kine 404 ch 4, 5, 6

big data

large datasets that are difficult to process using traditional data analysis methods; used to track disease outbreaks, study risk factors, and analyze health trends over time

orgs that porvide internaltional data

WHO (World Health Organization) - collects global health data on disease outbreaks, mortality rates, and health dispairities

orgs that porvide national data

NIH (national institute of health) - conducts medical research and disease surveillance

why are mortality data useful in the US

• identifies leading causes of death

• tracks public health trends

• determines where funding is needed

• identifies patterns in infectious diseases

what are US census data used for

• provides demographic info

• helps allocate gov funding for public health and social programs

• calculate disease rates by providing population demoninators

what are registries used for

• track births, deaths, marriages, and diseases

• help disease surveillanece

• used for epidemiologic research and public health interventions

Why is epidemiology considered a quantitative discipline

1. Uses statistical methods to analyze health data

2. Measures disease freq (incidence, prevalence)

3. Quantifies risk factors and associations (relative risk, odds ratio)

4. Helps in data driven decision making for public health policies

What are public health surveillance programs used for

1. Monitor disease trends and outbreaks 

2. Detect and respond to public health threats 

3. Evaluate the effectiveness of health interventions (ex: vaccination programs)

4. Identify populations at risk of targeted health initiatives

what is BRFSS

• Behavioral risk factor surveillance system 

  1. The largest us telephone health survey 

  2. Tracks health behaviors, chronic diseases, and preventative measures at the state level

  3. Monitors smoking, alcohol use, physical activity, and access to healthcare

What are vital events

births, deaths, marriages, divorces, fetal dealths

ex of surveillance systems

Fluview (CDC) - monitors seasonal influenza activity 

purpose of state cancer registries

1. Monitor cancer incidence and morbidity - helps identify trends by age, race, and geographic location

2. Evaluate prevention efforts - tracks the effectiveness of screening programs 

3. Support cancer research - provides data for epidemiologic studies 

4. Guide public health policies - helps allocate resources for cancer treatment and prevention 

types of information that are included in birth stats 

1. Demographic data

   1. Infant name, sex, dob, and place of birth

   2. Mothers name, age, race, education level

   3. Fathers name, age, race, occupation

2. Health and medical data 

   1. Birth weight

   2. Gestational age

   3. Type of delivery

   4. Congenital abnormalities

   5. Mothers medical risk factors 

   6. Prenatal care details  

how are reportable and notifiable disease stats collected

1. Healthcare providers report cases to local, state, and national health authorities

2. Data sent to CDC 

limitations of reportable and notifiable disease stats collection

1. Limitations 

   1. Underreporting - some diseases are not reported due to mild symptoms or lack of diagnosis 

   2. Delayed reporting - time lag in comforting and recording cases

   3. Inconsistent criteria - definitions and reporting standards vary by state and country 

the role of the medical examiner or coroner in completing a death certificate

1. Determines cause and manner of death 

2. Conduct autopsies when necessary

3. Verifies time of death 

4. Provides legal documentation for public health surveillance and legal matters 

3 factors that affect the quality of epidemiologic data

accuracy, completeness, and timeliness of reporting data sources for determining life expectancy

data sources for determining life expectancy

1. Vital statistics data (birth and death certificates)

2. Us census data (demographic estimates)

3. World health organization (WHO) reports

4. Social security administration (SSA) data

information included in US death certificate data

1. Demographic info

   1. Name, dob, age sex, race, marital status

2. Cause of death

   1. Immediate cause (ex: heart attack)

   2. Underlying condition (ex: hypertension)

   3. Manner of death (natural, accidental, homicide, suicide, undetermined)

   4. Place and time of death 

information included in fetal death certificate

1. Fetal data 

   1. Weight, gestational age, sex

   2. Presence of congenital anomalies

2. Maternal and pregnancy history

   1. Mother’s age,race, education, prenatal care history

   2. Pregnancy complication (ex: gestational diabetes)

3. Cause of fetal death

   1. Placental problem, genetic abnormalities, maternal health conditions

 information is collected in birth certificates

1. Infant data

   1. Birth weight, gestational age, APGAR score

2. Parental data

   1. Mother and fathers age, race, education, occupation

3. Maternal health and prenatal care

   1. Number of prenatal visits, complications, risk factors

What are the objectives and definitions of descriptive epidemiology

Descriptive epidemiology - classifies the occurrence of disease according to the following variables 

• Person (who is affected)

• Place (where the condition occurs)

• Time (when and over what time period the condition occurred)

Case reports and case series

1. a detailed account of a single case or a group of cases with a similar condition

   1. Pro: 

      1. Good for identifying new diseases and rare conditions

      2. Generates hypotheses for further study 

   2. Cons:

      1. Lack of a control group, cannot establish prevalence

   3. Ex: reporting a rare case of a new infectious disease in a patient

Cross sectional studies

1. observational studies that assess exposure and disease outcomes at a single point in time 

   1. Pro: quick and inexpensive

      1. Estimates prevalence of a condition in population

   2. Cons: 

      1. Cannot determine cause and effect relationships

      2. Susceptible to prevalence incidence bias

   3. Ex: a survey measuring the percentage of adults with hypertension in a city

Ecological studies

1. analyze data at the population or group level rather than the individual level

