Risk assessment of vector-borne diseases for public health governance

ObjectivesIn the context of public health, risk governance (or risk analysis) is a framework for the assessment and subsequent management and/or control of the danger posed by an identified disease threat. Generic frameworks in which to carry out risk assessment have been developed by various agencies. These include monitoring, data collection, statistical analysis and dissemination. Due to the inherent complexity of disease systems, however, the generic approach must be modified for individual, disease-specific risk assessment frameworks.Study designThe analysis was based on the review of the current risk assessments of vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, ECDC, FAO, CDC etc…).MethodsLiterature, legislation and statistical assessment of the risk analysis frameworks.ResultsThis review outlines the need for the development of a general public health risk assessment method for vector-borne diseases, in order to guarantee that sufficient information is gathered to apply robust models of risk assessment. Stochastic (especially spatial) methods, often in Bayesian frameworks are now gaining prominence in standard risk assessment procedures because of their ability to assess accurately model uncertainties.ConclusionsRisk assessment needs to be addressed quantitatively wherever possible, and submitted with its quality assessment in order to enable successful public health measures to be adopted. In terms of current practice, often a series of different models and analyses are applied to the same problem, with results and outcomes that are difficult to compare because of the unknown model and data uncertainties. Therefore, the risk assessment areas in need of further research are identified in this article.     

Unraveling R0: Considerations for Public Health Applications

We assessed public health use of R₀, the basic reproduction number, which estimates the speed at which a disease is capable of spreading in a population. These estimates are of great public health interest, as evidenced during the 2009 influenza A (H1N1) virus pandemic.We reviewed methods commonly used to estimate R₀, examined their practical utility, and assessed how estimates of this epidemiological parameter can inform mitigation strategy decisions.In isolation, R₀ is a suboptimal gauge of infectious disease dynamics across populations; other disease parameters may provide more useful information. Nonetheless, estimation of R₀ for a particular population is useful for understanding transmission in the study population. Considered in the context of other epidemiologically important parameters, the value of R₀ may lie in better understanding an outbreak and in preparing a public health response.During the spring of 2009, the 2009 H1N1 influenza pandemic began in North America and quickly spread around the world, sparking great interest in potential mitigation strategies for the first influenza pandemic in more than 40 years. Research focused on interventions such as social distancing that could be applied before a specific monovalent H1N1 vaccine became available in the fall of 2009. During the initial wave of the 2009 H1N1 outbreak, teams of modelers from around the world gathered available data from Mexico to estimate several of the novel virus’s characteristics.1,2 Efforts focused on the rapid estimation of the basic reproduction number, or R₀, of this virus. R₀ is a theoretical parameter that provides some information regarding the speed at which a disease is capable of spreading in a specific population. First estimates were published online by early May 2009.1,2 Estimates of R₀ continue to be published from other countries and as more data become available.3–11As an indicator of the interest in publications concerning R₀, an early publication on the pandemic potential of the 2009 H1N1 strain by Fraser et al.1 has garnered 654 citations as of February 7, 2013. Although the influenza pandemic explains much of the recent interest in the basic reproduction number, this interest is not limited to the field of influenza. Web of Science searches on the terms “reproduction number” or “reproductive number” revealed that there have been 710 publications on this topic from 2009 through February 7, 2013, across various disciplines, with most articles being published in journals covering infectious diseases and mathematical modeling. Table A (available as a supplement to this article at http://www.ajph.org) shows breakdown by journal. If the search is expanded to include data from previous years, it is clear that there has been exponential growth by calendar year in the number of publications on this topic (Figure 1). Why is there such growing interest in R₀ among the disciplines interested in the dynamics of infectious diseases? To help better understand the interest in the basic reproduction number among public health officials, infectious disease researchers, and theoretical modelers, we reviewed the derivation of R₀ and its history.Open in a separate windowFIGURE 1—The number of publications regarding infectious disease and mathematical modeling as reported by Web of Science.Note. The figure was produced by searching Web of Science on the terms “reproduction number” or “reproductive number” and limiting the results to the fields of infectious diseases, mathematical computational biology, and applied mathematics. Clearly, interest in research regarding the basic reproductive number has risen dramatically since the 1990s. The number of publications in this area currently appears to be growing exponentially.We present a basic epidemiological compartmental model (a susceptible–infected–recovered or SIR model with S, I, and R representing the 3 compartments) described by Kermack and McKendrick.12 In this relatively simple model designed to describe epidemics, individuals start as susceptible to a particular pathogen and then progress to the other 2 compartments if infected. The model is defined by a system of 3 ordinary differential equations (ODEs):in which β is the transmission rate, γ is the recovery rate (or the inverse of the infectious period), and N is the total population size such that N = S + I + R. The standard model in equation 1 assumes no births or deaths. At the beginning of the outbreak or epidemic (t = 0) we assume the population is composed entirely of susceptible individuals and a single infectious individual. With this model, if the transmission rate exceeds the recovery rate (i.e., β/γ > 1), the disease will spread (dI/dt > 0). Alternatively, β/γ is the number of new infections per unit time multiplied by the time period of infectiousness, and describes the number of new infections resulting from the initially infected individual. In the presented case of the simple SIR model, the basic reproduction number (or ratio) equals β/γ.The scientific community largely underappreciated the implications of the Kermack–McKendrick model until the late 1970s, when Anderson and May13 used the model to study strategies for controlling infectious diseases. R₀ is a parameter of importance for gauging the disease dynamics because it indicates when an outbreak might happen based on the threshold value of 1.0. More generally, if the effective reproduction number R_e = R₀ × (S/N) is greater than 1.0, we predict that the disease continues its spread; the effective reproduction reflects the fact that, as proportion of susceptible individuals decreases (S/N), disease transmission slows. From this simple mathematical perspective, epidemiologists frequently consider the basic reproductive number one of the most vital parameters in determining whether an epidemic is “controllable.”14,15 The objective of any public health response during an influenza pandemic, for example, is to slow or stop the spread of the virus by employing mitigation strategies that either (1) reduce R₀ by changing the transmission rate (e.g., via school closure) or the duration of infectiousness (e.g., through antiviral use) or (2) reduce R_e by reducing the number of susceptible individuals (e.g., by vaccination).     

