Epidemiology: Challenges and Answers in the Context of Globalization Moyses Szklo Professor of...

Epidemiology: Challenges and Answers in the Context of

Globalization

Moyses SzkloProfessor of Epidemiology and MedicineThe Johns Hopkins Bloomberg School of

Public Health and School of Medicine

Timeless Epidemiologic Principles

(Relatively) New Developments Related to

Epidemiology*

Public Health/Epidemiologic

Challenges

• The epidemiologic triad: agent, host, environment

• Natural history of disease: primary prevention, when possible, is usually more effective than secondary (and tertiary) prevention

• Population-wide strategies are more effective than high-risk strategies

• Evaluation of natural history phase – specific risk factors

• Evidence-based Public Health

• Sensitivity Analysis

• Mathematical Models – GeoInformation Systems

• Migrant Health

• Increasing urbanization

• Demographic transition

• Public Health Vs. Academic Epidemiology

Genetic Epidemiology• Resurgence of Social Epidemiology



Epidemiology*


Challenges







• Life Course Epidemiology

• Migrant Health




*Relevant to Public Health

• Resurgence of Social EpidemiologyGenetic Epidemiology



Epidemiology*


Challenges








• Migrant Health


• Population aging




Host

Environment

Vector

Increase the host’s

resistance to the

agent (e.g.,

vaccination)Prim

ary

Preventio

n Stra

tegies

Render the environmenthostile to the agent’s or thevector’s growth (e.g., eliminate poverty, store food in cold temperatures)

Agent

Kill the agent and/or the vector (e.g., use ofpesticides, prophylacticanti-Tb therapy)

THE EPIDEMIOLOGIC TRIAD: AGENT, HOST AND ENVIRONMENT



Epidemiology*


Challenges








• Migrant Health






PRIMARY PREVENTIONSECONDARY PREVENTION

TERTIARY PREVENTION

NATURAL HISTORY

Early Detection (if possible)

Onset of exposure to risk factor(s)

Biologic onset of disease

Detection based on symptoms and signs after the beginning of the clinical phase

-or-

Detection based on symptoms or signs occurring at the beginning of the clinical phase

-or-

Delayed detection

TIME

Lev

el o

f ef

fec

tiv

enes

s

TIME



Epidemiology*


Challenges








• Migrant Health






Causal Components and Sufficient Causes* of Stroke: Hypothetical Causality Chain

*Sufficient cause: a set of minimal conditions and events that inevitably produce disease (Rothman K. Modern Epidemiology. Boston, Toronto: Little Brown and Company,1986)

mszklo

Neste exemplo, eu resolvi expandir o modelo de Rothman através da introdução do elemento de temporalidade. Eu tambem distinguo neste exemplo os diferentes tipos de causas suficientes, isto é, distais, intermediárias e proximais

STROKE PREVENTION

“In order to implement preventive measures, it is not necessary to understand the causal mechanisms in their entirety” (MacMahon B, Pugh TF, Ipsen J. Epidemiology: Principles and Methods. Boston: Little, Brown and Co., 1960, p. 18)

Stroke

Proximal Sufficient Cause

Arterial Hypertension

Genetic Susceptibility

Intermediate Sufficient Cause

Stress

High Salt Intake Obesity

Low Levels of Physical

Activity

Population-wide strategy: per capita salt intake= 33% stroke incidence ~ 22% (Law MR, et al. Br Med J 1991;302:819-24)

Low Social

Class

Distal Sufficient Cause

Excessive salt content of

processed foods

Poor access to healthy foods

mszklo

Eu não conheço todas as causas suficientes de AVC, mas eu sei que, se o consumo per capita de sal na população diminuir, a incidência de AVC tambem diminui, o que seguramente reflete a eliminação de pelo menos esta causa intermediária suficiente...O que nos reporta ao conceito do entrelaçamento de causas ou teia de causalidade de MacMahon, que --- baseado neste conceito, afirmou há quase meio século afirmou que..."A fim de implementar..."

