Data type source presentation im

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Data type source presentation im

  1. 1. Dr. Muhammedirfan H. Momin Assistant ProfessorCommunity Medicine DepartmentGovernment Medical College, Surat
  2. 2.  Yesterday one patient asked surgeon Will I survive this risky operation? surgeon replied: Yes, Im absolutely sure that you will survive the operation. He asked surgeon : How can you be so sure? surgeon replied: Well, 9 out of 10 patients die in this operation, and yesterday my ninth patient died.
  3. 3. ABOUT MEDICAL STATISTICS If I had only one more day to live in this world… I would spend it in a statistics class It would seem much longer!
  4. 4. Knowledge - Science – Research Describe the world Understand the world Change the world for betterment Predict events in the world
  5. 5. Science  Aspires for certainty  Hierarchy of sciences Objectivity, universality   Mathematics (Numbers) Constants  Physics (Natural world) Certainty  Medicine (Human body) Variables  Health sciences (Health of Community)  Social sciences (society) Context
  6. 6. • Data collected may be for profile or prospective studies at local, state, national or international level.• They are analyzed to assess changes in health or disease situations in the community or population by standard parameters.
  7. 7. Data Datum: Measured or counted facts or piece of information provided in a figure. Facts, observation & information that comes from investigation. Data: A set of Values. Once the measurements carried out and recorded values of results is called data.
  8. 8.  Statistics: Collection, compilation, presentation, analysis & interpretation of data. Uses: Business, demography, economics, operational research. Biostatistics: Medical statistics: Disease, disability or efficacy of vaccine or new drug. Health statistics: Public health importance. Vital statistics: Birth, death, marriage etc.
  9. 9. Variable Any character Characteristic Quality Attribute A measurable or observable characteristics of a person or things that take on different values. E.g. weight Sex, gender.
  10. 10. Parameter Summery value of a statistic e.g. mean, median, mode.
  11. 11. What is observationAn event and its measurement.Height: 155 cmSex: Male
  12. 12. FrequencyNumber of persons in each group is called Frequency…
  13. 13. Sources of dataExperimentsSurveysRecordsExperiments & surveys are specially applied to generate data needed for specific purpose.Records provide readymade data
  14. 14. ExperimentsCarried out in the laboratory.In the hospital ward.Fundamental research.Will provide a data.Compiled & analyzed
  15. 15. Surveys They provide useful information on Changing trends in the health status, morbidity, mortality, nutritional status or environmental hazards. Provide feedback which may be expected to modify policy & system. Provide timely warning of public health hazard.
  16. 16. RecordsVital statisticsHospital records
  17. 17. Types of dataQualitative/ quantitativeDiscrete/ continuousGrouped/ ungroupedPrimary/ secondaryNominal/ ordinal
  18. 18. •Primary data• is that which is collected by the researcher to address the current research.•Secondary data• refers to data gathered by others or from other studies like official records, publications, documents etc.
  19. 19. Approaches to data collection Secondary data Primary data Readily available  need to be generatedHospital, laboratory ,  First hand information blood bank, labour  Questionnaire, room interview Delivery register, OT register, case papers schedule, Proforma second hand  Purpose served information  Time duration , cost ? Purpose served  ? Poor existing records
  20. 20. •Qualitative data• refers to data having counting of the individuals for the same characteristic and not by measurement.
  21. 21. • There is no notion of magnitude or size of the characteristic or attribute as the same cannot be measured• They are classified by counting the individuals having the same characteristic or attribute and not by measurement.• Persons with the same characteristic are counted to form specific groups or classes
  22. 22. • Classes such as Attacked escaped died cured relieved vaccinated Males Young Old treated not treated on drug etc.• The characteristic such as being attacked by a disease or being treated by a drug is not a measurable variable, only the frequency of persons treated or diseased, varies.• e.g. By one line of treatment 2o survives out of 25 while on other line of treatment 15 may survive
  23. 23. • Data are discrete in nature such as number of deaths in different years, population of different towns, persons with different blood groups in a population and so on• In medical studies such data are mostly collected in1) Pharmacology to find the action of a drug, in2) Clinical practice to test or compare the efficacy of a drug, vaccine, operation or line of treatment and3) Demography to find births , deaths still births, etc.
  24. 24. • The results thus obtained are expressed as a ratio, proportion, rate or percentage.• The statistical methods commonly employed in analysis of such data are standard error of proportion and chi-square tests
  25. 25. •Quantitative data•refers to data having magnitude and the characteristic is measured either on an interval or on a ratio scale.•The resulting data are set of numbers.
  26. 26. Numerical data These data can be either measured or counted. Blood sugar level is a measured data where as number of children in a family is a counted data. Numerical data is also called “interval data.” It can be further classified as “discrete or continuous.”
