Probabilistic linkage of multiple data sources for estimating prevalence of problem drug users in England in 2018/19.

Objectives Problem drug use (PDU) prevalence is an essential part of evidence base to formulate policy, inform service provision, and assess interventions. Opiate and crack use, particularly injecting, is associated with infectious diseases. Multiple data sources, where PDU are observed, are linked to estimate the hidden population of PDU using capture-recapture. Approach Datasets consisted of (1) community treatment, (2) prison treatment, (3) probation (Ministry of Justice), and (4) drug-related deaths, for financial year 2018/19. The following offenders’ attributes and geography are used: first and second initials, date of birth, gender, multiple areas of residence and area of prison release. Geography information consists of local authority code and region name. Probabilistic linkage approach of Fellegi–Sunter is applied through FastLink package in R, where pairs of records are classified as match, possible match, or non-match at 85% threshold. Estimates of error rates, sensitivity and specificity are used to test the results. Results There were 138,341 records in community treatment, 41,700 records in prison, 18,849 in probation and 2,368 deaths from opiate or cocaine. The total number of individuals observed in at least one linked dataset is estimated at 170,307 records at 85% threshold. The number of exact and possible matches overlapping between community treatment and prison is estimated at 17,544 records, from which 13,731 (78.3%) are exact matches, and 3,813 (21.7%) are possible matches at 85% threshold. The number of exact and possible matches overlapping between linked community treatment/prison and probation is estimated at 12,586 records, from which 9,872 (78.4%) are exact and 2,714 (21.6%) are possible matches at 85% threshold. False discovery rate was estimated at 0.3%, sensitivity and specificity were 99.8%. Conclusions Information on PDUs attending community treatment is poor and relying on exact matches underestimate the overlap between data sources. This tend to bias hidden population estimates derived from capture-recapture. Probabilistic approaches, and use of multiple candidate geographic areas for matching, maximise linkages between individuals and thus likely to improve estimates

Beyond location and dispersion models: The Generalized Structural Time Series Model with Applications

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of time-varying conditional skewness and kurtosis is a challenging task. We propose a class of parameter-driven time series models referred to as the generalized structural time series (GEST) model. The GEST model extends Gaussian structural time series models by a) allowing the distribution of the dependent variable to come from any parametric distribution, including highly skewed and kurtotic distributions (and mixed distributions) and b) expanding the systematic part of parameter-driven time series models to allow the joint and explicit modelling of all the distribution parameters as structural terms and (smoothed) functions of independent variables. The paper makes an applied contribution in the development of a fast local estimation algorithm for the evaluation of a penalised likelihood function to update the distribution parameters over time \textit{without} the need for evaluation of a high-dimensional integral based on simulation methods

Beyond location and dispersion models: The Generalized Structural Time Series Model with Applications

In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of time-varying conditional skewness and kurtosis is a challenging task. We propose a class of parameter-driven time series models referred to as the generalized structural time series (GEST) model. The GEST model extends Gaussian structural time series models by a) allowing the distribution of the dependent variable to come from any parametric distribution, including highly skewed and kurtotic distributions (and mixed distributions) and b) expanding the systematic part of parameter-driven time series models to allow the joint and explicit modelling of all the distribution parameters as structural terms and (smoothed) functions of independent variables. The paper makes an applied contribution in the development of a fast local estimation algorithm for the evaluation of a penalised likelihood function to update the distribution parameters over time \textit{without} the need for evaluation of a high-dimensional integral based on simulation methods