StrokeStroke





Stress



Activity

STROKE PREVENTION

Systolic Blood Pressure

No

. o

f in

div

idu

als

Low Social

Class



processed foods


mszklo

Uma estratégia alternativa é a estratégia de alto risco. O problema com esta estratégia é que a maior parte dos casos de AVC ocorrem em pessoas com níveis de pressão arterial apenas modestamente elevados.

High risk strategy: identification, treatment and control of all patients with hypertension stroke incidence ~ 15% (Law MR, et al. Br Med J 1991;302:819-24)

Stroke


Stress



Activity

STROKE PREVENTION

BP levels with high RR, low ARPOP


No

. o

f in

div

idu

als




Low Social

Class



processed foods


mszklo


High risk strategy: identification, treatment and control of all patients with hypertension stroke incidence ~ 15% (Law MR, et al. Br Med J 1991;302:819-24)

Stroke


Stress



Activity

STROKE PREVENTION


Blood pressure levels with low RR

and high ARPOP


No

. o

f in

div

idu

als




Low Social

Class



processed foods


mszklo



Ris

k of

Str

oke

Stroke


Stress



Activity

STROKE PREVENTION




Low Social

Class



processed foods


mszklo



Stroke


Stress



Activity

STROKE PREVENTION


No

. o

f in

div

idu

als






and high ARPOP

Low Social

Class



processed foods


mszklo



Stroke


Stress



Activity

STROKE PREVENTION

BP levels high RR, low ARPOP


and high ARPOP


No

. o

f in

div

idu

als




Low Social

Class



processed foods


mszklo


Ken

ia n

omad

s

Civil servants

(Rose G, Int J Epidemiol 1985;14:32-38)


Stroke


Stress



Activity

STROKE PREVENTION


No

. o

f in

div

idu

als




Low Social

Class



processed foods


mszklo

A supremacia da estratégia populacional foi conceitualizada por Geoffrey Rose há mais ou menos 20 anos atrás, baseado na observação da diferença na distribuição da pressão arterial entre nômades de Kênia e funcionários públicos ingleses. Notem que o conceito de teia de causalidade, o modelo de Rothman e o conceito da supremacia da estratégia de prevenção a nível populacional convergem. Obviamente, este exemplo tambem coloca em evidência a importância da epidemiologia social para a prevenção e políticas de saúde.

Life Time Associated Risks of Cancer

Possible increased Possible increased risk of other cancers risk of other cancers (eg, prostate, colon)(eg, prostate, colon)

Breast cancerBreast cancer - often - often early age at onset early age at onset

(50%-85%(50%-85%)

Second primary breast Second primary breast cancercancer (40%-60%) (40%-60%)

ovarian cancerovarian cancer (15%-45%)(15%-45%)

BRCA1

breast cancerbreast cancer (50%-85%)(50%-85%)

ovarian cancerovarian cancer (10%-20%)(10%-20%)

male breast cancermale breast cancer (6%)(6%)

Increased risk of prostate, Increased risk of prostate, laryngeal, and pancreatic laryngeal, and pancreatic

cancers (magnitude cancers (magnitude unknown)unknown)

BRCA2

How Much Breast and Ovarian Cancer Is Hereditary?

SporadicSporadic

HereditaryHereditary

Ovarian CancerOvarian CancerBreast CancerBreast Cancer

5%–10%5%–10% 5%–10%5%–10%

15%15%20% 20%

GENETIC INHERITANCE:- High Relative Risk- Low Population Attributable Risk

High Risk Screening Program Sensitivity= 0.54 × 0.93 = 0.50

SENSITIVITY OF A HIGH RISK SCREENING PROGRAM FOR INCIDENT BREAST CANCER CASES DURING ONE YEAR IN A TARGET POPULATION OF 100 000 WOMEN¶

•Average yearly incidence in S. Paulo, 1998-2000 278/100 000§

¶Szklo M. J Gen Intern Med 1990; 5(Suppl):S47-S49*Hartmann et al, New Eng J Med 2005;353:229-37**Mushlin et al, Am J Prev Med 1998;14:143-53

