Tables and Figures Sample Clauses

Tables and Figures. TABLE 1. Etiologic agents of fish-associated outbreaks, United States, 1998- 2008. Etiologic Agent No. (%) of No. (%) of No. (%) of No. (%) of Outbreaks Illnesses Hospitalizations Deaths Chemical Scombroid toxin 317 (57.6) 1,321 (43.3) 54 (28.0) 0 (0) Ciguatoxin 173 (31.5) 719 (23.6) 92 (47.7) 1 (50) Other chemical* 8 (1.5) 40 (1.3) 0 (0) 0 (0) Paralytic shellfish poison 5 (0.9) 30 (1.0) 4 (2.1) 0 (0) Other natural toxins 3 (0.6) 9 (0.3) 0 (0) 0 (0) Heavy metals 1 (0.2) 2 (0.1) 0 (0) 0 (0) SUBTOTAL 507 (92.2) 2121 (69.6) 150 (77.8) 1 (50) Bacteria Salmonella 11 (2.0) 331 (10.8) 14 (7.3) 0 (0) Clostridium botulinum 10 (1.8) 32 (1.0) 21 (10.9) 1 (50) Bacillus cereus 4 (0.7) 19 (0.6) 0 (0) 0 (0) Staphylococcus 5 (0.9) 12 (0.4) 0 (0) 0 (0) Shigella sonnei 2 (0.4) 55 (1.8) 6 (3.1) 0 (0) Campylobacter 1 (0.2) 3 (0.1) 0 (0) 0 (0) Vibrio 1 (0.2) 2 (0.1) 0 (0) 0 (0) Other bacterial 1 (0.2) 5 (0.2) 0 (0) 0 (0) SUBTOTAL 35 (6.4) 459 (15.0) 41 (21.3) 1 (50) Virus Norovirus 6 (1.1) 453 (14.8) 0 (0) 0 (0) Rotavirus 1 (0.2) 5 (0.2) 2 (1.0) 0 (0) SUBTOTAL 7 (1.3) 458 (15.0) 2 (1.0) 0 (0) Parasite Anisakidae 1 (0.2) 14 (0.5) 0 (0) 0 (0) SUBTOTAL 1 (0.2) 14 (0.5) 0 (0) 0 (0) TOTAL 550 (100) 3,052 (100) 193 (100) 2 (100) *Gempylotoxin (1/8) and unspecified chemical toxins (7/8) TABLE 2. Univariate and multivariate logistic regression modeling of etiologic agent and fish as predictors of severe illness in fish-associated outbreaks, United States, 1998- 2008. Odds of predictor resulting in severe illness Univariate Analysis Multivariate Logistic Regression Model* Predictor Crude OR 95% CI Adjusted OR 95% CI Etiologic Agent Other⌃ 1.0 Reference 1.0 Reference Scombroid toxin 1.0 0.6-1.5 1.0 0.6-1.5 Ciguatoxin 3.3 2.2-5.1 4.8 3.0-7.9 Salmonella 3.1 1.6-6.1 3.1 1.6-6.1 Clostridium botulinum 51.1 20.1-129.7 97.2 35.3-267.2 Fish Type Other° 1.0 Reference 1.0 Reference Barracuda 12.1 7.9-18.8 11.4 7.2-17.9 Grouper 3.1 1.9-5.1 2.9 1.8-4.9 *Model controls for setting ⌃Etiologic agents other than scombroid toxin, ciguatoxin, salmonella, and Clostridium botulinum °Fish types other than barracuda and grouper N = 2,222 observations OR = odds ratio CI = confidence interval TABLE 3. Outbreak state, etiology, setting and preparation by fish type, Xxxxxx Xxxxxx, 0000-0000. FISH TYPE Tuna Mahi Mahi Grouper Escolar Barracuda Jack Other* No. of Outbreaks (Total = 607) 199 78 49 39 30 27 185 No. of Illnesses (Total = 3,317) 837 270 197 321 159 166 1,367 No. of Hospitalizations (Total = 211) 32 7 2...

