epidemiologia e oncologia: uma relaÇÃo...

EPIDEMIOLOGIA E ONCOLOGIA: UMA RELAÇÃO ÍNTIMARELAÇÃO ÍNTIMA

Moysés Szklo

Professor de Epidemiologia e Medicina

Universidade Johns Hopkins

Editor-Chefe, American Journal of Epidemiology

Things have to be as

simple as possible, but

not simpler.

(Einstein)

“Tempo de antecipação”

Detecção precoce (se possível),

Início da exposição a fatores de risco

Início da enfermidade

Detecção baseada em sintomas e sinais, que ocorre com atraso depois do início da fase clínica

Detecção baseada em sintomas ou sinais que ocorrem no início da fase clínica,

Conceito Fundamental Para o Controle de Câncer: História Natural

Morte, cura ou recidiva

(Baseado em Gordis L. Epidemiology, 3rd edition)







As áreas de pesquisa em câncerse “encaixam” na história natural


Avaliação de programas de atenção a pacientes terminais

Estudos de fatores prognósticos biológicos, ambientais e terapêuticos

Estudos de fatores determinantes de atraso diagnóstico

Estudos de avaliação de programas de detecção precoce (rastreamento/ prevenção secundária)

Estudos de processo e estrutura do sistema de atenção à saúde

Estudos de qualidade de vida







Correspondência entre as diferentes fases da história natural do

câncer e níveis de controle


Prevenção secundária

(rastreamento)

Prevenção terciária• Prevenção de exposição

a fatores de risco• Cessação de exposição

Prevenção primária:

Estudos epidemiológicos

• Estudos experimentais (ensaios clinicos) e estudos observacionais




Início “da enfermidade






PrevenPrevenPrevenção ção ção secundsecundsecundáááriariaria

PrevenPrevenPrevenção tercição tercição terciáááriariaria• Prevenção de exposição

a fatores de risco• Cessação de exposição

Prevenção primária:

Prevalência de tabagismo entre adultos de 18 anos ou mais de idade e estratégias nacionais de controle de tabaco implementadas entre 1986 e 2008

O GRANDE SUCESSO DA PREVENÇÃO PRIMÁRIA

(Adaptado de Figueiredo VC [Tese de Doutorado]. RJ: Instituto de Medicina Social, UERJ, 2007)

Environment

Primary Prevention of Cancer Is Based on the Epidemiologic Triad of

Risk Factors

Agent

Human host

Vector

Environment


Risk Factors Three basic strategies in

primary prevention

- Kill the agent and/or the vector

- Make the environment hostile to the agent

- Increase human host’s resistance

Agent

Human host

Vector

Environment: air

Vector: tobacco


Risk Factors – Example: Tobacco Exposure

Example: Prohibition of indoor smoking

Example: Decrease in

Example: lobby against tobacco

industry

Agent: tobacco

Human Host: behavior

Vector: tobacco industry

Example: Health education, smoking cessation therapy

Decrease in tobacco

cultivation

Risk Factors (Causes) for Cancer Established by Epidemiologic Research: Some Examples

• Estrogen replacement therapy and breast and endometrial cancers

• Asbestos and respiratory cancer

• Smoking and cancers

• Ionizing radiation and cancers

• Down’s syndrome and leukemia

• Helicobacter pylori and gastric cancer

• HPV and cervical cancer

• Obesity and post-menopausal breast cancer

• Diet low in fibers and colon cancer

COMO SÃO GERADAS AS HIPÓTESES SOBRE FATORES DE RISCO (ETIOLOGIA) DE CÂNCER?

Epidemiologia é a arte de olhar

A melhor estratégia de

análise de dados é olhar

os dados e pensar no que os dados e pensar no que

se está vendo.

(G. W. Comstock)

Generating Hypothesis from Descriptive (Available) Population Data: Average Annual Breast Cancer Age-specific Incidence Rates per 100,000, USA 1997-

2001 (based on SEER data)

40

50

60

70

80

90

100

3-D Column 1

3-D Column 2

3-D Column 3

Inciden

ce Rates/ 100,000

0

10

20

30

40 3-D Column 3

Age 50-5430-34 75+

Inciden

ce Rates/ 100,000

The decrease in the slope at around menopause led to the hypothesis that breast cancer is estrogen-dependent, which was subsequently confirmed in analytic

epidemiologic studies

Smoking

High Salt Intake

High intake of processed foods

H. Pylori

Modelo de causalidade de Rothman

Causa suficienteConstelação de

componentes causais que, quando presente,

causa inevitavelmente a

O MODELO DE ROTHMAN EXPLICA DOIS FENÔMENOS RELACIONADOS

-POPULAÇÕES COM PREVALÊNCIA ELEVADA DE UM COMPONENTE CAUSAL OU UMA CAUSA NECESSÁRIA (FATOR DE RISCO) TÊM RISCO BAIXO DA ENFERMIDADE �� FALTAM COMPONENTES CAUSAIS QUE COMPLETEM AS CAUSAS SUFICIENTES;

- ELIMINAÇÃO DE UM FATOR DE RISCO (COMPONENTE CAUSAL) DIMINUI O RISCO DA ENFERMIDADE �� TODOS AS CAUSAS SUFICIENTES QUE CONTÊM O COMPONENTE CAUSAL SÃO ELIMINADAS

Taxa

s p

or

10

0 0

00

Taxas de mortalidade por câncer, ajustadas por idade, sexo feminino, Estados Unidos 1930-2001

(American Cancer Society, 2005)

causa inevitavelmente a doença

(American Cancer Society, 2005)

Smoking

High Salt Intake

High intake of processed foods

H. Pylori*

Causa necessária, mas não suficiente

Tempo de antecipação

Detecção precoce éfeita,





Momento em que a detecção

precoce se torna possivel

Fase pré-clínica detectável (FPCD)




••• Prevenção da exposição Prevenção da exposição Prevenção da exposição a fatores de riscoa fatores de riscoa fatores de risco

••• Cessação de exposiçãoCessação de exposiçãoCessação de exposição

Prevenção primária:Prevenção primária:Prevenção primária: Prevenção secundária

(rastreamento)

