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Designing effective tables Kostas Danis

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Competency to be gained from this lecture Lay out data effectively in tables

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Key areas Essential rules when arranging a table Common tables in field epidemiology

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Communicating patterns and messages contained in your data Show the patterns inherent in the data Focus attention on these patterns Serve as a basis for narrative or discussion Lead observer to insight, discussion, conclusions

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Avoid visual puzzles in tables Poorly organized data Series of complicated numbers Important data obscured Unnecessary frames, lines, coloring Decoration Basic table rules

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Column headings Data Footnotes Title Row headings Typical table layout with components

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Making sure that a table is understandable without referral to other material Title Person Time Place Content of cells (any measurement found in all columns) Row and column headings Content of the row or column Any modifier applied to all cells of a row or column Unit of measurement Abbreviations, if necessary Eliminate acronyms, unless standard (eg.OR) Avoid excessive use of capitals Basic table rules

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Using footnotes in a table Clarify points of potential ambiguity Explain all: Abbreviations Symbols Codes Note exclusions Mention data source if applicable Basic table rules

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Table 2. Cases And Controls Among Customers at UMFS CasesControlsTotal OR (95%CI) Swordfish34205413.2 (5.3-33.0) Paella862700.1 (0.01-0.95) Chicken1223351.0 (0.4-1.9) Flan caramel2040600.9 (0.2-2.9) Crema catalan1022320.3 (0.1-1.4) Lemon tarte080120- Incomplete title Absence of necessary footnotes Excess use of capitals Acronyms

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REVISED Table 2. Frequency of exposures among 42 cases of gastrointestinal illness and 82 controls by fish consumption, Uncle Mikes Fish & Chips, Berlin, 2005 Exposure Cases * n=42 Controls n=82 Odds Ratio (95CI% ) Swordfish342013 (5.3-33) Paella8620.1 (0.0-0.9) Chicken12231.0 (0.4-1.9) Flan caramel20400.9 (0.2-2.9) Crema catalan10220.3 (0.1-1.4) Lemon tarte080Reference * 2 cases were excluded 95% Confidence Interval

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*ASC Ehrenberg, J R Statis Soc A, 140(3):277-297, 1977 Suggestions for data arrangement in tables* 1.Round data to 2 meaningful figures 2.Summarize rows and columns 3.Compare numbers in columns 4.Arrange key data by magnitude 5.Help the reader with easy table layout 6.Align numbers by decimalures Basic table rules

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Table with excessive number of meaningful figures FactorCasesRate Rate Ratiopapa None274512.3451.000Ref b A342113.4331.4640.1011 B110025.6612.4140.0133 C56436.0012.5590.0005 a. p-value b. Reference exposure category Up to five meaningful figures Rate ratios difficult to compare 1. Round data to 2 meaningful figures Basic table rules

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Rounding data in a table to 2 meaningful figures Factor Cases (1000s)Rate Rate ratiop None272.32.31.01.0Ref* C343.43.41.51.5>0.100 A115.75.72.42.4<0.050 B06066.06.02.62.6<0.001 a. p-value b. Reference exposure category 2 meaningful figures Rate ratios easier to compare 1. Round data to 2 meaningful figures Basic table rules

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Rounding tips Cut decimals for percentages, eg 56.78 % Use of thousand dividers, eg 18,526 Round up measures of associations to 2 meaningful figures: 2 decimals between 0-0.99 1 decimal between 1-9.9 0 decimals between 10-99 round to nearest 10 between 100-999 ORs symmetrical around 1 on log scale 134 same precision as 13.4 or 1.34 or 0.134 X

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Rounding tips: p-values Basic table rules P-value Number of decimalsExample >0.10 20.21 <0.10-0.001 30.041 <0.001 3p<0.001

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YearMF Both Sexes 197350099600 197058087670 196846089550 196626071330 Mean43086520 Summary of the columns Summary of the rows 2. Summarize rows and columns Summarizing rows and columns with totals, averages or other statistics Basic table rules

