us to provide a flexible framework in order to obtain trend estimates for any area.
Infection with high-risk (hr) human papillomavirus (HPV) is considered the necessary cause of cervical cancer. Vaccination against HPV16 and 18 types, which are responsible of about 75% of cervical cancer worldwide, is expected to have a major global impact on cervical cancer occurrence. Valid estimates of the parameters that regulate the natural history of hrHPV infections are crucial to draw reliable projections of the impact of vaccination. We devised a mathematical model to estimate the probability of infection transmission, the rate of clearance, and the patterns of immune response following the clearance of infection of 13 hrHPV types. To test the validity of our estimates, we fitted the same transmission model to two large independent datasets from Italy and Sweden and assessed finding consistency. The two populations, both unvaccinated, differed substantially by sexual behaviour, age distribution, and study setting (screening for cervical cancer or Chlamydia trachomatis infection). Estimated transmission probability of hrHPV types (80% for HPV16, 73%-82% for HPV18, and above 50% for most other types); clearance rates decreasing as a function of time since infection; and partial protection against re-infection with the same hrHPV type (approximately 20% for HPV16 and 50% for the other types) were similar in the two countries. The model could accurately predict the HPV16 prevalence observed in Italy among women who were not infected three years before. In conclusion, our models inform on biological parameters that cannot at the moment be measured directly from any empirical data but are essential to forecast the impact of HPV vaccination programmes.
Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major cause of skin and soft tissue infections (SSTIs) in the US. We developed an age-structured compartmental model to study the spread of CA-MRSA at the population level and assess the effect of control intervention strategies. We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model using monthly time series data on SSTIs incidence in children (≤19 years) during January 2004 -December 2006 in Maricopa County, Arizona. Our model-based forecast for the period January 2007–December 2008 also provided a good fit to data. We also carried out an uncertainty and sensitivity analysis on the control reproduction number, which we estimated at 1.3 (95% CI [1.2,1.4]) based on the model fit to data. Using our calibrated model, we evaluated the effect of typical intervention strategies namely reducing the contact rate of infected individuals owing to awareness of infection and decolonization strategies targeting symptomatic infected individuals on both and the long-term disease dynamics. We also evaluated the impact of hypothetical decolonization strategies targeting asymptomatic colonized individuals. We found that strategies focused on infected individuals were not capable of achieving disease control when implemented alone or in combination. In contrast, our results suggest that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination.
Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is a bacteria that causes skin infections in the US. We developed a mathematical model of CA-MRSA transmission among different age groups at the population level. We parameterized the model using monthly time series data on number of SSTIs in children during the period January 2004–December 2006 in Maricopa County, Arizona. Our model-based forecast to additional time series data covering the period 2007–2008 yielded a good fit to data. Using our calibrated model, we calculated that an infected individual generates on average 1.3 infected people in a totally susceptible population in the study area. We assessed the impact of intervention strategies including reductions in contact rates between infected and non-infected individuals and the effect of decolonization strategies aimed at infected individuals by drug treatment, and found that neither of the two strategies when implemented alone or in combination were able to control the disease. In contrast, we found that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination.
Methicillin-resistant Staphylococcus aureus (MRSA) nasal colonization among inpatients is a well-established risk factor for MRSA infection during the same hospitalization, but the long-term risk of MRSA infection is uncertain. We performed a retrospective cohort study to determine the one-year risk of MRSA infection among inpatients with MRSA-positive nasal polymerase chain reaction (PCR) tests confirmed by positive nasal culture (Group 1), patients with positive nasal PCR but negative nasal culture (Group 2), and patients with negative nasal PCR (Group 3).
