BIOSTATISTICS IN MEDICINE & PUBLIC HEALTH.pptx

An introduction class on biostatistics and its use in medicine & public health

1. INTRODUCTION TO BIOSTATISTICS

2. “ Statistics is the science which deals with collection, classification and tabulation of numerical facts as the basis for explanation, description and comparison of phenomenon”. ------ Lovitt

3. “ BIOSTATISICS ” (1) Statistics arising out of biological sciences, particularly from the fields of Medicine and public health. (2) The methods used in dealing with statistics in the fields of medicine, biology and public health for planning, conducting and analyzing data which arise in investigations of these branches.

4. Sources of Medical Uncertainties Intrinsic due to biological, environmental and sampling factors Natural variation among methods, observers, instruments etc. Errors in measurement or assessment or errors in knowledge Incomplete knowledge

5. Intrinsic variation as a source of medical uncertainties Biological due to age, gender, heredity, parity, height, weight, etc. Also due to variation in anatomical, physiological and biochemical parameters Environmental due to nutrition, smoking, pollution, facilities of water and sanitation, road traffic, legislation, stress and strains etc., Sampling variation because the entire world cannot be studied and at least future cases can never be included Chance variation due to unknown or complex to comprehend factors

6. Natural variation despite best care as a source of uncertainties In assessment of any medical parameter Due to partial compliance by the patients Due to incomplete information in conditions such as the patient in coma

7. Medical Errors that cause Uncertainties Errors in methods such as in using incorrect quantity or quality of chemicals and reagents, misinterpretation of ECG, using inappropriate diagnostic tools, misrecording of information etc. Instrument error due to use of non-standardized or faulty instrument and improper use of a right instrument. Not collecting full information Inconsistent response by the patients or other subjects under evaluation

8. Incomplete knowledge as a source of Uncertainties Diagnostic, therapeutic and prognostic uncertainties due to lack of knowledge Predictive uncertainties such as in survival duration of a patient of cancer Other uncertainties such as how to measure positive health

9. Biostatistics is the science that helps in managing medical uncertainties

10. Reasons to know about biostatistics: Medicine is becoming increasingly quantitative. The planning, conduct and interpretation of much of medical research are becoming increasingly reliant on the statistical methodology. Statistics pervades the medical literature.

11. CLINICAL MEDICINE Documentation of medical history of diseases. Planning and conduct of clinical studies. Evaluating the merits of different procedures. In providing methods for definition of “normal” and “abnormal”.

12. Role of Biostatistics in patient care In increasing awareness regarding diagnostic, therapeutic and prognostic uncertainties and providing rules of probability to delineate those uncertainties In providing methods to integrate chances with value judgments that could be most beneficial to patient In providing methods such as sensitivity-specificity and predictivities that help choose valid tests for patient assessment In providing tools such as scoring system and expert system that can help reduce epistemic uncertainties

13. PREVENTIVE MEDICINE To provide the magnitude of any health problem in the community. To find out the basic factors underlying the ill-health. To evaluate the health programs which was introduced in the community (success/failure). To introduce and promote health legislation.

14. Role of Biostatics in Health Planning and Evaluation In carrying out a valid and reliable health situation analysis, including in proper summarization and interpretation of data. In proper evaluation of the achievements and failures of a health programme

15. Role of Biostatistics in Medical Research In developing a research design that can minimize the impact of uncertainties In assessing reliability and validity of tools and instruments to collect the infromation In proper analysis of data

16. Example: Evaluation of Penicillin (treatment A) vs Penicillin & Chloramphenicol (treatment B) for treating bacterial pneumonia in children< 2 yrs. What is the sample size needed to demonstrate the significance of one group against other ? Is treatment A is better than treatment B or vice versa ? If so, how much better ? What is the normal variation in clinical measurement ? (mild, moderate & severe) ? How reliable and valid is the measurement ? (clinical & radiological) ? What is the magnitude and effect of laboratory and technical error ? How does one interpret abnormal values ?

17. WHAT DOES STAISTICS COVER ? Planning Design Execution (Data collection) Data Processing Data analysis Presentation Interpretation Publication

18. BASIC CONCEPTS Data : Set of values of one or more variables recorded on one or more observational units Categories of data 1. Primary data: observation, questionnaire, record form, interviews, survey, 2. Secondary data: census, medical record,registry Sources of data 1. Routinely kept records 2. Surveys (census) 3. Experiments 4. External source-research studies

19. TYPES OF DATA QUALITATIVE DATA DISCRETE QUANTITATIVE CONTINOUS QUANTITATIVE

20. QUALITATIVE Nominal Example: Sex ( M, F) Exam result (P, F) Blood Group (A,B, O or AB) Color of Eyes (blue, green, brown, black)

21. ORDINAL Example: Response to treatment (poor, fair, good) Severity of disease (mild, moderate, severe) Income status (low, middle, high)

22. QUANTITATIVE (DISCRETE) Example: The no. of family members The no. of heart beats The no. of admissions in a day QUANTITATIVE (CONTINOUS) Example: Height, Weight, Age, BP, Serum Cholesterol and BMI

23. Discrete data -- Gaps between possible values Continuous data -- Theoretically , no gaps between possible values Number of Children Hb

24. CONTINUOUS DATA QUALITATIVE DATA wt. (in Kg.) : under wt, normal & over wt. Ht. (in cm.): short, medium & tall

25. Table 1 Distribution of blunt injured patients according to hospital length of stay

26. Scale of measurement Qualitative variable: A categorical variable Nominal (classificatory) scale   - gender, marital status, race Ordinal (ranking) scale   - severity scale, good/better/best

27. Scale of measurement Quantitative variable : A numerical variable: discrete; continuous Interval scale : Data is placed in meaningful intervals and order. The unit of measurement are arbitrary. - Temperature (37 º C -- 36º C; 38º C-- 37º C are equal) and No implication of ratio (30º C is not twice as hot as 15º C)

28. Ratio scale: Data is presented in frequency distribution in logical order. A meaningful ratio exists. - Age, weight, height, pulse rate - pulse rate of 120 is twice as fast as 60 - person with weight of 80kg is twice as heavy as the one with weight of 40 kg.

29. Scales of Measure Nominal – qualitative classification of equal value: gender, race, color, city Ordinal - qualitative classification which can be rank ordered: socioeconomic status of families Interval - Numerical or quantitative data: can be rank ordered and sizes compared : temperature Ratio - Quantitative interval data along with ratio: Temperature scale .

30. CLINIMETRICS A science called clinimetrics in which qualities are converted to meaningful quantities by using the scoring system. Examples : (1) Apgar score based on appearance, pulse, grimace, activity and respiration is used for neonatal prognosis. (2) Smoking Index: no. of cigarettes, duration, filter or not, whether pipe, cigar etc., (3) APACHE( Acute Physiology and Chronic Health Evaluation) score: to quantify the severity of condition of a patient

31. THANK YOU