PrevenPrevenPrevenção tercição tercição terciáááriariaria














Prevenção primária:Prevenção primária:Prevenção primária: Prevenção secundária

(rastreamento)

PrevenPrevenPrevenção tercição tercição terciáááriariaria

Critical Point: A point in the natural history of the disease before which therapy may be less difficult and/or more

ANOTHER CRITICAL CONCEPT IN CANCER

CONTROL: THE CRITICAL POINT

before which therapy may be less difficult and/or more effective (If this disease is potentially curable, cure may be

possible before this point, but not after)


Biologiconset

A

Earliestpoint whendiagnosis ispossible

B D

Usualdiagnosisbased onsymptoms

Detectable Preclinical

Effective early diagnosis

Detectable PreclinicalPhase

(Adapted from Gordis,Epidemiology, 1996)

NATURAL HISTORY OF A DISEASE

When should early diagnosis be made?Critical point

Biologiconset

A


B D







Biologiconset

A


B D




NOT NECESSARY!




Biologiconset

A


B D





Critical point IN CERVICAL CANCER, THE DPCP PRIOR TO THE CRITICAL POINT IS LONG: SCREENING EVERY 3 YEARS IS FAIRLY CONSERVATIVE



Biologiconset

A


B D






Critical point IN BREAST CANCER, THE DPCP PRIOR TO THE CRITICAL POINT IS RELATIVELY SHORT:

SCREENING EVERY TWO YEARS IS REASONABLE (DPCP ∼3.3 years for post-

menopausal women; ∼1.7 years for <50 years old) (Tabar, et al, Cancer 1995;75:2507-17; Tabar et al, Int

J Cancer 1996;66:413-9)(Adapted from Gordis,Epidemiology, 1996)

Biologiconset

A


B D


Detectable PreclinicalDetectable PreclinicalPhase


Critical point IN LUNG CANCER, THE DPCP PRIOR TO THE CRITICAL POINT IS SO SHORT THAT

SCREENING IS NOT EFFECTIVE


Biologiconset

A


B D



1 2 3

Screening is ineffectivecure

Survival* 2 yrs

Survival* 5 yrs



Critical point Most diseases have multiple critical points; thus, the earlier the

diagnosis, the better the prognosis

*corrected for lead time


A2004 20101995

Diagnosis based on symptoms Death

Disease onset

Survival= 6 years

Patient

NATURAL HISTORY OF CANCER IN PATIENTS A AND B IS THE SAME

A KEY NOTION WHEN EVALUATING SURVIVAL OF PATIENTS WITH CANCER: LEAD TIME BIAS

B2002 20101995

Early diagnosis DeathDisease

onset

Survival= 8 years

Lead timePatient

Survival B = Survival A + lead time

Conclusion: Screening was not effective

A2004 20101995

Diagnosis based on symptoms Death

Disease onset

Survival= 6 years

Patient

NATURAL HISTORY OF CANCER IN PATIENTS A AND B IS DIFFERENT

A KEY NOTION WHEN EVALUATING SURVIVAL OF PATIENTS WITH CANCER: LEAD TIME BIAS

B2002 20101995

Early diagnosis Death

2012

Disease onset

Survival= 10 years

Lead timePatient Net gain

Survival B > Survival A + lead time

Conclusion: Screening was effective

100%Natural History of a

Disease: Lead Time Bias in Survival Analysis(Adapted from Frank, Am J Prev

1985;1:3-9)

40%

70%

Lead time bias

Cu

mu

lativ

e S

urv

iva

l

40%

5 years after usual diagnosis

5 years after early diagnosis

3 5 10 12

Lead Time

Usual diagnosis

Cu

mu

lativ

e S

urv

iva

l

-2Early

diagnosis


50

60

70

80

Lead-Time- Adjusted Five-Year Survival Among Breast Cancer Patients in Shapiro et al’s Randomized Clinical Trial

(Shapiro et al, JNCI 1982;69:349-55)

0

10

20

30

40

2nd QtrTotal Allocated to Screening

Control

Overdiagnosis Bias: Bleyer AB & Welch HG. Effect of Three Decades of Screening Mammography on Breast Cancer Incidence. New Eng J Med

2012;367:1998-2005.

• Effective cancer screening should increase the incidence of disease detected at an early stage and decrease late stage disease

• Examination of breast cancer incidence trends from 1976 through 2008, taking into account the transient excess incidence associated with hormone replacement therapy and adjusting for trends in the incidence of women younger than 40 years (that is, women without screening)

• During this period, incidence of early stage breast cancer doubled (an absolute increase of 122 cases per 100 000 women)

• The rate of late stage breast cancer decreased by 8% (from 102 to 94 cases per 100 000 women), corresponding to 8 cases.

• Thus, only 8 of the 122 cases were thus expected to progress to advanced disease

• It is estimated that in 2008 breast cancer was overdiagnosed in more than 70 000 women (31% of all breast cancers diagnosed)

Assumptions Justifying a Screening Program

• All or most clinical cases of a disease first go through a detectable pre-clinical phase prior to the last critical point, and

• In the absence of intervention, all or most cases in a pre-clinical phase progress to the clinical phase.phase progress to the clinical phase.