  27. 27. Examples: quantity DataHeightAgeSize of bicycle frameTime to complete a statistics test
  28. 28. Quantity data can be classified as‘Discrete or Continuous’ Quantity data Discrete Continuous
  29. 29. Discrete data: Only certain values are possible (there are gaps between the possible values). Implies counting. Continuous Data Theoretically, with a fine enough measuring device.
  30. 30. Discrete data -- Gaps between possible values- count 0 1 2 3 4 5 6 7 Continuous data -- Theoretically, no gaps between possible values- measure 0 1000
  31. 31. Examples:Discrete Data Number of children in a family Number of students passing a stats exam Number of crimes reported to the police Number of bicycles sold in a day. Generally, discrete data are counts.We would not expect to find 2.2 children in a family or 88.5 students passing an exam or 127.2 crimes being reported to the police or half a bicycle being sold in one day.
  32. 32. Examples:Continuous data Size of bicycle frame Height Time to run 500 metres Age ‘Generally, continuous data come from measurements. (any value within an interval is possible with a fine enough measuring device)
  33. 33. Qualitative ( Sex, Religion)• Data types Quantitative Continuous Discrete (measurable) (countable) Age No. of. Children Hb No. of Cases
  34. 34. Quantitative vs. Qualitative Structured • Unstructured interviews, Survey interviews; FGD Extensive • Intensive Determining • Describing and understanding, explore association & cause • Textual Numbers • To bring out variations To arrive at universal explanations • Empowering, participative
  35. 35. Relationships between Variables. (Source. Rowntree 2000: 33) Variables Category Quantity Discrete ContinuousNominal Ordinal (measuring) (counting) Ordered categories Ranks.
  36. 36. Categorical data classified asNominal, Ordinal, and/or Binary Categorical data Nominal Ordinal data dataBinary Not binary Binary Not binary
  37. 37. MeasurementScales of measurementi) Nominal scale-identifyii) Ordinal scale-magnitudeiii) Interval Scale-equal interval iv)ratio scale-absolute 0
  38. 38. 1. Qualitative/ Categorical variableNominalOrdinalIt can be binary/Dichotomous or not binary2. Quantitative/ Numerical variableDiscreteContinuous
  39. 39. Categorical Data/variable The objects being studied are grouped into categories based on some qualitative trait. The resulting data are merely labels or categories.
  40. 40. Examples: Categorical Data Eye colorblue, brown, hazel, green, etc. Smoking statussmoker, non-smoker Dead/Alive Immune/Non immune O,A,B,AB Mild, Moderate, Severe
  41. 41. Nominal dataA type of categorical data in which objects fall into unordered categories/ designations.Similar or dissimilarObservations are described, based on certain qualities or properties.Information fits in to categories but categories can not be ordered.
  42. 42. Nominal dataSex as a male or femaleDead or aliveColour of hairBlood groupAll based on certain quality
  43. 43. Ordinal data It is also a categorical data but there is a natural order among the categories so they can be ranked or arranged in order. Data is ordered Greater than and less than E.g. Pain – mild, moderate, severe PEM
  44. 44. Categorical Ordinal Nominal Ordered  Unordered categories categories Grade of breast  Male/ Female cancer – Better,  Dead/ Alive same, worsen  Blood group – O, Undergraduate & A, B, AB post graduate
  45. 45. Binary /Dichotomous Data A type of categorical data in which there are only two categories. Binary data can either be nominal or ordinal. Smoking status- smoker, non-smoker Attendance- present, absent Class of mark- pass, fail. Status of student- undergraduate, postgraduate.
  46. 46. Conversion of dataContinuous Ordinal Nominal
  47. 47. Parametric dataData whose distribution in the underlying population can be represented by normal distribution (Gussian curve) are known as parametric data.Data of more than 100 number are considered as parametric data
  48. 48. Non parametric dataUnderlying population distribution is not normal.Distribution freeUnknown distribution
  49. 49. Univariate, bivariate or multivariate dataWhen we try to analyze only one variable at a time in a particular study sample, the data set is called univariate
  50. 50. Univariate Age at diagnosis of Sex of diabetic patients diabetesNo Age No Sex1 43 1 M2 47 2 M3 49 3 F4 60 4 M5 55 5 F 6 F6 40 7 M7 63 8 F8 41 9 M9 56 10 M10 55
  51. 51. BivariateNo Age sex1 43 M2 47 M3 49 F4 60 M5 55 F6 40 F7 63 M8 41 F9 56 M10 55 M
  52. 52. MultivariateNo Age sex Medicines used1 43 M A2 47 M A3 49 F B4 60 M A5 55 F C6 40 F D7 63 M A+B8 41 F C9 56 M A10 55 M B
  53. 53. PRESENTATION OFSTATISTICAL DATA
  54. 54. • Two main methods 1) Tabulation 2) Drawing
  55. 55. Presentation should be such that data1. Become concise without losing its detail.2. Become simple to form impression.3. Arouse interest in the reader.4. Become helpful in further statistical analysis.
