Epidemiology of Non-communicable Diseases Raymark D. Salonga, RN MPH Course Outcomes • Identify the major known determinants of chronic disease and their distribution by country, community, and key demographic characteristics both in the Philippines and globally. • Assess how the social, political and economic environment shape policy and other responses to major non-communicable disease whilst examining the actions necessary to control key noncommunicable diseases Course Outcomes • Assess the organization, delivery, and financing of health services in the Philippines and globally influence the provision of health care aimed at the prevention, management, and treatment of chronic disease. • Analyze the factors that influence public health’s approach to addressing chronic disease, including health impact; availability of health data; political, economic, and cultural influences; and feasibility. Course Outcomes • Apply systematic approaches to develop, implement, and evaluate programs to prevent the occurrence of chronic disease and to improve quality of life for persons with these conditions. Learning Outcomes • Explore and explain the trends and patterns in the burden of noncommunicable disease, both globally and in the Philippines. • Critically discuss the epidemiology and risk factors of major non communicable diseases, globally and in the Philippines • Examine how disease burden is measured and critique the strengths and weaknesses of the various methods. Learning Outcomes • Critical understanding of health inequalities relating to key noncommunicable diseases. • Critically assess the importance of the social, political and economic environment in shaping policy and other responses to major noncommunicable disease. • Critically examine the actions necessary to control key noncommunicable diseases. • Propose possible strategies for disease prevention as applicable and discuss appropriate social determinants of diseases. Basic Epidemiologic Principles 1. 2. 3. 4. Principles of Disease Causation Study Designs for Epidemiology Measures of Disease Frequency/Occurrence Estimates of Association Principles of Disease Causation • Epidemiology is the study of the distribution and determinants of health-related states or events in specified populations, and the application of this study to the control of health problems • Epidemiology is often described as the basic science of public health Principles of Disease Causation • Measure disease frequency • Quantify disease • Assess distribution of disease • Who is getting disease? • Where is disease occurring? • When is disease occurring? Formulation of hypotheses concerning causal and preventive factors • Identify determinants of disease • Hypotheses are tested using epidemiologic studies Principles of Disease Causation Epidemiology is often described as the basic science of public health… • epidemiology is a quantitative discipline that relies on a working knowledge of probability, statistics, and sound research methods • it is a method of causal reasoning based on developing and testing hypotheses grounded in other fields of science • It provides the foundation for directing practical and appropriate public health action based on this science and causal reasoning Principles of Disease Causation A critical premise of epidemiology is that disease and other health events do not occur randomly in a population, but are more likely to occur in some members of the population than others because of risk factors that may not be distributed randomly in the population. Principles of Disease Causation Epidemiologic Triad Principles of Disease Causation Epidemiologic Triad • The triad consists of an external agent, a susceptible host, and an environment that brings the host and agent together. • Development of appropriate, practical, and effective public health measures to control or prevent disease usually requires assessment of all three components and their interactions. Principles of Disease Causation Epidemiologic Triad • Agent originally referred to an infectious microorganism or pathogen: a virus, bacterium, parasite, or other microbe. Generally, the agent must be present for disease to occur • Over time, the concept of agent has been broadened to include chemical and physical causes of disease or injury. Principles of Disease Causation Epidemiologic Triad • Host refers to the human who can get the disease. A variety of factors intrinsic to the host, sometimes called risk factors, can influence an individual's exposure, susceptibility, or response to a causative agent • Opportunities for exposure are often influenced by behaviors such as sexual practices, hygiene, and other personal choices as well as by age and sex. Principles of Disease Causation Epidemiologic Triad • Environment refers to extrinsic factors that affect the agent and the opportunity for exposure. • Environmental factors include physical factors such as geology and climate, biologic factors such as insects that