Suchergebnisse

ObjectivesProblem 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.
ApproachDatasets 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.
ResultsThere 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%.
ConclusionsInformation 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.

The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

OBJECTIVE: To evaluate the impact of the Cleanyourhands campaign on rates of hospital procurement of alcohol hand rub and soap, report trends in selected healthcare associated infections, and investigate the association between infections and procurement. DESIGN: Prospective, ecological, interrupted time series study from 1 July 2004 to 30 June 2008. SETTING: 187 acute trusts in England and Wales. INTERVENTION: Installation of bedside alcohol hand rub, materials promoting hand hygiene and institutional engagement, regular hand hygiene audits, rolled out nationally from 1 December 2004. MAIN OUTCOME MEASURES: Quarterly (that is, every three months) rates for each trust of hospital procurement of alcohol hand rub and liquid soap; Staphylococcus aureus bacteraemia (meticillin resistant (MRSA) and meticillin sensitive (MSSA)) and Clostridium difficile infection for each trust. Associations between procurement and infection rates assessed by mixed effect Poisson regression model (which also accounted for effect of bed occupancy, hospital type, and timing of other national interventions targeting these infections). RESULTS: Combined procurement of soap and alcohol hand rub tripled from 21.8 to 59.8 mL per patient bed day; procurement rose in association with each phase of the campaign. Rates fell for MRSA bacteraemia (1.88 to 0.91 cases per 10,000 bed days) and C difficile infection (16.75 to 9.49 cases). MSSA bacteraemia rates did not fall. Increased procurement of soap was independently associated with reduced C difficile infection throughout the study (adjusted incidence rate ratio for 1 mL increase per patient bed day 0.993, 95% confidence interval 0.990 to 0.996; P < 0.0001). Increased procurement of alcohol hand rub was independently associated with reduced MRSA bacteraemia, but only in the last four quarters of the study (0.990, 0.985 to 0.995; P < 0.0001). Publication of the Health Act 2006 was strongly associated with reduced MRSA bacteraemia (0.86, 0.75 to 0.98; P = 0.02) and C difficile infection (0.75, 0.67 to 0.84; P < 0.0001). Trust visits by Department of Health improvement teams were also associated with reduced MRSA bacteraemia (0.91, 0.83 to 0.99; P=0.03) and C difficile infection (0.80, 0.71 to 0.90; P=0.01), for at least two quarters after each visit. CONCLUSIONS: The Cleanyourhands campaign was associated with sustained increases in hospital procurement of alcohol rub and soap, which the results suggest has an important role in reducing rates of some healthcare associated infections. National interventions for infection control undertaken in the context of a high profile political drive can reduce selected healthcare associated infections.