Seasonality and the effects of weather on Campylobacter infections

Background Campylobacteriosis is a major public health concern. The weather factors that influence spatial and seasonal distributions are not fully understood. Methods To investigate the impacts of temperature and rainfall on Campylobacter infections in England and Wales, cases of Campylobacter were linked to local temperature and rainfall at laboratory postcodes in the 30 days before the specimen date. Methods for investigation included a comparative conditional incidence, wavelet, clustering, and time series analyses. Results The increase of Campylobacter infections in the late spring was significantly linked to temperature two weeks before, with an increase in conditional incidence of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship to temperature was not linear. Generalized structural time series model revealed that changes in temperature accounted for 33.3% of the expected cases of Campylobacteriosis, with an indication of the direction and relevant temperature range. Wavelet analysis showed a strong annual cycle with additional harmonics at four and six months. Cluster analysis showed three clusters of seasonality with geographic similarities representing metropolitan, rural, and other areas. Conclusions The association of Campylobacteriosis with temperature is likely to be indirect. High-resolution spatial temporal linkage of weather parameters and cases is important in improving weather associations with infectious diseases. The primary driver of Campylobacter incidence remains to be determined; other avenues, such as insect contamination of chicken flocks through poor biosecurity should be explored

A comparison of weather variables linked to infectious disease patterns using laboratory addresses and patient residence addresses

Background: To understand the impact of weather on infectious diseases, information on weather parameters at patient locations is needed, but this is not always accessible due to confidentiality or data availability. Weather parameters at nearby locations are often used as a proxy, but the accuracy of this practice is not known. Methods: Daily Campylobacter and Cryptosporidium cases across England and Wales were linked to local temperature and rainfall at the residence postcodes of the patients and at the corresponding postcodes of the laboratory where the patient’s specimen was tested. The paired values of daily rainfall and temperature for the laboratory versus residence postcodes were interpolated from weather station data, and the results were analysed for agreement using linear regression. We also assessed potential dependency of the findings on the relative geographic distance between the patient’s residence and the laboratory. Results: There was significant and strong agreement between the daily values of rainfall and temperature at diagnostic laboratories with the values at the patient residence postcodes for samples containing the pathogens Campylobacter or Cryptosporidium. For rainfall, the R-squared was 0.96 for the former and 0.97 for the latter, and for maximum daily temperature, the R-squared was 0.99 for both. The overall mean distance between the patient residence and the laboratory was 11.9 km; however, the distribution of these distances exhibited a heavy tail, with some rare situations where the distance between the patient residence and the laboratory was larger than 500 km. These large distances impact the distributions of the weather variable discrepancies (i.e. the differences between weather parameters estimated at patient residence postcodes and those at laboratory postcodes), with discrepancies up to ±10 °C for the minimum and maximum temperature and 20 mm for rainfall. Nevertheless, the distributions of discrepancies (estimated separately for minimum and maximum temperature and rainfall), based on the cases where the distance between the patient residence and the laboratory was within 20 km, still exhibited tails somewhat longer than the corresponding exponential fits suggesting modest small scale variations in temperature and rainfall. Conclusion: The findings confirm that, for the purposes of studying the relationships between meteorological variables and infectious diseases using data based on laboratory postcodes, the weather results are sufficiently similar to justify the use of laboratory postcode as a surrogate for domestic postcode. Exclusion of the small percentage of cases where there is a large distance between the residence and the laboratory could increase the precision of estimates, but there are generally strong associations between daily weather parameters at residence and laboratory

Effect of Pneumococcal Conjugate Vaccines on Pneumococcal Meningitis, England and Wales, July 1, 2000–June 30, 2016

We describe the effects of the 7-valent (PCV7) and 13-valent (PCV13) pneumococcal conjugate vaccines on pneumococcal meningitis in England and Wales during July 1, 2000–June 30, 2016. Overall, 84,473 laboratory-confirmed invasive pneumococcal disease cases, including 4,160 (4.9%) cases with meningitis, occurred. PCV7 implementation in 2006 did not lower overall pneumococcal meningitis incidence because of replacement with non–PCV7-type meningitis incidence. Replacement with PCV13 in 2010, however, led to a 48% reduction in pneumococcal meningitis incidence by 2015–16. The overall case-fatality rate was 17.5%: 10.7% among patients 65 years of age. Serotype 8 was associated with increased odds of death (adjusted odds ratio 2.9, 95% CI 1.8–4.7). In England and Wales, an effect on pneumococcal meningitis was observed only after PCV13 implementation. Further studies are needed to assess pneumococcal meningitis caused by the replacing serotypes