System factors to explain H1N1 state vaccination rates for adults in US emergency response to pandemic

Introduction

During the 2009–2010 H1N1 pandemic, vaccine in short supply was allocated to states pro rata by population, yet the vaccination rates of adults differed by state. States also differed in their campaign processes and decisions. Analyzing the campaign provides an opportunity to identify specific approaches that may result in higher vaccine uptake in a future event of this nature.

Objective

To determine supply chain and system factors associated with higher state H1N1 vaccination coverage for adults in a system where vaccine was in short supply.

Methods

Regression analysis of factors predicting state-specific H1N1 vaccination coverage in adults. Independent variables included state campaign information, demographics, preventive or health-seeking behavior, preparedness funding, providers, state characteristics, and H1N1-specific state data.

Results

The best model explained the variation in state-specific adult vaccination coverage with an adjusted R-squared of 0.76. We found that higher H1N1 coverage of adults is associated with program aspects including shorter lead-times (i.e., the number of days between when doses were allocated to a state and were shipped, including the time for states to order the doses) and less vaccine directed to specialist locations. Higher vaccination coverage is also positively associated with the maximum number of ship-to locations, past seasonal influenza vaccination coverage, the percentage of women with a Pap smear, the percentage of the population that is Hispanic, and negatively associated with a long duration of the epidemic peak.

Conclusion

Long lead-times may be a function of system structure or of efficiency and may suggest monitoring or redesign of distribution processes. Sending vaccine to sites with broad access could be useful when covering a general population. Existing infrastructure may be reflected in the maximum number of ship-to locations, so strengthening routine influenza vaccination programs may help during emergency vaccinations also. Future research could continue to inform program decisions.     

Assessing outcomes of a stigma-reduction intervention with venue-based analysis

Purpose

A randomized controlled trial with a matched design was conducted during October 2008 and February 2010, aiming at reducing HIV-related stigma in healthcare settings.

Methods

Forty county hospitals in Fujian and Yunnan provinces of China were matched into pairs and randomized to either an intervention condition or a control condition. Forty-four service providers were randomly selected from each hospital, yielding a sample of 1,760. Intervention outcomes were assessed at baseline, 6 and 12 months based on venue-based pair comparisons. We identified and trained 30 popular opinion leaders in each intervention hospital among service providers to disseminate stigma-reduction messages to their peer providers.