• Prognosis (recurrence free survival and survival) improve with early diagnosis

Causas de baixa efetividade de um programa de rastreamento








• Tempo de antecipação é curto (ex: câncer de pulmão)

• O tempo de antecipação é longo, mas a terapia não é eficaz

• A terapia é eficaz, mas o sistema de atenção à saúde é falho


Detecção precoce éfeita,













PrevençãoPrevençãoPrevenção primáriaprimáriaprimária::: PrevenPrevenPrevenção tercição tercição terciáááriariariaPrevençãoPrevençãoPrevençãosecundáriasecundáriasecundária: : :

rastreamentorastreamentorastreamento

EXPERIMENTAL (RANDOMIZED CLINICAL TRIAL)

Study Sample

Designs Used in Clinical Research (Clinical Epidemiology)

Random Allocation

Outcome Outcome

Follow-up

Intervention Control

100)(

)()(×

−=

GroupControlinIncidence

GrouponInterventiActiveinIncidenceGroupControlinIncidenceEfficacy

Randomized Clinical Trials of Screening for Prostate Cancer

Experimental Epidemiology: the Randomized Clinical Trial

Cumulative mortality. Schroder FH, et al. New Eng

J Med 2009;360:1320-8 (USA)

Cumulative number of deaths. Andriole GL et al, New Eng J

Med 2009;360:1310-9 (Europe)

Randomized Clinical Trial: Recurrence and Breast Cancer Mortality in 6846 Women with ER-positive Disease

Recurrence Mortality

(Davies C, Pan H, Godwin J, et al. Long term effects of continuing adjuvant tamoxifen to 10 years versus stopping at 5 years after diagnosis of oestrogen receptor-positive breast cancer:ATLAS, a randomised trial. Lancet 2012 [Electronic publication])

Two Designs Used in Clinical Research

(Clinical Epidemiology)

NON-EXPERIMENTAL (OBSERVATIONAL)

Study Sample

Non-Random Allocation

Study Sample

Random Allocation

EXPERIMENTAL (RANDOMIZED CLINICAL TRIAL)

Follow-up

Factor (+) Factor (-)

Outcome OutcomeOutcome Outcome

Follow-up

Intervention Control

Observational Study: Kaplan-Meier curves of survival after diagnosis of breast cancer. HER2 human epidermal growth factor receptor-2-positive; HR hormone receptor

(estrogen and/or progesterone); TN triple negative (estrogen, progesterone and HER2 negative)

All HR+

Stearoyl-CoA desaturase 1

(SCD1): essential regulator of fatty acid synthesis. Overexpression increases the

growth of breast

(Holder AM, Gonzalez-Angulo AM, Chen H, et al. High stearoyl-CoA desaturase 1 expression is associated with shorter survival in breast cancer patients. Breast Cancer Res Treat [epubl, Dec 2012])

HER2+TN

growth of breast cancer cell line.

It is the inverse of the difference in mortality between the control and the active intervention

groups. For example, if the mortality rate in the control group is 30% and in the active

intervention group it is 10%, the difference is 20% Thus, if for every 100 individuals, 20

deaths are prevented, to prevent 1 death, 1/0.2= 5.

How is the number that needs to be screened/treated to prevent one death calculated?

Detour: A USEFUL STATISTIC: THE NUMBER OF PEOPLE WHO HAVE TO BE TREATED TO PREVENT ONE EVENT

deaths are prevented, to prevent 1 death, 1/0.2= 5.

FOR EVERY 100 TREATED/SCREENED PERSONS 20 DEATHS ARE AVOIDED

1 AVOIDED DEATHx?

X= (100 × 1) ÷ 20= 5

• Investigate the etiology (risk factors) of disease;

• Assess effectiveness of preventive and curative strategies;

Some Key Objectives of Epidemiology(All Relevant to Cancer Control)

• Translate epidemiologic findings into public health policies.



Some Key Objectives of Epidemiology(All Relevant to Cancer Control)


TRANSLATIONAL EPIDEMIOLOGY

Aplicação da politica: planejamento

• Evidências• Obstáculos

Políticas baseadas em evidências

• Colaboração Cochrane

• Outras fontes (meta-análises publicadas em

Revisões sistemáticas

Custo-efetividade

Análise de sensibilidade

• Ensaios aleatorizados

• Estudos de coorte

• Estudos de casos e

Aquisição de evidências científicas

Processo de Implementação de Politicas de Controle do Câncer Baseadas em Evidências

• Obstáculos

• Avaliação de níveis de evidência

• Seleção de opções programáticas (análise de decisão)

• Recomendações

publicadas em revistas)

• Revisões Sistemáticas e meta-análisesrealizadas de

novo

• Estudos conduzidos de

novo

• Estudos de casos e controles

• Estudos de séries temporais

• Estudos de processo e estrutura

Tradução de conhecimentos(Modificado de Dickersin K)

Levels of Evidence on Effectiveness

• Meta-analysis of randomized trials

• Individual randomized trial of high quality

• Dramatic changes in the incidence/mortality after introduction of a given program (e.g., St Jude protocol and survival of acute lymphocytic leukemia in children)

• Meta-analysis of cohort studies

better

• Individual cohort study of high quality

• Meta-analysis of case-control studies

• Individual case-control study of high quality

• Expert opinion not based on the aboveworse

Forest plot of the effect of group counselling on the incidence of smoking abstinence. Use of nicotine patch, bupropion, and notriptyline varied among studies (Motillo S, et al. Eur Heart J 2008 [Epub ahead of print Dec 24]

Interface between epidemiology and the

decision-making process

• Meta-Analysis

• Decision Analysis

Evaluation of effectiveness


• Cost-Effectiveness Analysis

(Pettiti DB. Meta-Analysis, Decision Analysis and Cost-Effectiveness Analysis. New

York, Oxford, Oxford University Press, 1994)



• Meta-Analysis


Evaluation of effectiveness




York, Oxford, Oxford University Press, 1994

Meta-Analysis

Summary of the

efficacy/effectiveness of the

intervention

Decision Analysis

Assessment of the relative value of the

programmatic options, based on their

community effectiveness (ideally determined

by meta-analysis)

Relations between meta-analysis, decision analysis and cost-

effectiveness analysis

intervention

Cost-effectiveness Analysis

Assessment of the cost of the program, based on the relative value

of the options

Decision Analysis: uses a quantitative approach to evaluate the relative value (effectiveness) of one or more interventions, programs or services.

