"... In PAGE 2: ... Further, we show how such ecologically based criteria are better able to define where existing predictive models can and cannot be applied. Methods Infection data Prevalence data originate from school questionnaire surveys conducted in Tanzania ( Table1 and Figure 1). Infection prevalence was estimated from carefully valid- ated questionnaire surveys in which schoolchildren were asked whether they have urinary schistosomiasis or blood in urine (termed locally kichocho) (Lengeler et al.... In PAGE 2: ... 1999; Partnership for Child Devel- opment 1999a). Several studies show that prevalence in schools of self-reported kichocho underestimates the parasitological prevalence of infection, but by a consis- tent amount ( Table1 ). This means that for each school the prevalence of reported kichocho can be reliably calibrated and used to exclude areas of low transmission from control efforts (Red Urine Study Group 1995).... ..."

"... In PAGE 8: ...able 3. Comparison of the prevalence of antipsychotic use in Ontario and in Switzerland ..................38 Table4 . Comparison of the prevalence of antipsychotic use by appropriateness in Ontario and Switzerland.... In PAGE 49: ...etween nursing homes in the Ontario dataset, from 12% to 47% (p lt;.0001). However, the number of assessments per nursing home varied greatly as well, limiting the validity of this relationship. Table4 presents the prevalence of antipsychotics by appropriateness of use. Antipsychotics were prescribed to 62% and 65% of the residents with conditions appropriate for antipsychotic prescription in a similar pattern in the Ontario and Swiss sample respectively.... In PAGE 120: ... Table4 . Change in socially inappropriate behaviour between T1 and T2 in Ontario and Switzerland Ontario Switzerland T1 T2 (~3 months later) T1 T2 (~5 months later) Socially inap.... In PAGE 122: ... Table4 . Models of change in socially inappropriate behaviour: predictors in Ontario and Switzerland Ontario Switzerland AP Other predictors AP Other predictors Initiation ns Positive: male, dementia, resisting care, depressive symptoms, and Negative: 5+ diagnoses positive Positive: wandering, verbally abusive, cognitive impairment Cessation ni ns Positive: male, verbally abusive, restless, having depressive symptoms Negative: 5+ diagnoses Improvement ni negative Positive: insomnia Negative: ADL impairment ns = not significant in multivariate model ni = not investigated in multivariate analyses because not significant in bivariate analyses Table 5.... ..."

"... In PAGE 10: ...992). If true, we need to be careful about rural/urban comparisons of means. We analyzed demographic health survey (DHS) data from 11 countries to verify these facts using the prevalence of stunting as indicator of malnutrition. Our results, reported in Table2... ..."

"... In PAGE 8: ...witzerland.............................................................................................................................39 Table5 . Prevalence of antipsychotic use by demographic variables, crude OR and 95% CI, and p- values from bivariate analyses in Ontario and Switzerland .... In PAGE 52: ....3.1. Bivariate Analyses Table5 displays the prevalence of antipsychotic use by demographic variables, as well as crude odds ratios - unadjusted for other independent variables - with p-values associated with the chi-square test from bivariate analyses in the Ontario and Swiss samples separately. Gender was not associated with antipsychotic use, though it was more prevalent among males than females in both samples.... In PAGE 120: ... n = 61 Not socially inap. n = 926 Table5 . Change in resisting care behaviour between T1 and T2 in Ontario and Switzerland Ontario Switzerland T1 T2 (~3 months later) T1 T2 (~5 months later) Resisting care n = 653 Resisting care n = 578 Not resisting care n = 75 Resisting care n = 220 Resisting care n = 174 Not resisting care n = 46 Not resisting care n = 870 Resisting care n = 109 Not resisting care n = 761 Not resisting care n = 955 Resisting care n = 73 Not resisting care n = 882 Table 6.... In PAGE 122: ...Table 4. Models of change in socially inappropriate behaviour: predictors in Ontario and Switzerland Ontario Switzerland AP Other predictors AP Other predictors Initiation ns Positive: male, dementia, resisting care, depressive symptoms, and Negative: 5+ diagnoses positive Positive: wandering, verbally abusive, cognitive impairment Cessation ni ns Positive: male, verbally abusive, restless, having depressive symptoms Negative: 5+ diagnoses Improvement ni negative Positive: insomnia Negative: ADL impairment ns = not significant in multivariate model ni = not investigated in multivariate analyses because not significant in bivariate analyses Table5 . Models of change in resisting behaviour: predictors in Ontario and Switzerland Ontario Switzerland AP Other predictors AP Other predictors Improvement ns Positive: pain, shorter stay Negative: behaviour intervention, soc.... ..."

