Association between socioeconomic deprivation and bone health status in the UK biobank cohort participants.

The effect of deprivation on total bone health status has not been well defined. We examined the relationship between socioeconomic deprivation and poor bone health and falls and we found a significant association. The finding could be beneficial for current public health strategies to minimise disparities in bone health. PURPOSE: Socioeconomic deprivation is associated with many illnesses including increased fracture incidence in older people. However, the effect of deprivation on total bone health status has not been well defined. To examine the relationship between socioeconomic deprivation and poor bone health and falls, we conducted a cross-sectional study using baseline measures from the United Kingdom (UK) Biobank cohort comprising 502,682 participants aged 40-69 years at recruitment during 2006-2010. METHOD: We examined four outcomes: 1) low bone mineral density/osteopenia, 2) fall in last year, 3) fracture in the last five years, and 4) fracture from a simple fall in the last five years. To measure socioeconomic deprivation, we used the Townsend index of the participant's residential postcode. RESULTS: At baseline, 29% of participants had low bone density (T-score of heel < -1 standard deviation), 20% reported a fall in the previous year, and 10% reported a fracture in the previous five years. Among participants experiencing a fracture, 60% reported the cause as a simple fall. In the multivariable logistic regression model after controlling for other covariates, the odds of a fall, fracture in the last five years, fractures from simple fall, and osteopenia were respectively 1.46 times (95% confidence interval [CI] 1.42-1.49), 1.26 times (95% CI 1.22-1.30), 1.31 times (95% CI 1.26-1.36) and 1.16 times (95% CI 1.13-1.19) higher for the most deprived compared with the least deprived quantile. CONCLUSION: Socioeconomic deprivation was significantly associated with poor bone health and falls. This research could be beneficial to minimise social disparities in bone health.

Estimates of global seasonal influenza-associated respiratory mortality: a modelling study.

BACKGROUND: Estimates of influenza-associated mortality are important for national and international decision making on public health priorities. Previous estimates of 250 000-500 000 annual influenza deaths are outdated. We updated the estimated number of global annual influenza-associated respiratory deaths using country-specific influenza-associated excess respiratory mortality estimates from 1999-2015. METHODS: We estimated country-specific influenza-associated respiratory excess mortality rates (EMR) for 33 countries using time series log-linear regression models with vital death records and influenza surveillance data. To extrapolate estimates to countries without data, we divided countries into three analytic divisions for three age groups (<65 years, 65-74 years, and ≥75 years) using WHO Global Health Estimate (GHE) respiratory infection mortality rates. We calculated mortality rate ratios (MRR) to account for differences in risk of influenza death across countries by comparing GHE respiratory infection mortality rates from countries without EMR estimates with those with estimates. To calculate death estimates for individual countries within each age-specific analytic division, we multiplied randomly selected mean annual EMRs by the country's MRR and population. Global 95% credible interval (CrI) estimates were obtained from the posterior distribution of the sum of country-specific estimates to represent the range of possible influenza-associated deaths in a season or year. We calculated influenza-associated deaths for children younger than 5 years for 92 countries with high rates of mortality due to respiratory infection using the same methods. FINDINGS: EMR-contributing countries represented 57% of the global population. The estimated mean annual influenza-associated respiratory EMR ranged from 0·1 to 6·4 per 100 000 individuals for people younger than 65 years, 2·9 to 44·0 per 100 000 individuals for people aged between 65 and 74 years, and 17·9 to 223·5 per 100 000 for people older than 75 years. We estimated that 291 243-645 832 seasonal influenza-associated respiratory deaths (4·0-8·8 per 100 000 individuals) occur annually. The highest mortality rates were estimated in sub-Saharan Africa (2·8-16·5 per 100 000 individuals), southeast Asia (3·5-9·2 per 100 000 individuals), and among people aged 75 years or older (51·3-99·4 per 100 000 individuals). For 92 countries, we estimated that among children younger than 5 years, 9243-105 690 influenza-associated respiratory deaths occur annually. INTERPRETATION: These global influenza-associated respiratory mortality estimates are higher than previously reported, suggesting that previous estimates might have underestimated disease burden. The contribution of non-respiratory causes of death to global influenza-associated mortality should be investigated. FUNDING: None.

Rapid mapping of the spatial and temporal intensity of influenza.

