Data Interpretation

1. Data Interpretation Dr Amna R Siddiqui CMED 305 February 16, 2015

2. Objectives • To describe interpretation of epidemiological data • To classify the sub-group analysis based on hypothesized -exposure/risk/determinant with the -outcome/factor per study objectives • To apply the type of measure of disease occurrence and association

3. Measurements in epidemiological study • Main ideas and concepts • What is being assessed? • What are we answering? • What variables are included? • Calculations / Understanding ?

4. Data analysis • Place your objectives in front of you • Characterize the items you have mentioned in objective (to determine….) by the variables that are determining • Prevalence (mainly the 3rd yr studies are to determine prevalence of KAP; or any other factor • determinants; as hypothesized that any two factors will be associated • Outcome; as hypothesized • List the variables / determinant /sub-groups that will be compared

5. Methods: Review your methods and data • Study design – does it justify to the research question? • Study setting – internal and external validity concerns? • Sampling / inclusion / exclusion criteria; selection biases? • Subjects : demographic, socioeconomic characteristics; selection bias? • Variables: clarity of defining exposure, outcome, other variables? • Data management: information bias; how data were managed? • Data collection; questions vague, missing information? • Measurement error: instruments calibrated; data collectors trained? ; • Statistical methods: summary statistics given; appropriate statistical tests used?

6. Epidemiological Study design • Cross sectional study design: • Be cautious about associations between factors and risks and outcomes • As exposure and outcome are collected at the same time; terms like being overweight is a risk for an outcome ‘arthritis’ when data for weight and arthritis were collected at the same time. • Use ‘risk’ in case control, cohort & experimental studies

7. Selection of Study Participants : Examples (review selection criteria to assess representativeness) • “Participants in the Women’s Health Study were a random sample of all women ages 55 to 69 years derived from the state of Iowa automobile driver’s license list in 1985, which represented approximately 94% of Iowa women in that age group.... • “We aimed to select 5 controls for every case from among individuals in the study population who had no diagnosis of autism or other pervasive developmental disorders (PDD) recorded in their general practice record and who were alive and registered with a participating practice on the date of the PDD diagnosis in the case.

8. Variables: Example • “Only major congenital malformations were included in the analyses. Minor anomalies were excluded according to the exclusion list of European Registration of Congenital Anomalies (EUROCAT). • If a child had more than 1 major congenital malformation of 1 organ system, those malformations were treated as 1 outcome in the analyses by organ system ... • In the statistical analyses, factors considered potential confounders were maternal age at delivery and number of previous parities.”

9. Data Sources/Measurement: Example • “Total caffeine intake was calculated primarily using U.S. Department of Agriculture food composition sources. In these calculations, it was assumed that the content of caffeine was 137 mg per cup of coffee, 47 mg per cup of tea, 46 mg per can or bottle of cola beverage, and 7 mg per serving of chocolate candy. This method of measuring (caffeine) intake was shown to be valid in both the NHS I cohort and a similar cohort study of male health professionals... • Self-reported diagnosis of hypertension was found to be reliable in the NHS I cohort” • “Samples pertaining to cases and controls were always analyzed together in the same batch and laboratory personnel were unable to distinguish among cases and controls”

10. Measures of disease occurrence : Prevalence Comparison of smoking consumption pattern of KSU students 2009 Source: Mandil A. Bin Saeed AA, Ahmed S, Al-Dabbagh R, AlSaadi M, Khan M. Smoking among university students: A gender analysis Journal of Infection and Public Health (2010) 3, 179—187 Explain the descriptive characteristics in data

11. Descriptive data: SD=Standard Deviation Source: Humayun Q, Iqbal R, Azam I, Siddiqui AR, Khan A, Baig-ansari N. Development and validation of sunlight exposure measurement (SEM-Q) in adult population residing in Pakistan. BMC Public Health 2012, 12:421 doi:10.1186/1471-2458-12-421

12. Sun Exposure tested by interview /questionnaire and comparison with Serum Vitamin D levels and Source: Humayun Q, Iqbal R, Azam I, Siddiqui AR, Khan A, Baig-ansari N. Development and validation of sunlight exposure measurement (SEM-Q) in adult population residing in Pakistan. BMC Public Health 2012, 12:421 doi:10.1186/1471-2458-12-421

13. Mean pretreatment ALT at various liver inflammation grades ALT=Alanine transaminase Source: Mirza S, Siddiqui AR, Hamid S, Umar M, Bashir S. Extent of liver inflammation in predicting response to Interferon alpha & Ribavirin in chronic hepatitis C patients: a cohort study Journal: BMC Gastroenterology 2012 Jun 14;12:71. doi: 10.1186/1471-230X-12-71.