"... In PAGE 27: ... The UN estimates the number of disabled persons in MDCs in 1981 to be 100 million, which is in the same neighborhood as our estimate of 127 million for 1995 (see Table 5). Roughly speaking, then, the UN estimate of the disabled population in LDCs (400 million in 1981) is twice as high as the estimate in Table7 (236.9 million in 1995).... In PAGE 27: ... It appears safe to conclude that the disabled population in LDCs is set to grow more rapidly than in MDCs. In Table7 , we project growth at an annual average rate of 2.7% pa between 1995 and 2020 and 2.... ..."

"... In PAGE 3: ...the rapid growth of research related to neural networks is given in Table2 from two dis- tinct searches for neural network articles, one from business and one from medicine, which demonstrate a doubling of published research between the past 3 years and the pre- vious 4 years, although it must be remembered that the authors are also citing organiza- tional research literature in addition to neural network literature, which may account for several earlier citations. Given these shortcomings, though, the authors do make an effort to provide a workable list of references for neural network applications specifically in organization research topics.... ..."

"... In PAGE 8: ...alues from bivariate analyses in Ontario and Switzerland ...................................................42 Table6 . Prevalence of antipsychotic use by clinical and behavioural variables, with crude OR, 95% CI, and p-values from bivariate analyses in Ontario and Switzerland .... In PAGE 52: ....27 (0.97-1.66) 0.0062 not available a: reference category Table6 presents the prevalence of antipsychotic use by clinical and behavioural characteristics as well as crude odds ratios with p-values associated with the chi-square test from bivariate analyses in the Ontario and Swiss samples separately. In both settings, dementia was significantly associated with ... In PAGE 55: ... Table6 . Prevalence of antipsychotic use by clinical and behavioural variables, with crude OR, 95% CI, and p-values from bivariate analyses in Ontario and Switzerland (continued) Ontario Switzerland Variable AP use (%) Crude OR (95%CI) p-valuea AP use (%) Crude OR (95%CI) p- valuea Social engagement None 0 Low level 1-2 High level 3-6 31.... In PAGE 120: ... n = 926 Table 5. Change in resisting care behaviour between T1 and T2 in Ontario and Switzerland Ontario Switzerland T1 T2 (~3 months later) T1 T2 (~5 months later) Resisting care n = 653 Resisting care n = 578 Not resisting care n = 75 Resisting care n = 220 Resisting care n = 174 Not resisting care n = 46 Not resisting care n = 870 Resisting care n = 109 Not resisting care n = 761 Not resisting care n = 955 Resisting care n = 73 Not resisting care n = 882 Table6 . Change in aggressive behaviour between T1 and T2 in Ontario and Switzerland Ontario Switzerland T1 T2 (~3 months later) T1 T2 (~5 months later) Aggressive n = 841 Aggressive n = 764 Not aggressive n = 77 Aggressive n = 461 Aggressive n = 381 Not aggressive n = 80 Not aggressive n = 673 Aggressive n = 116 Not aggressive n = 557 Not aggressive n = 714 Aggressive n = 113 Not aggressive n = 601 82.... In PAGE 122: ... inap. behaviour ni ns = not significant in multivariate model ni = not investigated in multivariate analyses because not significant in bivariate analyses Table6 . Models of change in aggressive behaviour: predictors in Ontario and Switzerland Ontario Switzerland AP Other predictors AP Other predictors Initiation ni ns Positive: restless, cognitive impairment Negative: ADL impairment Cessation negative Positive: female, pain, dementia Negative: depressive symptoms, longer length of stay, chairs preventing raising ns Positive: pain Negative: restless, dementia Deterioration positive Positive: insomnia, cognitive impairment, antianxiety, trunk restraint Negative: antidepressant ni ns = not significant in multivariate model ni = not investigated in multivariate analyses because not significant in bivariate analyses ... ..."