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Aims: Lyme disease (LD) cases in the United States are estimated to be approaching 500,000 annually. Protective measures, such as repellent use and wearing protective clothing are recommended by public health officials. However, no protective measure has been proven to be consistently effective, partly because they require consistent and persistent behaviour change. While safe and effective vaccines are in development, it is unclear what factors influence the intention to vaccinate against LD. This study uses the Health Belief Model (HBM) framework to determine key drivers associated with vaccine intention. The HBM is widely applied in public health research and uses the following constructs: perceived susceptibility and severity of disease, perceived benefits and barriers to disease prevention, and cues to action for disease prevention to predict health behaviours. To date, the HBM framework has not been applied to vaccination intention for LD. Methods and Results: Data were collected from 874 adults and 834 caregivers of children residing in US states endemic to LD. Sampling adults and caregivers allows us to explore how the intention to vaccinate differs among those at‐risk. Estimates from structural equation modelling (SEM) show that the HBM constructs explain much of the variation in intention to vaccinate against LD. Both adult and caregiver intentions to vaccinate are positively influenced by cues to action, perceived susceptibility of LD, and perceived benefits to vaccination. However, there is variation in the influence of constructs across the samples. Caregiver's intention to vaccinate is positively influenced by the perceived severity of LD and negatively influenced by safety concerns about the vaccine, whereas adult intention is negatively influenced by perceived barriers to vaccination. Conclusion: A strong relationship of cues to action on vaccine intention in samples of adults and caregivers suggests the importance of a recommendation from a healthcare provider or the Centers for Disease Control and Prevention (CDC). [ABSTRACT FROM AUTHOR]

Aims: A growing number of Lyme disease (LD) cases in the U.S. are reported in states neighbouring those with high‐incidence (>10 cases per 100,000 population) rates. Considering the evolving epidemiology, high‐incidence counties in many of these "neighbouring states," and the forthcoming vaccines, understanding the drivers of vaccination intention is critical, particularly how drivers of intention in neighbouring states vary relative to regions currently classified as high incidence. This study uses the Health Belief Model (HBM) to determine the key drivers associated with vaccine intention for U.S. adults and caregivers of children residing in neighbouring states. Methods and Results: Using an established panel with quotas for age, sex, race/ethnicity and urbanity, we surveyed 887 adults and 822 caregivers of children residing in U.S. neighbouring states. Survey items included measures of intention and HBM constructs, all of which were assessed using 5‐point Likert scales. We subsequently used structural equation modelling to understand the influence of the HBM constructs on LD vaccine intention. Estimates from structural equation modelling show that the HBM constructs explain much of the variation in intention to vaccinate against LD. Intentions to vaccinate for both adults and caregivers are positively influenced by cues to action, perceived susceptibility of LD, and perceived benefits to vaccination. Both are also negatively influenced by perceived barriers to vaccination. The caregiver's intention to vaccinate is also positively influenced by the perceived severity of LD. Conclusion: The intention to vaccinate for respondents residing in LD neighbouring states is strongly influenced by recommendations from healthcare providers or the Centers for Disease Control and Prevention (CDC). As incidence rises in neighbouring states and the need to prevent disease becomes more overt, public health officials should strongly recognize the influence of healthcare providers and CDC recommendations on intention to vaccinate against LD. [ABSTRACT FROM AUTHOR]

