Beyond deaths per capita: comparative COVID-19 mortality indicators

ObjectivesFollowing well-established practices in demography, this article discusses several measures based on the number of COVID-19 deaths to facilitate comparisons over time and across populations.SettingsNational populations in 186 United Nations countries and territories and populations in first-level subnational administrative entities in Brazil, China, Italy, Mexico, Peru, Spain and the USA.ParticipantsNone (death statistics only).Primary and secondary outcome measuresAn unstandardised occurrence/exposure rate comparable to the Crude Death Rate; an indirectly age-and-sex standardised rate that can be derived even when the breakdown of COVID-19 deaths by age and sex required for direct standardisation is unavailable; the reduction in life expectancy at birth corresponding to the 2020 number of COVID-19 deaths.ResultsTo date, the highest unstandardised rate has been in New York, at its peak exceeding the state 2017 crude death rate. Populations compare differently after standardisation: while parts of Italy, Spain and the USA have the highest unstandardised rates, parts of Mexico and Peru have the highest standardised rates. For several populations with the necessary data by age and sex for direct standardisation, we show that direct and indirect standardisation yield similar results. US life expectancy is estimated to have declined this year by more than a year (−1.26 years), far more than during the worst year of the HIV epidemic, or the worst 3 years of the opioid crisis, and to reach its lowest level since 2008. Substantially larger reductions, exceeding 2 years, are estimated for Panama, Peru, and parts of Italy, Spain, the USA and especially, Mexico.ConclusionsWith lesser demand on data than direct standardisation, indirect standardisation is a valid alternative to adjust international comparisons for differences in population distribution by sex and age-groups. A number of populations have experienced reductions in 2020 life expectancies that are substantial by recent historical standards.

Feasibility of handheld mid-infrared spectroscopy to predict particle size distribution: influence of soil field condition and utilisation of existing spectral libraries

Partial least-squares regression (PLSR), using spectra from a handheld mid-infrared instrument (the ExoScan), was tested for the prediction of particle size distribution. Soils were sampled from agricultural sites in the Eyre Peninsula under field conditions and with varying degrees of soil preparation. Issues relevant to field sampling were identified, such as sample heterogeneity, micro-aggregate size and moisture content. The PLSR models for particle size distribution were derived with the varying degrees of preparation. Cross-validation of clay content in the as-received in situ soils resulted in low accuracy: coefficient of determination (R2) = 0.55 and root mean square error (RMSE) = 7%. This was improved by manual mixing, drying, sieving to < 2 mm and fine grinding, resulting in R2 values of 0.64, 0.75 and 0.81, and RMSE of 6%, 5% and 4% respectively; less improvement resulted for sand, with corresponding R2 values of 0.82, 0.88, 0.91 and 0.89, and RMSE of 10%, 8%, 6% and 7%. Predictions for silt remained poor. Where only archival benchtop calibration models were available, predictions of clay contents for spectra scanned with the handheld ExoScan spectrometer resulted in high error because of spectral intensity mismatch between benchtop and handheld spectra (R2 = 0.72, RMSE = 24.2% and bias = 21%). Pre-processing the benchtop spectra by piecewise direct standardisation resulted in more successful predictions (R2 = 0.73, RMSE = 6.7% and bias = –1.5%), confirming the advantage of piecewise direct standardisation for prediction from archival spectral libraries.

