Lecture 2: Ecologic Studies and Cross-Sectional Studies Dr. Dick Menzies June 7th, 2006

Ecologic studies Basic design: Study populations: Exposures at group level Diseases (outcomes) at group level Association is between group characteristics, and disease occurrence in the group Study populations: Any level or type of grouping of subjects Country, Province, city, neighbourhood, city block KEY - group should be relatively homogeneous At least for exposure of interest Differences between groups should be more than differences within groups

Ecologic studies - exposures Exposures are measured at group level National/regional data - Per capita income, consumption of alcohol, cigarettes Reported illnesses (if 1 disease leads to another) Environmental data – climate, air pollution Census data – income, education, housing Down to city block KEY – exposures should be uniform for all of group

Ecologic studies – comparisons: Temporal – Disease changes over time Adv – same population (genetics, lifestyles) Disadv – Many other things can change Spatial – Different incidence of disease in different places Adv – same time so temporal changes less Disadv – may be many other differences

Ecologic studies – analysis: Correlation (can answer) Is there a relationship between X and Y? (agreement) Is it statistically significant? What direction (positive or negative)? Regression (can answer) Is there a relationship between X and Y? What is magnitude of effect? Y = a + bX

R = +1.0 Perfect correlation (rare)

R = +0.6 Strong correlation, but not perfect

R = -0.8 Strong correlation, but negative

R = 0 No correlation at all

R = 0 Is the correlation zero for the same reason?

Correlation – tells us about agreement of tests Regression - estimates change of X per unit change in Y Figure 10-2. Scatterplot of C1q-binding complexes and IgG-containing complexes (Adapted from N Engl J Med 1978; 298: 126.)

Ecologic study - example Skin test sensitivity to coccidiomycosis and place of residence What might be a source of error? What does it tell us an individual’s skin test?

Ecologic studies - the observation National mortality rates from Cardio-vascular disease

Ecologic study – CHD mortality National mortality rates from Cardio-vascular disease differ widely What could account for observed differences? Some plausible hypotheses: How to test these hypotheses?

Ecologic study – air pollution Observation: Rates of lung cancer lower in rural areas than urban. Why – what are plausible hypotheses: Confounding - what else might be different? How to control for this?

Ecologic studies and Tuberculosis

Decreasing coal use and decreasing TB USA: 1953 – 2003 Coal use ( ) and TB incidence ( ) .

Increasing coal use and increasing TB China: 1978 - 2004 Total coal combustion ( ) Notified cases of of TB ( ).

Ecologic studies – summary of problems Exposures – Assumed uniform for all individuals, but this is rarely true Smoking or alcohol – NOT at all Income – Depends on size of group Environmental – More uniform Fluoridation of water - ?Uniform exposure? KEY – heterogeneity within population should be less than between populations Confounding – HUGE problem. Temporal – many things change over time Spatial – many other differences between places

Cross-Sectional or Prevalence Studies Snap shot of disease and exposures at the same time in a population Like a case control study – measures disease that is present/has occurred Measures exposures at same time. Key difference - strategy to identify controls

Uses Define risk factors for a disease: Personal – demographic, life-style Medical (lipids, BP, other meds) Environmental Occupation Define prevalence of disease and of exposures Defines population impact of a given exposure. Useful for Health policy, planning health services utilization, public health programmes

Limitations Not useful for Etiologic research (cannot be sure of cause and effect) Temporal trends – increasing prevalence may reflect greater incidence, or longer duration (better survival), or changes in population (aging, or selective in – or out-migration) Problems of cross-sectional surveys in general Higher prevalence may be associated with factors because: Causes higher incidence of disease = BAD Or, longer duration = GOOD

Diseases best studied Diseases studied should be reasonably common, ie high prevalence Otherwise study will involve too many controls without condition - this is inefficient Chronic disease with long duration (Higher prevalence) Or, acute disease with very high incidence

Study Population Without specific exposures General population samples This is difficult and so not done commonly: One method is random digit dialing (telephone list) or random household selection (using GIS) Staged cluster sampling Must have a complete list of persons, communities to select sample