  56. 56. TABULATION
  57. 57. • They are devices for presenting data from a mass of statistical data.• Preparation of frequency distribution table is the first requirement.• Table can be simple or complex depending upon measurement of single set of items or multiple sets of items.
  58. 58. • In most studies, information is collected in large quantity and the data should be classified and presented in the form of a frequency distribution table.• It groups large number of series or observations of master table and presents the data very concisely, giving all information at a glance.
  59. 59. • All the frequencies considered together form the frequency distribution.• The number of persons in each group is called the frequency of that group.• It records how frequently a characteristic or an event occurs in persons of the same group• The frequency distribution table of most biological variables develops a distribution which can be compared with the standard distributions such as normal , binomial or Poisson.• Tabulation of frequencies may be for Qualitative data or Quantitative data
  60. 60. Tabular presentation• Classification: Dividing the total group of observations into smaller groups according to similarities or dissimilarities of the items w.r.t. character under study.• The quantitative type of data can be classified by dividing the Range into suitable number of Groups or Class Intervals. 0 20 40 60 80 -Age-C.I. : 0 – 20, 20- 40, 40 – 60 and 60 - 80
  61. 61. GUIDELINES TO PREPARE A TABLE1. Find Min. & Max. (9.1 & 15.7)2. Calculate difference (Max. – Min.) (15.7 – 9.1 = 6.6)3. Decide No. of Classes ( 5-15)4. Decide width of classes (Equal /Unequal)
  62. 62. GUIDELINES TO PREPARE A TABLE5. Decide class limits (Closed / Open ) Precise ( 9.0 - 9.9 / 9 -10 ) Non-overlapping ( 9.0 - 9.9, 10.0 - 10.9, …/ 9 - 10, 10 - 11…) 6. Prepare a dummy table(Hb, Tally, Frequency) 7. Tabulate (using tally marks)
  63. 63. Steps in tabular presentation of quantitative data Range = Largest observation - Smallest observation K = Number of Class Intervals(C.I.) = 1+3.322xlog n or choose K according to your interest. W = Class Interval = Range / K Prepare table mainly with three columns with headings C.I., frequency and percentage and K rows representing C.I. e.g. 110-120, 120-130,……,etc. Start C.I. with smallest observation.
  64. 64. Type of class intervals Range=49-10= 39 yrs, K=4 and W =10 yrs Inclusive type Exclusive type C.I. C.I. 10 – 19 10 - 20 20 - 29 20 - 30 30 - 39 30 - 40 40 - 49 40 - 50• Closed ended C.I. e.g. 10-15• Open ended C.I. e.g. <20 or >=30• BMI: <18, 18-25, 25-30, 30+
  65. 65. ELEMENTS OF A TABLE1. Number (To refer )2. Title (What, How classified, Where & When)3. Column headings (concise & clear)4. Foot-note (Headings, Special cell, Source)
  66. 66. Presentation of data• Raw data is arranged in the order in which they are collected.Age: 39, 47, 51, 44, 39, 35, 30, 45, 50, 41, 45, 46, 55,49, 53, 37, 36, 32, 34, 59,33, 55, 45, 40, 32, 36, 48, 50,38, 44, 50,52• Data in this form is difficult to understand and interpret.• To get information from the raw data they must be organized in some orderly fashion. Age Number 30-39 12 40-49 11 50-59 9
  67. 67. SAMPLE DATA SETPt. No. Hb. Pt. No. Hb. Pt. No. Hb. 1 12.0 11 11.2 21 14.9 2 11.9 12 13.6 22 12.2 3 11.5 13 10.8 23 12.2 4 14.2 14 12.3 24 11.4 5 12.3 15 12.3 25 10.7 6 13.0 16 15.7 26 12.7 7 10.5 17 12.6 27 11.8 8 12.8 18 9.1 28 15.1 9 13.5 19 12.9 29 13.4 10 11.2 20 14.6 30 13.1
  68. 68. TABLE FREQUENCY DISTRIBUTION OF30 ADULT MALE PATIENTS BY Hb Hb (g/dl) No. of patients 9.0 – 9.9 1 10.0 – 10.9 3 11.0 – 11.9 6 12.0 – 12.9 10 13.0 – 13.9 5 14.0 – 14.9 3 15.0 – 15.9 2 Total 30
  69. 69. In Qualitative data• There is no notion of magnitude or size of attribute, hence the presentation of frequency distribution is very simple because the characteristic is not variable but discrete
  70. 70. Sex Colour of Cloths Total White Pink Green Yellow BlueBoys 5 6 6 2 2 21Girls 16 12 17 10 14 69Total 21 18 23 12 16 90
  71. 71. DIMENSION OF A TABLEDimension = No. of variables according to which the data are classifiedOne-way Table - Freq. distn. of 30 adult male pts. by HbTwo-way Table - Freq. distn. of 30 adult pts. by Hb & SexThree-way Table - Freq. distn. of 30 pts. by Hb, Sex & AgeFour Way Table- Freq. distn. of 30 pts. by Hb, Sex, Age & Caste