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Compare numbers in columns 23 42 34 109 87 42 27 98 114 75 Difficult to compare numbers in rows 23 42 34 109 87 42 27 98 114 75 1st improvement: Right-justify numbers vertically 23 27 34 42 75 87 98 109 114 2nd improvement: Sort numbers 3. Compare numbers in columns Basic table rules

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Organize data by magnitude Exposure Cases (1000s)Rate Rate ratioPaPa A112.91.3> 0.100 B069.94.3< 0.001 C345.42.3> 0.100 None272.31.0Ref b 4. Arrange key data by magnitude a. p-value b. Reference exposure category Basic table rules

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Organize data by magnitude Exposure Cases (1000s)Rate Rate ratiopapa B69.94.3< 0.010 C345.42.3< 0.050 A112.91.3> 0.001 None272.31.0Ref b a. p-value b. Reference exposure category 4. Arrange key data by magnitude Basic table rules

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Year Both sexesMaleFemale 197360050099 197067058087 196855046089 196633026071 Spaced out table layout: Comparisons difficult for the reader 5. Help the reader with easy table layout Basic table rules

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Year Both sexesMaleFemale 197360050099 197067058087 196855046089 196633026071 5. Help the reader with easy table layout Drawing columns and rows close together facilitates comparisons Basic table rules

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Intervening statistics: Separated numbers are harder to compare Rate per 1000 (SE) YearMaleFemaleAll 1993837880 2.32.21.9 1994626663 2.52.71.8 1995585456 2.12.01.7 1996554551 2.0 1.7 5. Help the reader with easy table layout Basic table rules

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Rate per 1000 (SE) YearMaleFemaleAll 199383 (2.3) 78 (2.2) 80 (1.9) 199462 (2.5) 66 (2.7) 63 (1.8) 199558 (2.1) 54 (2.0) 56 (1.7) 199655 (2.0) 45 (2.0) 51 (1.7) Moving and minimizing intervening numbers facilitates readability 5. Help the reader with easy table layout Basic table rules

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Rate per 1000 a YearMFAll 1993837880 1994626663 1995585456 1996554551 a. Standard errors for all rates less than 5% of rate. Remove intervening numbers entirely if consequence minimal 5. Help the reader with easy table layout Basic table rules

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Align columns by decimal 23 42 34 10.9 8.7 42 27 9.8 114 75 23.0 42.0 34.0 10.9 8.7 42.0 27.0 9.8 114.0 75.0 Difficult to compare numbers in rows Keeping the zeros or not is a question of personal style 6. Align numbers by decimal Basic table rules

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More suggestions 1.Use one column for each of figures 2.Use only horizontal lines between sections of table 3.Avoid redundant (duplicated) data 4.Use landscape format to display more information, if needed 5.Merge tables that share the same denominator, but do not mix data from different populations, denominators, indicators (medians/proportions) Basic table rules

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Table 1: Distribution of the Households (n=506) by per capita monthly income, Place X, 20012 Monthly income per capita (Euros) Number (%) Up to 500 268 (53.0) 501 – 1000 131 (25.94) 1001 – 2000 75 (14.82) >200032 (6.31) Place number and % in separate columns Excessive use of formatting lines, vertical divider not needed Text not aligned to the left Proportions not rounded

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REVISED Table 1: Distribution of the households (n=506) by per capita monthly income, Place X, 2012 Monthly income Per capita (Euros) NumberPercentage Up to 50026853 500-1,00013126 1,001-2,0007515 >2,000326

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Table 2- Baseline characteristics of parents/guardians and their children, vaccination coverage survey, Greece, 2006 Common tables SexNumberPercentage Female1,91949.6 Male1,94950.4 Total3,868 100 Redundant: Proportion of females will indicate proportion of males X