Steps in Decision Analysis

• Identification and description of the problem

• Collection of the information needed to construct the decision tree (ideally by means of meta-analysis) decision tree (ideally by means of meta-analysis)

• Construction of a decision tree

• Analysis of the decision tree

Decision Node

High Social Class (0.10)

Low Social Class (0.90)

SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)

Example of decision tree with two chance nodes

For those who tolerate the intervention, D has a lower

mortality than C

Decision Node



SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)


For those who tolerate the interventions, D has a lower

mortality than C

Decision Node



SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)

Example of decision tree with two chance nodes However, tolerance is better for Program C

Thus,C: better tolerance, higher mortalityD: worse tolerance, lower mortality

Table 2a – Program C: less efficacious but better drug tolerance (70%)

Tolerance? Joint probability of death

Yes 0.70 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015

0.30 x 0.90 x 0.50= 0.135

Table 2b – Program D: more efficacious, but less drug tolerance (only 30%)


Yes 0.30 ×××× 0.10 ×××× 0.05= 0.0015

0.30 ×××× 0.90 ×××× 0.10= 0.027

No 0.70 ×××× 0.10 ×××× 0.50= 0.035

0.70 ×××× 0.90 ×××× 0.50= 0.315

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

0.70 ×××× 0.90 ×××× 0.50= 0.315

0.0015+ 0.027 + 0.035 + 0.315= 0.3785 or 37.85%

Conclude: Program D is more efficacious (i.e., those who tolerate the drug have a lower mortality than in Program C), but because tolerance to Program C is higher, its community effectiveness is higher.

Community effectiveness of C (compared with D)= {[37.85% - 28.30%] ÷ 37.85%} ×100= 25.2%

THE ENDTHE END

Objectives of Epidemiology(All Relevant to Cancer Control)

• Describe the magnitude of the disease burden in the population

• Examine the distribution of the disease in the population using vital statistics data according to factors related to persons (e.g., age, gender), time and place;




Who, 2010. Global status report on noncommunicable diseases 2010

Homens195190

Mulheres189150

Número estimado de casos novos, por sexo, Brasil, 2012

Localização Primária Casos novos

%

Próstata 60180 30,8

Traqueia, Brônquios e Pulmão

17210 8,8

Localização Primária Casos novos

%

Mama 52680 27,9

Colo do útero 17540 9,3

Cólon e reto 15960 8,4

*Fonte:: MS/INCA/ Estimativa de Câncer no Brasil, 2012MS/INCA/Conprev/Divisão de Informação


Estômago 12670 6,5

Cavidade oral 9990 5,1

Esôfago 7770 4,0

Bexiga 6210 3,2

Laringe 6110 3,1

Linfoma Não-Hodgkin 5190 2,7

Cérebro, Sistema Nervoso 4820 2,5


Tireoide 10590 5,6

Traqueia, Brônquios e Pulmão

10110 5,3

Estômago 7420 3,9

Ovário 6190 3,3

Corpo do útero 4520 2,4

Cérebro, Sistema Nervoso 4450 2,4

Linfoma Não-Hodgkin 4450 2,4

Distribuição das taxas brutas por 100 000 habitantes de incidência

estimadas para o ano de 2008, em homens e mulheres, segundo os

principais tipos de câncer.

52,43

18,86

14,927,93

14,88

9,72

50,71

Próstata

Mama Feminina

Colo do Útero

Traquéia, Brônquio e Pulmão

Estômago

13,23

11,00

8,35

5,52

3,093,03

4,44

2,72

3,88

19,18

60 40 20 0 20 40 60

Cólon e Reto

Cavidade Oral

Esôfago

Leucemias

Pele Melanoma

Fonte: MS/INCA/Conprev/Divisão de Informação

Registros de Câncer de Base Populacional (RCBP)

Situação atual

Dos 32 implantados

27 RCBP estão ativos

**

*

* *

**

***

*

*

*

27 RCBP estão ativos*

*

*

*

*

*

**

**

**

**

*

Situação atual

241 RHC em atividade operacional em

40-75% das informações da bases dedados dos RHC já são transferidasautomaticamente para os RCBP pormeio da exportação do sistemaSisRHC/importação pelo BPW

Registro Hospitalar de Câncer - RHC

241 RHC em atividade operacional em CACON/UNACON

UNACON 87% tem RHC

CACON 100% tem RHC



• Examine the distribution of the disease in the population using vital statistics data according to factors related to persons(e.g., age, gender), time and place;




Taxas de incidência estimadas (não ajustadas) para os tipos de

câncer mais frequentes (exceto câncer de pele não melanoma) em

homens, Brasil e regiões geográficas, 2012*

BrasilRegiãoNorte

RegiãoNordeste

Região Centro-Oeste

Região Sudeste

Região Sul

1ºPróstata (62,54)

Próstata (29,72)

Próstata (43,08)

Próstata (74,65)

Próstata (77,89)

Próstata (68,36)

2ºTraqueia, Brônquio

e Pulmão Estômago (10,67)

Estômago (8,99)

Traqueia, Brônquio e Pulmão

Cólon e Reto (22,12)


BrasilRegiãoNorte

RegiãoNordeste


Região Sudeste

Região Sul

1ºPróstata (62,54)

Próstata (29,72)

Próstata (43,08)

Próstata (74,65)

Próstata (77,89)

Próstata (68,36)


e Pulmão Estômago (10,67)

Estômago (8,99)




PLACE and PERSON (MEN)

*por 100.000 habitantesFonte: MS/INCA/ Estimativa de Câncer no Brasil, 2011/2012

MS/INCA/Conprev/Divisão de Informação e Análise de Situação

2º e Pulmão (17,90)

(10,67) (8,99)e Pulmão (16,64)

(22,12)e Pulmão (37,02)

3ºCólon e reto

(14,75)

Traqueia, Brônquio e Pulmão (8,11)





4ºEstômago (13,20)


Cavidade Oral (6,15)

Estômago (13,84)

Estômago (15,52)

Estômago (15,72)

5ºCavidade Oral

(10,41)Leucemias

(3,54)Cólon e Reto

(5,31)Cavidade Oral

(8,58)Cavidade Oral

(14,61)Esôfago (15,27)

2º e Pulmão (17,90)

(10,67) (8,99)e Pulmão (16,64)

(22,12)e Pulmão (37,02)

3ºCólon e reto

(14,75)






4ºEstômago (13,20)


Cavidade Oral (6,15)

Estômago (13,84)

Estômago (15,52)

Estômago (15,72)

5ºCavidade Oral

(10,41)Leucemias

(3,54)Cólon e Reto

(5,31)Cavidade Oral

(8,58)Cavidade Oral

(14,61)Esôfago (15,27)