Surveillance of influenza epidemics is a priority for risk assessment and pandemic preparedness, yet representation of their spatiotemporal intensity remains limited. Using the epidemic of influenza type A in 2016 in Australia, we demonstrated a simple but statistically sound adaptive method of mapping epidemic evolution over space and time. Weekly counts of persons with laboratory confirmed influenza type A infections in Australia in 2016 were analysed by official national statistical region. Weekly standardised epidemic intensity was represented by a standard score (z-score) calculated using the standard deviation of below-median counts in the previous 52 weeks. A geographic information system (GIS) was used to present the epidemic progression. There were 79,628 notifications of influenza A infections included. Of these, 79,218 (99.5%) were allocated to a geographical area. The GIS maps indicated areas of elevated epidemic intensity across Australia by week and area that were consistent with the observed start, peak and decline of the epidemic when compared with counts aggregated at the state and territory level. This simple, adaptable approach could improve local level epidemic intelligence in a variety of settings and for other diseases. It may also facilitate increased understanding of geographic epidemic dynamics.

Hospitalizations for Influenza-Associated Severe Acute Respiratory Infection, Beijing, China, 2014-2016.

We analyzed surveillance data for 2 sentinel hospitals to estimate the influenza-associated severe acute respiratory infection hospitalization rate in Beijing, China. The rate was 39 and 37 per 100,000 persons during the 2014-15 and 2015-16 influenza seasons, respectively. Rates were highest for children <5 years of age.

Dengue Fever in Burkina Faso, 2016.

We report 1,327 probable cases of dengue in Burkina Faso in 2016. Of 35 serum samples tested by a trioplex test, 19 were confirmed dengue virus (DENV)‒positive: 11 DENV-2, 6 DENV-3, 2 nontypeable, and 1 DENV-2/DENV-3 co-infection. Molecular testing should be conducted to correctly identify causative agents in this complex infectious disease landscape.

Preliminary results of official influenza and acute respiratory infection surveillance in two towns of Burkina Faso, 2013-2015.

BACKGROUND: In 2010, influenza, influenza-like illness (ILI) and acute respiratory infection (ARI) surveillance was established by the government of Burkina Faso. We provide preliminary descriptive results from this surveillance activity. METHODS: The study period was 2013 through 2015. Two primary healthcare facilities in Bobo-Dioulasso district reported ILI in outpatients. Influenza virology, using reverse transcription-polymerase chain reaction (rRT-PCR), was available for a proportion of ILI patients. One hospital, in the capital Ouagadougou, reported ARI in both outpatients and inpatients (hospitalized). Inpatients admitted with ARI were considered severe ARI (SARI). We estimated the proportion of primary care outpatient visits that were ILI, and the proportion of those that were due to influenza, by age. We estimated the proportion of hospital outpatient visits that were ARI and the proportion of those that were SARI, by age. RESULTS: Among combined outpatient visits in the Bobo-Dioulasso facilities, 19.6% were for ILI. One half (49.9%) of outpatient visits in infants and 30.9% in 1-4 year-olds were ILI. Among ILI outpatient visits 14.8% were due to influenza virus and, of these, 58.5% were type A and 41.5% type B. At the Ouagadougou hospital, 6.7% of outpatient visits were ARI, and 22.3% of those were SARI. The highest proportions of ARI were among infants (19.8%) and 1-4 year-olds (16.0%). The proportion of ARI that was SARI was highest among ≥15 year-olds (31.5%) followed by 1-4 year-olds (22.4%). Overall, 4.1% of SARI patients died. CONCLUSIONS: These preliminary data indicate the importance of respiratory infections among health care attendances in Burkina Faso, and influenza may be an important contributor to these.

Translation of Real-Time Infectious Disease Modeling into Routine Public Health Practice.

Infectious disease dynamic modeling can support outbreak emergency responses. We conducted a workshop to canvas the needs of stakeholders in Australia for practical, real-time modeling tools for infectious disease emergencies. The workshop was attended by 29 participants who represented government, defense, general practice, and academia stakeholders. We found that modeling is underused in Australia and its potential is poorly understood by practitioners involved in epidemic responses. The development of better modeling tools is desired. Ideal modeling tools for operational use would be easy to use, clearly indicate underlying parameterization and assumptions, and assist with policy and decision making.