  72. 72. The dist of blood group of patients with Lung Cancer Blood Group Number(%) A 10(20) B 12(24) AB 13(26) O 15(30) Total 50(100)
  73. 73. Two way presentation Awareness about HIV/AIDS by socioeconomic status. Awareness about HIV/AIDSSocioeconomic Status Poor Satisfactory V Good Total Low 30 15 05 050 Middle 15 22 13 050 High 10 25 15 050 Total 55 62 33 150
  74. 74. Three way presentationPersonality type by SBP by CHD SBP140 SBP<140PERSONAL CHD No CHD No ITY TYPE yes CHD Tot yes CHD Tot A 69 319 388 109 1092 1201 B 27 278 305 52 1208 1260 Total 96 597 693 161 2300 2461 OR = 2.28 OR=2.32
  75. 75. Four way presentation Personality type by SBP by CHD by SexMale:PERSONALITY SBP140 SBP<140 TYPE CHD No CHD Tot CHD No CHD Tot A 69 319 388 109 1092 1201 B 27 278 305 52 1208 1260 Total 96 597 693 161 2300 2461Female:PERSONALITY SBP140 SBP<140 TYPE CHD No CHD Tot CHD No CHD Tot A 69 319 388 109 1092 1201 B 27 278 305 52 1208 1260 Total 96 597 693 161 2300 2461
  76. 76. DIAGRAMS
  77. 77. Graphical presentation of dataGraphical presentation of data is useful for• Giving a visual impression of the data.• Studying hidden patterns or relationships in the data.• Identifying outliers or extreme observations.• Easy and quick understanding.
  78. 78. TYPES OF DIAGRAMSType of Variable DiagramQualitative or discrete Bar diagram (religion, gender, Pie/sector chart place of residence) Pictogram Map diagram Spot map
  79. 79. TYPES OF DIAGRAMS Type of Variable Diagram Continuous (height, weight, blood sugar )1. Histograms.2. Frequency polygon3. Frequency curve4. Line Chart / diagrams5. Cumulative Frequency Diagram6. Scatter or dot diagram7. Box and whiskers ( The Box Plot )8. Stem and leaf display ( Stem plot)
  80. 80. BAR DIAGRAM• Used when data are qualitative or discrete• Height of a bar is proportional to the frequency• Width of each bar is same.• Multiple bars can be drawn in the samediagram.
  81. 81. Simple Bar DiagramIt is mainly used for the presentation of qualitative data. The frequency distribution of blood group of patients with Lung Cancer Blood Group Freq A 25 B 45 AB 15 O 20
  82. 82. Multiple Bar DiagramIn Multiple bar diagram two or more bars can begrouped together.The frequency distribution of blood group ofpatients with Lung Cancer and Throat cancer Group Throat Lung Cancer cancer A 20 25 B 48 45 AB 10 15 O 22 20
  83. 83. AIDS Awareness Percent of women and men age 15-49 who have heard of AIDS Urban Rural TotalWomen 81 46 57 Men 94 73 80 DR IRFAN MOMIN
  84. 84. Component Bar Diagram/ Proportional Bar DiagramThe bars may be divided into twoor more parts, each partrepresents a certain item andproportional to the magnitude ofthat particular item.
  85. 85. Component Bar Diagram Awareness about HIV/AIDS by socioeconomic status. Awareness about HIV/AIDSStatus Poor Satisfactor V Good Total y Low 30 15 05 050Middle 15 22 13 050High 10 25 15 050Total 55 62 33 150
  86. 86. Component Bar Diagram SES Level of knowledge
  87. 87. Wild poliovirus cases, India 2000 1 WPV case in 2011 compared to 42 in 1750 2010 1500 1250 1000 750 500 250 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011* P1 wild P3 wild* data as on 2 March 2012
  88. 88. Three dimensional bar diagramIn three dimensional axis, along one of the axisfrequency / proportion/percentage is marked while onother axis categories of two characteristics are marked.The proportional distribution of IHD patients by systolicblood pressure by serum cholesterol levels. SBP Serum Cholesterol Levels < 140 140 - 160 > 160 <180 0.06 0.08 0.11 180 - 200 0.12 0.14 0.16 200 -260 0.30 0.28 0.29 > 260 0.52 0.50 0.44
  89. 89. Three dimensional bar diagram
  90. 90. Pie diagram/chartIt is commonly used for the presentation of qualitative type of data.Circle is used for the presentation of data, area enclosed by it being taken as 100%.Circle is divided into number of sectors by drawing angles at the centre.Area of each sector varies with the corresponding frequency or percentage.Since full angle at the centre is 360, for any particular category the angle should be 3.6 times corresponding frequency or percentage.