End of season influenza vaccine effectiveness in adults and children in the United Kingdom in 2017/18

Background: In the United Kingdom (UK), in recent influenza seasons, children are offered a quadrivalent live attenuated influenza vaccine (LAIV4), and eligible adults mainly trivalent inactivated vaccine (TIV). Aim: To estimate the UK end-of-season 2017/18 adjusted vaccine effectiveness (aVE) and the seroprevalence in England of antibodies against influenza viruses cultured in eggs or tissue. Methods: This observational study employed the test-negative case–control approach to estimate aVE in primary care. The population-based seroprevalence survey used residual age-stratified samples. Results: Influenza viruses A(H3N2) (particularly subgroup 3C.2a2) and B (mainly B/Yamagata/16/88-lineage, similar to the quadrivalent vaccine B-virus component but mismatched to TIV) dominated. All-age aVE was 15% (95% confidence interval (CI): −6.3 to 32) against all influenza; −16.4% (95% CI: −59.3 to 14.9) against A(H3N2); 24.7% (95% CI: 1.1 to 42.7) against B and 66.3% (95% CI: 33.4 to 82.9) against A(H1N1)pdm09. For 2–17 year olds, LAIV4 aVE was 26.9% (95% CI: −32.6 to 59.7) against all influenza; −75.5% (95% CI: −289.6 to 21) against A(H3N2); 60.8% (95% CI: 8.2 to 83.3) against B and 90.3% (95% CI: 16.4 to 98.9) against A(H1N1)pdm09. For ≥ 18 year olds, TIV aVE against influenza B was 1.9% (95% CI: −63.6 to 41.2). The 2017 seroprevalence of antibody recognising tissue-grown A(H3N2) virus was significantly lower than that recognising egg-grown virus in all groups except 15–24 year olds. Conclusions: Overall aVE was low driven by no effectiveness against A(H3N2) possibly related to vaccine virus egg-adaption and a new A(H3N2) subgroup emergence. The TIV was not effective against influenza B. LAIV4 against influenza B and A(H1N1)pdm09 was effective

The seasonality and effects of temperature and rainfall on Campylobacter infections

ABSTRACT Background Campylobacteriosis is a major public health concern. Despite evidence that climate factors influence the spatio-temporal patterns of the infections; their impact is not fully described and understood. Objectives To examine methods for determining the impact of rainfall and temperature on Campylobacter cases in England and Wales. Methods Reported cases for England and Wales were linked to local temperature and rainfall at laboratory postcode locations in the 30 days before the specimen date. Descriptive, statistical and spatial methods included a novel Comparative Conditional Incidence (CCI), wavelet analysis, hierarchical clustering, generalized additive model (GAM) and generalized structural time series model (GEST). Results The Campylobacter increase in late spring was linked to temperature two weeks prior, with an increase in CCI of 0.175 cases per 100,000 per week for weeks 17 to 24; the relationship was non-linear and changed through the year. GEST with penalized varying temperature coefficient found 33% of the seasonal change was attributable to temperature, while with a fixed temperature coefficient found 8%. Wavelet analysis showed a strong annual cycle, with harmonics at six and four months and no simple association with temperature or rainfall. Geographic clustering showed three clusters with geographic similarities, representing metropolitan, rural, and other areas. Conclusions Our analyses provide more robust and convincing associations than simple regression analysis. The association with temperature is likely to be indirect and the primary driver remains to be determined. Local-temporal linkage of weather parameters and cases is important in improving the resolution of climate associations with infectious diseases and provides methods which can improve disease predictions. Further examination of data from a wider geographic area and longer time series should improve the understanding of the epidemiology and drivers of human Campylobacter infections