Part, Chapter, Paragraph
1 II, 5. 2. 2| the treatments. As already explained, MONICA data, although collected
2 II, 5. 2. 3| deaths than breast cancer.~As explained in chapter 5.2.2 ‘Data sources’,
3 II, 5. 2. 3| different populations could be explained by changes in the average
4 II, 5. 2. 3| above the age of 75. As explained in chapter 4.2.2 ‘Data sources’,
5 II, 5. 2. 3| east-West gap might be mostly explained by increased levels of traditional
6 II, 5. 2. 4| in classic risk factors explained only a part of the change
7 II, 5. 2. 5| including secondary prevention explained only the remaining 42% of
8 II, 5. 5. 3| among study estimates can be explained by the age of the children
9 II, 5. 5. 3| of reports can be largely explained by suboptimal case ascertainment
10 II, 5. 5. 3| and women can be mostly explained by the differing genetic
11 II, 5. 5. 3| The differences are mostly explained by the structure of the
12 II, 5. 5. 3| French study can be mostly explained through accuracy in case
13 II, 5. 5. 3| seizures may be largely explained by the different distribution
14 II, 5. 5. 3| studies may be partially explained by the inclusion of acute
15 II, 5. 5. 3| in children may be thus explained by the rate expected in
16 II, 5. 5. 3| difference in rates may be mostly explained by the small size of the
17 II, 5. 5. 3| prevalence rate since 1978 was explained by the changing population
18 II, 5. 6. 3| al, 1997) (Figure 5.6.1), explained partly by a cumulative effect
19 II, 5. 8. 3| osteoporosis could not be explained by gluco-corticoid use alone,
20 II, 5. 9. FB| Europe could be partially explained by the higher physicians’
21 II, 5. 9. FB| allergies. This correlation is explained by the shift from Th-1 phenotype (
22 II, 5. 9. 3| of centre level variation explained), hay fever (61 and 73%)
23 II, 5. 11. 3| increases could probably be explained by increased cumulative
24 II, 9. 1. 1| than neonatal mortality, as explained in Table 9.1.1.1. In addition,
25 II, 9. 3. 2| deterioration can also be explained by increased risk factors
26 III, 10. 1. 1| Conflicting results may be explained by the fact that the food
27 III, 10. 5. 3| something that cannot be fully explained by the ‘healthy worker effect’,
28 IV, 11. 5. 4| differences cannot be easily explained. They are probably due to
29 IV, 11. 5. 4| understand. They could be explained by the wide variability
30 IV, 11. 5. 5| access that cannot merely be explained by differences in donation
31 IV, 13. 2. 1| more diseases which can be explained by that risk factor (or