transmit the agent, and socioeconomic factors such as crowding, sanitation, and the availability of health services. Principles of Disease Causation Epidemiologic Triad • What are the limitations of Epidemiologic Triad as a model for disease causation for non-communicable diseases? Principles of Disease Causation Principles of Disease Causation Component causes and causal pies (Rothman, 1976) Principles of Disease Causation Component causes and causal pies (Rothman, 1976) • The individual factors are called component causes. • The complete pie, which might be considered a causal pathway, is called a sufficient cause. A disease may have more than one sufficient cause, with each sufficient cause being composed of several component causes that may or may not overlap. • A component that appears in every pie or pathway is called a necessary cause, because without it, disease does not occur. Principles of Disease Causation Component causes and causal pies: Types of Causal Relationships • Necessary and sufficient – without the factor, disease never develops • Necessary but not sufficient – the factor in and of itself is not enough to cause disease • Sufficient but not necessary – the factor alone can cause disease, but so can other factors in its absence • Neither sufficient nor necessary – the factor cannot cause disease on its own, nor is it the only factor that can cause that disease Principles of Disease Causation • For each group, draw two complete/sufficient causal pies for lung cancer. • Identify the necessary causes for lung cancer Principles of Disease Causation For each of the following risk factors and health outcomes, identify whether they are necessary causes, sufficient causes, or component causes. _____ Hypertension / Stroke _____ Treponema pallidum / Syphilis _____ Type A personality / Heart disease _____ Skin contact with a strong acid /Burn Measures of Disease Frequency/Occurrence • Incidence is the number of new cases of a disease occurring in an atrisk population during a defined time interval Example: The incidence of kidney disease in a community is 30 new cases for this year Measures of Disease Frequency/Occurrence • Incidence proportion - the number of new cases of a disease occurring divided by the total number of people at risk Example: The incidence proportion of kidney disease in a community is 3 new cases per 10 adults Measures of Disease Frequency/Occurrence • Incidence rate/density - The numerator is the same as the numerator of incidence proportion but the denominator accumulates time at risk of the event. The denominator is not just the number of people at risk. Example: In a study of breast cancer, an individual who was followed for 5 years will contribute 5 person-years of follow-up to the denominator, while another individual with 3 years of follow-up will contribute 3 years to the denominator Incidence rate of breast cancer = 25 per 10,000 person-years Measures of Disease Frequency/Occurrence • Prevalence - the proportion of population with the disease Example: global prevalence of lung cancer was 32% (1.86 billion people). Measures of Disease Frequency/Occurrence • Prevalence - the proportion of population with the disease - It measures the extent (amount) of the event (disease) in the population in a specified time - The numerator includes both new and existing cases of disease Measures of Disease Frequency/Occurrence • A chronic, incurable disease, such as diabetes, can have a ???? incidence but ???? prevalence. • A short-duration, curable disease, such as the common cold, can have a ???? incidence but ???? prevalence Study Designs for Epidemiology • Disease is not randomly distributed throughout a population • Epidemiology uses systematic approach to study the differences in disease distribution in subgroups • Allows for study of causal and preventive factors Study Designs for Epidemiology • Measure disease frequency • Quantify disease • Assess distribution of disease • Who is getting disease? • Where is disease occurring? • When is disease occurring? Formulation of hypotheses concerning causal and preventive factors • Identify determinants of disease • Hypotheses are tested using epidemiologic studies Study Designs for Epidemiology • Descriptive studies (to generate hypotheses) • Case-reports • Case-series Study Designs for Epidemiology • Analytic studies (to test hypotheses) • Experimental studies • Clinical trials • Field trials • Intervention studies • Observational studies • Cross sectional (can also be descriptive) • Case-control studies • Cohort studies • Ecologic studies Study Designs for Epidemiology •Types by timeframe • Prospective Study - looks forward, looks to the future, examines future events, follows a condition, concern or disease into the future • Retrospective Study - “to look back”, looks back in time to study events that have already occurred Descriptive Study: Case Reports • Detailed presentation of a single case or handful of cases • Generally report a new