Health information system strengthening and malaria elimination in Papua New Guinea.

BACKGROUND: The objective of the study was to describe an m-health initiative to strengthen malaria surveillance in a 184-health facility, multi-province, project aimed at strengthening the National Health Information System (NHIS) in a country with fragmented malaria surveillance, striving towards enhanced control, pre-elimination. METHODS: A remote-loading mobile application and secure online platform for health professionals was created to interface with the new system (eNHIS). A case-based malaria testing register was developed and integrated geo-coded households, villages and health facilities. A malaria programme management dashboard was created, with village-level malaria mapping tools, and statistical algorithms to identify malaria outbreaks. RESULTS: Since its inception in 2015, 160,750 malaria testing records, including village of residence, have been reported to the eNHIS. These case-based, geo-coded malaria data are 100% complete, with a median data entry delay of 9 days from the date of testing. The system maps malaria to the village level in near real-time as well as the availability of treatment and diagnostics to health facility level. Data aggregation, analysis, outbreak detection, and reporting are automated. CONCLUSIONS: The study demonstrates that using mobile technologies and GIS in the capture and reporting of NHIS data in Papua New Guinea provides timely, high quality, geo-coded, case-based malaria data required for malaria elimination. The health systems strengthening approach of integrating malaria information management into the eNHIS optimizes sustainability and provides enormous flexibility to cater for future malaria programme needs.

Demand for Emergency Services Trends in New South Wales Years 2010-2014 (DESTINY): Age and Clinical Factors Associated with Ambulance Transportation to Emergency Departments.

OBJECTIVES: The study aimed to analyze ambulance transportations to Emergency Departments (EDs) in New South Wales (NSW) and to identify temporal changes in demographics, acuity, and clinical diagnoses. METHODS: This was a retrospective analysis of a population based registry of ED presentations in New South Wales. The NSW Emergency Department data collection (EDCC) collects patient level data on presentations to designated EDs across NSW. Patients that presented to EDs by ambulance between January 2010 and December 2014 were included. Patients dead on arrival, transferred from another hospital, or planned ED presentations were excluded. RESULTS: A total of 10.8 million ED attendances were identified of which 2.6 million (23%) were transported to ED by ambulance. The crude rate of ambulance transportations to EDs across all ages increased by 3.0% per annum over the five years with the highest rate observed in those 85 years and over (620.5 presentations per 1,000 population). There was an increase in the proportion of category 1 and 2 (life-threatening or potentially life-threatening) cases from 18.1% to 24.0%. CONCLUSION: Demand for ambulance services appears to be driven by older patients presenting with higher acuity problems. Alternative models of acute care for elderly patients need to be planned and implemented to address these changes.

The Sydney Triage to Admission Risk Tool (START) to predict Emergency Department Disposition: A derivation and internal validation study using retrospective state-wide data from New South Wales, Australia.

BACKGROUND: Disposition decisions are critical to the functioning of Emergency Departments. The objectives of the present study were to derive and internally validate a prediction model for inpatient admission from the Emergency Department to assist with triage, patient flow and clinical decision making. METHODS: This was a retrospective analysis of State-wide Emergency Department data in New South Wales, Australia. Adult patients (age ≥ 16 years) were included if they presented to a Level five or six (tertiary level) Emergency Department in New South Wales, Australia between 2013 and 2014. The outcome of interest was in-patient admission from the Emergency Department. This included all admissions to short stay and medical assessment units and being transferred out to another hospital. Analyses were performed using logistic regression. Discrimination was assessed using area under curve and derived risk scores were plotted to assess calibration. RESULTS: 1,721,294 presentations from twenty three Level five or six hospitals were analysed. Of these 49.38% were male and the mean (sd) age was 49.85 years (22.13). Level 6 hospitals accounted for 47.70% of cases and 40.74% of cases were classified as an in-patient admission based on their mode of separation. The final multivariable model including age, arrival by ambulance, triage category, previous admission and presenting problem had an AUC of 0.82 (95% CI 0.81, 0.82). CONCLUSION: By deriving and internally validating a risk score model to predict the need for in-patient admission based on basic demographic and triage characteristics, patient flow in ED, clinical decision making and overall quality of care may be improved. Further studies are now required to establish clinical effectiveness of this risk score model.