14. Mean levels of pretreatment ALT by inflammation grades in males & females: Source: Mirza S, Siddiqui AR, Hamid S, Umar M, Bashir S. Extent of liver inflammation in predicting response to Interferon alpha & Ribavirin in chronic hepatitis C patients: a cohort study Journal: BMC Gastroenterology 2012 Jun 14;12:71. doi: 10.1186/1471-230X-12-71.

15. Correlation: Increase in mean ALT with increase in liver inflammation grades

16. Comparison of age and pretreatment ALT and Alpha-fetoprotein tests in HCV patients with high and low grades of inflammation on liver biopsy. Statistical Tests: a:Chi Square; b: Student’s t test, c: Mann Whitney Test ALT=Alanine transaminase Source: Mirza S, Siddiqui AR, Hamid S, Umar M, Bashir S. Extent of liver inflammation in predicting response to Interferon alpha & Ribavirin in chronic hepatitis C patients: a cohort study Journal: BMC Gastroenterology 2012 Jun 14;12:71. doi: 10.1186/1471-230X-12-71.

17. Measure of association Risk Factors for Diarrhea in Children less than 5 years in Low-income Settlements in Karachi A case control study • Cases: children <5 years with diarrhea/dysentery • Controls: healthy children matched to cases on age and gender from the same community

18. Inclusion Criteria CASE • Diarrhea1, or Dysentery2of <7day • No antibiotic use within the last 7 days of enrolment • Moderate-to-severe diarrhea, defined as at least one of the following: • a. Sunken eyes, more than normal • b. Loss of skin turgor • c. Intravenous rehydration administered or prescribed CONTROL • No diarrhea within 7 days of enrollment • Should not have taken antibiotics in the previous one week • Age, gender and neighborhood matched to index case • Concomitant: within 14 days of presentation of the index case 1Defined as 3 or more abnormally loose stools during the previous 24 hours. 2 Presence of blood in stools

19. Household characteristics of diarrhea of cases and controls * Wealth index: index based on proportionate weighted sum of household assets ** Number of people in HH / Number of rooms in HH

20. Water and sanitation practices in the household of children with diarrhea and asymptomatic matched controls. *:p=0.055 (not significant; interpretation?

21. cont’d: Water and sanitation practices in the households of study participants

22. Finalizing data analysis • Writing an abstract

23. Predicting tobacco use among high school students by using the global youth tobacco survey in Riyadh, Saudi Arabia. Time Sample size missing Eligible persons Good response-low selection bias Place/setting OBJECTIVE: To identify the predictors that lead to cigarette smoking among high school students by utilizing the global youth tobacco survey in Riyadh, Kingdom of Saudi Arabia (KSA). METHODS: A cross-sectional study was conducted among high school students (grades 10-12) in Riyadh, KSA, between April 24, 2010, and June 16, 2010. RESULTS: The response rate of the students was 92.17%. The percentage of high school students who had previously smoked cigarettes, even just 1-2 puffs, was 43.3% overall. This behavior was more common among male students (56.4%) than females (31.3%). The prevalence of students who reported that they are currently smoking at least one cigarette in the past 30 days was 19.5% (31.3% and 8.9% for males and females, respectively). "Ever smoked" status was associated with male gender (OR = 2.88, confidence interval [CI]: 2.28-3.63), parent smoking (OR = 1.70, CI: 1.25-2.30) or other member of the household smoking (OR = 2.11, CI: 1.59-2.81) who smoked, closest friends who smoked (OR = 8.17, CI: 5.56-12.00), and lack of refusal to sell cigarettes (OR = 5.68, CI: 2.09-15.48). CONCLUSION: Several predictors of cigarette smoking among high school students were identified. Vague predictor Who is selling? Outcomes Defined; clear Low information bias Predictors shown by data; OR, 95% CI