Tables and Figures. Table 1. Characteristics of High School Students who had sexual intercourse, National XXXX 0000-0000 Had sexual intercourse, 2005-2011 No. % 95% CI Overall Sex of the subject 28,177 Female 13,614 47.9 47.1-48.7 Male 14,463 52.1 51.3-52.9 Missing 100 Grade of the subject 9th grade 4,749 19.8 18.9-20.6 10th grade 6,147 23.7 23.0-24.4 11th grade 7,944 26.8 26.1-27.5 12th grade 9,154 29.7 28.9-30.6 Missing 183 Race and ethnicity White 10,684 55.6 52.4-58.8 Black or African America 6,834 18.7 16.6-21.1 Hispanic 8,106 18.7 16.8-20.7 Other 2,085 6.9 5.9-8.1 Missing 468 Ever been tested for HIV Yes 5,706 21.9 21.0-22.8 No, not sure 18,801 78.1 77.2-79 Missing 3,670 Used condom at last sexual intercourse No 9,919 35.4 34.3-36.4 Yes 17,614 64.6 63.6-65.7 Missing 644 Had 4 or more sexual partners in life Yes 9,116 31.1 30.1-32.0 No 18,771 68.9 68.0-69.9 Missing 290 First sexual intercourse before 13 Yes 3,966 13.5 12.8-14.2 No 24,077 86.5 85.8-87.2 Missing 134 Ever forced to have intercourse Yes 3,856 14.1 13.4-14.8 No 24,156 85.9 85.2-86.6 Missing 165 Lifetime illegal injection drug use Yes 930 3.5 3.2-3.9 No 26,630 96.5 96.1-96.8 Missing 617 Abbreviations: CI, confidence interval; HIV, human immunodeficiency virus.

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Tables and Figures. Table 4.1 Characteristics of the sample and prevalence of polygyny, DHS surveys in sub-Saharan Africa 1999-2004 9 Table 4.2 Percent distribution of couples by spousal agreement on approval of family planning, DHS surveys in sub-Saharan Africa 1999-2004 10 Table 4.3 Percent distribution of couples by spousal agreement on discussion of family planning issues, DHS surveys in sub-Saharan Africa 1999-2004 11 Table 5.1.1 Percentage of couples in which both partners approve of family planning, by selected characteristics: West and Central Africa 14 Table 5.1.2 Percentage of couples in which both partners approve of family planning, by selected characteristics: Eastern and Southern Africa 15 Table 5.2.1 Percentage of couples in which both partners discussed family planning, by selected characteristics: West and Central Africa 17 Table 5.2.2 Percentage of couples in which both partners discussed family planning, by selected characteristics: Eastern and Southern Africa 18 Table 5.3.1 Percentage of wives who used any modern contraceptive method, by selected characteristics: West and Central Africa 20 Table 5.3.2 Percentage of wives who used any modern contraceptive method, by selected characteristics: Eastern and Southern Africa 21

Tables and Figures. Table 1. Demographic Information of the Study Population by Dosing Method Variables (mean ± SD , n (%) or median [IQR]) Patients Met Inclusion Criteria (N=1256) Traditional Dosing (n=572) Alternative Dosing (n=684) P-Value Age 57.8±16.5 57.0±17.1 58.4±16.0 0.16 Male Sex (vs. Female Sex) 668 (53.2) 287 (50.2) 381 (55.7) 0.05 White Race (vs. Non- White Race) 572 (45.5) 239 (41.8) 333 (48.7) 0.01 ICU (=Yes) 617 (49.1) 268 (46.9) 349 (51.0) 0.14 Xxxxxxxx comorbidity index >2 vs. ≤2 700 (55.7) 309 (54.0) 391 (57.2) 0.26 Xxxxxxx vs. Midtown 931 (74.1) 438 (76.6) 493 (72.1) 0.07 Inpatient Mortality=Yes 208 (16.6) 100 (17.5) 108 (15.8) 0.42 Table 2. Table of Odds Ratios Calculated with Multivariable Logistic Regression Effect Odds Ratio Estimate 95% Confidence Limits P- Value Alternative Dosing vs. Traditional 0.781 0.568 1.075 0.13 Age 1.015 1.004 1.026 0.01 ICU= Yes 5.182 3.548 7.567 <0.01 Xxxxxxxx comorbidity index >2 vs. ≤2 2.327 1.622 3.339 <0.01 Table 3. Demographic Information of the Study Population by Inpatient Mortality Variables (mean ± SD , n (%) or median [IQR]) Inpatient Mortality = No (n=1048) Inpatient Mortality =Yes (n=208) P- Value Age 56.8±16.3 62.5±16.6 <0.01 Male Sex (vs. Female Sex) 545 (52.0) 123 (59.1) 0.06 White Race (vs. Non- White Race) 484 (46.2) 88 (42.3) 0.31 ICU (=Yes) 447 (42.7) 170 (81.7) <0.01 Xxxxxxxx comorbidity index >2 vs. ≤2 540 (51.5) 160 (76.9) <0.01 Xxxxxxx vs. Midtown 797 (76.1) 134 (64.4) <0.01 Figure 1. Xxxxxx-Xxxxx Survival Analysis of Traditional vs. Alternative Dosing of Meropenem (P-value of Log Rank Test Displayed)