Results

Hospital and participant characteristics were comparable between the intervention and control conditions. Thirteen out of twenty pairs of hospitals showed significant reduction in the stigma outcome measure at the 6-month follow-up assessment. For most hospitals, the intervention effects were maintained at the 12-month follow-up assessment. Among the 13 pair of hospitals, which showed intervention effects at 6 months, eight were in Fujian and five were in Yunnan. The non-significant hospitals at 6 months had more beds than significant hospitals. However, the difference did not reach statistical significance.

Conclusions

A matched design and venue-based analysis provide more insight in assessing intervention effects for facility-based intervention trials. The identification of venue-based or hospital characteristics that are associated with intervention efficacy provides additional implications for the adaptation and implementation of future interventions.     

Sibling concordance for clinical features of Duchenne and Becker muscular dystrophies

Introduction: The correlation of markers of disease severity among brothers with Duchenne or Becker muscular dystrophy has implications for clinical guidance and clinical trials. Methods: Sibling pairs with Duchenne or Becker muscular dystrophy (n = 60) were compared for ages when they reached clinical milestones of disease progression, including ceased ambulation, scoliosis of ≥ 20°, and development of cardiomyopathy. Results: The median age at which younger brothers reached each milestone, compared with their older brothers ranged from 25 months younger for development of cardiomyopathy to 2 months older for ceased ambulation. For each additional month of ambulation by the older brother, the hazard of ceased ambulation by the younger brother decreased by 4%. Conclusions: The ages when siblings reach clinical milestones of disease vary widely between siblings. However, the time to ceased ambulation for older brothers predicts the time to ceased ambulation for their younger brothers. Muscle Nerve 49 : 814–821, 2014     

Women's perceptions of breast cancer screening. Spanish screening programme survey

ObjectivesParticipants in breast cancer screening programmes may benefit from early detection but may also be exposed to the risks of overdiagnosis and false positives. We surveyed a sample of Spanish women to assess knowledge, information sources, attitudes and psychosocial impact.Materials and methodsA total of 434 breast cancer screening programme participants aged 45–69 years were administered questionnaires regarding knowledge, information sources, attitudes and psychosocial impact. Scores of 5 or more (out of 10) and 12 or less (out of 24) were established as indicating adequate knowledge and a positive attitude, respectively. Psychosocial impact was measured using the Hospital Anxiety and Depression Scale and the Cancer Worry Scale.ResultsOnly 42 women (9.7%) had adequate knowledge. The mean (SD) knowledge score was 2.97 (1.16). Better educated women and women without previous false positives had higher scores. The main sources of information were television, press, Andalusian Health Service documentation and family and friends. Most participants (99.1%) had a positive attitude, with a mean (SD) score of 3.21 (2.66). Mean (SD) scores for anxiety, depression and cancer worry were 1.86 (3.26), 0.72 (1.99) and 9.4 (3.04), respectively.ConclusionWomen have a very positive attitude to breast cancer screening, but are poorly informed and use television as their main information source. They experience no negative psychosocial impact from participation in such programmes.     

Update on the disease burden and circulating strains of rotavirus in China: A systematic review and meta-analysis

Background

Rotavirus is the most common cause of severe diarrhea in children, and most associated deaths occur in developing countries. Two new internationally licensed vaccines are expected to be launched in the near future in China. We performed a systematic review and meta-analysis of rotavirus studies to update information on the burden of rotavirus disease in China.

Materials and methods

Eligible studies published before 2011 were identified using PubMed/Medline, Embase, Cochrane Library, LILACS, WHOLIS, and two Chinese literature databases, CNKI, and WANFANG. Arc-sine transformations and the DerSimonian–Laird random-effects or fixed-effects models were used for meta-analysis.

Results

A total of 211 studies were included in this review, of which 63 (29.9%) were inpatient studies, 26 (12.3%) were outpatient, 122 (57.8%) were combined. Community subjects were investigated in two combined studies. Rates of gastroenteritis caused by rotavirus in inpatients, outpatients, and community children were 42.6%, 32.5% and 9.3%, respectively. The most common G type was G3 (39.3%), followed by G1 (30.3%), G2 (7.2%), and G9 (3.3%). The most common P types were P[8] (50.2%), P[4] (18.2%), and P[6] (7.2%). The most prevalent G-P combinations were G3P[8] (32.1%), G1P[8] (23.0%), and G2P[4] (7.9%).

Conclusion

Rotavirus is an important cause of both severe and mild diarrheal disease in children <5 years of age in China; G3P[8] is the most prevalent strain. The introduction of an effective rotavirus vaccine to Chinese pediatric immunization programs is necessary.