Alcopops, taxation and harm: a segmented time series analysis of emergency department presentations.

BACKGROUND: In Australia, a Goods and Services Tax (GST) introduced in 2000 led to a decline in the price of ready-to-drink (RTD) beverages relative to other alcohol products. The 2008 RTD ("alcopops") tax increased RTD prices. The objective of this study was to estimate the change in incidence of Emergency Department (ED) presentations for acute alcohol problems associated with each tax. METHODS: Segmented regression analyses were performed on age and sex-specific time series of monthly presentation rates for acute alcohol problems to 39 hospital emergency departments across New South Wales, Australia over 15 years, 1997 to 2011. Indicator variables represented the introduction of each tax. Retail liquor turnover controlled for large-scale economic factors such as the global financial crisis that may have influenced demand. Under-age (15-17 years) and legal age (18 years and over) drinkers were included. RESULTS: The GST was associated with a statistically significant increase in ED presentations for acute alcohol problems among 18-24 year old females (0 · 14/100,000/month, 95% CI 0 · 05-0 · 22). The subsequent alcopops tax was associated with a statistically significant decrease in males 15-50 years, and females 15-65 years, particularly in 18-24 year old females (-0 · 37/100,000/month, 95% CI -0 · 45 to -0 · 29). An increase in retail turnover of liquor was positively and statistically significantly associated with ED presentations for acute alcohol problems across all age and sex strata. CONCLUSIONS: Reduced tax on RTDs was associated with increasing ED presentations for acute alcohol problems among young women. The alcopops tax was associated with declining presentations in young to middle-aged persons of both sexes, including under-age drinkers.

Automatic classification of diseases from free-text death certificates for real-time surveillance.

BACKGROUND: Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV. METHODS: Two classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000-2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors. RESULTS: Classification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness. CONCLUSIONS: The high accuracy and low cost of the classification methods allow for an effective means for automatic and real-time surveillance of diabetes, influenza, pneumonia and HIV deaths. In addition, the methods are generally applicable to other diseases of interest and to other sources of medical free-text besides death certificates.

Age before acuity: the drivers of demand for emergency department services in the Greater Sydney Area.

OBJECTIVE: To contrast long-term population-based trends in general practice (GP) presentations and acute inpatient admissions from the emergency department (ED) in the elderly population within the Greater Sydney Area. METHODS: This was a retrospective analysis of population-based ED presentation data over 11 years, between January 2001 and December 2011, conducted within the Greater Sydney Area in Australia. De-identified data were obtained from the New South Wales Emergency Department Data Collection database on all patients presenting to 30 public hospital EDs located within the Greater Sydney Area. The outcomes of interest were GP presentations to ED (triage category 4 or 5, self-referred and discharged from ED) and of acute inpatient admissions from ED per 1000 population. RESULTS: Over 11 million presentations were identified. Around 40% of presentations were classified as a GP presentation and 23% were classified as acute inpatient admissions. There was a 2.9% per annum increase in acute inpatient admissions per 1000 population in those ≥80 years of age and no appreciable change in other age groups. Rates of GP presentations were higher in those <65 years of age. GP presentations increased 1.9% per annum in those aged <65 years of age. CONCLUSIONS: The increase in ED demand appears to be driven by the elderly presenting with acute problems requiring inpatient admission. There has been a modest increase in the rate of GP presentations to ED.

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance.

BACKGROUND: Syndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits. METHODS: We selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other's classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A "mental health problem presentation" for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians' assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic. RESULTS: Agreement between clinician's classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 - 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99). CONCLUSIONS: Mental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

Uptake of pneumococcal vaccines in older Australian adults before and after universal public funding of PCV13.