Introduction: Lyme disease (LD), caused by the spirochete Borrelia burgdorferi, is the most common vector‐borne disease in the United States. Although most surveillance‐reported cases are in people who are White, data suggest worse outcomes among people from racial and ethnic minority groups. Methods: We conducted a systematic literature review to describe racial disparities in LD. We described the epidemiology of LD by race and ethnicity, including clinical presentation at diagnosis, and summarised the literature on knowledge, attitudes and practices related to LD and ticks by race and ethnicity. Results: Overall, the incidence and prevalence of LD were 1.2–3.5 times higher in White persons than in persons who identified as Asian or Pacific Islander and 4.5–6.3 times higher in White persons than in persons who identified as Black. Across multiple studies, people from racial and ethnic minority groups were more likely than White people to have disseminated manifestations of LD, including neurological manifestations and arthritis, and less likely to have erythema migrans. People from racial and ethnic minority groups were also more likely to report disease onset in the fall and less likely to report disease onset in the summer. Possible reasons for these disparities include lack of recognition of the disease in people with darker skin tones, lack of knowledge of disease risk for some groups and differences in exposure risk. Conclusions: Taken together, these results reinforce that all people residing in high‐incidence areas are at risk of LD, regardless of race or ethnicity. Future prevention measures should be broadly targeted to reach all at‐risk populations. [ABSTRACT FROM AUTHOR]

Introduction: Information regarding availability of electronic healthcare databases in the Asia‐Pacific region is critical for planning vaccine safety assessments particularly, as COVID‐19 vaccines are introduced. This study aimed to identify data sources in the region, potentially suitable for vaccine safety surveillance. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). Methods: Nineteen countries targeted for database reporting were identified using published country lists and review articles. Surveillance capacity was assessed using two surveys: a 9‐item introductory survey and a 51‐item full survey. Survey questions related to database characteristics, covariate and health outcome variables, vaccine exposure characteristics, access and governance, and dataset linkage capability. Other questions collated research/regulatory applications of the data and local publications detailing database use for research. Results: Eleven databases containing vaccine‐specific information were identified across 8 countries. Databases were largely national in coverage (8/11, 73%), encompassed all ages (9/11, 82%) with population size from 1.4 to 52 million persons. Vaccine exposure information varied particularly for standardized vaccine codes (5/11, 46%), brand (7/11, 64%) and manufacturer (5/11, 46%). Outcome data were integrated with vaccine data in 6 (55%) databases and available via linkage in 5 (46%) databases. Data approval processes varied, impacting on timeliness of data access. Conclusions: Variation in vaccine data availability, complexities in data access including, governance and data release approval procedures, together with requirement for data linkage for outcome information, all contribute to the challenges in building a distributed network for vaccine safety assessment in the Asia‐Pacific and globally. Common data models (CDMs) may help expedite vaccine safety research across the region. [ABSTRACT FROM AUTHOR]

Purpose Estimating disease incidence based on secondary data requires a look‐back period to exclude patients with pre‐existing disease from the incidence risk set. However, the optimal length of the look‐back period and its impact on incidence rates are often unknown. We studied the impact of the length of the look‐back period on incidence rates of 24 different chronic diseases. Methods: Everyone residing in Sweden between January 1, 2005 and December 31, 2013 were identified from national registries and followed up to 2 years (through December 31, 2015). Outcome events were identified from inpatient and outpatient hospital contacts and incidence rates were calculated per 100 000 person‐years. The length of the look‐back period was varied with 6‐month increments, starting at 6 months. The maximum look‐back period of 9 years was used as reference period. Results: There were 7 943 807 individuals with a look‐back period of at least 9 years (mean age 46.5 years) and a mean follow‐up time of 1.97 years. Incidence rates were higher across all diseases when restricting the look‐back to 1 year compared to 9 years, with a magnitude of overestimation of the incidence rates between 13% (temporal arteritis) and 174% (type 1 diabetes). However, for most diseases the effect of extending the look‐back period beyond 3‐5 years appeared comparably small. Conclusions: This study illustrates how short look‐back periods cause overestimation of the incidence rates of chronic diseases, suggesting that sensitivity analyses with respect to look‐back period are considered, particularly using data sources with limited information on past medical history. [ABSTRACT FROM AUTHOR]