Subjects were adults admitted to a four-hospital system between November 1, 2006 and March 31, 2011, comprising 195,255 admissions. Patients underwent nasal swab for MRSA PCR upon admission; if positive, nasal culture for MRSA was performed; if recovered, MRSA was tested for Panton-Valentine Leukocidin (PVL). Outcomes included MRSA-positive clinical culture and skin and soft tissue infection (SSTI). Group 1 patients had a one-year risk of MRSA-positive clinical culture of 8.0% compared with 3.0% for Group 2 patients, and 0.6% for Group 3 patients (p<0.001). In a multivariable model, the hazard ratios for future MRSA-positive clinical culture were 6.52 (95% CI, 5.57 to 7.64) for Group 1 and 3.40 (95% CI, 2.70 to 4.27) for Group 2, compared with Group 3 (p<0.0001). History of MRSA and concurrent MRSA-positive clinical culture were significant risk factors for future MRSA-positive clinical culture. Group 1 patients colonized with PVL-positive MRSA had a one-year risk of MRSA-positive clinical culture of 10.1%, and a one-year risk of MRSA-positive clinical culture or SSTI diagnosis of 21.7%, compared with risks of 7.1% and 12.5%, respectively, for patients colonized with PVL-negative MRSA (p = 0.04, p = 0.005, respectively).
MRSA nasal colonization is a significant risk factor for future MRSA infection; more so if detected by culture than PCR. Colonization with PVL-positive MRSA is associated with greater risk than PVL-negative MRSA.
Previous studies demonstrated that fewer mosquitoes enter houses which are screened or have closed eaves. There is little evidence about the effect on malaria infection in humans that changes in house construction may have. This study examines the impact of protective housing improvements on malaria infection on Bioko Island.
Data from the annual malaria indicator surveys between 2009 and 2012 were used to assess trends in housing characteristics and their effect on RDT confirmed malaria infection in household members. Odds ratios were adjusted for socio-economic status of the household.22726 children between the ages of 2 and 14 years were tested for P. falciparum. Prevalence of infection in those living in houses with open eaves was 23.0% compared to 18.8% for those living in houses with closed eaves (OR = 0.81, 95% CI 0.67 - 0.98). The prevalence of infection for children in screened houses was 9.1% versus 20.1% for those living in unscreened houses (OR = 0.44, 95% CI 0.27 - 0.71). The proportion of houses with closed eaves increased from 66.0% in 2009 to 74.3% in 2012 (test for trend p = 0.01). The proportion of screened houses remained unchanged over time at 1.3%.
As a malaria control intervention, house modification has the advantages that it is not affected by the growing threat of insecticide resistance; it protects all household members equally and at all times while indoors; and it offers protection against a number of vector borne diseases. The study provides evidence in support of efforts to regulate or encourage housing improvements which impede vector access into residences as part of an integrated vector control approach to complement existing measures which have been only partially successful in reducing malaria transmission in some parts of Bioko.
Baguio City, Philippines experienced its first influenza A(H1N1)pdm09 [A(H1)pdm09] case in May 2009. In spite of numerous reports describing the epidemiological and clinical features of A(H1)pdm09 cases, there are no studies about A(H1)pdm09 epidemiology in the Philippines, where year-round influenza activity was observed.
We aimed to investigate the epidemiological and clinical features of A(H1)pdm09 in pandemic and post-pandemic periods.
Data were collected under enhanced surveillance of influenza-like illness (ILI) and severe acute respiratory infection (SARI) from January 2009 to December 2010. RT-PCR was used to detect A(H1)pdm09, following the protocol of the United States Centers for Disease Control and Prevention. The reproduction number was computed as a simple exponential growth rate. Differences in proportional and categorical data were examined using chi-square test or Fishers’ exact test.
Results and Conclusions
The outbreak was observed from week 25 to 35 in 2009 and from week 24 to 37 in 2010. The highest proportion of cases was among children aged 5–14 years. The number of ILI outpatients was 2.3-fold higher in 2009 than in 2010, while the number of inpatients was 1.8-fold higher in 2009. No significant difference in gender was observed during the two periods. The clinical condition of all patients was generally mild and self-limiting, with only 2 mortalities among inpatients in 2009. The basic reproduction number was estimated as 1.16 in 2009 and 1.05 in 2010 in the assumption of mean generation time as 2.6 days. School children played a significant role in facilitating influenza transmission.