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Table 4- Complete vaccination coverage of children by place of residence, vaccination coverage survey, Greece, 2006 Place of residencen (weighted %)95% CI Urban Rural 1676 (65%) 448 (58%) 63.2-67.5 52.5-61.3 Table 3- Complete vaccination coverage of children by maternal belief, vaccination coverage survey, Greece, 2006 n (weighted %) [95%CI] Positive attitude of mother towards her childs vaccination No 1993 (64.5) [62.2-70.5] 24 (52.3) [49.1-62.1] Row heading takes more than one line-too wordy Use one column for each figure Consider landscape format Merge tables with identical structrure Use thousand dividers Cut decimals from percentages Explain 95%CI in a footnote

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n% *95% CI Place of residence Urban Rural 1,676 448 65 58 63-67 52-61 Maternal attitude Positive Negative 1,993 24 65 52 62-71 49-62 REVISED Table 3- Complete vaccination coverage of children by selected characteristics, vaccination coverage survey, Greece, 2006 * Weighted % allowing for clustering 95% Confidence Interval

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Table 2. Clinical characteristics of 102 cases of campylobacteriosis, Ireland, 2002 CharacteristicsValue Total cases102 Median age (years) Range (years) Fever Diarrhoea Joint pain 35 5-83 65 (65.6 %) 102 (100 %) 4 (4.3) Headache Muscle pain Isolation of organism 12 (12.4%) 4(4.4%) Stool samples (5/93%) Text must be alighned to the left The table presents frequency of symmptoms Quantitave variables/other info should not be here Sort rows. Decreasing order

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REVISED Table 2. Frequency of clinical characteristics of 102 cases of campylobacteriosis, Ireland, 2002 Symptomsn% Diarrhoea102100 Fever6566 Headache1213 Joint pain44 Muscle pain44

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Arranging common types of tables in epidemiology Line listing Two variable table Complex table Cohort study Case-control study Common tables

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StateAge 1 SexDays 2 Dose New York02M031 California03M 1 Pennsylvania06M031 Pennsylvania02M041 Colorado04F 1 California07M042 Kansas02F051 Colorado03M051 New York03F051 North Carolina04F051 Missouri11M051 Pennsylvania03F071 California04F142 Pennsylvania02M291 California05M591 1. Age in months * MMWR, 48 (27):577 2. Days from dose to symptom onset Reported cases of intussusception among recipients of rotavirus vaccine, by state, United States, 1998-1999* a. Line listing Common tables

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New cases of primary and secondary syphilis by age group and sex, United States, 1989 Age groupCases (100s) (years)MaleFemaleTotal 14 0.41.91.92.32.3 15-1917.427.944.3 20-2451.453.9100.3 25-2953.442.996.3 30-3455.431.986.3 35-4450.419.969.3 45-5421.449.926.3 55 11.413.913.3 Total260.4180.9440.3 b. Two variable table Common tables

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Complex table Children CharacterExp % (n=205) Not exp % (n=8729)p Gestational age (weeks) at birth <255.8140.04 25-2918.019NS Birthweight (kg) 1.515.015NS 2.539.043NS c. Complex table Common tables

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ate ham did not ham illnot ill 49 49 98 4 6 10 2x2 table for calculation of measure of effect d. Cohort study

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Tab. IV Fish consumption and gastro-intestinal illness among customers at Uncle Mikes Fish & Chips, Berlin, 2005 IllTotal Attack rate Relative risk Ate fish425872%9.3 (3.9-22) Did not eat fish 5648%Ref Total47122 39% d. Cohort study 2x2 table for caclulations Not for presentation

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Exposed Exposure % Res. a Yes No RR c (95% CI d ) nAR b n Type 1 Sub Type 1-A( - ) Sub Type 1-B( - ) Sub Type 1-C( - ) Type 2( - ) Type 3( - ) Type 4: a. Res. = Responded c. RR = Risk Ratio b. AR = Attack Rate – cases per ___ d. 95% CI = 95% confidence interval of the RR d. Cohort study Risk of ______ by exposure, among #### residents of Place, time Common tables

