That thepresence of an underlying disease increased the risk of dying

That thepresence of an underlying disease increased the risk of dying to by 6.1 fold in Mexico. We found that the OR for death cases with underlying disease was 2.218 (95 CI, 1.504?.271) adjusted by all other variables including the phase of prescription refill. Our study is an aggregated case report including almost all cases of confirmed and suspected infection during and around the pandemic peak. Individual case reports at an early stage of a pandemic are important to make appropriate policy decisions. However, while such reports at the early stage of a pandemic can explain groups susceptible to transmission; they cannot help identify risk groups in the total population. Moreover, these data cannot predict the degree of severity, because the aim of hospitalization at this time is isolation in general [27]. Including the probable cases of novel influenza A (H1N1) in this study would not likely result in overestimating the incidence rate considering that novel influenza A (H1N1) is a relatively mild infection [28] or even an L 663536 biological activity asymptomatic infection for which a majority of cases were not captured [29]. Our study had several limitations. Given that we used data from the ADSS, there was an absence of detailed clinical symptom information. Data related to the type of medication may be limited in its ability to reflect the true conditions of the infection. Another limitation was that prescription information was entered by staff at hundreds of clinics across the county, which may have reduced reliability of the data, but antiviral drugs distributed from nationalPLOS ONE | www.plosone.org2009 Novel Influenza in KoreaTable 6. Multivariate Monocrotaline cost behavioral predictors associated with a severe outcome in relation to a nonsevere outcome among all cases.Total (N = 397,390) Characteristics Female sex Age (yrs) 20?9 30?9 40?9 50?9 60+ BMI (kg/m2) Underweight (BMI,18.5) Normal (18.5#BMI,25.0) Obese (25.0#BMI) Smoking{ Drinking{ region, province 1 underlying disease Lung disease Cardiovascular disease Diabetes Mellitus Kidney disease Liver disease Malignancy Immune supp. others NOTE. {current smokers. drink more than once or twice per week. doi:10.1371/journal.pone.0047634.t{Inpatients, OR (95 CI) 1.117 (1.070?.165)ICU, OR (95 CI) 2.601 (1.883?.591)1.046 (0.987?.108) reference 0.952 (0.896?.012) 1.435 (1.351?.523) 2.829 (2.670?.998)0.922 (0.327?.600) reference 2.793 (1.219?.400) 6.266 (2.927?3.417) 31.021 (15.382?2.562)1.436 (1.334?.546) reference 0.903 (0.867?.941) 1.052 (1.000?.107) 0.959 (0.911?.010) 1.041 (1.004?.080) 1.428 (1.372?.486) 1.367 (1.302?.436) 1.192 (1.118?.272) 1.218 (1.145?.296) 1.739 (1.552?.949) 1.029 (0.969?.094) 2.076 (1.915?.251) 1.280 (1.169?.400) 1.139 (1.024?.266)2.953 (1.830?.767) reference 0.840 (0.614?.150) 1.305 (0.907?.879) 0.789 (0.515?.209) 1.503 (1.120?.017) 2.378 (1.712?.302) 1.734 (1.299?.383) 1.937 (1.380?.350) 1.249 (0.885?.764) 1.146 (0.531?.473) 0.785 (0.505?.221) 4.274 (2.9945?.100) 1.575 (0.914?.714) 0.905 (0.422?.942)stores were counted and rechecked by the district health center to verify their use and the number of remaining drugs. We were also unable to gather information on underlying disease severity, which precluded a conclusion as to which type of underlying disease most influenced outcomes.Author ContributionsConceived and designed the experiments: HK. Performed the experiments: KC SC. Analyzed the data: KC. Contributed reagents/materials/analysis tools: MH . Wrote the paper: KC.
Physical, Chemical and B.That thepresence of an underlying disease increased the risk of dying to by 6.1 fold in Mexico. We found that the OR for death cases with underlying disease was 2.218 (95 CI, 1.504?.271) adjusted by all other variables including the phase of prescription refill. Our study is an aggregated case report including almost all cases of confirmed and suspected infection during and around the pandemic peak. Individual case reports at an early stage of a pandemic are important to make appropriate policy decisions. However, while such reports at the early stage of a pandemic can explain groups susceptible to transmission; they cannot help identify risk groups in the total population. Moreover, these data cannot predict the degree of severity, because the aim of hospitalization at this time is isolation in general [27]. Including the probable cases of novel influenza A (H1N1) in this study would not likely result in overestimating the incidence rate considering that novel influenza A (H1N1) is a relatively mild infection [28] or even an asymptomatic infection for which a majority of cases were not captured [29]. Our study had several limitations. Given that we used data from the ADSS, there was an absence of detailed clinical symptom information. Data related to the type of medication may be limited in its ability to reflect the true conditions of the infection. Another limitation was that prescription information was entered by staff at hundreds of clinics across the county, which may have reduced reliability of the data, but antiviral drugs distributed from nationalPLOS ONE | www.plosone.org2009 Novel Influenza in KoreaTable 6. Multivariate behavioral predictors associated with a severe outcome in relation to a nonsevere outcome among all cases.Total (N = 397,390) Characteristics Female sex Age (yrs) 20?9 30?9 40?9 50?9 60+ BMI (kg/m2) Underweight (BMI,18.5) Normal (18.5#BMI,25.0) Obese (25.0#BMI) Smoking{ Drinking{ region, province 1 underlying disease Lung disease Cardiovascular disease Diabetes Mellitus Kidney disease Liver disease Malignancy Immune supp. others NOTE. {current smokers. drink more than once or twice per week. doi:10.1371/journal.pone.0047634.t{Inpatients, OR (95 CI) 1.117 (1.070?.165)ICU, OR (95 CI) 2.601 (1.883?.591)1.046 (0.987?.108) reference 0.952 (0.896?.012) 1.435 (1.351?.523) 2.829 (2.670?.998)0.922 (0.327?.600) reference 2.793 (1.219?.400) 6.266 (2.927?3.417) 31.021 (15.382?2.562)1.436 (1.334?.546) reference 0.903 (0.867?.941) 1.052 (1.000?.107) 0.959 (0.911?.010) 1.041 (1.004?.080) 1.428 (1.372?.486) 1.367 (1.302?.436) 1.192 (1.118?.272) 1.218 (1.145?.296) 1.739 (1.552?.949) 1.029 (0.969?.094) 2.076 (1.915?.251) 1.280 (1.169?.400) 1.139 (1.024?.266)2.953 (1.830?.767) reference 0.840 (0.614?.150) 1.305 (0.907?.879) 0.789 (0.515?.209) 1.503 (1.120?.017) 2.378 (1.712?.302) 1.734 (1.299?.383) 1.937 (1.380?.350) 1.249 (0.885?.764) 1.146 (0.531?.473) 0.785 (0.505?.221) 4.274 (2.9945?.100) 1.575 (0.914?.714) 0.905 (0.422?.942)stores were counted and rechecked by the district health center to verify their use and the number of remaining drugs. We were also unable to gather information on underlying disease severity, which precluded a conclusion as to which type of underlying disease most influenced outcomes.Author ContributionsConceived and designed the experiments: HK. Performed the experiments: KC SC. Analyzed the data: KC. Contributed reagents/materials/analysis tools: MH . Wrote the paper: KC.
Physical, Chemical and B.

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