Catastrophic declines in African great ape populations due to disease outbreaks have been reported in recent years, yet we rarely hear of similar disease impacts for the more solitary Asian great apes, or for smaller primates. We used an age-structured model of different primate social systems to illustrate that interactions between social structure and demography create ‘dynamic constraints’ on the pathogens that can establish and persist in primate host species with different social systems. We showed that this varies by disease transmission mode. Sexually transmitted infections (STIs) require high rates of transmissibility to persist within a primate population. In particular, for a unimale social system, STIs require extremely high rates of transmissibility for persistence, and remain at extremely low prevalence in small primates, but this is less constrained in longer-lived, larger-bodied primates. In contrast, aerosol transmitted infections (ATIs) spread and persist at high prevalence in medium and large primates with moderate transmissibility;, establishment and persistence in small-bodied primates require higher relative rates of transmissibility. Intragroup contact structure – the social network - creates different constraints for different transmission modes, and our model underscores the importance of intragroup contacts on infection prior to intergroup movement in a structured population. When alpha males dominate sexual encounters, the resulting disease transmission dynamics differ from when social interactions are dominated by mother-infant grooming events, for example. This has important repercussions for pathogen spread across populations. Our framework reveals essential social and demographic characteristics of primates that predispose them to different disease risks that will be important for disease management and conservation planning for protected primate populations.
Background and Aims
A substantial recrudescent wave of pandemic influenza A/H1N1 affected the Mexican population from December 1, 2011–March 20, 2012 following a 2-year period of sporadic transmission.
We analyzed demographic and geographic data on all hospitalizations with severe acute respiratory infection (SARI) and laboratory-confirmed A/H1N1 influenza, and inpatient deaths, from a large prospective surveillance system maintained by a Mexican social security medical system during April 1, 2009– March 20, 2012. We also estimated the reproduction number (R) based on the growth rate of the daily case incidence by date of symptoms onset.
A total of 7569 SARI hospitalizations and 443 in-patient deaths (5.9%) were reported between December 1, 2011, and March 20, 2012 (1115 A/H1N1-positive inpatients and 154 A/H1N1-positive deaths). The proportion of laboratory-confirmed A/H1N1 hospitalizations and deaths was higher among subjects ≥60 years of age (χ2 test, p <0.0001) and lower among younger age groups (χ2 test, p <0.04) for the 2011–2012 pandemic wave compared to the earlier waves in 2009. The reproduction number of the winter 2011–2012 wave in central Mexico was estimated at 1.2–1.3, similar to that reported for the fall 2009 wave, but lower than that of spring 2009.
We documented a substantial increase in the number of SARI hospitalizations during the period December 2011–March 2012 and an older age distribution of laboratory-confirmed A/H1N1 influenza hospitalizations and deaths relative to 2009 A/H1N1 pandemic patterns. The gradual change in the age distribution of A/H1N1 infections in the post-pandemic period is consistent with a build-up of immunity among younger populations.
A/H1N1 influenza pandemic; Hospitalizations; Deaths; Age distribution; Transmissibility
Disease surveillance allows prospective monitoring of patterns in disease incidence in the general community, specific institutions (e.g. hospitals, elderly care homes), and other important population subgroups. Surveillance activities are now routinely conducted in many developed countries and in certain easy-to-reach areas of the developing ones. However due to limited health resources, population in rural area that consisted of the most the vulnerable groups are not under surveillance. Cheaper alternative ways for disease surveillance were needed in resource-limited settings.
Methods and Findings
In this study, a syndromic surveillance system using disease specific absenteeism rates was established in 47 pre-schools with 1,417 students 3–6 y of age in a rural area of Kampot province, Cambodia. School absenteeism data were collected via short message service. Data collected between 1st January and 31st December 2012 was used for system evaluation for future potential use in larger scale. The system appeared to be feasible and acceptable in the rural study setting. Moderate correlation was found between rates of school absenteeism due to illness and the reference data on rates of attendance at health centers in persons <16 y (maximum cross-correlation coefficient = 0.231 at lag = −1 week).
School absenteeism data is pre-existing, easily accessible and requires minimum time and resources after initial development, and our results suggest that this system may be able to provide complementary data for disease surveillance, especially in resource limited settings where there is very little information on illnesses in the community and traditional surveillance systems are difficult to implement. An important next step is to validate the syndromic data with other forms of surveillance including laboratory data.
Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matérn correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.