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Exposed ExposurenAR a RR b 95% CI c Type or Level 3 Type or Level 2 Type or Level 1 None or Level 01.0Referent b. RR = Risk Ratio c. 95% CI = 95% confidence interval of the RR a. AR = Attack Rate – cases per ___ Risk of ______ by exposure, among #### residents of Place, time d. Cohort study (reference group) Common tables

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Exposed Not exposed Cases Controls Odds ratio Case control study 50 20 4 a b 50 80 c d Total 100

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Exposed % (n) a ExposureCasesControlsOR b 95% CI c Type 1 (n) ( – ) Sub Type 1-A (n) ( – ) Sub Type 1-B (n) ( – ) Sub Type 1-C (n) ( – ) Type 2 (n) ( – ) Type 3 (n) ( – ) c. 95% CI = 95% confidence interval of the OR a. n = subjects respondingb. OR = Odds Ratio Exposures (%) among ### cases and ### controls, Place, Time e. Case control study Common tables

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Table from a case control study

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Food Specific Attack Rates, Outbreak of Salmonellosis, Prison X, Dover, Delaware, September 1992

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REVISED for oral presentation Food specific attack rates, outbreak of Salmonellosis, prison X, Dover, Delaware, September 1992

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Take home message Design your table around the message that is contained in your data

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Practical 1 Spot the errors of the following tables

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2.3. Reported laboratory diagnosis methods for chronic and acute infections Lab. methodanti-HCV + RNA-HCV RNA-HCVData missing Chronic cases (n=10403) 4084 (40.3%) 2659 (25.4%) 2057 (20%) 1603 (15%) Acute cases (n=956) 383 (40.4%) 260 (27.3%) 199 (21.2%) 114 (12.4%) SmiNet database 2005-2011 Seroconversion could not be verified for the VHC acute cases. - Place acute and chronic vertically to facilitate comparison - Round up proportions - Add thousand dividers

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Reported laboratory diagnosis methods for chronic and acute HCV infections, SmiNet database 2005-2011 Seroconversion could not be verified for acute hepatitis C cases. Information available among cases Acute casesChronic cases n%n% Anti HCV4,0844038340 Anti HCV + RNA2,6592526027 RNA HCV2,0572019921 Data missing1,6031514412 Total10,403100956100 - Vertical comparisons - Rounded proportions - Thousand dividers

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CMOs reporting procedures 19/21 CMOs replied the questionnaire Easy to apply case definitions? Yes (Both chronic and acute) Yes (Only chronic) Yes (Only acute) No Replies (n=19)9 (47.5%)1 (5%)09 (47.5%) Reporting instructions for labs Report after confirmation by imunoblot positive test Report after any antibody positive test Wait for RNA confirmatio n test Other Replies (n=19)12 (63%)2 (10%)1 (5%)4 (21%) - Two tables with identical structure - Incomplete title

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Hepatitis C reporting procedures described by 19 of the 21 Chief Medical Officers (CMOs) surveyed, Sweden, 2012 ItemAnswersN% Case definition easily applicable For chronic and acute cases947 For chronic cases only15 For acute cases only00 No947 Reporting instruction for laboratory After confirmation (Iblot)1263 After any antibody test210 Wait for RNA15 Other421 Total19100 - Merged table - Time, place and person title

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Sexually transmitted infections (STIs) Main public health concern Prevention of STI transmission is a major PH challenge Number of new STI diagnoses in 2009-11, and changes in trend in 2002-11, England New STI diagnoses Year% Change 2009201020112009-102010-112002-11 Chlamydia189,356189,314186,1960%-2%135% Gonorrhoea16,14416,83520,9654%25%-13% Syphilis*2,8512,6502,915-7%10%87% Herpes**27,53629,79431,1548%5%81% Warts**77,84575,41576,071-3%1%21% Total***426,735419,773426,867-2%2%49% * Syphilis: primary, secondary & early latent **Anogenital herpes / warts ***Total includes diagnoses stated in the table, plus Non-specific genital infection, Pelvic inflammatory disease & epididymitis and Other new STI diagnoses Source: http://www.hpa.org.uk/webc/HPAwebFile/HPAweb_C/1215589015024 53 - Two parts in table: Values and changes - Footnote too small / detailed - Heterogeneous content indicator-wise

