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. Figure 1: NETT Hubs and Centers10 Table 1: Geographic Distribution of Participants (n= 2620) Hub n % n Arizona 26 0.99% Cincinnati 248 9.47% Emory 241 9.20% HFHS 97 3.70% Kentucky 904 34.50% Maryland 85 3.24% OHSU 75 2.86% Stanford 216 8.24% Temple 263 10.04% Texas 324 12.37% UCSF 93 3.55% Xxxxx 48 1.83% TOTAL 2620 100% Table 2: Study Population Characteristics n % Gender (n= 2578, 42 missing) Male 1135 44.03% Female 1443 55.97% Age Distribution (n=2567, 53 missing) Mean (Std Dev) 40.08 (16.63) 15-17 8 0.31% 18-24 688 26.80% 25-34 407 15.86% 35-49 620 24.15% 50-64 639 24.89% ≥ 65 205 7.99% Highest Education Level Completed (n=2566, 54 missing) Never attended school or only attended Kindergarten 3 0.12% Grade 12 or GED (High school graduate) 299 11.65% College 1-3 years (Some college or technical school) 920 35.85% College 4 years or more (College graduate) 1290 50.27% Ethnicity (n=2506, 114 missing) Hispanic 136 5.43% Not Hispanic 2355 93.97% Unsure/Don‟t know 15 0.60% Race (n=2538, missing 82) White 1977 77.90% Black or African American 340 13.38% Asian 145 5.71% Native Hawaiian or Other Pacific Islander 16 0.63% American Indian or Alaska Native 28 1.10% Other 79 3.50% Table 3: Personal Experience with TBI among Participants (n= 2578, 42 missing) TBI Experience n % (≠100%) Who do you know that has experienced a TBI? Self 214 8.30% Family or Friend 461 17.88% Someone else 731 28.36% No 1317 51.09% Personal Experience Yes (If answered yes to self or family/friend) 636 24.67% No 1942 75.33% Table 4: Characteristics Analyzed for Differences between Those with Personal TBI Experience and Those Without Personal Exp n (%) No Personal Exp n (%) Difference P-Value GENDER n=2540 (80 missing) Male 264 (42.58%) 856 (44.58%) Chi Square= 0.7625 0.3825 Female 356 (57.42%) 1064 (55.42%) AGE n=2529 (91 missing) Mean (SD) 42.14 (16.12) 39.67 (16.71) Xxxxxx T- test= 3.25 0.0012* 15-17 4 (0.65%) 3 (0.16%) Chi Square= 28.2660 XX Xxx Square= 10.0973 <.0001* 0.0015* 18-24 131 (21.16%) 531 (27.80%) 25-34 80 (12.92%) 323 (16.91%) 35-49 178 (28.76%) 438 (22.93%) 50-64 180 (29.08%) 458 (23.98%) ≥ 65 46 (7.43%) 157 (8.22%) EDUCATION n= 2530 (90 missing) Never attended or only Kindergarten 0 (0.00%) 2 (0.10%) Chi Square= 8.3636 XX Xxx Square= 3.7201 0.1373 0.0538 Grades 1-8 (Elementary) 3 (0.48%) 10 (0.52%) Grades 9-11 (Some high school) 16 (2.58%) 24 (1.26%) Grade 12 or GED (High school graduate) 75 (12.08%) 216 (11.31%) College 1-3 years (Some college or technical school) 232 (37.3...
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 Xxxx 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 28,177 Sex of the subject 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.
Tables and Figures. Where graphs or tables are used, all source data files must be supplied at the initial stage. Tables and Figures should be of reproducible quality, should include captions and should not duplicate material presented in the text. All Tables and Figures must be cited in the text. Tables should be numbered consecutively in the order of their first citation in the text. The table number and caption should be placed consistently throughout the report (either above or below the table). The default style is for the table number and caption to be placed above the table. The same guidance applies to numbering of Figures. Where figures have been imported from other software packages (e.g. graphs imported as pictures from Microsoft Excel) care must be taken to ensure that legends and axis titles are large enough to be legible in the printed report – see guidance on line artwork below. Explanatory notes should be placed in footnotes, not in the table or figure heading. Explain in footnotes all non-standard abbreviations. Pictures and other Images Contractors should check with the DCLG what the publication route is likely to be (web or hard copy). This may influence the format and file size of any pictures and images either embedded in the report or provided separately. For printed reports, all pictures and illustrations (line drawings, diagrams, graphs, boxes, photographs etc) must be suitable for high-quality reproduction at their intended size in the final publication. COMMS will not accept sub-standard illustrations, such as photocopies, pencil sketches, rough drawings, distorted or damaged material, or photographs that are too light, too dark or out of focus. Images scanned from printed media or downloaded from the Internet are not acceptable. Please supply each illustration both as an electronic file and in hard copy form (the latter should be a good-quality printout). You may also embed low resolution copies of the illustrations in your main document to give COMMS an indication of size and positioning. If you do this, please supply a separate text-only document, indicating within it the position of each illustration. Figures, pictures etc provided as electronic files should be identified with a file name that matches the number given to the figure, picture etc in the report e.g. Fig04.04.tif or Fig12.01.wmf. Printouts should be labelled with the corresponding electronic filenames. Note that all illustrations – drawings, graphs, photographs etc – should be identi...
