A systematic review on integration mechanisms in human and animal health surveillance systems with a view to addressing global health security threats

Lymphatic filariasis and onchocerciasis are neglected tropical diseases (NTDs) targeted for elimination by mass (antifilarial) drug administration. These drugs are predominantly active against the microfilarial progeny of adult worms. New drugs or combinations are needed to improve patient therapy and to enhance the effectiveness of interventions in persistent hotspots of transmission. Several therapies and regimens are currently in (pre-)clinical testing. Clinical trial simulators (CTSs) project patient outcomes to inform the design of clinical trials but have not been widely applied to NTDs, where their resource-saving payoffs could be highly beneficial. We demonstrate the utility of CTSs using our individual-based onchocerciasis transmission model (EPIONCHO-IBM) that projects trial outcomes of a hypothetical macrofilaricidal drug. We identify key design decisions that influence the power of clinical trials, including participant eligibility criteria and post-treatment follow-up times for measuring infection indicators. We discuss how CTSs help to inform target product profiles

Predictive performance of telenursing complaints in influenza surveillance : a prospective cohort study in Sweden

Syndromic data sources have been sought to improve the timely detection of increased influenza transmission. This study set out to examine the prospective performance of telenursing chief complaints in predicting influenza activity. Data from two influenza seasons (2007/08 and 2008/09) were collected in a Swedish county (population 427,000) to retrospectively determine which grouping of telenursing chief complaints had the largest correlation with influenza case rates. This grouping was prospectively evaluated in the three subsequent seasons. The best performing telenursing complaint grouping in the retrospective algorithm calibration was fever (child, adult) and syncope (r=0.66; p<0.001). In the prospective evaluation, the performance of 14-day predictions was acceptable for the part of the evaluation period including the 2009 influenza pandemic (area under the curve (AUC)=0.84; positive predictive value (PPV)=0.58), while it was strong (AUC=0.89; PPV=0.93) for the remaining evaluation period including only influenza winter seasons. We recommend the use of telenursing complaints for predicting winter influenza seasons. The method requires adjustments when used during pandemics

RIR diagrams for pandemic and seasonal influenza outbreaks in Östergötland county 2006–2010.

<p>The RIR diagrams (95% Confidence Intervals) represent the A pH1N1 outbreak in 2009 and mean values for the seasonal outbreaks 2006–2010, respectively. * p<0.05 **p<0.01 ***p<0.001 ¤ Too few observations to allow statistical analysis.</p

Influenza outbreaks in Östergötland county 2006–2010.

<p>Influenza cases (ICD-10 codes 10.0–11.8) per day in Östergötland county 2005–2010. The influenza activity as accumulated into five outbreaks lasting between 2006-01-01–2006-04-20 (circulating virus types B, A/H3 and H1N1), 2007-01-31–2007-04-11 (A/H3N2), 2008-01-21–2008-04-30 (B and A/H1), 2008-12-24–2009-03-30 (A/H3N2), and 2009-08-21–2009-12-22 (A pH1N1).</p

Correlation (95% C.I.) between mean time (day) of diagnosis and RIR for age groups during influenza outbreaks in Östergötland county 2005–2009.

<p>Correlation (95% C.I.) between mean time (day) of diagnosis and RIR for age groups during influenza outbreaks in Östergötland county 2005–2009.</p

RIR diagrams for seasonal influenza outbreaks in Östergötland county 2006–2009.

<p>RIR diagrams for seasonal influenza outbreaks in Östergötland county 2006–2009.</p

Mean time (day) for diagnosis (95% C.I.) for age groups during influenza outbreaks in Östergötland county 2005–2009 with reference to the total mean for the outbreak.

<p>Mean time (day) for diagnosis (95% C.I.) for age groups during influenza outbreaks in Östergötland county 2005–2009 with reference to the total mean for the outbreak.</p

Age as a determinant for dissemination of seasonal and pandemic influenza : An open cohort study of influenza outbreaks in Östergötland County, Sweden

An understanding of the occurrence and comparative timing of influenza infections in different age groups is important for developing community response and disease control measures. This study uses data from a Scandinavian county (population 427,000) to investigate whether age was a determinant for being diagnosed with influenza 2005–2010 and to examine if age was associated with case timing during outbreaks. Aggregated demographic data were collected from Statistics Sweden, while influenza case data were collected from a county-wide electronic health record system. A logistic regression analysis was used to explore whether case risk was associated with age and outbreak. An analysis of variance was used to explore whether day for diagnosis was also associated to age and outbreak. The clinical case data were validated against case data from microbiological laboratories during one control year. The proportion of cases from the age groups 10–19 (p&lt;0.001) and 20–29 years old (p&lt;0.01) were found to be larger during the A pH1N1 outbreak in 2009 than during the seasonal outbreaks. An interaction between age and outbreak was observed (p&lt;0.001) indicating a difference in age effects between circulating virus types; this interaction persisted for seasonal outbreaks only (p&lt;0.001). The outbreaks also differed regarding when the age groups received their diagnosis (p&lt;0.001). A post-hoc analysis showed a tendency for the young age groups, in particular the group 10–19 year olds, led outbreaks with influenza type A H1 circulating, while A H3N2 outbreaks displayed little variations in timing. The validation analysis showed a strong correlation (r = 0.625; p&lt;0.001) between the recorded numbers of clinically and microbiologically defined influenza cases. Our findings demonstrate the complexity of age effects underlying the emergence of local influenza outbreaks. Disentangling these effects on the causal pathways will require an integrated information infrastructure for data collection and repeated studies of well-defined communities