  91. 91. Table - 2 Distribution of newly detected leprosypatients by Type, Govt. Leprosy Treatment & Study Centre, Arakandanallur, 1955-57 Type P a tie n ts A n g le No. % (D e g re e s ) L 689 1 7 .9 64 N?L 157 4 .1 15 N 2999 7 8 .0 281 T o ta l 3845 1 0 0 .0 360
  92. 92. Table - 2 Distribution of newly detected leprosypatients by Type, Govt. Leprosy Treatment & Study Centre, Arakandanallur, 1955-57 N?L 4% L 18% N 78%
  93. 93. Freq dist of blood group of patients (Lung Cancer)Group Frequency Percentage Angle A 10 20 72 B 25 50 180 AB 08 16 57.6 O 07 14 50.4
  94. 94. Pie Chart
  95. 95. RULE OF HALVES Total Employees Employees on 1493 (100%) regular Treatment 139 (9.3%) Total Hypertensive 455 (30.5%) Employees having controlledKnown HypertensionHypertensive 71 (4.7%)197 (13.2%) DR IRFAN MOMIN
  96. 96. - 914 Sex-ratio (0-6 yrs)DR IRFAN MOMIN
  97. 97. Endemicity of Leprosy – March.09 P.R.- 0.87 PM Dahod-2.25 2.22 Vadodara – 1.10 Bharuch Narmada- 1.26 P.R. 2.7 >5 7 Surat-1. 39 3-5 Navsari Dang – 4.26 2-3 2. 04 Valsad –2. 1 - 2Gujaratachieved elimination as 18 <1 on Oct-04 DR IRFAN MOMIN
  98. 98. Pictogram•It is commonly used for the presentation ofqualitative type of data.•Here suitable symbol is first chosen torepresent certain number of units of variable.•Next each value in the given series of data isrepresented either by taking similar symbol, itssize being proportional to the value or by takingnumber of symbols of same size.
  99. 99. PictogramFreq distribution of bld gr of patients with Lung Cancer. Group Freq Symbol No of units of variable A 25  5 B 30  10 AB 20  10 O 5  1    
  100. 100. HistogramIt is used for the presentation of quantitative type of data.Along one of the axis frequency is marked while on the other axis class intervals or scale is marked.Frequency of each group will form rectangle or column.All the rectangles are adjacent to each other.
  101. 101. HISTOGRAM• Essentially a bar diagram• Bars are drawn continuously• Width - usually equal• Area - proportional to frequencies
  102. 102. Table 3 Frequency distribution of Haemoglobin levels of adult male patients (n=30) Hb (g /d l) N o . o f p a tie n ts 9 .0 - 9 .9 1 1 0 .0 - 1 0 .9 3 1 1 .0 - 1 1 .9 6 1 2 .0 - 1 2 .9 10 1 3 .0 - 1 3 .9 5 1 4 .0 - 1 4 .9 3 1 5 .0 - 1 5 .9 2 T o ta l 30
  103. 103. Fig. 3 Frequency distribution of Haemoglobin levels 12 of adult male patients (n=30) 10No. of patients 8 6 4 2 0 9.0 - 9.9 10.0 - 10.9 11.0 - 11.9 12.0 - 12.9 13.0 - 13.9 14.0 - 14.9 15.0 - 15.9 Hb level (g/dl)
  104. 104. Freq dist of Pulse Rate Pulse Rate Frequency 30 – 35 5 35 – 40 9 40 – 45 13 45 – 50 18 50 – 55 15 55 – 60 11 60 – 65 8 65 – 70 7 70 – 75 5 75 – 80 3Note: C.I. should exclusive type
  105. 105. Histogram
  106. 106. Frequency Polygon
  107. 107. Frequency Curve
  108. 108. Stem and leaf diagram 86 9 00224 466810 0 0 0 2 2 2 4 4 4 6 6 7 8 911 0 0 0 2 2 4 4 4 6 6 6 7 7 7 8 8 8 8 9 9 9 9 912 0 0 0 0 0 0 0 1 1 1 2 2 2 3 4 4 4 5 56 6 6 7 8 8 9 9 913 0 0 1 2 3 4 5 6 7 8 914 0 1 2 3 4 5 615 0 1 2
  109. 109. LINE DIAGRAM• Diagram is drawn by taking X – axis - time (e.g., Years) Y – axis - value of any index or quantity (e.g., couple protection rate)• Displays how a variable has changed over time
  110. 110. Line chartLine charts are commonly for used studyingchronological variation (trend) in a given set ofdata. F r e q Time
  111. 111. Table 4 Number of smear- positive new leprosy cases registered at the Acworth Municipal Leprosy Hospital, Mumbai, 1985-1995 No. of cases Year R e g is te re d 1985 1681 1986 1319 1987 1143 1988 1287 1989 1317 1990 1103 1991 1060 1992 1176 1993 825 1994 706 1995 528Source: Juwatkar PS, Chulawala RC, Naik SS.Correspondence Indian J Lepr 1997;62 (2):197
  112. 112. Fig 4 Number of smear- positive new leprosy cases registered at the Acworth Municipal Leprosy 2000 Hospital, Mumbai, 1985-1995 1500 1000 500 0 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995
  113. 113. Line chartTable shows year-wise distribution of MorbidityRate per 1000 due to all causes in three services. Year Army Navy AF 88 123 121 143 89 125 120 134 90 119 115 131 91 116 133 123 92 121 125 120 93 119 117 112 94 124 95 114 95 129 116 110 96 143 97 117 97 143 140 100
  114. 114. Sex Ratio & Child Sex-Ratio DR IRFAN MOMIN
  115. 115. Scatter Diagram:It is used to depict the relationshipbetween two variables which arequantitative. One of the variable ismeasured along X-axis while othervariable is measured along Y-axis.Series of pair observations such as (x,y)marked in X-Y axis system.