Gram-negative bacterial bloodstream infection (BSI) is a serious condition with estimated 30% mortality. Clinical outcomes for patients with severe infections improve when antibiotics are appropriately chosen and given early. The objective of this study was to estimate the association of prior healthcare exposure on time to appropriate antibiotic therapy in patients with gram-negative BSI.
We performed a multicenter cohort study of adult, hospitalized patients with gram-negative BSI using time to event analysis in nine community hospitals from 2003-2006. Event time was defined as the first administration of an antibiotic with in
vitro activity against the infecting organism. Healthcare exposure status was categorized as community-acquired, healthcare-associated, or hospital-acquired. Time to appropriate therapy among groups of patients with differing healthcare exposure status was assessed using Kaplan-Meier analyses and multivariate Cox proportional hazards models.
The cohort included 578 patients with gram-negative BSI, including 320 (55%) healthcare-associated, 217 (38%) community-acquired, and 41 (7%) hospital-acquired infections. 529 (92%) patients received an appropriate antibiotic during their hospitalization. Time to appropriate therapy was significantly different among the groups of healthcare exposure status (log-rank p=0.02). Time to first antibiotic administration regardless of drug appropriateness was not different between groups (p=0.3). The unadjusted hazard ratios (HR) (95% confidence interval) were 0.80 (0.65-0.98) for healthcare-associated and 0.72 (0.63-0.82) for hospital-acquired, relative to patients with community-acquired BSI. In multivariable analysis, interaction was found between the main effect and baseline Charlson comorbidity index. When Charlson index was 3, adjusted HRs were 0.66 (0.48-0.92) for healthcare-associated and 0.57 (0.44-0.75) for hospital-acquired, relative to patients with community-acquired infections.
Patients with healthcare-associated or hospital-acquired BSI experienced delays in receipt of appropriate antibiotics for gram-negative BSI compared to patients with community-acquired BSI. This difference was not due to delayed initiation of antibiotic therapy, but due to the inappropriate choice of antibiotic.
On 31 March 2013, the first human infections with the novel influenza A/H7N9 virus were reported in Eastern China. The outbreak expanded rapidly in geographic scope and size, with a total of 132 laboratory-confirmed cases reported by 3 June 2013, in 10 Chinese provinces and Taiwan. The incidence of A/H7N9 cases has stalled in recent weeks, presumably as a consequence of live bird market closures in the most heavily affected areas. Here we compare the transmission potential of influenza A/H7N9 with that of other emerging pathogens and evaluate the impact of intervention measures in an effort to guide pandemic preparedness.
We used a Bayesian approach combined with a SEIR (Susceptible-Exposed-Infectious-Removed) transmission model fitted to daily case data to assess the reproduction number (R) of A/H7N9 by province and to evaluate the impact of live bird market closures in April and May 2013. Simulation studies helped quantify the performance of our approach in the context of an emerging pathogen, where human-to-human transmission is limited and most cases arise from spillover events. We also used alternative approaches to estimate R based on individual-level information on prior exposure and compared the transmission potential of influenza A/H7N9 with that of other recent zoonoses.
Estimates of R for the A/H7N9 outbreak were below the epidemic threshold required for sustained human-to-human transmission and remained near 0.1 throughout the study period, with broad 95% credible intervals by the Bayesian method (0.01 to 0.49). The Bayesian estimation approach was dominated by the prior distribution, however, due to relatively little information contained in the case data. We observe a statistically significant deceleration in growth rate after 6 April 2013, which is consistent with a reduction in A/H7N9 transmission associated with the preemptive closure of live bird markets. Although confidence intervals are broad, the estimated transmission potential of A/H7N9 appears lower than that of recent zoonotic threats, including avian influenza A/H5N1, swine influenza H3N2sw and Nipah virus.
Although uncertainty remains high in R estimates for H7N9 due to limited epidemiological information, all available evidence points to a low transmission potential. Continued monitoring of the transmission potential of A/H7N9 is critical in the coming months as intervention measures may be relaxed and seasonal factors could promote disease transmission in colder months.