Study Populations Proxy General Population School populations Primary school will be more complete than high school Requires 90-100% of children in school to be representative - but still ignores older, younger, child-less Work-forces - eg Electricians, Nurses Not as representative of total population (age, SES, education, healthy worker effect) But can be used for non-occupational determinants - (as well as occupational determinants)

Study Population - Exposure based Workforce Studies Workforce studies for occupational exposures eg Asbestos workers and Lung Ca or mesothelioma Health care workers and TB But healthy worker effect And some characteristics might be quite specific to work force Special Populations Prisoners, military, mental institutions Useful for studying selected exposures in these populations

Selecting the Study Population Census survey - means survey all of the population Feasible if - you are the government or - you have a small group Sample surveys – random selection Individuals - from the entire population Need a LIST of population Cluster sampling – select population groups Need a LIST of all groups Take all persons in selected units Eg., workers on certain wards in hospitals or residents of certain neighbourhoods

Study methods - Detecting the disease Case definition: Must be very clear, Same as other published studies Include mild or asymptomatic cases ? Diagnostic method Questionnaires - “Have you been diagnosed with …?” Quicker, cheaper Validity – has questionnaire been used before?? Direct - sero-prevalence, diabetes, lipids, TST Will this be practical, feasible, acceptable Will it be valid?

Measures of Disease Occurrence - Prevalence Prevalence = number of persons with condition or disease at a given point in time Prevalence is really a ratio Numerator = number of persons with disease Denominator = all persons in population Prevalence can be expressed as: At a given point in time - eg, January 1st, 2004 Or on entry to university or military service Or can be for a period or time, eg., prevalence during medical school or a five year period of time

Specific definitions Prevalence (P) = Persons with disease/Total population – this is a ratio (NOT A RATE) Point Prevalence = number of persons with disease at a specific point in time Period Prevalence = number of persons with disease during a specific period of time Annual Prevalence = number of persons with disease over one year. Sero-Prevalence = number of persons with serologic evidence of disease or infection or exposure

Measures of Disease Occurrence - Incidence Incidence = number who develop Disease X in a population initially free of Disease X in YY time. Numerator = persons with newly developed Disease X Denominator = persons without Disease X at the beginning of the period of study Time - YY weeks, months, years Incidence = NDiseaseX in TimeYY / Ntotal Births and deaths are a form of incidence Birth rate, mortality rate

Relationship between Incidence and Prevalence Prevalence = Incidence x Duration This holds ONLY when: Incidence is stable Duration is also stable These conditions are often not true Eg., HIV – incidence is changing Duration is also changing with new effective therapy

Prevalence and incidence - examples In population in small town: Prevalence of Asthma = 6% Incidence of asthma (past 20 years) = 2/1,000/year Duration = ?? Pub Health official needs estimate of ART needed for next 5 years Incidence symptomatic HIV infection: 0.1% annually Median survival with ART: 20 years Prevalence now: 2% Prevalence in 5 years?

Types of Incidence and Prevalence Measures Rate Type Numerator Denominator Mortality rate Incidence Number of deaths from a disease (or all causes) Person-years at risk in the population Attack rate Number of new cases of a disease Total population at risk, for a limited period of observation Period prevalence Prevalence Number of existing plus all new cases during given time period Total population (at risk)

Types of Incidence and Prevalence Measures (cont’d) Rate Type Numerator Denominator Point Prevalence Prevalence Number of cases with disease at one moment in time Total population (Number of persons surveyed) Neonatal mortality rate Incidence Number of deaths in infants under 28 days of age N. live births in same period, usually per 1000 annually Infant mortality rate Number of deaths in a year of children less than 1 year of age N. live births in the same period, usually per 1000 annually

Incidence or prevalence? Number of homicides in Montreal in 2005 Number of homicide detectives in Montreal in 2005 Number of new homicide detectives hired by Mtl police force in 2005 Female homicide detectives in Montreal on Dec 31 2005

