A quick survey of A quick survey of Epidemiology and its Epidemiology and its

methodsmethods

Seminar by:Seminar by:

Diego VillarrealDiego Villarreal

OutlineOutline

What is epidemiology and what is it What is epidemiology and what is it used forused for

A quick glance at statistical A quick glance at statistical hypothesishypothesis

The normal distribution and p-valuesThe normal distribution and p-values Example of an epidemiological Example of an epidemiological

survey conducted in Indiasurvey conducted in India Concluding remarksConcluding remarks

What is Epidemiology?What is Epidemiology?

Epidemiology is often defined as the study of Epidemiology is often defined as the study of factors that determine the occurrence and factors that determine the occurrence and distribution of diseases in a populationdistribution of diseases in a population

Epidemiology is used to determine the course of Epidemiology is used to determine the course of medical treatments, public health and scientific medical treatments, public health and scientific research.research.

Areas in which epidemiology plays an important Areas in which epidemiology plays an important role: Nutrition, Environmental Health, HIV etc…role: Nutrition, Environmental Health, HIV etc…

Jekel J.F, Katz D., Elmore J. Epidemiology, Biostatistics, and Preventive Medicine. W.B Saunders Company. Philadelphia USA 2001.

Epidemiology is often divided into two Epidemiology is often divided into two different groups:different groups:

Classical Epidemiology and Clinical Classical Epidemiology and Clinical EpidemiologyEpidemiology

Classical Epidemiology- population oriented, Classical Epidemiology- population oriented, studies the risk factors associated with a studies the risk factors associated with a population. (HIV, Pb level in blood etc…)population. (HIV, Pb level in blood etc…)

Clinical Epidemiology- Studies patients in Clinical Epidemiology- Studies patients in health care settings in order to improve health care settings in order to improve the diagnosis and treatment of various the diagnosis and treatment of various diseases. ( Drugs, nutrition etc.)diseases. ( Drugs, nutrition etc.)

How do we know that the observations we How do we know that the observations we make of a specific population are correct? make of a specific population are correct?

Are they always the same? Are they always the same?

Can we quantify our “Correctness” Can we quantify our “Correctness”

Epidemiologists use tools such as Epidemiologists use tools such as probability and statistics to assess and probability and statistics to assess and validate their findings.validate their findings.

A little Probability and StatisticsA little Probability and Statistics

Before testing a hypothesis, we must set up the hypothesis Before testing a hypothesis, we must set up the hypothesis in a quantitative manner.in a quantitative manner.

The measurements done in epidemiological studies must be The measurements done in epidemiological studies must be a number of some sort. (i.e. number of patients that did not a number of some sort. (i.e. number of patients that did not receive a drug and died, mean blood pressure in HIV receive a drug and died, mean blood pressure in HIV patients, Pb levels of children in India etc…)patients, Pb levels of children in India etc…)

In statistics, there are usually two types of variables; In statistics, there are usually two types of variables; Continuous and Discrete. Continuous and Discrete.

Continuous Variables can assume an infinite number of Continuous Variables can assume an infinite number of values (Pb levels, blood pressure, age, height)values (Pb levels, blood pressure, age, height)

Discrete variables can only assume a fixed number of Discrete variables can only assume a fixed number of numerical values ( Sex, Pregnancy etc…)numerical values ( Sex, Pregnancy etc…)

Montgomery C.D, Runger C.G. Applied Statistics and Probability for Engineers. Jon Wiley & Sons. New York NY: 2003.

The Null HypothesisThe Null HypothesisA hypothesis that is tested statistically is called a A hypothesis that is tested statistically is called a

Null hypothesis (HNull hypothesis (Hoo).).

The null hypothesis usually takes the form of a The null hypothesis usually takes the form of a “no difference hypothesis”, and we try to reject it “no difference hypothesis”, and we try to reject it with the gathered data. with the gathered data.

Example:Example:

We want to test the efficiency of a drug that We want to test the efficiency of a drug that reduces the death rates in two different groups.reduces the death rates in two different groups.

HHoo: Death rate group A = Death rate group B : Death rate group A = Death rate group B or or HHo o : Death rate group A- Death rate group B = 0: Death rate group A- Death rate group B = 0

The null hypothesis is tested against an The null hypothesis is tested against an Alternative hypothesis HAlternative hypothesis HAA

HHAA: Death rate A –Death rate B ≠0: Death rate A –Death rate B ≠0

The HThe HOO is rejected if there is an observed is rejected if there is an observed difference in the death rates of both difference in the death rates of both groups. groups.