Purpose To describe published validation studies of administrative health care claims data in the Asia‐Pacific region. Methods: A comprehensive literature search was conducted in PubMed for English language articles published through 31‐Oct‐2017 in humans from 10 Asian‐Pacific countries or regions (Japan, Australia, New Zealand, China, Hong Kong, India, Singapore, South Korea, Taiwan, and Thailand) that validated claims‐based diagnoses with a gold standard data source. Search terms included the: validation, validity, accuracy, sensitivity, agreement, specificity, positive predictive value, kappa, kappa coefficient, and Cohen's kappa. Results: Forty‐three studies across six countries were identified: Australia (21); Japan (6); South Korea (6); Taiwan (7); Singapore (2); and New Zealand (1). Gold standard diagnoses were obtained from: medical records (18); registry data (11); self‐reported questionnaires (5); and other data sources (9). Validity measures used included sensitivity, specificity, positive and negative predictive values (12); sensitivity, specificity, and positive predictive value (4); sensitivity and specificity (4); sensitivity and positive predictive value (4); and combinations of other measures (19). Validated outcomes included medical conditions (28); disease‐specific comorbidities (8); death, smoking, and other (ie, injury, hospital outcome measures) (5); medication/transfusion (2). Approximately 72% of the studies were published within the last 5 years. Conclusions: Validation studies of claims data published in the English language in the Asia‐Pacific region are very limited. Given the increased reliance on administrative health care databases for pharmacoepidemiology and the need for ensuring the credibility of results from such data, additional support for the conduct of validation research of claims data in the Asia‐Pacific region is needed. [ABSTRACT FROM AUTHOR]

Please cite this paper as: Stebbins et al. (2011) Sensitivity and specificity of rapid influenza testing of children in a community setting. Influenza and Other Respiratory Viruses 5(2), 104-109. Rapid influenza testing (RFT) allows for a rapid point-of-care diagnosis of influenza. The Quidel QuickVue Influenza A+B test (QuickVue) has a reported manufacturer's sensitivity and specificity of 73% and 96%, respectively, with nasal swabs. However, investigators have shown sensitivities ranging from 22% to 77% in community settings. The QuickVue rapid influenza test was evaluated in a population of elementary (K-5) school children, using testing in the home, as part of the Pittsburgh Influenza Prevention Project during the 2007-2008 influenza season. The QuickVue test was performed with nasal swab in full accordance with package instructions and compared with the results of nasal swab semi-quantitative RT-PCR. Sensitivity of the QuickVue was found to be 27% in this sample. There was no statistically valid correlation between the semi-quantitative PCR result and the QuickVue result. This study is consistent with the low sensitivity of the QuickVue test also reported by others. Viral load, technique, and the use of nasal swabs were examined as contributing factors but were not found to be explanations for this result. Community testing includes patients who are on the lower spectrum of illness which would not be the case in hospital or clinic samples. This suggests that RFT is less sensitive for patients at the lower spectrum of illness, with less severe disease. [ABSTRACT FROM AUTHOR]

Please cite this paper as: Cooley et al. (2010) Protecting health care workers: a pandemic simulation based on Allegheny County. Influenza and Other Respiratory Viruses 4(2), 61–72. Background and Objectives The Advisory Committee on Immunization Practices has identified health care workers (HCWs) as a priority group to receive influenza vaccine. Although the importance of HCW to the health care system is well understood, the potential role of HCW in transmission during an epidemic has not been clearly established. Methods Using a standard SIR (Susceptible–Infected–Recovered) framework similar to previously developed pandemic models, we developed an agent-based model (ABM) of Allegheny County, PA, that incorporates the key health care system features to simulate the spread of an influenza epidemic and its effect on hospital-based HCWs. Findings Our simulation runs found the secondary attack rate among unprotected HCWs to be approximately 60% higher (54·3%) as that of all adults (34·1%), which would result in substantial absenteeism and additional risk to HCW families. Understanding how a pandemic may affect HCWs, who must be available to treat infected patients as well as patients with other medical conditions, is crucial to policy makers’ and hospital administrators’ preparedness planning. [ABSTRACT FROM AUTHOR]