BACKGROUND: In 2020 Australia changed the funded universal older adult pneumococcal vaccination program from use of the 23-valent pneumococcal polysaccharide vaccine (PPV23) at age 65 to the 13-valent pneumococcal conjugate vaccine (PCV13) at age 70 years. We investigated uptake of both PCV13 and PPV23 in older adults before and after the program change. METHODS: We analysed a national dataset of records of patients attending general practices (GPs). We included regular attendees aged 65 or above in 2020. Cumulative uptake of PCV13 and monthly uptake of PPV23 was compared for the two periods before (January 2019 to June 2020) and after (July 2020 to May 2021) the program change on 1 July 2020, by age groups and presence of comorbid conditions. RESULTS: Our study included data from 192,508 patients (mean age in 2020: 75.1 years, 54.2 % female, 46.1 % with at least one comorbidity). Before July 2020, for all adults regardless of underlying comorbidities, the cumulative uptake of PCV13 was < 1 % but by May 2021, eleven months after the program changes, cumulative uptake of PCV13 had increased among those aged 70-79 years (without comorbidity: 16.3 %; with comorbidity: 21.1 %) and 80 + years (without comorbidity: 13.5 %; with comorbidity: 17.7 %), but not among those aged 65-69 years (without comorbidity: 1.3 %; with comorbidity: 3 %). Monthly uptake of PPV23 dropped following the program change across all age groups. CONCLUSIONS: Changes in uptake of PCV13 and PPV23 among those aged 70 + years were consistent with program changes. However, PCV13 uptake was still substantially lower in individuals aged 65-69 years overall and in those with comorbidities.

Estimates of Seasonal Influenza Burden That Could Be Averted by Improved Influenza Vaccines in the Australian Population Aged Under 65 Years, 2015-2019.

BACKGROUND: The interpretation of relative vaccine effectiveness (rVE) of improved influenza vaccines is complex. Estimation of burden averted is useful to contextualise their potential impact across different seasons. For the population aged under 65 years in Australia, this study estimated the additional morbidity and mortality that could be averted using improved influenza vaccines. METHODS: We used observed, season-specific (2015-2019) influenza notification and influenza-coded hospitalisation frequencies and published modelled estimates of influenza-associated hospitalisations and deaths that occurred under the prevailing influenza vaccination coverage scenario. After back-calculating to the estimated burden in the population without vaccination, we applied published standard influenza vaccine effectiveness and coverage estimates to calculate the burden potentially averted by standard and improved influenza vaccines. A plausible range of rVE values were used, assuming 50% coverage. RESULTS: The percentage point difference in absolute vaccine effectiveness (VE) of an improved vaccine compared to a standard vaccine is directly proportional to its rVE and inversely proportional to the effectiveness of the standard vaccine. The incremental burden averted by an improved vaccine is a function of both its difference in absolute VE and the severity of the influenza season. Assuming an rVE of 15% with 50% coverage, the improved vaccine was estimated to additionally avert 1517 to 12,641 influenza notifications, 287 to 1311 influenza-coded hospitalisations and 9 to 33 modelled all-cause influenza deaths per year compared to the standard vaccine. CONCLUSIONS: Improved vaccines can have substantial clinical and population impact, particularly when the effectiveness of standard vaccines is low, and burden is high.

The association between influenza vaccination uptake and influenza and pneumonia-associated deaths in the United States.

BACKGROUND: The influenza mortality burden has remained substantial in the United States (US) despite relatively high levels of influenza vaccine uptake. This has led to questions regarding the effectiveness of the program against this outcome, particularly in the elderly. The aim of this evaluation was to develop and explore a new approach to estimating the population-level effect of influenza vaccination uptake on pneumonia and influenza (P&I) associated deaths. METHODS: Using publicly available data we examined the association between state-level influenza vaccination and all-age P&I associated deaths in the US from the 2013-2014 influenza season to the 2018-2019 season. In the main model, we evaluated influenza vaccine uptake in all those age 6 months and older. We used a mixed-effects regression analysis with generalised least squares estimation to account for within state correlation in P&I mortality. RESULTS: From 2013-2014 through 2018-2019, the total number of all-age P&I related deaths during the influenza seasons was 480,111. The mean overall cumulative influenza vaccine uptake (age 6 months and older) across the states and years considered was 46.7%, with higher uptake (64.8%) observed in those aged ≥ 65 years. We found that overall influenza vaccine uptake (6 months and older) had a statistically significant protective association with the P&I death rate. This translated to a 0.33 (95% CI: 0.20, 0.47) per 100,000 population reduction in P&I deaths in the influenza season per 1% increase in overall influenza vaccine uptake. DISCUSSION: These results using a population-level statistical approach provide additional support for the overall effectiveness of the US influenza vaccination program. This reassurance is critical given the importance of ensuring confidence in this life saving program. Future research is needed to expand on our approach using more refined data.

