{"content":[{"text":"Methodological Methodological Methodological Methodological note note note note \n PIN Flash 21 – Reducing Road Deaths for Young People Aged 15-30","bbox":{"left":0.118991,"top":0.12285,"width":0.735105,"height":0.041650000000000006}},{"text":"Regression Regression Regression Regression estimation estimation estimation estimation of of of of the the the the average average average average annual annual annual annual percentage percentage percentage percentage change change change change in in in in road road road road deaths deaths deaths deaths \n or or or or mortality mortality mortality mortality rates rates rates rates over over over over the the the the past past past past decade decade decade decade","bbox":{"left":0.11899,"top":0.197386,"width":0.7493989999999999,"height":0.035914}},{"text":"To estimate the average yearly percentage change in road deaths occurring over \n a given period, one should make use of the whole time series of count, not just \n the counts in the first and the last year.","bbox":{"left":0.11899,"top":0.25083,"width":0.757668,"height":0.05344500000000002}},{"text":"The task is to estimate the average annual change in road deaths in the period \n 2001-2010 using the yearly recorded values in each country. The procedure is the \n same for mortality rates","bbox":{"left":0.11899,"top":0.322089,"width":0.758817,"height":0.05344500000000002}},{"text":"We assume a priori a reduction in deaths or mortality rate over time, so to fix \n the sign of a change; we will assume reduction, so that a minus sign indicates an \n increase. Let the average reduction per year as a percentage of the previous year \n be p. If Dn is the number of deaths in year n, then we wish to fit a model Dn = \n K*D0(1 – p/100)n, where K is typically close to 1 and takes account of the extent \n to which D0 was abnormally high or low. In this case year 0 is 2001 and n = 9 in \n 2010.","bbox":{"left":0.117267,"top":0.393348,"width":0.763022,"height":0.12470400000000009}},{"text":"This is equivalent to ln(Dn/D0) = lnK + n. ln(1–p/100) so if we fit ln(Dn/D0) = a + bn \n by linear regression, then a is the estimate of lnK , b is the estimate of ln(1 – \n p/100) and p is estimated by 100(1 – eb).","bbox":{"left":0.117267,"top":0.535723,"width":0.760496,"height":0.05358700000000005}}],"images_bbox":[{"left":0.560874,"top":0.055296,"width":0.28558,"height":0.057862}],"images_bbox_not_text_overlap":[{"left":0.560874,"top":0.055296,"width":0.28558,"height":0.057862}]}