Influenza A/H7N9; Transmissibility; Reproduction number; Spillover; Animal reservoir; Emerging infection; Influenza A/H5N1; Swine influenza; Transmission potential; China; Real-time estimation
The 2012-13 influenza season had an unusually early and severe start in the US, succeeding the record mild 2011-12 influenza season, which occurred during the fourth warmest winter on record. Our analysis of climate and past US influenza epidemic seasons between 1997-98 to present indicates that warm winters tend to be followed by severe epidemics with early onset, and that these patterns are seen for both influenza A and B. We posit that fewer people are infected with influenza during warm winters, thereby leaving an unnaturally large fraction of susceptible individuals in the population going into the next season, which can lead to early and severe epidemics.
In the event of continued global warming, warm winters such as that of 2011-12 are expected to occur more frequently. Our results thus suggest that expedited manufacture and distribution of influenza vaccines after mild winters has the potential to mitigate the severity of future influenza epidemics.
In 2008, 800 rural Thai adults living within Kamphaeng Phet Province were enrolled in a prospective cohort study of zoonotic influenza transmission. Serological analyses of enrollment sera suggested this cohort had experienced subclinical avian influenza virus (AIV) infections with H9N2 and H5N1 viruses.
After enrollment, participants were contacted weekly for 24mos for acute influenza-like illnesses (ILI). Cohort members confirmed to have influenza A infections were enrolled with their household contacts in a family transmission study involving paired sera and respiratory swab collections. Cohort members also provided sera at 12 and 24 months after enrollment. Serologic and real-time RT-PCR assays were performed against avian, swine, and human influenza viruses.
Over the 2 yrs of follow-up, 81 ILI investigations in the cohort were conducted; 31 (38%) were identified as influenza A infections by qRT-PCR. Eighty-three household contacts were enrolled; 12 (14%) reported ILIs, and 11 (92%) of those were identified as influenza infections. A number of subjects were found to have slightly elevated antibodies against avian-like A/Hong Kong/1073/1999(H9N2) virus: 21 subjects (2.7%) at 12-months and 40 subjects (5.1%) at 24-months. Among these, two largely asymptomatic acute infections with H9N2 virus were detected by >4-fold increases in annual serologic titers (final titers 1∶80). While controlling for age and influenza vaccine receipt, moderate poultry exposure was significantly associated with elevated H9N2 titers (adjusted OR = 2.3; 95% CI, 1.04–5.2) at the 24-month encounter. One subject had an elevated titer (1∶20) against H5N1 during follow-up.
From 2008–10, evidence for AIV infections was sparse among this rural population. Subclinical H9N2 AIV infections likely occurred, but serological results were confounded by antibody cross-reactions. There is a critical need for improved serological diagnostics to more accurately detect subclinical AIV infections in humans.
Methicillin resistant Staphylococcus aureus (MRSA) is currently a major cause of skin and soft tissue infections (SSTI) in the United States. Seasonal variation of MRSA infections in hospital settings has been widely observed. However, systematic time-series analysis of incidence data is desirable to understand the seasonality of community acquired (CA)-MRSA infections at the population level. In this paper, using data on monthly SSTI incidence in children aged 0–19 years and enrolled in Medicaid in Maricopa County, Arizona, from January 2005 to December 2008, we carried out time-series and nonlinear regression analysis to determine the periodicity, trend, and peak timing in SSTI incidence in children at different age: 0–4 years, 5–9 years, 10–14 years, and 15–19 years. We also assessed the temporal correlation between SSTI incidence and meteorological variables including average temperature and humidity. Our analysis revealed a strong annual seasonal pattern of SSTI incidence with peak occurring in early September. This pattern was consistent across age groups. Moreover, SSTIs followed a significantly increasing trend over the 4-year study period with annual incidence increasing from 3.36% to 5.55% in our pediatric population of approximately 290,000. We also found a significant correlation between the temporal variation in SSTI incidence and mean temperature and specific humidity. Our findings could have potential implications on prevention and control efforts against CA-MRSA.