By rejecting HBy rejecting Hoo we automatically accept H we automatically accept HA A

Notice that HNotice that HAA does not specify whether does not specify whether Death rate A > Death rate BDeath rate A > Death rate B

Wassertheil-Smoller S. Biostatistics and Epidemiology. Springer. New York NY: 2004.

Errors Associated with accepting or Errors Associated with accepting or rejecting a hypothesis:rejecting a hypothesis:

Rejecting the Null hypothesis incorrectly- Type I Rejecting the Null hypothesis incorrectly- Type I ErrorError

Failing to reject the Null hypothesis- Type II ErrorFailing to reject the Null hypothesis- Type II Error

TRUE STATE OF NATURE

Drug has no effect (Ho True)

Drug Has Effect; Ho False, Ha Ture

Decision on Basis of SampleDo Not Reject Ho No Error

Type II Error

Reject Ho (Accept Ha)

Type I Error No Error

It is important to understand that we can never It is important to understand that we can never eliminate the risk of making one of these errors. eliminate the risk of making one of these errors.

However we may lower the probability of making However we may lower the probability of making these errors. these errors.

The probability of making a Type I error is known The probability of making a Type I error is known as the as the Significance Level of a Statistical TestSignificance Level of a Statistical Test

Due to the intrinsic nature of the Null hypothesis, Due to the intrinsic nature of the Null hypothesis, by lowering the probability of a Type I error you by lowering the probability of a Type I error you increase the probability of a Type II errorincrease the probability of a Type II error

To lower both probabilities, one must increase To lower both probabilities, one must increase sample size. sample size.

The most widely used model for the The most widely used model for the distribution of a random variable is the distribution of a random variable is the normal distributionnormal distribution, which is described by , which is described by the following mathematical expression:the following mathematical expression:

Where:Where:

µ = Mean µ = Mean

σσ = Standard Deviation = Standard Deviation

It is important to notice the following facts:It is important to notice the following facts:

68%68% of the observations fall within of the observations fall within 1 standard deviation1 standard deviation of the of the meanmean, , that is, between that is, between µ-µ- σσ and and µ +µ + σσ . .

95%95% of the observations fall within of the observations fall within 2 standard deviations2 standard deviations of the of the meanmean, , that is, between that is, between µ-µ- 2 2σσ and and µ+µ+22σσ . .

99.7%99.7% of the observations fall within of the observations fall within 3 standard deviations3 standard deviations of the of the meanmean, that is, between , that is, between µ-µ- 3 3σσ and and µ+µ+ 3 3σσ . .

1)( dxxf

P-valuesP-values

The The pp-value is an index of the strength of -value is an index of the strength of the evidence with regard to rejecting the the evidence with regard to rejecting the null hypothesis. null hypothesis.

The The p-p-value gives us an idea of whether or value gives us an idea of whether or not our data arises from mere chance, or not our data arises from mere chance, or is indeed reliable and “true”is indeed reliable and “true”

By convention, if a By convention, if a p-p-value > 0.05, we say value > 0.05, we say that the result is NOT statistically that the result is NOT statistically significant, therefore accepting the Null significant, therefore accepting the Null hypothesis. hypothesis.

Blood lead levels in Bombay:Blood lead levels in Bombay:A Case StudyA Case Study

As stated at the beginning, Epidemiology studies As stated at the beginning, Epidemiology studies the occurrence and distribution of diseases in a the occurrence and distribution of diseases in a population.population.

In 2002 a study was done to compare the blood In 2002 a study was done to compare the blood lead levels (BLL) of children in Bombay after the lead levels (BLL) of children in Bombay after the use of lead in gasoline was prohibited. use of lead in gasoline was prohibited.

The data collected was compared against existing The data collected was compared against existing data from 1997 (before Pb was prohibited). data from 1997 (before Pb was prohibited).

In 1997 the Georges Foundation conducted a study of 291 In 1997 the Georges Foundation conducted a study of 291 children (ages 6-10) in Bombay to test the BLL of children in children (ages 6-10) in Bombay to test the BLL of children in metropolitan areas in Indiametropolitan areas in India

This study showed the following:This study showed the following: 61.8% (n=180) had BLL > 10 61.8% (n=180) had BLL > 10 μμg/dLg/dL

14.7% (n=43) had BLL > 20 14.7% (n=43) had BLL > 20 μμg/dLg/dL2.7% (n=8) had BLL > 30 2.7% (n=8) had BLL > 30 μμg/dLg/dL

0.6% (n=2) had BLL > 40 0.6% (n=2) had BLL > 40 μμg/dLg/dL

The study also pointed out that the mean BLL was in the The study also pointed out that the mean BLL was in the range of 8.6-14.4 range of 8.6-14.4 μμg/dLg/dL

Concentrations of lead in air in various locations ranged Concentrations of lead in air in various locations ranged from 0.10 – 1.18 from 0.10 – 1.18 μμg/mg/m33

At the time of this study, seasonal variations (monsoon vs. At the time of this study, seasonal variations (monsoon vs. non-monsoon season) were not studied. non-monsoon season) were not studied.

