Data Interpretation

Dr Amna R Siddiqui CMED 305

February 16, 2015

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

Measurements in epidemiological study

• Main ideas and concepts• What is being assessed?• What are we answering?• What variables are included?• Calculations / Understanding ?

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

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?

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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

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.

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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.”

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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”

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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

Study variables Cigarette No.

(%)

Shisha No.

(%)

Shamma No.

(%)

Others No.

(%) Total No. (%)

Gender

Male

475 (51.1) 344 (37) 40 (4.3) 61 (6.6) 929 (81.8)

Female

72 (34.8) 69 (33.3) 10 (4.8) 56 (27.1) 207 (18.2)

Total

547 (48.1) 413 (36.3) 50 (4.4) 117 (10.2) 1136 (100)

Explain the descriptive characteristics in data

Demographic Characteristics of Study Participants by Sunlight Exposure Group

Variables Sunlight exposure groups Total (n = 54) Low

(n = 19) Moderate (n = 18)

High (n = 17)

Age, yrs [Mean(SD*)]

33 (9) 31 (7) 41 (11) 35 (10)

Sex [n (%)]

Male 6 (31.6) 7 (38.9) 16 (94.1) 29 (53.7)

Female 13 (68.4) 11 (61.1) 1 (5.8) 25 (46.3)

Literacy rate [n (%)]

I lliterate 0 0 9 (52.9) 9 (16.7)

Literate 19 (100) 18 (100) 8 (47.1) 45 (83.3)

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

Descriptive data:

Variables Sunlight exposure groups

Low (n = 19) Moderate (n = 18)

High (n = 17)

Serum vitamin D (ng/ml) [Mean(SD)]

9.8 (4.7)

11.1 (4.6)

17.0 (6.5)

Time (minutes) outdoors in summer [Mean(SD)]

54.5 (30.0)

81(62.7) 331.2 (63.8)

Mean (SD) time outdoors in winters

59.7 (32.5)

89.4 (65.0)

310 (85.0)

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

Mean pretreatment ALT at various liver

inflammation grades

ALT in U/L for: Males Females Minimal inflammation (mean ± SD)

85.1 ± 59 59.1 ± 36.7

Mild inflammation (mean ± SD)

112.4 ± 74.5 85.5 ± 75.3

Moderate inflammation (mean ± SD)

129.6 ± 74.5 102.9 ± 69.5

Severe Inflammation (mean ± SD)

166 ± 81.7 137.1 ± 111.85

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.

ALT=Alanine transaminase

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.

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

Comparison of age and pretreatment ALT and Alpha-fetoprotein tests in HCV patients with high and low grades of inflammation on liver biopsy.

ALT test

Characteristics

Liver Biopsy with High grades of inflammation

(moderate and severe)

Liver Biopsy with Low grades of inflammation

(minimal and mild)

P- Value

n Test response n Test response

ALT U/L Mean (SD) In males In females

38 49

133.92(75.4) 107.85 (76.4)

241 267

98.26(68.2) 74.26 (63.2)

0.003b 0.001b

Age < 40 years ≥ 40 years

53 50

51.4% 48.5%

357 212

62.7% 37.2%

0.031a

Alfafetoprotein ng/mL

(median & IQR) 4

3.3 (0.2 – 11.7)

46

2.2 (0.2 – 16.5)

0.65c

Statistical Tests: a:Chi Square; b: Student’s t test, c: Mann Whitney TestALT=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.

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

Inclusion CriteriaCASE

• Diarrhea1, or Dysentery2 of <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

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Household characteristics of diarrhea of cases and controls

Household characteristics Cases (n = 154)n (%)

Controls (n = 268)n (%)

OR(95% CI)

Socio-economic status *

First tertile (upper) 47 (30.5) 99 (37) 1

Second tertile (middle) 58 (37) 99 (38) 1.3 (0.8-2.2)

Third tertile (lower) 49 (32) 70 (26) 1.5 (0.9-2.5)

Caretaker’s educational status

No school education 65 (42) 142 (53) 1

Primary education 27 (17.5) 63 (24)0.9 (0.5-1.5)

Secondary education 17 (11) 19 (7)

Crowding index ** 5.6 (±4.6) 5.3 (±3.5) 1.0 (0.9-1.1)

Mean no. of children < 5 years in HH (± SD)

2.25 (± 1.2) 2.42 (±1.9) 0.9 (0.8-1)

* Wealth index: index based on proportionate weighted sum of household assets** Number of people in HH / Number of rooms in HH

Water and sanitation practices in the household of children with diarrhea and asymptomatic matched controls.

Water and sanitation practices

Cases (n = 154)n (%)

Controls (n = 268)n (%) Unadjusted MOR

(95% CI)

Water source in last 2 weeks

Piped water 87 (56.5) 156 (58) 1

Bought / tanks 51 (33) 76 (28.4) 2.6 (0.9-8.0)

Public tap/ rain water/ borehole

11 (7) 9 (3.3) 1.3 (0.7-2.6)

Other sources 5 (3.2) 27 (10) 0.3 (0.1-0.9)*

Fetch drinking water everyday

No 48 (31) 50 (18.6) 1.81 (0.94-3.51)*

Yes 27 (17.5) 51 (19) 1

sometimes 79 (51) 166 (62) 0.90 (0.51-1.60)

Untreated drinking water given to child in last 2 weeks

No 110 (71) 199 (74.5) 1

Yes 44 (29) 68 (25.5) 1.1 (0.7-1.7)

*:p=0.055 (not significant; interpretation?

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

Cases (n = 154) n (%)

Controls (n = 268) n (%)

Unadjusted MOR (95% CI)

Treatment of drinking water

Yes 82 (53) 110 (41) 1

No 72 (46.7) 157 (59) 1.9 (1.2-3.0)

Sometimes 6 (4) 10 (4) 2.4 (0.6-10.0)

Method used to treat drinking water

Boil 46 (30) 74 (27) 1

Leave in the sun/alum 2 (1.3) 8 (2.6) 0.5 (0.1-2.6)

Filtration (cloth/ other filters) 38 (25) 30 (11.2) 2.7 (1.3-5.7)

No treatment 68 (44) 156 (58) 0.6 (0.4-1.0)

Method of stool disposal

Toilet 112 (73) 164 (62) 1

Bury / scatter in yard 13 (8) 14 (5) 1.4 (0.6-3.3)

Bush /open sewer /field 29 (19) 88 (33) 0.4 (0.2-0.7)

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Finalizing data analysis

• Writing an abstract

Predicting tobacco use among high school students by using the global youth tobacco survey in Riyadh, Saudi Arabia.

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 wasassociated 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.

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Eligible personsPlace/setting

Time Good response-low selection bias

Outcomes Defined; clearLow information bias Predictors shown by data; OR, 95% CI

Vague predictorWho is selling?

Sample size missing