Leukaemia

BrasilRegião Norte

Região Nordeste


Região Sudeste

Região Sul

1ºMama feminina

(52,50)Colo do útero

(23,62)Mama feminina




(64,80)

2ºColo do útero




(27,71)Cólon e Reto


(19,85)

BrasilRegião Norte

Região Nordeste


Região Sudeste

Região Sul

1ºMama feminina






(64,80)

2ºColo do útero






(19,85)

Taxas de incidência estimadas (não ajustadas) para os tipos de câncer mais

frequentes (exceto câncer de pele não melanoma) em mulheres, Brasil e

regiões geográficas, 2012*

Breast

PLACE and PERSON (WOMEN)

2º(17,49) (19,38) (17,96) (27,71) (23,01) (19,85)

3ºCólon e Reto

(15,94)Glândula Tireoide

(7,34)Cólon e Reto

(6,66)Cólon e Reto


(15,53)


4ºGlândula Tireoide

(10,59)Estômago


(6,01)


Glândula Tireoide (15,02)

Colo do útero (13,88)


e Pulmão (10,08)



Estômago (6,76)



2º(17,49) (19,38) (17,96) (27,71) (23,01) (19,85)

3ºCólon e Reto


(7,34)Cólon e Reto

(6,66)Cólon e Reto


(15,53)


4ºGlândula Tireoide

(10,59)Estômago


(6,01)



Colo do útero (13,88)


e Pulmão (10,08)



Estômago (6,76)



Thyroid

*por 100.000 habitantesFonte: MS/INCA/ Estimativa de Câncer no Brasil, 2011/2012

MS/INCA/Conprev/Divisão de Informação e Análise de Situação




• Investigate the etiology (risk factors) of disease:



• Case-control studies• Cohort studies







• Pre-post studies• Quasi-experimental studies• Follow-up studies

- Prognostic studies- Randomized Clinical Trials

1. Estudo Pré-Pós

Mo

rta

lida

de

Inferências causais relacionadas a esse desenho são robustas no caso de

“experimentos naturais” (exemplo: insulina, estreptomicina)

ESTUDOS BASEADOS EM SÉRIES TEMPORAIS UTILIZANDO DADOS DE VIGILÂNCIA

Mo

rta

lida

de

Tempo

Introdução do programa

Outros programas? Mudanças nas características da população alvo?

A= intervençãoB= controle

2. Estudo Quasi-experimental (Pré-Pós com Controles)

ESTUDOS BASEADOS EM SÉRIES TEMPORAIS UTILIZANDO DADOS DE VIGILÂNCIA

Mo

rta

lida

de

A B

A B

Seguimento

(Adapted by Ibrahim M from D. Gillings et al. Am J Pub Hlth 71(1)38-46)

20

30

40

50 (1)(2)

(3)

(4)

(5)Taxas de

mortalidade

perinatal

Antes de o programa ser implementado,

a área que recebeu a intervenção tinha

taxas the mortalidade perinatal mais

elevadas do que a área controle, o que

pode explicar porque foi selecionada para

receber a intervenção

10

20

01935 1945 1955 1965 1975 1985

Intervenção

ControlePrograma

Year

(Adapted by Ibrahim M from D. Gillings et al. Am J Pub Hlth 71(1)38-46)

20

30

40

50 (1)(2)

(3)

(4)

(5)

O aspecto mais importante no estudo quasi-experimental é a comparação dos ângulos

Taxas de

mortalidade

perinatal

10

20

01935 1945 1955 1965 1975 1985

Year

Intervenção

ControlePrograma

Follow-up Studies (Prognostic Studies and RCTs)

time

Initialsample

Finalsample

Events (death, recurrence)

Losses to follow-up

Factor (+)

INCIDENCEFACTOR (+)

time

time

Initialsample

Finalsample

Events (death, recurrence)

Losses to follow-up

Factor (-)

INCIDENCEFACTOR (-)

= RR

Age-Adjusted Death Rates/100 000, Relative Risks, and Attributable Risks for Lung Cancer—Current Cigarette Smokers Vs Nonsmokers, Cancer Prevention Study I and CPS-II*

CPS-I (1959-1965) CPS-II (1982-1988)

Nonsmoker CurrentSmoker

Nonsmoker CurrentSmoker

MenMen

Rate 15.7 187.1 14.7 341.3

Relative Risk 1.0 11.9 1.0 23.2

Women

Rate 9.6 26.1 12.0 154.6

Relative Risk 1.0 2.7 1.0 12.8

BC= beta caroteneAT= alpha tocopherol

Às vezes, resultados de estudos epidemiológicos de prevenção primária desapontam...

Cumulative Incidence of Lung Cancer by Intervention Allocation (Albanes D, et al. JNCI 1996;88:1560-70)

Forest plot of the effect of group counselling on the incidence of smoking abstinence. Use of nicotine patch, bupropion, and notriptyline varied among studies (Motillo S, et al. Eur Heart J 2008 [Epub ahead of print Dec 24]



• Meta-Analysis

• Decision AnalysisEvaluation of community

effectiveness• Decision Analysis



York, Oxford, Oxford University Press, 1994)

effectiveness



• Meta-Analysis

• Decision AnalysisEvaluation of community

effectiveness• Decision Analysis



York, Oxford, Oxford University Press, 1994

effectiveness

Meta-Analysis

Summary of the

efficacy/effectiveness of the

intervention

Decision Analysis

Assessment of the relative value of the

programmatic options, based on their

community effectiveness (ideally determined

by meta-analysis)

Relations between meta-analysis, decision analysis and cost-

effectiveness analysis

intervention

Cost-effectiveness Analysis

Assessment of the cost of the program, based on the relative value

of the options

Decision Node



SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)


For those who tolerate the intervention, D has a lower

mortality than C

Decision Node



SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)


For those who tolerate the interventions, D has a lower

mortality than C

Decision Node



SC




SC

Mortality (0.10)

Mortality(0.20)

Mortality (0.50)

Mortality (0.50)

Mortality (0.05)

No(0.30)

Yes (0.70)