Nichani, V; Li-W.I; Smith M.A; Noonan G; Kulkarni M.; Kodavor M; Naeher L.P; Science of the Total Environment 2005 (In Press).

In the 2002 study, measurements were done in two In the 2002 study, measurements were done in two different campaigns (Monsoon and non-monsoon season)different campaigns (Monsoon and non-monsoon season)

A total of 754 children under 12 yrs were sampled. A total of 754 children under 12 yrs were sampled.

276 (36.6%) during non-monsoon season and 478 (63.4%) 276 (36.6%) during non-monsoon season and 478 (63.4%) during monsoon season.during monsoon season.

BLL were measured using an ESA Lead Care Portable BLL were measured using an ESA Lead Care Portable Analyzer.Analyzer.

The study locations were Panchseel Hospital (Mulund) and The study locations were Panchseel Hospital (Mulund) and low socioeconomic areas in Mulund and Thane. This was low socioeconomic areas in Mulund and Thane. This was done to include children with different socioeconomic status done to include children with different socioeconomic status (SES). (SES).

SES was determined by parental occupation and SES was determined by parental occupation and geographic location. geographic location.

The dependent variable used in the The dependent variable used in the analysis was BLL.analysis was BLL.

Independent variables were age, sex, SES, Independent variables were age, sex, SES, and season. and season.

Sex, SES and season were treated as Sex, SES and season were treated as discrete variables, while age and BLL were discrete variables, while age and BLL were treated as continuous variables.treated as continuous variables.

Since the distribution of BLL was not Since the distribution of BLL was not normal, the data was normalized using a normal, the data was normalized using a Box-Cox transformation. Box-Cox transformation.

Results:Results:

Sample t-tests showed that BLL’s Sample t-tests showed that BLL’s differences across SES were significant differences across SES were significant (t=-5.9; (t=-5.9; pp<0.0001), with lower SES having <0.0001), with lower SES having higher BLLhigher BLL

BLL’s across seasons were also statistically BLL’s across seasons were also statistically significant (t=5.4, significant (t=5.4, pp <0.0001). Higher BLLs <0.0001). Higher BLLs in the monsoon season. in the monsoon season.

BLL between boys and girls were not BLL between boys and girls were not statistically significant (t=1.1, statistically significant (t=1.1, pp = 0.28) = 0.28)

Age is associated with increasing BLLs to a Age is associated with increasing BLLs to a small degree (small degree (pp = 0.014). = 0.014).

Nichani, V; Li-W.I; Smith M.A; Noonan G; Kulkarni M.; Kodavor M; Naeher L.P; Science of the Total Environment 2005 (In Press).

From the study done in India many things can be concluded.From the study done in India many things can be concluded.

1. Eliminating the Pb from gasoline is extremely important 1. Eliminating the Pb from gasoline is extremely important in in lowering the BLL of individuals.lowering the BLL of individuals.

2. Children with lower SES are more 2. Children with lower SES are more susceptible to blood susceptible to blood poisoning.poisoning.3. During monsoon season, the BLL’s of children tend to be 3. During monsoon season, the BLL’s of children tend to be higher.higher.

4. Developing countries around the world MUST prohibit the use 4. Developing countries around the world MUST prohibit the use of Pb in gasoline in order to secure the health of their citizens.of Pb in gasoline in order to secure the health of their citizens.

ConclusionConclusion

Epidemiology is a great tool in order to Epidemiology is a great tool in order to shape public health policyshape public health policy

Statistics help epidemiologists determine Statistics help epidemiologists determine whether or not observations within a whether or not observations within a population are relevant and significantpopulation are relevant and significant

Epidemiological results MUST be used not Epidemiological results MUST be used not only by scientists and doctors, but by only by scientists and doctors, but by politicians in order to have a healthy and politicians in order to have a healthy and productive society.productive society.

QUESTIONS?!?!?!QUESTIONS?!?!?!