Exposure Assessment - Misclassification Often exposures measured retrospectively Major weakness in cross-sectional studies Especially diseases with long latency Or prevalent disease with long duration Inaccurate recall – random misclassification Strategies – measure current exposures Use objective measures: age, sex, BP, weight Or easily remembered Smoking history Pregnancies and children Occupation

Exposure Assessment – Recall bias Cases with disease remember exposures better Often prompted by their doctors Example - Fetal malformations – both parents spend all their time remembering things…. So – almost all exposures significantly more common in diseased Control by – measure disease directly at time of survey, and assess exposure before disease status known. (eg screening for HIV, TST) If disease ascertained by questionnaire use more objective exposure measures

Misclassification Random – poor measurement Questionnaire – memory problems Direct measurements – poor tests Reduces chance of finding real associations Non-random – biased measurement Questionnaire – recall bias Direct – rarely a problem Will produce biased estimates No, or spurious, or even reverse associations

Measures of Disease Association Prevalence Odds Ratios Summary measure of disease association in prevalence studies General formula: odds of exposure given disease odds of exposure given no disease Like case control studies, prevalence studies identify subjects on the basis of disease status.

Measures of disease association Prevalence Odds Ratio In a prevalence survey, 60 individuals were found to have diabetes out of 1,000 surveyed Prevalence of diabetes total = 6% Prevalence of diabetes among obese persons = 27/200 = 13.5% Prevalence of diabetes in non obese persons = 33/773 = 4.3% Obesity Not Obesity Totals Diabetes 27 33 60 No Diabetes 200 740 940

Prevalence Odds Ratio, cont’d Obesity Not Obesity Totals Diabetes 27 33 60 No Diabetes 200 740 940 Express the findings as prevalence odds i.e., odds of exposure if disease or, odds of obesity if diabetes = 27/33 = 0.81 Odds of obesity if not diabetes = 200/740 = 0.27 Prevalence odds ratio (POR) = 0.81/0.27 = 3.0 For cross-sectional or prevalence studies the prevalence odds ratio is the same as the ratio of the prevalence of disease in persons with and without the risk factor

POR: Effect of random mis-classification CHILDHOOD obesity and adult diabetes Child Obesity Not obese as child Total Yes No Diabetes The truth Answers 27 14 13 33 16 17 60 No diabetes 200 100 740 370 940

POR: Effect of random mis-classification CHILDHOOD obesity and adult diabetes Child obesity Not obese as child Totals Diabetes 30 60 No diabetes 470 940 Odds of childhood obesity if diabetic: 30/30 = 1.0 Odds of childhood obesity if not diabetic 470/470 = 1.0 POR = 30/30 / 470/470 = 1.0

POR: Non-random mis-classification Adult diabetics more likely to recall obesity in childhood Child Obesity Not obese as child Total Yes No Diabetes The truth Answers 27 -- 33 15 18 60 No diabetes 200 740 940

POR: Non-random mis-classification Recall bias - CHILDHOOD obesity and adult diabetes Child obesity Not obese as child Totals Diabetes 45 15 60 No diabetes 200 740 940 Odds of childhood obesity if diabetic: 45/15= 3.0 Odds of childhood obesity if not diabetic 200/740 = 0.27 POR = 3.0 / 0.27 = 11.4

“Longitudinal prevalence studies” In some cross-sectional studies inferences can be made about incidence, as if a cohort design was used When population has spectrum of years of exposure/age Tuberculin or HIV sero-prevalence survey Years of work as health professional However, this design still has same problems of retrospective exposure assessment

Longitudinal prevalence study - example Results of HIV sero-prevalence in males and females in different age groups: Sex Age groups 0-14 15-18 19-22 23-26 27-30 Males 1% 2% 4% 10% 18% Females 8% 14% 16% What can one say about incidence By age? By age and sex?

Longitudinal prevalence study - example Estimated annual incidence in each age/sex category Sex Age groups 0-14 15-18 19-22 23-26 27-30 Males 0.06% 0.25% 0.5% 1.5% 2% Females 0.12% Incidence higher in younger females, but then males ‘catch up’