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Practical 2 Prepare dummy tables for a: case-control study cross-sectional study

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Practical 2a Prepare dummy tables for a: case-control study to identify risk factors for Campylobacter infection Exposures: travel food consumption (chicken, lettuce) domestic animals Demographics

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Practical 2b Prepare dummy tables for a: Sero-prevalence study to identify risk factors for West Nile virus infection Exposures: rural place of residence mosquito protection employment status

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Exposed % (n) a ExposureCasesControlsOR b 95% CI c Age>median Food (n) ( – ) Chicken (n) ( – ) Lettuce (n) ( – ) Travel abroad (n) ( – ) Domestic animal (n) ( – ) c. 95% CI = 95% confidence interval of the OR a. n = subjects respondingb. OR = Odds Ratio Exposures (%) among ### cases of campylobacter and ### controls, Place, Time

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Exposed Exposure%P a PR b 95% CI c Population size Urban Rural 1.0Referent Mosquito protection Often Rarely Never1.0Referent b. RR = Prevalence Ratio c. 95% CI = 95% confidence interval of the RR a. P = Prevalence– cases per ___ Prevalence of West Nile virus infection by exposure, among #### residents of Place, time Common tables

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Group ERS Group KLO Group MGI Group NHO Group NEA Group KLN Day 1-2 Day 3-4 Factory Atada NDPH 13**124553346734622425 Factory Seuda457235165899i056943§ Factory Desda111 (56)43 (96)35 (97)46 (53)56 (75)567 (42) Factory Rioja1103 Mean age233423452332 Travel hours64455667889890 H C AB level677089043565676 HIV +54566785678909080 Primary school3453445e655678 Secondary school234546556876878

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BACK-UP SLIDES

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61 Results Number of cases submitted to USISS Age group Type <11 to 45 to 1415 to 4445 to 6465+Total A(H1N1)24165725 A(H3N2)58316181767 A(unknown)10208342140133 B20428319 Total193216585267244 Information hard to follow as table

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62 Number of cases submitted to USISS, by age and virus, {Place}, {Time} Data presented at as graph

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ExposedUnexposed ExposureTotalCasesAR%TotalCases AR%RR Confidence intervalP blood glucose monitoring30826.67560 0.0020.26[3.18-]<0.001 diabetes mellitus408204600.0011.05[1.71-]0.003 insulin injection25624.00612 3.287.32[1.58-33.84]0.003 chiropody53815.09330 0.006.45[1.01-.]0.048 upper floor57814,04290 0.005.62[0.87-]0.056 ground floor4312.33437 16.280.14[0.02-1.11]0.058 urethral catheter4125.00767 9.212.71[0.43-17.06]0.350 eye drops10110.00737 9.591.04[0.14-7.62]1.000 sex2627.69606 10.000.77[0.17-3.56]1.000 dialysis100.00788 10.260.00[.-.]1.000 63 Multivariable analysis: only blood glucose monitoring significant Results - Redundant stats - Alignment - Decimals - Neutral title

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ExposedUnexposed ExposureTotalCasesAR%TotalCases AR% Relative risk Confidence interval Glucose monitoring30827560 0203.2- Diabetes mellitus408204600111.7- Insulin injection25624612 37.31.6-34 Chiropody53815330 06.41.0-. Upper floor57814290 05.60.87- Ground floor4312 7 160.140.0-1.1 Urethral catheter4125767 92.70.43-17 Eye drops101 737 101.00.14-7.6 Sex2627606 100.770.17-3. 6 Dialysis100788 100.0.-. 64 Multivariable analysis: Only blood glucose monitoring significant Risk of hepatitis B according to selected exposures, nursing home, Saxony, Germany, 2011 - Full title - Rounding off - Alignment - P values deleted

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