  116. 116. Diagram shows the relationship between heightand weight for children of age 10 yrs. The relationship between height and weight 70 60 W 50 e i 40 g 30 h 20 t 10 0 125 130 135 140 145 150 155 160 Height
  117. 117. Dot Diagram:It used for presentation of quantitative data. Scale ismarked on a line and then each value is represented bya dot.Data on age: 40, 41, 41, 41, 45, 46, 45, 46,47, 47, 47, 50, 50, 50, 50, 56, 56, 57, 57, 58, 59,60, 60
  118. 118. BOX AND WHISKERS DIAGRAM
  119. 119. PARTITION VALUES:  The values which divide the given data in to number of equal parts are called the partition values/ percentiles.  The most commonly used partition values are QUARTILES, QUINTILES, DECILES, PERCENTILE S.
  120. 120. QUARTILES: The values which divide the given data in to four equal parts when observations are arranged in order of magnitude are quartiles. obviously there will be three quartiles Q1,Q2 & Q3.Q1(1st quartile):25%below &75%aboveQ2(2nd quartile): same as median 50% above & belowQ3(3rd quartile):75%below &25% above
  121. 121. QUINTILES & DECILES:Quintiles : It contains four points so it will divide data in to five equal parts.Deciles : it contain 9 points & it will divide data in to ten equal parts.
  122. 122. Quartile  For quartiles, we want to divide our data into 4 equal pieces.Suppose we had the following data set (alreadyin order) 2 3 7 8 8 8 9 13 17 20 21 21
  123. 123. Box and Whisker plot•It is used for the presentation of quantitativetype of data.•It is useful in comparing the dist of two or moregroups.•Determine: smallest, largest, Q1, Q2 and Q3•Mark the scale on Y axis( or X axis ).•Draw a box (a rectangle with width as much aspossible and length as Q3-Q1) with ends troughQ3 & Q1.
  124. 124. Box and Whisker plot•Draw a horizontal line through the box at Q2.•Draw the whiskers ( lines ) from each end ofthe box to the smallest and largest values.•More extreme observations are plottedindividually.Obs<Q1 – 1.5*(Q3- Q1) or >Q3 + 1.5*(Q3- Q1) andsmallest and largest values: after excludingextreme observations.
  125. 125. Box and Whisker plot Largest ValueSc Q3ale Q2 Q1 Smallest Value
  126. 126. The following are the pulse rate of 20 subjects.82, 48, 45, 81, 83, 74, 76, 60, 75, 79, 63, 55, 68, 46,60, 58, 54, 51, 47, 64 Box and Whisker Plot 80 70 PR 60 50 1
  127. 127. PULSE RATE GR-A82, 48, 45, 81, 83, 74, 76, 60, 75, 79, 63, 5 5, 68, 46, 60, 58, 54, 51, 44, 58 GR-B72 , 64 , 45 , 68 , 72 , 74 , 70 , 64 , 72 , 71 , 63 , 48 , 60 , 46 , 60 , 40 , 54 , 51 , 47 , 59
  128. 128. Comparison of PR between Group-A and B 80PR 60 40 A B Group
  129. 129. The following table shows the score on certain test for two groups. Score-A 2 22 25 16 20 20 22 18 23 19 25 20 25 38 39 Score-B 4 30 25 16 20 20 24 18 23 14 25 25 25 38 30
  130. 130. Column Bar Error Diagram•It is used for the presentation of quantitativetype of data. It is useful in comparing mean andS.E.( Standard Error  S . D . ) of two or more groups. n•In the rectangular axes system, along one of theaxis scale is marked while other axis is taken asbase line or guideline.•For each group under study bar is projectedfrom the base line proportional to the mean andthe whiskers (lines) are drawn from the end ofthe bar to the values mean  S.E.