or unique finding • e.g. previous undescribed disease • e.g. unexpected link between diseases • e.g. unexpected new therapeutic effect • e.g. adverse events Descriptive Study: Case Series • Experience of a group of patients with a similar diagnosis (more than 2) • Assesses prevalent disease • Cases may be identified from a single or multiple sources • Generally report on new/unique condition • May be only realistic design for rare disorders Descriptive Studies • Advantages • Useful for hypothesis generation • Informative for very rare disease with few established risk factors • Characterizes averages for disorder • Disadvantages • Cannot study cause and effect relationships • Cannot assess disease frequency Analytic Study: Experimental • treatment and exposures occur in a “controlled” environment • planned research designs • clinical trials are the most well known experimental design. Clinical trials use randomly assigned data. • Community trials use nonrandom data Analytic Study: Experimental • investigator can “control” the exposure • akin to laboratory experiments except living populations are the subjects • generally involves random assignment to groups • clinical trials are the most well known experimental design Analytic Study: Experimental • In an experiment, we are interested in the consequences of some treatment on some outcome. • The subjects in the study who actually receive the treatment of interest are called the treatment group. • The subjects in the study who receive no treatment or a different treatment are called the comparison group. Analytic Study: Experimental • Randomized Controlled Trials (RCTs) • a design with subjects randomly assigned to “treatment” and “comparison” groups • provides most convincing evidence of relationship between exposure and effect • not possible to use RCTs to test effects of exposures that are expected to be harmful, for ethical reasons outcome RANDOMIZATION Intervention no outcome Study population outcome Control no outcome baseline future time Study begins here (baseline point) Analytic Study: Experimental • Randomized Controlled Trials (RCTs) • the “gold standard” of research designs • provides most convincing evidence of relationship between exposure and effect Analytic Study: Intervention Studies 1. Pretest-posttest control-group design -samples are randomized into two groups: one intervention and one control group; both were tested before and after intervention 2. Posttest-only control-group design -samples are randomized into two groups: one intervention and one control group; both were tested after intervention 3. Non-randomized comparison group pretest-posttest -samples are allocated into intervention and control groups but not randomly; both were tested before and after intervention 4. Time-series quasi-experimental design / interrupted time-series -one or two groups are tested in multiple time points Analytic Study: Cross-sectional • An “observational” design that surveys exposures and disease status at a single point in time (a cross-section of the population) Analytic Study: Cross-sectional factor present No Disease factor absent Study population factor present Disease factor absent time Study only exists at this point in time Analytic Study: Cross-sectional • Often used to study conditions that are relatively frequent with long duration of expression (nonfatal, chronic conditions, e.g. non-communicable diseases) • It measures prevalence, not incidence of disease • Example: community surveys • Not suitable for studying rare or highly fatal diseases or a disease with short duration of expression Analytic Study: Cross-sectional • Disadvantages • Weakest observational design, (it measures prevalence, not incidence of disease). • The temporal sequence of exposure and effect may be difficult or impossible to determine • Usually don’t know when disease occurred • Rare events a problem. Quickly emerging diseases a problem Analytic Study: Case-Control • an “observational” design comparing exposures in disease cases vs. healthy controls from same population • exposure data collected retrospectively • most feasible design where disease outcomes are rare factor present Cases (disease) factor absent Study population factor present Controls (no disease) factor absent present past time Study begins here Analytic Study: Case-Control • Strengths • Less expensive and time consuming • Efficient for studying rare diseases • Limitations • Inappropriate when disease outcome for a specific exposure is not known at start of study • Exposure measurements taken after disease occurrence (We do not know the time period between exposure and disease) Analytic Study: Cohort Study • an “observational” design comparing individuals with a known risk factor or exposure with others without the risk factor or exposure • looking for a difference in the risk (incidence) of a disease over time • best observational design • data usually collected prospectively (some retrospective) disease Factor