P01 Incidence of juvenile idiopathic arthritis in the United Kingdom: estimates from a national primary care dataset

Poster presentation Tuesday 8 October Background Juvenile idiopathic arthritis (JIA) is the most common childhood onset inflammatory arthritis. The last estimates of incidence of JIA in the UK are from nearly 30 years ago, prior to international classification consensus and the emergence of paediatric and adolescent rheumatology as a specialty. The aim of this study was to estimate incidence of JIA from primary care records in the UK since 2000. Methods The study used data from the Clinical Practice Research Datalink (CPRD), a database of UK primary care records broadly representative of the UK in terms of age, gender and ethnicity. A pre-defined list of JIA Read codes were used to identify incident cases annually from 2000-2018. Incidence rates (IR) with 95% confidence intervals(CI) were calculated and stratified by age and gender. The denominator was the population of CPRD <16 years old on the 31st December each year. Direct standardisation, applying Office for National Statistics population data, was used to estimate the UK IR. IRs in 5-year groupings were calculated 2000-2015 to identify change over time. Results Between 2000-2018, there were 1927 incident cases of JIA, from a total of 23,328,676 children <16 years old in CPRD. The total IR (95% CI) was 8.26 (7.90-8.64) per 100,000 population; for females and males respectively it was 9.83 (9.27-10.43) and 6.78 (6.33-7.27) per 100,000. Age-adjusted direct standardisation to the UK population estimated a total IR of 9.66 per 100,000 person-years. By age group, there appeared to be a lower incidence in middle childhood compared to early childhood and adolescents, as well as infancy where lower rates may be due to difficulties recognising the disease (Table 1). IRs over time did not appear to change. P01 Table 1: Incidence rates by age group Age (years) Denominator Cases IR (95% CI) Per 100,000 0-2 1739417 113 6.5 (5.40-7.81) >2-6 5763003 534 9.27 (8.51 – 10.08) >6-12 9464988 693 7.32 (6.80-7.90) >12-15 6361268 587 9.22 (8.51-10.01) Conclusion This is the first study to provide contemporary UK estimates of the incidence of JIA for nearly 30 years. JIA was more common in females compared to males, and in early childhood and adolescence compared to other age groups. Incidence appeared to be stable over fifteen years. These data provide important information for patients, their families and healthcare providers; in addition, they are vital for appropriate resource planning and service provision in paediatric and adolescent rheumatology. Conflicts of Interest The authors declare no conflicts of interest.

Evaluation of Standardisation Methods of near Infrared Calibration Models

The standardisation of eight partial least squares calibration models for the prediction of diesel oil properties was studied. The models were developed using spectra acquired on a laboratory accousto-optic tunable filter (AOTF) near infrared (NIR) spectrophotometer (with a quartz cuvette) and transferred to another AOTF-NIR spectrophotometer (with a fibre-optic probe) and to an Fourier transform NIR spectrometer, both designed for on-line application. Thirteen standardisation methods, using different approaches, were studied: standardisation by the pretreatment of spectra, (piecewise) direct standardisation and (piecewise) reverse standardisation. The reverse approach proved to be the best strategy to transfer the models.

Variations in practice admission rates: the policy relevance of regression standardisation

Objectives: To explore variations in general practice admission rates, comparing standardisation by regression with direct standardisation of the data to identify explained and unexplained variation. Methods: Data from hospital episode statistics and the attribution dataset on 8048 cataract admissions from 109 practices in an English health district (North Yorkshire) between 1995 and 1998. Multiple regression was used to estimate the effect of practice characteristics, socio-economic factors, waiting times and distance on practice admission rates. Rankings of practices by the residuals from the regression were compared with rankings by directly standardised admission rates. Results: The regression model yielded intuitively plausible results and explained 35% of the cross-practice variation in directly standardised admission rates. Standardisation by regression, compared with direct standardisation, made as least as much difference to the ranking of practices as direct standardisation compared with crude admission rates. Regression standardisation suggested that 10 practices not identified as 'unusual' by comparison of their rates to the district mean were in fact 'unusual', and that six practices identified as unusual by comparison with the district mean were not unusual once allowing for the explanatory factors used in the regression model. Conclusions: Given the increasing importance of systematic performance assessment to support quality improvement, care must be taken when interpreting variations in health care activity even after conventional standardisation of the data. If significant variations are detected, regression analysis can assist in explaining some of it, which is the starting point in informing discussions about whether variations are justified or unjustified.