Tolerance to

intervention



SC



SC

Mortality (0.05)

Mortality (0.10)

Mortality (0.50)

Mortality (0.50)

Tolerance to

intervention

Yes (0.30)

No(0.70)

Example of decision tree with two chance nodes However, tolerance is better for Program C

Thus,C: better tolerance, higher mortalityD: worse tolerance, lower mortality

Table 2a – Program C: less efficacious but better drug tolerance (70%)


Yes 0.70 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015

0.30 x 0.90 x 0.50= 0.135

Table 2b – Program D: more efficacious, but less drug tolerance (only 30%)


Yes 0.30 ×××× 0.10 ×××× 0.05= 0.0015

0.30 ×××× 0.90 ×××× 0.10= 0.027

No 0.70 ×××× 0.10 ×××× 0.50= 0.035

0.70 ×××× 0.90 ×××× 0.50= 0.315

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

0.70 ×××× 0.90 ×××× 0.50= 0.315

0.0015+ 0.027 + 0.035 + 0.315= 0.3785 or 37.85%

Conclude: Program D is more efficacious (i.e., those who tolerate the drug have a lower mortality than in Program C), but because tolerance to Program C is higher, its community effectiveness is higher.

Community effectiveness of C (compared with D)= {[37.85% - 28.30%] ÷ 37.85%} ×100= 25.2%

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 certain model parameters (or assumptions) over a reasonable range (Szklo & Nieto. Epidemiology: Beyond the Basics.

2nd Edition, Jones & Bartlett, 2006).

Table 2a – Program C: less efficacious, but with better tolerance (70%)


Yes 0.7 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015

Sensitivity Analysis: Assume that tolerance to the intervention in Program D is increased to 50%

0.30 x 0.90 x 0.50= 0.135

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

Table 2c – Program D with tolerance improved to 50%


Yes 0.50 ×××× 0.10 ×××× 0.05= 0.0025

0.50 ×××× 0.90 ×××× 0.10= 0.045

No 0.50 ×××× 0.10 ×××× 0.50= 0.025



Yes 0.7 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015


0. 50 ×××× 0.90 ×××× 0.50= 0.225

0.0025 + 0.045 + 0.025 + 0.225= 0.2975 or 29.75%

0.30 x 0.90 x 0.50= 0.135

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

Community effectiveness of C (vis-a-vis D)= {[29.75% - 28.30%] ÷ 29.75%} × 100= 4.9%

Program C is still a bit more effective than Program D, but if the cost of D is lower, it may be cost-effective to implement Program D

(Before: 37.85%)

FIMFIM

Risk Factors for Brain Tumors in Subjects Aged <20 years: A Case-Control Study

(Gold et al, Am J Epidemiol 1979;109:309-19)

• Exploratory study of risk factors for brain tumors

• Subjects < 20 yrs old

• Cases: primary malignant brain tumors in Baltimore in 1965-75

• Normal controls: chosen from birth certificates on file, and matched on cases by sex, • Normal controls: chosen from birth certificates on file, and matched on cases by sex, date of birth (±1 year) and race

• Interviews with parents of children

• Exposed: children with birthweight equal or above the median (3629 g)

• Unexposed: children with birthweight below the median

• Odds ratio= 2.6

Exposed(1) Investigator

selects

Cohort (prospective) study

(2) Follow-up from present to future

INCIDENCEEXP

INCIDENCE

= RR

Unexposed

2009

selects present to future

2029

INCIDENCEUNEXP

Non-concurrent cohort (prospective) study (also

known as historical cohort study

Follow-up from past to present:

(2) Investigatorreconstructs cohort:

2009

(1) At presentinvestigator has

access to databasecreated in 1988

1989

Exposed

Unexposed

cohort:

“Follow-up”from ’89-’09

INCEXP

INCUNEXP

Exposed

Unexposed

2009

Follow-up from present to future

2029

CONCURRENT

INCEXP

INCUNEXP

NON-CONCURRENT

2009

Follow-upfrom ’89-’09

1989

Exposed

Unexposed

NON-CONCURRENT

INCEXP

INCUNEXP

Both studies startin year 2009

Exposed

Unexposed

2009

Follow-up from present to future

2029

CONCURRENT

INCEXP

INCUNEXP

NON-CONCURRENT

2009

Follow-upfrom ’89-’09

1989

Exposed

Unexposed

NON-CONCURRENT

INCEXP

INCUNEXP

In both studies, the initial selection (or classification) is based on the exposure, and the outcome (unknown

variable) is incidence

Example of Non-concurrent Prospective Study (Yeh et al, Am J Epidemiol 2001;153:749-56)

• Study subjects identified by investigators in 2000 as having been seen in a Clinic for Prevention of Deafness in Washington County, MD, from 1940-60, for elimination of nasopharyngeal lymphoid tissues.

• Exposed: Individuals who had nasopharyngeal radium • Exposed: Individuals who had nasopharyngeal radium treatment (n=808).

• Unexposed: Individuals who received other treatments (e.g., tonsillectomy) (n=1819).

• Outcome: Cancer Incidence

• Follow-up through 1995.

Design of Study Done by Yeh et al, Am J

Epidemiol 2001;153:749-56

FOLLOW-UP:35 YEARS

FOLLOW-UP THROUGH 1995

Adjustment for variable follow-up times: Rate per

person-years and Survival analysis

Study done in 2000

19601940

Reconstructionof cohort

1995

FOLLOW-UP: 55 YEARS

Survival analysis

Time

Relative Risk of Cancer Incidence (95% Confidence Interval) According to Exposure to Nasopharyngeal Radium Therapy,

Washington County, Maryland 1940-95

Cancer type

No. cases in exposed group (PY= 31,005)

No. cases in unexposed group

(PY= 65,502)

Adjusted Relative Risk (95% CI)

All neoplasms, 41 83 1.02 (0.7, 1.5)All neoplasms, except skin

41 83 1.02 (0.7, 1.5)

Oral cavity, pharynx, and thyroid

4 2 4.22 (0.38, 46.6)

(Yeh et al, Am J Epidemiol 2001;153:749-56)

Biologically plausible

800 200 500 500

80 100 50 250

180 300

18% 30%

Intervention No intervention

No. Deaths:

Mortality:

Absent, mortality = 10%

Present, mortality = 50%

Confounding variable:

One of the solutions to eliminate confounding: stratify

Mortality according to the intervention, stratified by the confounder

Confounding variable:

Intervention: N No. of deaths

Mortality

Present Yes 200 100 50.0%

No 500 250 50.0%

Absent Yes 800 80 10.0%

No 500 50 10.0%

Confounder

Exposure

Outcome

CONFOUNDING EFFECT

` and not in the causality pathway between ` and not in the causality pathway between exposure and outcome:

Confounder

Exposure

Outcome

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 certain model parameters (or assumptions) over a reasonable range (Szklo & Nieto. Epidemiology: Beyond the Basics.