  131. 131. Serum cholesterol by BMI status Group Sample size Mean SD (mg/dl) (mg/dl) S.E.BMI>25 258 191.2 31.11 1.937BMI<=25 283 184.7 26.96 1.603
  132. 132. Error Bar Diagram 95% C.I. Group n Mean S.D. S.E. Mean -1.96*S.E. Mean+1.96*S.E 1 30 50.40 5.25 0.96 48.52 52.28 2 30 53.37 4.58 0.84 51.73 55.01The graph depicts 95% C I for weight for group 1 and 2.
  133. 133. Exercise / Examples of Tables and Graphs
  134. 134. DR IRFAN MOMIN
  135. 135. Infant Mortality Rates 79 65 6457 57 27 11 6 DR IRFAN MOMIN
  136. 136. Infant Mortality Rate NFHS-1 NFHS-2 NFHS-3 85 79 73 68 62 56 57 47 42 Urban DR IRFAN MOMIN Rural Total
  137. 137. WPV1 cases, India, 1998 - 20111750 1735 1487150012501000750 648500 397 212 203250 139 127 62 83 75 80 18 1 0 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011* Year * data as on 2 March 2012
  138. 138. Marital StatusPercent of women age 20-24 married by 53 50 age 18 54 45 28 NFHS-1 NFHS-2 NFHS-3 Urban Rural NFHS-3 DR IRFAN MOMIN
  139. 139. Desire for No More Children amongWomen with 2 Children 83 90 88 72 76 66 61 47 37 NFHS-1 NFHS-2 NFHS-3 2 sons 1 son and 1 daughter 2 daughters DR IRFAN MOMIN
  140. 140. Trends in Child Nutritional Status Percent of children age under 3 years NFHS-3 NFHS-2 51 45 43 40 23 20 Stunted Wasted Underweight (Low-weight-for-(Low-height- (Low-weight- age) for-age) DR IRFAN MOMIN for-height)
  141. 141. Anaemia among ChildrenPercent of children 6-35 months with anemia 79 81 72 74 Total Urban Rural NFHS-2 DR IRFAN MOMIN
  142. 142. Nutritional Status of Adults Percent of women and men age 15-49 Women Men 55 36 34 24 13 9 BMI below normal Overweight/Obese Anaemic DR IRFAN MOMIN
  143. 143. Malnutrition of Women by Residence and Education Percent of women age 15-496050 7 7 13 13 14 1140 24 213020 41 42 36 35 35 36 25 25100 al n an s l l s s ta ta ar io ar ar ur rb To to at ye ye ye R U uc 2 + 9 <8 S- 10 8- ed FH o N N DR IRFAN MOMIN Underweight Overweight
  144. 144. Malnutrition of Men by Residence and Education Percent of men age 15-49 50 45 3 6 40 5 5 8 35 14 30 14 25 20 38 40 38 40 34 15 Overweight 27 25 10 Underweight 5 0 an al l s rs n s ta ar ar io ur a rb To ye ye at ye R U uc <8 + 9 8- 10 ed o N DR IRFAN MOMIN
  145. 145. Child Immunization Trends Percent of children age 12-23 months vaccinated 62 BCG 72 78 54 Polio3 63 78 52 DPT3 55 55 42 Measles 51 59 35All Vaccines 42 44 NFHS-1 NFHS-2 NFHS-3 DR IRFAN MOMIN
  146. 146. Urban and Rural Population in Gujarat in2011 DR IRFAN MOMIN
  147. 147. Major causes of death in children under 5 withdisease-specific contribution of undernutrition DR IRFAN MOMIN
  148. 148. State wise ContributionNew Leprosy Cases - year 2007-08 Karnataka, Tamilnadu, 3.28, Delhi, Orissa, Others, 4, 3% 0.97, 4.13, 4.85, 4% 1% 4% 5% Uttar Pradesh, 22.54, Madhya Pradesh, 23% 4.4, 4% Jharkhand, .94, 5% Bihar, Gujarat, 13.83, 5.25, 14% 5% Chhattisgarh, West Bengal, 5.67, Andhra Pradesh, Maharashtra, 9.84, 6% 7.3, 9, 10% 7% 9% Contribution by six states 20.8% pop & 34.5% new cases DR IRFAN MOMIN
  149. 149. DR IRFAN MOMIN
  150. 150. Trends in Global Deaths 2002-30 DR IRFAN MOMIN Source: World Health Statistics 2007
  151. 151. DR IRFAN MOMIN
  152. 152. Trend of Leprosy Prevalence & Annual New Case Detection (ANCDR) Rates 30 25.9 PRPrevalence & ANCDR 25 20.0 ANCDR 20 15 13.7 10.9 8.9 10 8.4 7.0 5.9 5.9 5.8 5.5 5.5 4.4 5 3.3 6.4 5.1 5.6 2.3 1.4 5.9 6.2 5.7 4.9 4.6 5.3 5.3 1.17 4.2 1.2 3.7 3.2 2.4 1.3 0 0.84 0.72 0.74 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 DR IRFAN MOMIN Year (March End)