present Study population free of disease Factor absent no disease disease no disease present future time Study begins here Prospective Cohort study Exposed Outcome Non-exposed Outcome Measure exposure and confounder variables Baseline time Study begins here Retrospective Cohort study Exposed Outcome Non-exposed Outcome Measure exposure and confounder variables Baseline time Study begins here Analytic Study: Cohort Study • Strengths • Exposure status determined before disease detection • Subjects selected before disease detection • Can study several outcomes for each exposure • Limitations • Expensive and time-consuming • Inefficient for rare diseases or diseases with long latency • Loss to follow-up Study Designs for Epidemiology ?????? ?????? ?????? ?????? Study Designs for Epidemiology ____ 1. Representative sample of residents were telephoned and asked how much they exercise each week and whether they currently have (have ever been diagnosed with) heart disease. ____ 2. Occurrence of cancer was identified between April 1991 and July 2002 for 50,000 troops who served in the first Gulf War (ended April 1991) and 50,000 troops who served elsewhere during the same period. ____ 3. Persons diagnosed with new-onset Lyme disease were asked how often they walk through woods, use insect repellant, wear short sleeves and pants, etc. Twice as many patients without Lyme disease from the same physician's practice were asked the same questions, and the responses in the two groups were compared. ____ 4. Subjects were children enrolled in a health maintenance organization. At 2 months, each child was randomly given one of two types of a new vaccine against rotavirus infection. Parents were called by a nurse two weeks later and asked whether the children had experienced any of a list of side-effects Measures of Association The findings for most epidemiologic studies can be presented in the 2x2 table Disease Yes No Total Yes a b a+b No c d c+d a+c b+d a+b+c+d Exposure Total Measures of Association Cohort Study: the outcome measure is the relative risk (or risk ratio or rate ratio) • In cohort studies you begin with the exposure of interest and then determine the rate of developing disease • RR measures the likelihood of getting the disease if you are exposed relative to those who are unexposed • RR = incidence in the exposed/incidence in the unexposed Measures of Association Case-control study: the outcome measure is an estimate of the relative risk or the odds ratio (relative odds) • In a case-control study, you begin with disease status and then estimate exposure • RR is estimated because patients are selected on disease status and we cannot calculate incidence based on exposure • The estimate is the odds ratio (OR) or the likelihood of having the exposure if you have the disease relative to those who do not have the disease Measures of Association • If the risk/odds ratio is 1, it suggests no difference in risk/odds (incidence in each group is the same). • A risk/odds ratio > 1 suggests an increased risk/odds of that outcome in the exposed group. The outcome is associated to the exposure and it is a risk factor. • A risk/odds ratio < 1 suggests a reduced risk/odds in the exposed group. The outcome is associated to the exposure and it is a protective factor. • The confidence interval tells us if the association is statistically significant Measures of Association • If the risk/odds ratio is 1, it suggests no difference in risk/odds (incidence in each group is the same). • A risk/odds ratio > 1 suggests an increased risk/odds of that outcome in the exposed group. The outcome is associated to the exposure and it is a risk factor. • A risk/odds ratio < 1 suggests a reduced risk/odds in the exposed group. The outcome is associated to the exposure and it is a protective factor. • The confidence interval and p-value tell us if the association is statistically significant Measures of Association Table1. Relationship between Variables and Obesity in Preschool Children (2–5 Years) Variable Odds Ratio 95% Confidence Interval P-value # of hours of sleep at night Parent’s BMI 0.89 0.75-0.92 0.001 1.89 1.56-2.02 0.01 High Fat diet 1.10 0.89-1.15 0.10 High caloric intake 2.21 1.50-2.40 0.002 # of hours of physical activity per week 0.72 0.70-1.01 0.06 Measures of Association • A cohort study examined the association between smoking and lung cancer after following 400 smokers and 600 non-smokers for 15 years. At the conclusion of the study the investigators found a risk ratio = 17 (95% CI = 15.0-18.0) for smoking. What is the interpretation of this risk ratio? • A study is done to examine whether there is an association between the daily use of vitamins C & E and risk of coronary artery disease (heart attacks) over a 10 year period. When subjects who took both vitamins were compared to those who took not vitamins at all, the risk ratio was found to be 0.70 (95% CI = 0.50 – 0.90). What is the correct interpretation of this finding? Thank you!