§Câncer no Brasil- Dados do Registro de Câncer de Base Populacional. Disponível em http://www.inca.gov.br/vigilancia/CONPREV-INCA-MS e IBGE-MP

1st Phase: Sensitivity of Family History of Breast Cancer = 0.54*; 2nd Phase: Sensitivity of Mammography = 0.93**

False Negatives

True Positives

278

150

1st Phase: Sensitivity of Family History = 0.54*

128

150

2nd Phase: Sensitivity of Mammography = 0.93**

10

140

http://www.inca.gov.br/vigilancia/









mszklo

A estratégia de alto risco tambem é inferior à estratégia populacional do ponto de vista de efetividade de um programa de rastreamento.

SENSITIVITY OF A HIGH RISK SCREENING PROGRAM FOR INCIDENT BREAST CANCER CASES DURING ONE YEAR IN A TARGET POPULATION OF 100 000 WOMEN¶

•Average yearly incidence in S. Paulo, 1998-2000 278/100 000§

¶Szklo M. J Gen Intern Med 1990; 5(Suppl):S47-S49*Hartmann et al, New Eng J Med 2005;353:229-37**Mushlin et al, Am J Prev Med 1998;14:143-53

§Câncer no Brasil- Dados do Registro de Câncer de Base Populacional. Disponível em http://www.inca.gov.br/vigilancia/CONPREV-INCA-MS e IBGE-MP

1st Phase: Sensitivity of Family History of Breast Cancer = 0.54*; 2nd Phase: Sensitivity of Mammography = 0.93**

False Negatives

True Positives

278

150

1st Phase: Sensitivity of Family History = 0.54*

128

150

2nd Phase: Sensitivity of Mammography = 0.93**

10

140

Total Target Population-Wide

Screening: Sensitivity of

Mammography= 0.93

170

278

20

258

Total No. of False Negatives at the End of the 2nd Phase= 128 + 10= 138

High Risk Screening Program Sensitivity= 0.54 × 0.93 = 0.50










mszklo

A estratégia de alto risco tambem é inferior à estratégia populacional do ponto de vista de efetividade de um programa de rastreamento.

Use of Genetic Epidemiology in Public Health

• Identification of genotypes that are effect modifiers: equivalent to selective screening – two steps: (1) identification of susceptibility gene, and (2) prevention or cessation of risk factor in those with the gene.

– Possible negative consequence: shifts the responsibility of prevention to the individual [“Genetic Epidemiology: the epidemiology of the individual” (Wynder, personal communication)].

• Advantages: study of disease mechanisms, pharmacogenetics, vector resistance, etc.

ONE OF THE MAIN FOUNDATIONS OF THE POPULATION-WIDE PREVENTION STRATEGY: SOCIAL EPIDEMIOLOGY

Stroke





Stress



Activity

Excessive salt

content of

processed

foods

Distal Sufficient Cause of the Intermediate

Cause

Absence of supermarkets

selling healthy foods

GROUP LEVELExample: reduction of

salt content in processed foods

High crime rate

INDIVIDUAL LEVELINDIVIDUAL LEVEL

STROKE PREVENTION

mszklo

Neste exemplo, eu resolvi expandir o modelo de Rothman através da introdução do elemento de temporalidade. Eu tambem distinguo neste exemplo os diferentes tipos de causas suficientes, isto é, distais, intermediárias e proximais

Men Women

Neighborhood group socio-economic score

No. of events

Rate per 1000 PYs (95% CI)

No. of events

Rate per 1000 PYs (95% CI)

Whites

1 (low) 119 9.5 (7.7, 11.5) 68 3.9 (2.9, 5.1)

2 109 8.3 (6.8, 10.0) 45 2.6 (2.0, 3.6)

3 (high) 70 4.9 (3.8, 6.3) 24 1.5 (1.0, 2.3)

Blacks

1 (low) 38 9.8 (7.0, 13.7) 40 5.1 (3.7, 7.2)

2 27 7.5 (5.1, 11.0) 34 5.1 (3.6, 7.2)