  153. 153. The dist of Serum Cholesterol Levels of patients with IHD. Serum Cholesterol Levels Number % (mg/dl) 150-160 10 8.55 160-170 15 12.82 170-180 13 11.11 180-190 16 13.68 190-200 19 16.24 200-210 22 18.80 210-220 22 18.80 Total 117 100.00
  154. 154. Freq dist of Pulse Rate by Sex SexPulse Rate Male Female Total No(%) No(%) No(%) 30 – 35 05(55.56) 04(44.44) 09(100) 35 – 40 09(42.86) 12(57.14) 21(100) 40 – 45 13(56.52) 10(43.48) 23(100) 45 – 50 18(54.55) 15(45.45) 33(100) 50 – 55 15(51.72) 14(48.28) 29(100) 55 – 60 11(45.83) 13(54.17) 24(100) 60 – 65 08(61.54) 05(38.46) 13(100) 65 – 70 07(53.85) 06(46.15) 13(100) 70 – 75 05(55.56) 04(44.44) 09(100) 75 – 80 03(75.00) 01(25.00) 04(100) Total 94(52.81) 84(47.19) 178(100)
  155. 155. Freq dist of Weight by SBP SBPWeight <= 140 mmHg > 140 mmHg50 – 55 15 155 – 60 20 360 – 65 28 565 – 70 10 670 – 75 8 1075 – 80 6 11
  156. 156. Case-control study or Cohort study Outcome Yes No TotalExposure Yes a b a+b No c d c+d Total a+c b+d a+b+c+d Child with HIVNevirapine Yes No Total Yes 9 71 80 No 45 75 120 Total 54 146 200
  157. 157. Year Cases Deaths No. of Affected Villages 1996 40 9 37Table-2: 1997 659 76 344AN OVERVIEW OF 1998 537 42 295 1999 365 32 251PATTERN OF SUSPECTED 2000 156 16 124CASES OF LEPTOSPIROSIS 2001 4 0 4IN SOUTH GUJARAT 2002 58 2 50DURING LAST 15 YEARS 2003 371 57 291 2004 630 92 364 2005 390 80 254 2006 270 78 199 2007 523 133 351 2008 566 124 374 2009 224 55 196 2010 633 127 396
  158. 158. Table-3: Month wise Suspected cases of Leptospirosis 2004-2010Month 2004 2005 2006 2007 2008 2009 2010 Jan 9 0 2 0 0 0 0 Feb 7 0 2 0 0 0 0 Mar 2 0 1 0 0 0 0 Apr 0 0 3 0 0 0 0 May 5 0 4 0 0 0 0 Jun 4 0 0 0 4 0 2 Jul 54 41 54 60 62 20 29 Aug 398 159 102 194 264 103 184 Sep 75 127 79 197 175 93 351 Oct 8 73 11 69 47 6 59 Nov 2 1 6 3 0 0 0 Dec 4 0 4 0 0 0 0
  159. 159. Table-4: District wise distribution of Leptospirosis cases in 2010 Deaths Deaths Suspected among Confirmed Among Dist cases Suspected cases Confirmed cases cases VALSAD 106 25 67 10 NAVSARI 197 27 135 16 SURAT 193 48 133 36 TAPI 112 22 72 12 SMC 9 1 4 1 Others 16 4 9 3 Total 633 127 420 78
  160. 160. Table-5: Block wise Cases of Leptospirosis and Deaths due to Leptospirosis- 2010District Block Cases Deaths District Block Cases DeathsVALSAD Dharampur 14 2 SURAT Bardoli 40 6 Kaprada 2 1 Choriyasi 8 1 Pardi 28 10 Kamrej 15 5 Umergam 8 3 Mahuva 33 8 Valsad 51 9 Mandvi 35 13 Valsad City 3 0 Mangrol 2 1NAVSARI Chikhli 79 14 Olpad 6 2 Gandevi 38 5 Palsana 53 11 Jalalpore 32 1 Umarpada 1 1 Navsari City 42 6 TAPI Songadh 9 3 Vansda 6 1 Uchhal 6 2 Valod 19 2 Vyara 78 15
  161. 161. Table-6: Age wise Distribution of SuspectedCases and Deaths due to Leptosirosis-2010 Deaths Cured Age Suspected among among Group cases Suspected Susp. (Years) cases cases 0-14 5 0 5 15-25 114 20 94 26-45 334 67 267 46-65 168 39 129 >=66 12 1 11
  162. 162. Table-7: Sex wise Distribution of Suspected Cases,Deaths and Cured among Leptosirosis-2010 Male Female Total Susp cases 435 198 633Deaths among Susp 90 37 127 casesCured among Susp 345 161 506 cases
  163. 163. KEEP SMILING drmhmomin@yahoo.co.inMobile: +91-9426845307

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