Relationship between the population incidence of pertussis in children in New South Wales, Australia and emergency department visits with cough: a time series analysis.

BACKGROUND: Little is known about the potential of syndromic surveillance to provide early warning of pertussis outbreaks. We conducted a time series analysis to assess whether an emergency department (ED) cough syndrome would respond to changes in the incidence of pertussis in children aged under 10 years in New South Wales (NSW), Australia, and to evaluate the timing of any association. A further aim was to assess the lag between the onset of pertussis symptoms and case notification in the infectious diseases surveillance system in NSW. METHODS: Using routinely collected data, we prepared a daily count time series of visits to NSW EDs assigned a provisional diagnosis of cough. Separate daily series were prepared for three independent variables: notifications of cases of pertussis and influenza and ED visits with bronchiolitis (a proxy measure of respiratory syncytial virus (RSV) infection). The study period was 1/1/2007-31/12/2010. A negative binomial multivariate model was used to assess associations between the outcome and independent variables. We also evaluated the median delay in days between the estimated onset of a case of pertussis and the date the local public health authority was notified of that case. RESULTS: When notified pertussis increased by 10 cases in one day, ED visits with cough increased by 5.2% (95% confidence interval (CI): 0.5%-10.0%) seven days later. Daily increases in the other independent variables had a smaller impact on cough visits. When notified influenza increased by 10 cases in one day, ED visits with cough increased by 0.8% (95% CI: 0%-1.7%) seven days later. When ED visits with bronchiolitis increased by 10 visits in one day, ED visits with cough increased by 4.8% (95% CI: 1.2%-8.6%) one day earlier. The median interval between estimated onset of pertussis and case notification was seven days. CONCLUSIONS: Pertussis appears to be an important driver of ED visits with cough in children aged under 10 years. However, the median delay in notification of cases of pertussis was similar to the lag in the pertussis-associated short-term increases in ED visits with cough. Elevations in RSV and influenza activity may also explain increases in the ED cough syndrome. Real time monitoring of ED visits with cough in children is therefore unlikely to consistently detect a potential outbreak of pertussis before passive surveillance.

Bayesian structural time series, an alternative to interrupted time series in the right circumstances.

OBJECTIVES: Compare two approaches to analyzing time series data-interrupted time series with segmented regression (ITS-SR) and Bayesian structural time series using the CausalImpact R package (BSTS-CI)-highlighting advantages, disadvantages, and implementation considerations. STUDY DESIGN AND SETTING: We analyzed electronic health records using each approach to estimate the antibiotic prescribing reduction associated with an educational program delivered to Australian primary care physicians between 2012 and 2017. Two outcomes were considered: antibiotics for upper respiratory tract infections (URTIs) and antibiotics of specified formulations. RESULTS: For URTI indication prescribing, average monthly prescriptions changes were estimated at -4,550; (95% confidence interval, -5,486 to -3,614) and -4,270; (95% credible interval, -5,934 to -2,626) for ITS-SR and BSTS-CI, respectively. Similarly for specified formulation prescribing, monthly average changes were estimated at -7,923; (95% confidence interval, -15,887 to 40) for ITS-SR and -20,269; (95% credible interval, -25,011 to -15,635) for BSTS-CI. CONCLUSION: Differing results between ITS-SR and BSTS-CI appear driven by divergent explanatory and outcome series trends. The BSTS-CI may be a suitable alternative to ITS-SR only if the explanatory series represent the secular trend of the outcome series before the intervention and are equally affected by exogenous or confounding factors. When appropriately applied, BSTS-CI provides an alternative to ITS with more readily interpretable Bayesian effect estimates.

Comparison of statistical methods for the early detection of disease outbreaks in small population settings.

Objectives: This study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases. Methods: To address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions. Results: The study found that while the Early Aberration Reporting System-C1 algorithm developed by Hutwagner et al. outperformed others, no single approach provided reliable monitoring across all outbreak types. Furthermore, aberration detection approaches could only detect very large and acute outbreaks with any reliability. Conclusion: The findings of this study suggest that algorithm-based approaches to outbreak signal detection perform poorly when applied to settings with small numbers of background cases and should not be relied upon in these contexts. This highlights the need for alternative approaches for accurate and timely outbreak detection in small population settings, particularly those that are resource-constrained.