3 (high) 26 6.4 (4.3, 9.4) 15 2.7 (1.6, 4.5)

Neighborhood groups correspond to 3 race-specific groups of neighborhoods defined according to summary socioeconomic scores. Group 1 (scores in the lower third): most disadvantaged neighborhood. Group 3 (highest third): most advantaged neighborhoods

*Adjusted for center and age (5-year categories. CI, confidence interval

INCIDENCE OF CORONARY EVENTS IN WHITES AND BLACKS, ARIC STUDY* (Diez Roux et al,New Engl J Med 2001;345:99-106)

(J Epidemiol Community Health 2002;56:588-594)


(Relatively) New Developments Related

to Epidemiology*


Challenges








• Migrant Health






Examples of Epidemiologic Research Findings that Influenced Policy

• Seatbelts• Active smoking• Indoor smoking (environmental tobacco smoking)• Salt content in baby foods• Disclosure of saturated fat content of food items• Radiation standards• Screening for cervical cancer

RULES OF EVIDENCE TO ASSESS THE EFFECTIVENESS OF A PREVENTIVE OR CURATIVE INTERVENTION OR PROGRAM ARE BASED ON EPIDEMIOLOGIC STUDY DESIGNS

LEVELS

• META-ANALYSIS OF RANDOMIZED CLINICAL TRIALS WITH HOMOGENEITY

• AT LEAST ONE RANDOMIZED CLINICAL TRIAL

• TEMPORAL TREND DEMONSTRATING A DRAMATIC EFFECT OF THE INTERVENTION (“NATURAL EXPERIMENT”) – E.G., INSULIN IN DIABETES

• META-ANALYSIS OF OBSERVATIONAL STUDIES WITH HOMOGENEITY • AT LEAST ONE OBSERVATIONAL STUDY (COHORT STUDIES ARE ASSIGNED A HIGHER LEVEL THAN CASE-CONTROL STUDIES)

• QUASI-EXPERIMENTAL STUDIES

• TEMPORAL TREND NOT SHOWING DRAMATIC EFFECT (WITHOUT CONTROLS)

• CASE SERIES

• EXPERT OPINION NOT BASED ON THE ABOVE STUDIES

Based on: US Task Force on Preventive Services; Canadian Task Force on Periodic Health Examination

Better

Worse

Bigby M, Szklo M: Evidence-Based Dermatology. In : Fitzpatrick’, Dermatology in General Medicine (ed. By Freedberg IM et al). Vol. II; 2003: McGraw-Hill Medical Publishing Division, New York, pp. 2301-2311, 2003

mszklo

Há uma série de grupos de critérios, entre os quais este é apenas um exemplo.

LEVELS

• META-ANALYSIS OF RANDOMIZED CLINICAL TRIALS WITH HOMOGENEITY

• AT LEAST ONE RANDOMIZED CLINICAL TRIAL

• TEMPORAL TREND DEMONSTRATING A DRAMATIC EFFECT OF THE INTERVENTION (“NATURAL EXPERIMENT”) – E.G., INSULIN IN DIABETES

• META-ANALYSIS OF OBSERVATIONAL STUDIES WITH HOMOGENEITY • AT LEAST ONE OBSERVATIONAL STUDY (COHORT STUDIES ARE ASSIGNED A HIGHER LEVEL THAN CASE-CONTROL STUDIES)

• QUASI-EXPERIMENTAL STUDIES

• TEMPORAL TREND NOT SHOWING DRAMATIC EFFECT (WITHOUT CONTROLS)

• CASE SERIES

• EXPERT OPINION NOT BASED ON THE ABOVE STUDIES

Based on: US Task Force on Preventive Services; Canadian Task Force on Periodic Health Examination

Better

Worse

Bigby M, Szklo M: Evidence-Based Dermatology. In : Fitzpatrick’, Dermatology in General Medicine (ed. By Freedberg IM et al). Vol. II; 2003: McGraw-Hill Medical Publishing Division, New York, pp. 2301-2311, 2003

RULES OF EVIDENCE TO ASSESS THE EFFECTIVENESS OF A PREVENTIVE OR CURATIVE INTERVENTION OR PROGRAM ARE BASED ON EPIDEMIOLOGIC STUDY DESIGNS

mszklo

Há uma série de grupos de critérios, entre os quais este é apenas um exemplo.