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. 32 INTRODUCTION Depression is the leading cause of disability and the fourth leading contributor to the global burden of disease and is recognized as an important cause of morbidity and mortality worldwide (1,2). This disorder is the most common type of mental illness, affecting more than 26% of the U.S. adult population (3) and is characterized as a mental disorder associated with a range of emotional, cognitive, and physical behavioral symptoms including loss of interest or pleasure, feelings of guilt, disturbed sleep or appetite, low energy, and poor concentration (2,4). Depressive symptoms interfere with daily life and can contribute to personal adverse health effects (4,5). Women are twice as likely as men to report a lifetime history of major depressive episodes (1). According to the 2009-2012 National Health and Nutrition Examination Survey (NHANES), among a population of 18-39 year old, the prevalence of those displaying significant depressive symptoms was 9.3% in women compared to 5.8% in men (6). Biological processes are thought to be involved in the predisposition of women to depression, including genetically determined vulnerability and hormonal fluctuations (7). Psychosocial events, such as role-stress and sex-specific socialization, are considered to increase the vulnerability of women to depression (7). Depression among 50% of reproductive aged women is undiagnosed and untreated due to high cost, opposition to treatment, and stigma related to perceived mental illness in society (1). Women with depression are at a high risk for adverse reproductive outcomes, including lower fertility and negative pregnancy outcomes (e.g., preterm births and low birth weight infants), and impaired maternal functioning and bonding (1,8,9). Nutrition can play a key role in the onset as well as the severity and duration of depression (10,11). Evidence has suggested that low folate levels may play an additional role in the risk of depression. (12–14). Folate is a B-vitamin that is derived from diet or supplementation. Dietary folate is found in green leafy vegetables, legumes, beans, liver, citrus fruits, and yeast (15). Depressed patients have consistently been found to have lower serum folate concentrations, and patients with very low folate levels were associated with higher depression scores than patients with normal folate levels (12,16). Folate plays an important role in critical brain metabolic pathways (12), this vitamin is involved in the methylation proc...
Tables and Figures. Appendix Table 1. Schedule of Assessments Procedure Enrolment Visit During Mepolizumab Treatment (months)i Months from Baseline Observation Up to 1 month post or prior 1 3 6 12 Interval Allowance (weeks) ±2 ±4 ±4 ±4 Observation Number T1 T2 T3 T4 T6 Patient Characteristics Confirmation of Eligibility (including Inclusion/Exclusion) X Consent to Release Information X Screen Failure X Age X Sex at Birth X Height and Weight X Body Mass Index X Smoking Status X History of Smoking X Occupational Exposures X Clinical Variables Family History of Asthma in First or Second-degree Relatives X Comorbid Conditionsa X History of Covid Infection in Last 6 Months X Allergic Sensitisation X Allergy and Allergy Type X Allergic Asthma Diagnosis X Age of Asthma Onset X CRSwNP / NP Historyb X History of Biologic Treatment X Aspirin-exacerbated Respiratory Disease X OCS Utilisation Xc X X X X Asthma Exacerbationsd X X X X X Clinical assessments and biomarkerse FEV1, if available X X X X X Blood Eosinophils X X X X X FeNO X X X X X Rescue Medication Use X X X X X Nasal Endoscopy Results and/or Paranasal Sinus CT Scansf X X X X X IgE Levels X X X X X Treatment for SEA/asthma or CRSwNP/NP Mepolizumab Start Dateg X Mepolizumab Treatment, Mode, and Dosage X X X X X Other SEA/Asthma Treatment and Dosage X X X X X Other CRSwNP/NP Treatment and Dosage X X X X X Concomitant Medications X X X X X Patient Reported Outcomes SNOT-22e X X X X X VAS Score for Smell Dysfunctione X X X X X VAS Score for Nasal Obstructione X X X X X ACQ-5 X X X X X Adverse Events X End of study (Reason for Study Participation Termination) Xh
Tables and Figures. Figure 1. Comparison between QFEDv2.4 and EPA estimates of PM2.5 Figure 2. Comparison between FINNv1.5 12 km resolution and EPA estimates of PM2.5 Figure 3. Comparison between FINNv1.5 4 km resolution and EPA Estimates of PM2.5 Table 1a. Odds Ratios for respiratory and cardiovascular endpoints for continuous change in 1-h PM2.5 concentrations for XXXX 4 km resolution.
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...