2nd Edition, Jones & Bartlett, 2006).



Yes 0.7 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015


0.30 x 0.90 x 0.50= 0.135

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

Table 2c – Program D with tolerance improved to 50%


Yes 0.50 ×××× 0.10 ×××× 0.05= 0.0025

0.50 ×××× 0.90 ×××× 0.10= 0.045

No 0.50 ×××× 0.10 ×××× 0.50= 0.025



Yes 0.7 ×××× 0.10 ×××× 0.10= 0.007

0.70 × 0.90 × 0.20= 0.126

No 0.30 × 0.10 × 0.50= 0.015


0. 50 ×××× 0.90 ×××× 0.50= 0.225

0.0025 + 0.045 + 0.025 + 0.225= 0.2975 or 29.75%

0.30 x 0.90 x 0.50= 0.135

0.007 + 0.126 + 0.015 + 0.135= 0.283 or 28.30%

Community effectiveness of C (vis-a-vis D)= {[29.75% - 28.30%] ÷ 29.75%} × 100= 4.9%

Program C is still a bit more effective than Program D, but if the cost of D is lower, it may be cost-effective to implement Program D

(Before: 37.85%)







• (Optional): Sensitivity analysis

DECISION TREE

• Decision node: under the investigator’s control

• Chance (or probability) node: not under the investigator’s control

Decision Node

Program AChance node

Outcome*

Outcome*

Outcome*

Example of a Decision Tree

High Social Class

Low Social Class

High Social Class

Program BChance node

Outcome*

Outcome*

*For example, mortality

Low Social Class

Decision Node

Program AChance node

High Social Class

Low Social Class

Mortality (0.20)

Mortality (0.40)

Example of Decision Tree (Probabilities)

(0.10)

(0.90)

Decision Node

Program B Chance node

High Social Class

Low Social Class

Mortality (0.90)

Mortality (0.90)

(0.10)

(0.90)

Note that the distribution of social class is same in both programs, as they are being considered for the same target population

Decision Node

Chance node

High Social ClassMortality (0.20)


(0.10)

Program A

Decision Node


High Social Class

Low Social Class

Death (0.90)

Survival (0.10)

Death (0.90)

Survival (0.10)What is the joint probability of high social class (HSC) and mortality in program A?

Proportion HSC × Mort. A = 0.10 × 0.20= 0.02

Decision Node

Chance node

Low Social Class

Mortality (0.40)


(0.90)

Program A

Decision Node


High Social Class

Low Social Class

Death (0.90)

Survival (0.10)

Death (0.90)

Survival (0.10)

(0.10)

(0.90)What is the joint probability of low social class (LSC) and mortality in program A?

Proportion LSC × Mort. A = 0.90 × 0.40= 0.36

Decision Node

Chance node

High Social Class

Low Social Class

Mortality (0.20)

Mortality (0.40)


(0.10)

(0.90)

Program A

Decision Node


High Social Class

Low Social Class

Death (0.90)

Survival (0.10)

Death (0.90)

Survival (0.10)

(0.10)

(0.90)What is the joint mortality of all individuals in program A?

Mort. HSC + LSC= (0.10 × 0.02) + (0.90 × 0.40)= 0.02 + 0.36= 0.38

Decision Node

Chance node

High Social Class

Low Social Class

Mortality (0.20)

Mortality (0.40)


(0.10)

(0.90)

Program A

Decision Node

Chance node

High Social Class

Low Social Class

Mortality (0.90)

Mortality (0.90)

(0.10)

(0.90)

Program B

Decision Tree: Program A versus Program B

Program A

Social Class Proportion in Each Social Class

Mortality in Each Social Class

Overall Mortality

High 0.10 0.20 0.10 × 0.20 = 0.02

Low 0.90 0.40 0.90 × 0.40 = 0.36

Total 0.02 + 0.36 = 0.38

Program BProgram B

Social Class Proportion in Each Social Class

Mortality in Each Social Class

Overall Mortality

High 0.10 0.90 0.10 × 0.90 = 0.09

Low 0.90 0.90 0.90 × 0.90 = 0.81

Total 0.09 + 0.81 = 0.90

Community effectiveness of A versus B*= [(0.90 – 0.38) / 0.90] × 100 = 57.8%

* That is, treating program A as the “experimental” program and program B as the “control” program

2. ETIOLOGYIdentify and assess possible causes of disease and risk factor burden

1. BURDEN OF ILLNESSDetermine health status

(mortality, prevalence, incidence, years of potential life lost, etc.

6. REASSESSMENTReassessment of magnitude of burden of illness or risk factor

INTERFACE BETWEEN EPIDEMIOLOGY AND PUBLIC

HEALTH POLICY

INTERFACE BETWEEN EPIDEMIOLOGY AND PUBLIC HEALTH POLICY

factor burden

3. COMMUNITY EFFECTIVENESSAssess benefit/harm ratio of potentially feasible interventions and estimate reduction of burden of illness if programs are effective

4. COST-EFFECTIVENESSDetermine relationships between costs and effectiveness of options within and across programs

5. MONITORING OF PROGRAMOngoing monitoring using markers selected to indicate success

HEALTH POLICY

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

Other Cancer ControlsLung,

Non-tumor ControlsOther,

Non-cancer Controls

Case Control Case Control Case Control

Smokers 236(a) 666 (b) Smokers 236 124 Smokers 236 481

Non-122(c) 722 (d)

Non-122 110

Non-122 612

Odds ratio= (ad) / (bc)

Non-smokers

122(c) 722 (d)Non-

smokers122 110

Non-smokers

122 612

OR= (236 × 722) ÷ (666 × 122)= 2.10 OR = 1.72 OR = 2.46

95% CI: 1.6-2.7 95% CI: 1.2-2.4 95% CI: 1.9-3.2

Levin ML, Goldstein H and Gerhardt PR: Cancer and Tobacco Smoking. A Preliminary Report. JAMA, 143:336-338, 1950.