PUBLICATION BIAS: SELECTION BIAS THAT OCCURS WHEN PUBLICATION OF A PAPER REPORTING

FINDINGS OF A STUDY IS BASED ON FACTORS OTHER THAN THE QUALITY OF THE STUDY

(Chan AW et al. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA 2004;291:2457-65)

20 400-20-40

Efficacy of an intervention (%)

-60 60

SAMPLING A “POPULATION” OF STUDIES: PUBLICATION BIAS

Total population of studies

Num

ber

of s

tudi

es

Valid sample of studies

BIASED sample of studies

(Chan AW et al. CMAJ 2004;171:735-740)

Identification of published RCTs approved for funding by the Canadian Institutes of Health Research from 1990-98

105 RANDOMIZED TRIALS

Outcom e reporting b iasO dds o f pub lica tion for sta t sign if resu lt

O dds o f pub lica tion for non sta t sign if resu lt

. .*

. .* *. ( . , . )2 7 1 5 5 0

*p≤0.05**p>0.05

QUESTIONS: • IS PUBLICATION BIAS ENCOURAGED BY PEER REVIEWED JOURNALS?

PUBLICATION BIAS MAY BE THE RESULT OF THE JOURNAL’S EDITORIAL POLICY…

• ARE AUTHORS AFRAID OF REJECTION OF “NEGATIVE” REPORTS?

Factors Associated with the Odds of Publication: Multivariate Analysis (No. of studies= 285)

Factor Odds Ratio (95% Confidence Limits)

Null result 1.00 Non-significant trend 0.61 (0.23, 1.59) Statistically significant at ά=0.05 2.32 (1.25, 4.28)

Perceived importance of results by author: low

1.00

Perceived importance of results by author: high

3.50 (1.45, 8.45)

(Easterbrook et al, 1991)

Direction of findings in published trials by source of support

Source of Support No. of studies Results favored new therapy

Pharmaceutical 37 (100%) 33 (89%)

General 70 (100%) 43 (61%)

(Davidson, 1986)

Language Bias: Egger M, et al. Lancet. 1997;350:326–329.

• Randomized trials published by German investigators in either German or English journals from 1985 through 1994

• No differences in quality between German- and English-written papers

• Statistically significant results:– English-written papers: 63%– German-written papers: 35%

• Conclusion: using language as a criterion for inclusion in a systematic review or meta-analysis may result in publication biasODDS RATIO= 3.8 (95% CI= 1.3, 11.3)



Epidemiology*


Challenges








• Migrant Health






Sensitivity Analysis: a Tool for Public Health Policy

• Approach to examine the changes in the output (results) of a given model resulting from varying certain model parameters (or assumptions) over a reasonable range (Szklo

& Nieto. Epidemiology: Beyond the Basics. 2nd Edition, Jones & Bartlett, In press).

(I) Probability of event (E)

(II)

Joint probability of eligibility and event

(E) (III) = (I) × (II)

Eligible to receive vaccine S

Yes 0.90 0.10 0.90 × 0.10= 0.09

No 0.10 0.40 0.10 × 0.40= 0.04

Total 1.00 0.09 + 0.04= 0.13 or 13%

Eligible to receive vaccine N

Sim 0.70 0.05 0.70 × 0.05= 0.035

Nao 0.30 0.40 0.30 × 0.40= 0.12

Total 1.00 0.035 + 0.12= 0.155 or 15.5%

Effectiveness of vaccine N vis-à-vis vaccine S= [(0.13 – 0.155) ÷ 0.13] × 100= -19.2%

Sensitivity Analysis -- Assumption: Incidence of vaccine N* side effects is same as for vaccine S