Theory suggests that human behavior has implications for disease spread. We examine the hypothesis that individuals engage in voluntary defensive behavior during an epidemic. We estimate the number of passengers missing previously purchased flights as a function of concern for swine flu or A/H1N1 influenza using 1.7 million detailed flight records, Google Trends, and the World Health Organization's FluNet data. We estimate that concern over “swine flu,” as measured by Google Trends, accounted for 0.34% of missed flights during the epidemic. The Google Trends data correlates strongly with media attention, but poorly (at times negatively) with reported cases in FluNet. Passengers show no response to reported cases. Passengers skipping their purchased trips forwent at least $50 M in travel related benefits. Responding to actual cases would have cut this estimate in half. Thus, people appear to respond to an epidemic by voluntarily engaging in self-protection behavior, but this behavior may not be responsive to objective measures of risk. Clearer risk communication could substantially reduce epidemic costs. People undertaking costly risk reduction behavior, for example, forgoing nonrefundable flights, suggests they may also make less costly behavior adjustments to avoid infection. Accounting for defensive behaviors may be important for forecasting epidemics, but linking behavior with epidemics likely requires consideration of risk communication.
Identification of individuals or subpopulations that contribute the most to disease transmission is key to target surveillance and control efforts. In a recent study in BMC Medicine, Smieszek and Salathé introduced a novel method based on readily available information about spatial proximity in high schools, to help identify individuals at higher risk of infection and those more likely to be infected early in the outbreak. By combining simulation models for influenza transmission with high-resolution data on school contact patterns, the authors showed that their proximity method compares favorably to more sophisticated methods using detailed contact tracing information. The proximity method is simple and promising, but further research is warranted to confront this method against real influenza outbreak data, and to assess the generalizability of the approach to other important transmission units, such as work, households, and transportation systems.
See related research article here http://www.biomedcentral.com/1741-7015/11/35
contact network; hotspot; dynamic network; contact pattern; wireless sensing devices; collocation ranking; class schedule; high school; influenza; disease transmission.
Pandemic influenza is said to 'shift mortality' to younger age groups; but also to spare a subpopulation of the elderly population. Does one of these effects dominate? Might this have important ramifications?
We estimated age-specific excess mortality rates for all-years for which data were available in the 20th century for Australia, Canada, France, Japan, the UK, and the USA for people older than 44 years of age. We modeled variation with age, and standardized estimates to allow direct comparison across age groups and countries. Attack rate data for four pandemics were assembled.
For nearly all seasons, an exponential model characterized mortality data extremely well. For seasons of emergence and a variable number of seasons following, however, a subpopulation above a threshold age invariably enjoyed reduced mortality. 'Immune escape', a stepwise increase in mortality among the oldest elderly, was observed a number of seasons after both the A(H2N2) and A(H3N2) pandemics. The number of seasons from emergence to escape varied by country. For the latter pandemic, mortality rates in four countries increased for younger age groups but only in the season following that of emergence. Adaptation to both emergent viruses was apparent as a progressive decrease in mortality rates, which, with two exceptions, was seen only in younger age groups. Pandemic attack rate variation with age was estimated to be similar across four pandemics with very different mortality impact.
In all influenza pandemics of the 20th century, emergent viruses resembled those that had circulated previously within the lifespan of then-living people. Such individuals were relatively immune to the emergent strain, but this immunity waned with mutation of the emergent virus. An immune subpopulation complicates and may invalidate vaccine trials. Pandemic influenza does not 'shift' mortality to younger age groups; rather, the mortality level is reset by the virulence of the emerging virus and is moderated by immunity of past experience. In this study, we found that after immune escape, older age groups showed no further mortality reduction, despite their being the principal target of conventional influenza vaccines. Vaccines incorporating variants of pandemic viruses seem to provide little benefit to those previously immune. If attack rates truly are similar across pandemics, it must be the case that immunity to the pandemic virus does not prevent infection, but only mitigates the consequences.
Pandemic influenza; mortality due to influenza; recycling; pandemic attack rates; vaccination; protective immunity
We discuss models for rapidly disseminating infectious diseases during mass gatherings (MGs), using influenza as a case study. Recent innovations in modeling and forecasting influenza transmission dynamics at local, regional, and global scales have made influenza a particularly attractive model scenario for MG. We discuss the behavioral, medical, and population factors for modeling MG disease transmission, review existing model formulations, and highlight key data and modeling gaps related to modeling MG disease transmission. We argue that the proposed improvements will help integrate infectious-disease models in MG health contingency plans in the near future, echoing modeling efforts that have helped shape influenza pandemic preparedness plans in recent years.
