Effect of Smoke-Free Legislation on Adult Smoking Behaviour in England in the 18 Months following Implementation

Comprehensive smoke-free legislation covering all enclosed public places and workplaces was implemented in England on 1 July 2007. This study examines the impact of this legislation on smoking prevalence, number of cigarettes smoked and location of smoking, controlling for secular trends through the end of 2008.Repeat cross sectional survey using nationally representative data from the Health Survey for England (HSE). In total there are 54,333 respondents from 2003-2008. Logit and linear regression models were used to examine the effect of the legislation on smoking prevalence and the number of cigarettes smoked daily among continuing smokers which took the underlying trend into account. Our finding suggest that smoking prevalence (current smoker) decreased from 25% in 2003 to 21% in 2008 (AOR = 0.96 per year, 95% CI = 0.95-0.98, P<0.01) and the mean number of cigarettes consumed daily by smokers decreased from 14.1 in 2003 to 13.1 in 2008 (coefficient for time trend = -0.28±0.06 SE cig/day per year, P<0.01). After adjusting for these trends the introduction of smoke-free legislation was not associated with additional reductions in smoking prevalence (AOR = 1.02, 95% CI = 0.94-1.11, P = 0.596) or daily cigarette use in smokers (0.42±0.28 SE; P = 0.142). The percentage of respondents reporting smoking 'at work' and 'inside pubs or bars' decreased significantly from 14% to 2% (p<0.001) and from 34% to 2% (p<0.001), respectively, after the legislation. The percentage reporting smoking 'inside restaurants, cafes, or canteens' decreased significantly from 9% to 1% (p<0.001) and 'inside their home' decreased significantly from 65% to 55% (p<0.01).There is widespread compliance with the smoke-free legislation in England, which has led to large drops in indoor smoking in all venues, including at home. Declines in smoking prevalence and consumption continued along existing trends; they did not accelerate during the 18 months immediately following implementation

Klasifikasi Epileptiform dan Wicket Spikes Menggunakan Metode Key-Point Based Local Binary Pattern

Epilepsi adalah gangguan kronis otak disebabkan oleh adanya lepasan muatan listrik abnormal yang berlebihan di neuron-neuron otak secara berlebihan di neuron-neuron otak secara paroksismal dan disebabkan oleh berbagai etiologi, bukan disebabkan oleh penyakit otak akut. Sampai saat ini jumlah penderita Epilepsi mencapai 50 juta di seluruh dunia. Di Indonesia diperkirakan kesalahan diagnosis epilepsi mencapai 20-30%. Salah satu metode yang lazim digunakan untuk pemeriksaan epilepsi adalah dengan melakukan perekaman Electroencephalogram (EEG) kemudian dilanjutkan dengan melakukan diagnosis berdasarkan hasil rekaman sinyal EEG yang dihasilkan. Proses pemeriksaan secara manual oleh dokter ini merupakan proses yang panjang dan melelahkan sehingga tidak jarang menyebabkan terjadinya kesalahan dan over-diagnosis. Salah satu jenis sinyal EEG yang cukup sering salah dianggap sebagai sinyal tanda epilepsi adalah Wicket spikes. Wicket spikes merupakan sinyal wicket yang muncul saat pasien mengalami tidur ringan pada saat pemeriksaan EEG, bentuknya yang mirip sering disalah-artikan sebagai epileptiform sharp wave. Pada penelitian ini, diajukan metode Key-Point Local Binary Pattern dan Support Vector Machine untuk melakukan klasifikasi antara Epileptiform dan Wicket spikes. Metode yang diajukan termasuk pendeteksian Key-Point pada sinyal yang sebelumnya telah melalui proses konvolusi dengan filter gaussian. Local Binary Pattern kemudian akan dihasilkan berdasarkan lokasi Key-Point. Kemudian hasil histogram tersebut akan dimasukkan ke dalam Support Vector Machine untuk diklasifikasikan. Hasil proses klasifikasi berupa hyperplane yang mengklasifikasikan tiga kelas yaitu normal, epileptiform dan wicket spikes. Didapatkan metode yang diajukan memiliki tingkat keberhasilan sebesar 96% persen untuk klasifikasi pada Epileptiform dan Wicket spikes dan lebih besar dari yang ada pada saat ini

Associations between multimorbidity, healthcare utilisation and health status: evidence from 16 European countries

Background: with ageing populations and increasing exposure to risk factors for chronic diseases, the prevalence of chronic disease multimorbidity is rising globally. There is little evidence on the determinants of multimorbidity and its impact on healthcare utilisation and health status in Europe. Methods: we used cross-sectional data from the Survey of Health, Ageing and Retirement in Europe (SHARE) in 2011–12, which included nationally representative samples of persons aged 50 and older from 16 European nations. Negative binomial and logistic regression models were used to assess the association between number of chronic diseases and healthcare utilisation, self-perceived health, depression and reduction of functional capacity. Results: overall, 37.3% of participants reported multimorbidity; the lowest prevalence was in Switzerland (24.7%), the highest in Hungary (51.0%). The likelihood of having multimorbidity increased substantially with age. Number of chronic conditions was associated with greater healthcare utilisation in both primary (regression coefficient for medical doctor visits = 0.29, 95% CI = 0.27–0.30) and secondary setting (adjusted odds ratio (AOR) for having any hospitalisation in the last year = 1.49, 95% CI = 1.42–1.55) in all countries analysed. Number of chronic diseases was associated with fair/poor health status (AOR 2.13, 95% CI = 2.03–2.24), being depressed (AOR 1.48, 95% CI = 1.42–1.54) and reduced functional capacity (AOR 2.12, 95% CI = 2.02–2.22). Conclusion: multimorbidity is associated with greater healthcare utilisation, worse self-reported health status, depression and reduced functional capacity in European countries. European health systems should prioritise improving the management of patients with multimorbidity to improve their health status and increase healthcare efficiency

Multivariable regression model of the association between the rate of self-referred discharged ED visits and characteristics of general practices and their registered populations in England, 2010-11.

*<p>Inclusion of the access variable in the model resulted in a statistically significant improvement in model fit; likelihood ratio test statistic  = 18.78; <i>P</i><0.001.</p