Eligible to receive vaccine N*

Yes 0.90 0.05 0.90 × 0.05= 0.045

No 0.10 0.40 0.10 × 0.40= 0.04

Total 1.00 0.045 + 0.04= 0.085 or 8.5%

Effectiveness of vaccine N* vis-à-vis vaccine S= [(0.13 – 0.085) ÷ 0.13] × 100= +34.6%

Probability of the Event (E) that Vaccines S (standard) and N (new) Are Expected to Prevent. Assume That, In Order To Determine Eligibility,

Individuals in Whom Side Effects Occur Can be Identified in Advance

(I) Probability of event (E)

(II)

Joint probability of eligibility and event

(E) (III) = (I) × (II)

Eligible to receive vaccine S

Yes 0.90 0.10 0.90 × 0.10= 0.09

No 0.10 0.40 0,10 × 0.40= 0.04

Total 1.00 0.09 + 0.04= 0.13 or 13%

Eligible to receive vaccine N

Sim 0.70 0.05 0.70 × 0.05= 0.035

Nao 0.30 0.40 0.30 × 0.40= 0.12

Total 1.00 0.035 + 0.12= 0.155 or 15.5%

Effectiveness of vaccine N vis-à-vis vaccine S= [(0.13 – 0.155) ÷ 0.13] × 100= -19.2%

Sensitivity Analysis -- Assumption: Incidence of vaccine N* side effects is same as for vaccine S

Eligible to receive vaccine N*

Yes 0.90 0.05 0.90 × 0.05= 0.045

No 0.10 0.40 0.10 × 0.40= 0.04

Total 1.00 0.045 + 0.04= 0.085 or 8.5%

Effectiveness of vaccine N* vis-à-vis vaccine S= [(0.13 – 0.085) ÷ 0.13] × 100= +34.6%

Probability of the Event (E) that Vaccines S (standard) and N (new) Are Expected to Prevent. Assume That, In Order To Determine Eligibility,

Individuals in Whom Side Effects Occur Can be Identified in Advance



Epidemiology*


Challenges








• Migrant Health






Issues in Migrant Health

• Increased international travel is an integral component of the growing process of globalization.

• Foreign-born migrant labour force: nearly 90 million worldwide (International Labour Organization. Report VI Conference, 2004. Geneva, 2004

• Migration influences during past 50 years:– Decolonization (Africa, Middle East, Caribbean)– Large refugee movements following conflicts – Collapse of the Soviet Union

(Modified from: Gushulak BD, MacPherson DW. Emerging Themes in Epidemiology 2006;3:3-42)

The Impact of Different Health Environments for Migrants: Consequences at Destination

• Endemic diseases

• Health problems resulting from trauma (physical and psychosocial), deprivation, violence, and injury

• Use of health services and adoption of a healthy life style

• Psychologic trauma in illegal workers

(Modified from: Gushulak BD, MacPherson DW. Emerging Themes in Epidemiology 2006;3:3-42)

Incidence of Complications of Delivery in Colorado Singleton Births, 1998-1999

Complications of delivery

Undocumented migrants (n= 5,961) (%)

All other women (n=112,943) (%)

No complications 60.1 73.5

Meconium staining 11.2 4.3

Excessive bleeding 2.3 0.8

Premature rupture 1.9 2.3

Precipitous labor 2.4 1.8

Malpresentation 3.5 3.0

Cord prolapse 0.7 0.3

Fetal distress 8.7 3.6

(Reed MM, et al. BMC Public Health 2005;5:100)

Analysis of Factors Influencing the Probability of Being Treated with HAART in 553 HIV-Infected Immigrants to Italy

HAART, highly active antiretroviral therapy*Only countries outside the European Union

(Saracino A, et al. AIDS Patient Care and STDs 2005;19:599-606)

(source: Albin B, et al. European Journal of Public Health 2005;15:511-517)

Mortality Odds Ratios by Country of Birth and Sex Among Approximately 362,000 Foreign-Born Living in Sweden and

362,000 Swedish Controls, Matched by Age, Sex, Occupation and Type of Employment

Some Health Service Issues Resulting From International Population Mobility

Immediate Responses

• Provision of services and facilities (including laboratories)