Model; mathematical; epidemic; outbreaks; epidemiology; mass gathering; school closure; clustering; reactive vaccination; movement; social networks/
Our age-specific analysis of the mortality patterns of the influenza A/H1N1 pandemic in Mexico suggests a high excess of mortality burden relative to other countries, especially among individuals aged 5-59 years.
Background. The mortality burden of the 2009 A/H1N1 influenza pandemic remains controversial, in part because of delays in reporting of vital statistics that are traditionally used to measure influenza-related excess mortality. Here, we compare excess mortality rates and years of life lost (YLL) for pandemic and seasonal influenza in Mexico and evaluate laboratory-confirmed death reports.
Methods. Monthly age- and cause-specific death rates from January 2000 through April 2010 and population-based surveillance of influenza virus activity were used to estimate excess mortality and YLL in Mexico. Age-stratified laboratory-confirmed A/H1N1 death reports were obtained from an active surveillance system covering 40% of the population.
Results. The A/H1N1 pandemic was associated with 11.1 excess all-cause deaths per 100 000 population and 445 000 YLL during the 3 waves of virus activity in Mexico, April–December 2009. The pandemic mortality burden was 0.6–2.6 times that of a typical influenza season and lower than that of the severe 2003–2004 influenza epidemic. Individuals aged 5–19 and 20–59 years were disproportionately affected relative to their experience with seasonal influenza. Laboratory-confirmed deaths captured 1 of 7 pandemic excess deaths overall but only 1 of 41 deaths in persons >60 years of age in 2009. A recrudescence of excess mortality was observed in older persons during winter 2010, in a period when influenza and respiratory syncytial virus cocirculated.
Conclusions. Mexico experienced higher 2009 A/H1N1 pandemic mortality burden than other countries for which estimates are available. Further analyses of detailed vital statistics are required to assess geographical variation in the mortality patterns of this pandemic.
The role of demographic factors, climatic conditions, school cycles, and connectivity patterns in shaping the spatio-temporal dynamics of pandemic influenza is not clearly understood. Here we analyzed the spatial, age and temporal evolution of the 2009 A/H1N1 influenza pandemic in Chile, a southern hemisphere country covering a long and narrow strip comprising latitudes 17°S to 56°S.
We analyzed the dissemination patterns of the 2009 A/H1N1 pandemic across 15 regions of Chile based on daily hospitalizations for severe acute respiratory disease and laboratory confirmed A/H1N1 influenza infection from 01-May to 31-December, 2009. We explored the association between timing of pandemic onset and peak pandemic activity and several geographical and demographic indicators, school vacations, climatic factors, and international passengers. We also estimated the reproduction number (R) based on the growth rate of the exponential pandemic phase by date of symptoms onset, estimated using maximum likelihood methods.
While earlier pandemic onset was associated with larger population size, there was no association with connectivity, demographic, school or climatic factors. In contrast, there was a latitudinal gradient in peak pandemic timing, representing a 16-39-day lag in disease activity from the southern regions relative to the northernmost region (P < 0.001). Geographical differences in latitude of Chilean regions, maximum temperature and specific humidity explained 68.5% of the variability in peak timing (P = 0.01). In addition, there was a decreasing gradient in reproduction number from south to north Chile (P < 0.0001). The regional mean R estimates were 1.6-2.0, 1.3-1.5, and 1.2-1.3 for southern, central and northern regions, respectively, which were not affected by the winter vacation period.
There was a lag in the period of most intense 2009 pandemic influenza activity following a South to North traveling pattern across regions of Chile, significantly associated with geographical differences in minimum temperature and specific humidity. The latitudinal gradient in timing of pandemic activity was accompanied by a gradient in reproduction number (P < 0.0001). Intensified surveillance strategies in colder and drier southern regions could lead to earlier detection of pandemic influenza viruses and improved control outcomes.
A/H1N1 influenza pandemic; Acute respiratory infection; Influenza-like-illness; Reproduction number; Spatial heterogeneity; School cycles; Climatological variables, Specific humidity; Temperature; Chile