Tables and Figures. Figure 1. Screenshot of Diabetes App Lite by BHI Technologies, Inc. Figure 2. Screenshot of Glucose Buddy by Azumio Figure 3. Prevalence of functionalities found in selected glucose tracking apps Table 1. Survey results, all respondents (n=1601) Number (%) Country United States 1103 (68.89) Xxxxxx Xxxx 000 (00.00) Xxxxxx 46 (2.87) Othera 45 (2.81) Unknown 54 (3.37) Do you have diabetes? Yes 588 (36.73) No 491 (30.67) I don’t know 246 (15.37) I take care of a family member with diabetes 276 (17.24) Smartphone platform Android 815 (50.91) Ios 415 (25.92) Blackberry 17 (1.06) Do not have smartphone 354 (22.11) aCountries in this category included Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Nicaragua, Panama, Peru, Spain, Switzerland, and Venezuela Table 2. Survey results among patients reporting a history of diabetes (n=588) Number (%) Country United States 415 (70.6) Xxxxxx Xxxx 000 (00.0) Xxxxxx 15 (2.6) Othera 8 (1.4) Unknown 17 (2.9) Diabetes type Type I 74 (12.6) Type II 408 (69.4) Don’t know 106 (18.0) Do you use insulin? Yes 161 (27.4) No 427 (72.6) Do you use insulin? (type I only, n=74) Yes 29 (39.2) No 45 (60.8) Do you use insulin? (type II only, n=408) Yes 111 (27.2) No 297 (72.8) Do you use a diabetes app? Yes 18 (3.1) No 570 (96.9) aCountries in this category included Argentina, Brazil, Canada, Chile, Colombia, Costa Rica, the Dominican Republic, Ecuador, El Salvador, Guatemala, Nicaragua, Panama, Peru, Spain, Switzerland, and Venezuela Table 3. Characteristics of app usage among diabetic respondents reporting use of diabetes apps (n=18) Number (%) Language in which app is used English 4 (22) Spanish 10 (56) I don’t know 4 (22) How much did you pay for the app? Free 8 (44) $0.99 1 (6) $2.99 1 (6) More than $3.00 3 (17) I don’t remember 5 (28) Proportion of respondents reporting frequent use of the following documentation functionalities Oral medications 9 (50) Blood glucose 8 (44) Blood pressure 6 (33) Diet-related 6 (33) Weight 5 (28) Exercise 4 (22) HgbA1c 3 (17) Insulin 3 (17) None of these 4 (22) Proportion of respondents reporting frequent use of the following reminder features Reminder to check blood glucose 9 (50) Reminder to take medications 8 (44) None 4 (22) Information sharing Shares with physician only 10 (56) Does not share with anyone 5 (28) Diabetes forums 2 (11) Facebook 1 (6)