• Education of health care providers in global health issues

• Development of specialized reference centers and international networks

Long Term Responses

• Provision of appropriate diagnostic and treatment services

• Translation and interpretation services

• Culture awareness and sensitivity programs and training

• Establishment of appropriate graduate, post-graduate and continuing education training

(Gushulak BD, MacPherson DW. Emerging Themes in Epidemiology 2006;3:3-42)



Epidemiology*


Challenges








• Migrant Health






Public Health in the World -- Challenges



Coexistence of poor country and rich country problems (e.g., high infant mortality rate and cardiovascular disease incidence/mortality)

50

100

150

200

250

300

350

400

450

500

550

10 20 30 40 50 60 70 80 90 100 110

o

o

o

oo

o

o

o

o

o

oo

o

o

o

o

oo

o

oo

o

o

o

Correlation between infant mortality rate (per 1,000 live births) and Age-Adjusted Mortality from Cardiovascular Diseases (per 100,000) (WHO, World Health Statistics 2006)

Infant Mortality Rate/1,000 Live Births

Age

-adj

uste

d C

ardi

ovas

clar

Mor

talit

y/10

0,00

0 P

opul

atio

n

Countries Brazil, Colombia, Congo, Cuba, Ecuador, Egypt, Estonia, Germany, France, Greece, Honduras, Hungary, Jamaica, Japan, Kwait, Mexico, Nepal, Nigeria, Pakistan, Sri Lanka, Turkey, Uganda, USA

Median

Median

50

100

150

200

250

300

350

400

450

500

550

10 20 30 40 50 60 70 80 90 100 110

o

o

o

o

o

o

oo

o

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o

o



Age

-adj

uste

d C

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clar

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talit

y/10

0,00

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opul

atio

n


Median

Median

OF 12 COUNTRIES WITH INFANT MORTALITY ABOVE THE MEDIAN, 10 (83%) HAVE A CARDIOVASCULAR MORTALITY ABOVE THE MEDIAN

50

100

150

200

250

300

350

400

450

500

550

10 20 30 40 50 60 70 80 90 100 110

oo

o

o

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o

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Age

-adj

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ovas

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talit

y/10

0,00

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opul

atio

n


OF 12 COUNTRIES WITH INFANT MORTALITY BELOW THE MEDIAN, ONLY 2 (17%) HAVE A CARDIOVASCULAR MORTALITY ABOVE THE MEDIAN

Median

Median

Worldwide Projections

Tobacco kills ~10,000 people every day, ~4 million a year.

Based on current trends this will increase to 8.4 million by 2020 (70% of deaths in developing countries).

By 2020, tobacco is predicted to cause 12% of all deaths globally (more than HIV, TB, maternal mortality, car accidents, suicide & homicide combined).

(Cubillos-Garzon LA, et al. Am Heart J 2004;147:412-7)

OBESITY IS A MAJOR RISK FACTOR FOR HYPERTENSION AND DIABETES

Prevalence of Obesity (BMI= 30+) in the Urban Population of Selected Latin American Countries

(Cubillos-Garzon LA, et al. Am Heart J 2004;147:412-7)

Prevalence of Hypertension in the Urban Population of Selected Latin American Countries

Age-Adjusted Prevalence of Diabetes in Adults in Selected Countries of the Americas (%)

(Diabetes in the Americas, PAHO Epidemiological Bulletin, June 2001;22(2))

Mexico

USA

Cuba

Brazil

51.4

16.4

14.9

13.9

12.7

12.6

11.8

8.7

8.6

8.2

7.6

7.6

7.2

7.2

7.0

5.8

4.4

Trinidad & Tobago

Jamaica

Suriname

Pima, USA

Barbados

Bolivia

Colombia

Argentina

Urban Peru*

Paraguay

Chile

Guadeloupe*

Venezuela*

PREVALENCE (%)

Prevalence of Diabetes Mellitus by Educational Level, Bolivia, 1998

Educational Level Prevalence of DM (%) Confidence Interval (95%)