Objectives of Epidemiology




• Assess effectiveness of preventive strategies;

• Translate epidemiologic findings into public health policies

(INCA-MS Estimativas 2006 – Incidencia de Cancer no Brasil)

Objectives of Epidemiology




• Assess effectiveness of preventive strategies.

Decision Tree with Multiple Chance Nodes

Decision tree for the treatment of high blood pressure based on 52 hypertensive patients. Values besides each outcome health state are median and inter-quartile range. CVE, cardiovascular event (newly diagnosed angina,

myocardial infarction, coronary heart disease, stroke or transient ischemic attack) (Montgomery AA, et al. Shared decision making in hypertension. Family Practice 2001;18:309-313).

2. ETIOLOGY AND EFFICACY

Identify risk factors and assess efficacy of primary


(mortality, incidence, etc.)



HEALTH POLICY

INTERFACE BETWEEN EPIDEMIOLOGY AND CANCER CONTROL POLICY

assess efficacy of primary and secondary prevention

strategies

4. COST-EFFECTIVENESSDetermine relationships between costs and effectiveness across

programs

5. MONITORING OF PROGRAM (SURVEILLANCE)Ongoing monitoring

HEALTH POLICY


3. COMMUNITY EFFECTIVENESSAssess effectiveness in the target

community





HEALTH POLICY





programs


HEALTH POLICY



community


strategies





HEALTH POLICY



Identify risk factors and assess efficacy of


programs


HEALTH POLICY



community

assess efficacy of primary and secondary prevention strategies





HEALTH POLICY





programs


HEALTH POLICY



community


strategies





HEALTH POLICY




4. COST-EFFECTIVENESSDetermine relationships

between costs and effectiveness across programs


HEALTH POLICY



community


strategies





HEALTH POLICY





programs

5. MONITORING OF PROGRAM (SURVEILLANCE)

Ongoing monitoring

HEALTH POLICY



community


strategies



6. REASSESSMENTReassessment of magnitude of burden of illness or risk

factor


HEALTH POLICY





programs


HEALTH POLICY



community


strategies

Aplicação da politica: planejamento

• Evidências• Obstáculos

Políticas baseadas em evidências

• Colaboração Cochrane

• Outras fontes (meta-análises publicadas em

Revisões sistemáticas

Custo-efetividade

Análise de sensibilidade

Vigilância

• Ensaios aleatorizados

• Estudos de coorte

• Estudos de casos e

Aquisição de evidências científicas

Magnitude das DCNT

Processo de Implementação de Politicas de Controle do Câncer Baseadas em Evidências

• Obstáculos

• Avaliação de níveis de evidência

• Seleção de opções programáticas (análise de decisão)

• Recomendações

publicadas em revistas)

• Revisões Sistemáticas e meta-análisesrealizadas de

novo

• Estudos conduzidos de

novo:

• Estudos de casos e controles

• Estudos de séries temporais

• Estudos de processo e estrutura

: análise de tendências

Tradução de conhecimentos(Modificado de Dickersin K)

• Hipóteses causais

• Avaliação de intervenções, políticas e programas

• Avaliação de niveis epidêmicos (inclusive “clusters)

Cumulative incidence

Prostate Cancer Deaths in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial

(Andriole GL et al, New Eng J Med 2009;360:1310-9)

Cumulative incidence

Prostate Cancer Deaths in the European Randomized Study of Screening for Prostate Cancer (Schroder FH, et al. New Eng J Med 2009;360:1320-8)

(Holder AM, Gonzalez-Angulo AM, Chen H, et al. High stearoyl-CoA desaturase 1 expression is associated with shorter survival in breast cancer patients. Breast Cancer Res Treat [epubl, Dec 2012])

Assumption Justifying a Screening Program

• In the absence of intervention, all or most cases in a pre-clinical phase progress to the clinical phase (may not be true for certain cancers; e.g., prostate)

Detecção baseada em sintomas ou





sintomas ou sinais que ocorrem no início da fase clínica,

possivel


Fase pré-clínica não detectável

Cura ou Morte

Fase clinica

(INSERT DATA FROM BREAST CANCER (Bleyer A, Welch HG. Effect of three decades of screening mammography on breast cancer incidence. New Eng J Med 2012;367:21)

• Proteção contra a fumaça do tabaco e proibição de fumo em ambientes públicos

• Alertas com relação aos perigos do uso de tabaco• Implementação da proibição de propaganda, promoção e patrocínio

de eventos por tabaco• Aumento de impostos de produtos de tabaco• Restrição de acesso a álcool em negócios• Implementação de proibição de propaganda de bebidas alcólicas• Aumento de impostos de bebidas alcólicas• Redução de consumo de sal e quantidade de sal em alimentos

Algumas atividades de prevenção primáriacusto-efetivas sugeridas por estudos epidemiologicos (OMS)

• Redução de consumo de sal e quantidade de sal em alimentos processados

• Substituição de gorduras “trans” por gorduras poli-não saturadas• Conscientização do público sobre importância de dieta saudável e

atividade física, inclusive através de meios de comunicação • Controle de glicemia em pacientes com 30 anos ou menos com

diabetes se o risco de eventos agudos cardiovasculares for ≥30% nos próximos 10 anos

• Aspirina para prevenção de infarto agudo do miocárdio• Mamografia a cada 2 anos para mulheres de 50 a 70 anos• Detecção precoce de câncer colo-retal

epidemiologia e oncologia: uma relaÇÃo...

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