Tables and Figures. ‌ Table 1: Characteristics of PSF health care professionals in Vespasiano, state of Minas Gerais, Brazil, 2010 (n=75) Professional category n (%) Median age in years, 5th quartile, 95th quartile) Median number of years of practice (5th quartile, 95th quartile) Median number of years working with PSF (5th quartile, 95th quartile) Doctor 7 (9) 28 (25, 39) 1.75 (0.50, 12.00) 0.33 (0.04, 1.50) Nurse Nurse Aid Community Health Agent 10 (13) 11 (15) 47 (63) 32 (27,44) 37 (20, 52) 31 (25, 49) 4.84 (1.33, 10.50) 10.00 (0.67, 29.25) 5.33 (1.17, 12.50) 2.67 (0.25, 7.58) 2.92(0.17, 11.25) 5.25(0.67, 11.25) Table 2: Trainings received by PSF non-doctor healthcare professionals stratified by category in Vespasiano, state of Minas Gerais, Brazil, 2010 (n=68)* Category n Infant feeding practices (%) n Child growth monitoring (%) Nurses Yes No Nurse Aids Yes No Community Health Agents Yes No p- value 55 38 29 18 50 50 27 73 62 38 0.11 37 38 31 16 30 70 27 73 66 34 0.02 * Doctors were not asked the questions regarding training in infant feeding practices and child growth monitoring because investigators assumed they received such training in medical school. Table 3: Knowledge and practices related to child growth monitoring among PSF healthcare professionals stratified by category in Vespasiano, state of Minas Gerais, Brazil, 2010 Professional category Identified normal growth curve on chart n (%) (n=75) Identified information needed for nutritional assessment n (%) (n=75) Always/almost always plot measurements on chart n (%) (n=27) Always/almost always record measurements in records n (%) (n=27) Always/almost always record measurements Child Booklet n (%) (n=27) Yes No/DK Yes No/DK Yes No/DK Yes No/DK Yes No/DK Doctors 7 (100) 0 (0) 3 (43) 4 (57) 6 (100) 0 (0) 6 (100) 0 (0) 6 (100) 0 (0) Nurses 9 (90) 1 (10) 8 (80) 2 (20) 10 (100) 0 (0) 9 (90) 1 (10) 10 (100) 0 (0) Nurse 5 (46) 6 (54) 5 (46) 6 (54) 1 (9) 10 (91) 9 (82) 2 (18) 4 (36) 7 (64) Community 19 (40) 28 (60) 15 (32) 32 (68) -- -- -- -- -- -- All professionals 40 (53) 35 (47) 31 (41) 44 (59) 17 (63) 10 (37) 24 (89) 3 (11) 20 (74) 7 (26) aids Health Agents *p-value 0.05 0.001 0.726 0.004 * Chi square p-value does not include ‘All professionals’ category -- XXXx were not asked the questions re: growth monitoring practices given that they were not part of their responsibilities. Table 4: Knowledge related to infant feeding practices and anemia prevention among PSF professionals stratified by category in Vespasiano, sta...

Tables and Figures. Figure 1. Distribution of the cycle threshold (CT) of positive admission nasal MRSA screens (Xpert MRSA assay) among Atlanta veterans (n = 205) Table 1: Baseline patient characteristics among MRSA colonized and non-colonized (N = 346) Nasal Colonization Status Patient Demographics Negative Positive P - valuea N (%) N (%) Total 141 205 CT n/a 27.1 Age (years) Mean (SD) 63.0 (13.3) 63.6 (12.4) 0.6876b > 71.7 (4th quartile) 39 (27.7) 51 (24.9) 0.5622 Gender Male 130 (92.2) 199 (97.1) 0.0393 Female 11 (7.8) 6 (2.9) Race Black 55 (39.0) 97 (47.3) 0.1571 White 81 (57.4) 104 (50.7) Other 5 (3.6) 4 (2.0) Clinical Characteristics Admit from other than home 11 (7.8) 29 (14.2) 0.0697 Admission to ICU 44 (31.2) 40 (19.5) 0.0127 Surgery in 12 months prior 35 (24.8) 63 (30.7) 0.2307 Admission in 12 months prior 67 (47.5) 115 (56.1) 0.1163 Antibiotics within 30 days 26 (18.4) 61 (29.8) 0.0171 Co-morbidities Wound present 10 (7.1) 55 (26.8) <0.0001 Device Present 8 (5.7) 32 (15.6) 0.0045 Previous / Concurrent MRSA 1 (0.7) 40 (19.5) <0.0001 CAD 55 (39.0) 68 (33.2) 0.2651 CHF 34 (24.1) 62 (30.2) 0.2108 PVD 16 (11.4) 38 (18.5) 0.0702 COPD 26 (18.4) 55 (26.8) 0.0702 DM 60 (42.6) 88 (42.9) 0.9450 Smoker 45 (31.9) 52 (25.4) 0.1826 Advanced Liver dz 5 (3.6) 15 (7.3) 0.1650 Active Malignancy 31 (22.0) 28 (13.6) 0.0430 ESRD 6 (4.3) 13 (6.3) 0.4026 CVA 18 (12.8) 38 (18.5) 0.1521 HIV 2 (1.4) 12 (5.9) 0.0507 Other 11 (7.8) 25 (12.2) 0.1884 ≥ 3 co-morbidities 58 (41.1) 90 (43.9) 0.6091 *CAD = coronary artery disease, CHF = congestive heart failure, PVD = peripheral vascular disease, COPD = chronic obstructive pulmonary disease, DM = diabetes mellitus, ESRD = end stage renal disease, CVA = cerebrovascular accident, HIV = human immunodeficiency virus. aP-value for Chi Square or Xxxxxx’x exact test bP-value for two-sample T test Table 2. A comparison of subsequent MRSA infection types, death during follow-up, and readmission within 4 years stratified by colonization status among a cohort of Atlanta veterans Characteristic Negative Colonization (N = 141) Low Colonization Burden (N = 141) High Colonization Burden (N = 64) P-value a Total Subsequent Infections 6 (4.3%) 26 (18.5%) 11 (17.2%) 0.0007 Subsequent Infection Skin/Soft Tissue 2 10 4 0.2040 Bone and Joint 4 Lower Respiratory 2 2 2 Surgical Site 1 Mean Time to Infection in Days (SD) 310.8 (283.9) 385.8 (398.4) 445.6 (444.9) 0.7979b Death Death during admission 7 (5.0%) 9 (6.4%) 4 (6.3%) 0.8683 Death during follow up 48 (34.0%) 73 (51...