None 13.1 6.0, 20.8

Elementary 8.4 7.1, 9.7

Secondary 6.8 4.8, 8.6

Technical 4.4 2.8, 5.9

TOTAL 7.2 6.2, 8.3

Low SES Obesity

Hypertension

Diabetes

High levels of LDL and VLDLCardiovascular

DiseasesDeath

(Diabetes in the Americas, PAHO Epidemiological Bulletin, June 2001;22(2))



Epidemiology*


Challenges








• Migrant Health






THE INTERFACE BETWEEN EPIDEMIOLOGY AND HEALTH

POLICY

Modified from: Tugwell et al, J Chronic Dis 38 (4)

EPIDEMIOLOGY IS RELEVANT TO ALL ASPECTS RELATED TO HEALTH POLICY AND PROGRAMS

1. BURDEN OF ILLNESS

2. ETIOLOGY

3. BURDEN OF RISK FACTORS

5. COST-EFFECTIVENESS

6. IMPLEMENTATION OF POLICY/PROGRAM

7. MONITORING OF POLICY/PROGRAM

8. REASSESSMENT OFBURDEN OF ILLNESS/RFs

4. EFFECTIVENESS OF INTERVENTION(S)

V

V

V

V

V

V

V

V

Descriptive Epidemiology

Cohort, case-control studies

Descriptive Epidemiology; surveys

Experimental

(RCTs) and non-

experimental studies

Sistematic reviews; meta-analysis

Surveillance

Descriptive

Epidemiology

EPIDEMIOLOGY’S “MODERN” SEMANTIC IS OFTEN NOT SUITABLE FOR PUBLIC HEALTH APPLICATION

Example: confounding effect

IN MANY MODERN EPIDEMIOLOGY TEXTBOOKS:

Classification of selection bias:

- Due to confounding

- Not due to confounding

Selection Bias

CASES CONTROLS

REFERENCE POPULATION

X +

X -

X +

X -

SELECTION OF THE STUDY

POPULATION (SAMPLING)

X +

X - X -

X + alc+

alc-

alc+

alc-

SELECTION OF THE STUDY

POPULATION (SAMPLING)

CASES CONTROLS

REFERENCE POPULATION

Alc +

Alc -

Alc +

Alc -

Confounding: Excessive Alcohol Consumption

and Respiratory Diseases

EPIDEMIOLOGY’S “MODERN” SEMANTIC IS OFTEN NOT SUITABLE FOR PUBLIC HEALTH APPLICATION

Example: confounding effect

Obesidade

Hipertensão Câncer de Mama

Prevenção secundária (rastreamento)Marcador de risco

associação estatística(não causal)

asso

ciaç

ão c

ausa

l

(des

conhec

ida

)

Taxas de Incidência de Enfermidade Coronariana por 1 000 Pessoas-Ano (PA), Ajustadas por Idade, Centro e Raça

Mulheres Homens

Taxa* RR** ARexp/1000 Taxa* RR** ARexp/1000

Diabetes

Sim 9.2 5.1 7.4 13.8 2.2 7.4

Não 1.8 1.0 Ref. 6.4 1.0 Ref.

(Chambless et al, Am J Epidemiol 1997;146:483-94)

*Por 1 000 PA**Risco relativo

Taxas de Incidência de Enfermidade Coronariana por 1 000 Pessoas-Ano (PA), Ajustadas por Idade, Centro e Raça

Mulheres Homens

Taxa* RR** RAdiab

/1000 Taxa* RR** RAdiab/1000

Diabetes

Sim 9.2 5.1 7.4 13.8 2.2 7.4

Não 1.8 1.0 Ref. 6.4 1.0 Ref.

(Chambless et al, Am J Epidemiol 1997;146:483-94)

*Por 1 000 PA**Risco relativoRisco atribuível em diabéticos

Epidemiology: Challenges and Answers in the Context of Globalization Moyses Szklo Professor of...

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Transcript of Epidemiology: Challenges and Answers in the Context of Globalization Moyses Szklo Professor of...