Tables and Figures. Table 1: A comparative analysis of Quality Management of Official Statistics in Sweden and UK 4 Figure 1: Functions and structure of management of Official Statistics in UK (including England, Northern Ireland, Wales, Scotland) 11 Figure 2: Quality in production of Sweden statistics 16 Table 2: The day-to-day activities in place at an organizational level for quality assurance, quality control, quality reporting and quality improvement in ONS Quality Framework 18 Table 3: A comparative table for the three layers of quality reporting in UK 19 Figure 3: The position of branding in the Office for Statistics Regulation within the UK statistical infrastructure 23

Tables and Figures. Table 1. Descriptive statistics of baseline covariates stratified by detection method in two cohorts LEAD Cohort MER Cohort Clinical vs. SI Clinical vs. SI Gender Ethnicity Stage at diagnosis Grade at diagnosis ECOG FLIPI B- symptoms Age at (Col %) N Covariate Statistics Level clinical radio P- clinical radio P- N=35 N=18 value* N=63 N=50 value* N female 21 (60.00) 8 (44.44) 20 (31.75) 24 (48.00) (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N (Col %) N diagnosis (95% CI) (35.35, 0. 453 (35.17, (36.39, 0.619 80.93) 79.61) 82.09) 82.99) (Col %) Mean 0.281 0.078 male 14 (40.00) 10 (55.56) 43 (68.25) 26 (52.00) white 24 (68.57) 14 (77.78) 0.539 61 (98.39) 47 (94.00) 0.323 other 11 (31.43) 4 (22.22) 1 (1.61) 3 (6.00) 1,2 7 (21.88) 2 (11.11) 0.459 8 (12.70) 9 (18.00) 0.434 3,4 25 (78.13) 16 (88.89) 55 (87.30) 41 (82.00) 1,2 29 (82.86) 14 (82.35) 1.000 54 (85.71) 43 (86.00) 0.965 3 6 (17.14) 3 (17.65) 9 (14.29) 7 (14.00) 0 8 (25.00) 7 (43.75) 0.186 36 (58.06) 36 (72.00) 0.126 ≥1 24 (75.00) 9 (56.25) 26 (41.94) 14 (28.00) low 6 (25.00) 1 (6.67) 15 (23.81) 12 (24.00) intermediate 6 (25.00) 6 (40.00) 0.308 24 (38.10) 16 (32.00) 0.767 high 12 (50.00) 8 (53.33) 24 (38.10) 22 (44.00) yes 13 (40.63) 1 (5.88) 0.018 10 (16.39) 7 (14.00) 0.728 no 19 (59.38) 16 (94.12) 51 (83.61) 43 (86.00) 54.20 57.48 58.63 59.69 (27.47, * The p-values were calculated by ANOVA or Kruskal-Wallis test for numerical covariates and chi-square test or Xxxxxx'x exact test for categorical covariates. Table 2. K-M survival probability estimates for three outcomes in LEAD cohort Median Group N Event Censored 98.5%) 71.5%) 55.5%) 3.6 94.4% 55.6% 22.2% 0 (0%) (1.8, (66.6%, (30.5%, (6.9%, 4.4) 99.2%) 74.8%) 42.9%) 35 Survival Time (95% CI)