   1. Pro: 

      1. Useful for generating hypothesis

      2. Can assess large scale environmental effects

   2. Cons:

      1. Prone to ecological fallacy

      2. Lacks individual level exposure data

   3. Ex: examining the association between air pollution levels and asthma rates in different cities

What are some of the factors considered when measuring socioeconomic status

1. Income (household, personal, wealth and assets)

2. Education (highest level of education attained, quality of education, assess to educational opportunities)

3. Occupation (job type, stability and security, workplace benefits)

4. Housing and neighborhood (home ownership vs renting, housing quality and living conditions, neighborhood environment 

5. Access to healthcare (health insurance coverage, quality of healthcare service available, health outcomes)

6. Social and cultural capital (social network, community involvement, cultural knowledge and resources)

7. Economic stability and mobility (ability to save money, intergenerational mobility, debt levels)

Describe the relationship between morbidity and mortality and the following: age, sex, SES, and race

1. Age 

   1. Morbidity - older adults experience higher rates of chronic diseases. Infants and children have higher susceptibility to infectious diseases 

   2. Mortality - higher in infants (infant mortality rate) and elderly due to weaker immune systems and age related diseases

2. Sex

   1. Morbidity - women tend to have higher morbidity rates (ex: autoimmune diseases), while men have higher risk taking behaviors leading to injuries

   2. Morbidity - men have higher morbidity rates from cardiovascular disease, accidents, and violent causes, while women generally have longer life expectancy 

3. Socioeconomic status 

   1. Morbidity - lower ses groups experience higher rates of infectious diseases, malnutrition, and chronic conditions due to limited healthcare access

   2. Mortality: higher among lower income populations due to lack of preventive care, unhealthy environments, and inc stress related illnesses

4. race/ethnicity

   1. Morbidity - certain racial groups are disproportionately affected by chronic conditions (hypertension in black americans, diabetes in hispanic populations)

   2. Motability - racial disparities exist in healthcare assess, environmental exposures, and genetic predispositions leading to variation in mortality 

 information provided by descriptive epidemiology and specific uses

1. Focuses on identifying patterns of disease occurrence using person, place, and time variables 

   1. Disease freq and distribution - helps determine who is affected, where, and when 

   2. Trends over time - tracks disease outbreaks, seasonal variations, and long term patterns

   3. Public health planning - guides resource allocation, vaccination programs, and prevention efforts 

   4. Hypothesis generation - helps identify potential causes of diseases for further analytical studies 

2. Specific uses 

   1. Identifying risks factors for diseases

   2. Allocating healthcare resources effectively

   3. Tracking epidemics 

provide examples of person, place, and time variables and how they relate to distribution of health outcomes

1. Person variables - age, race, sex, ses, occupation, lifestyle behaviors

   1. Ex: older adults have a higher prevalence of alzheimer's disease 

2. Place variables - geographic location, urban vs rural areas, environmental factors

   1. Ex: malaria is more common in tropical regions to due climate conditions 

3. Time variables - trends over time, seasonal variations, outbreaks

   1. Ex: influenza peaks in winter months 

factors that contribute to variation in infectious and chronic disease from one country to another

1. Healthcare systems - universal health care reduces morbidity while lack of healthcare access inc disease burden 

2. Economic status - higher income countries face more chronic disease, while low income countries experience more infectious diseases

3. Lifestyle and diet - processes food consumption leads to obesity and heart disease in developed nations, while undernutrition affects poorer countries 

4. Climate and environment - tropical climates have more vector borne diseases

5. Public health policies - vaccination programs, sanitation infrastructure, and regulations affect disease prevalence

Describe and differentiate between vital statistics data and reportable/notifiable disease statistics 

1. Vital stats data

   1. Collected from official records of births, deaths, marriages, and divorces 

   2. Used to track mortality rates, life expectancy, and population growth trends 

   3. Ex: infant mortality rates from birth and death certificates

2. reportable/notifiable disease stats

   1. Tracks infectious and certain chronic diseases that require reporting to health authorities

   2. Helps monitor and control outbreaks, pandemics, and emerging diseases

   3. Ex: covid, tuberculosis, and measles causes must be reported to public health agencies

Cyclic fluctuation

regular patterns of variation in disease occurrence over seasons, months, or years, often influenced by environmental or behavioral factors

• Ex: influenza rates inc during the winter months every year

localized/spatial clustering

the grouping of disease cases within a specific geographic area, suggesting environmental or genetic influences 

Ex: higher cancer rates near and industrial waste site due to pollution exposure

Secular trend

long term changes in disease occurrence over an extended period, often linked to lifestyles changes, medical advancements, or policy changes

Temporal clustering

the occurrence of cases of health events in a population within a short time period, suggesting an outbreak or common exposure

Ex: an inc in heart attack following a heat wave

Causal association

a factor (exposure) directly influence the occurrence of an outcome (disease)

Noncausal association variable

relationship between 2 variables that does not imply causation

Epidemic curve

representation of the number of cases of a disease over time, used to identify patterns and potential sources of an outbreak

Association positive

an inc in one variable is associated with a inc in another variable

Negative association

an inc in one variable w a dec in another variable

Dose response relationship

a pattern where the risk of a disease inc (or dec) wit the level of exposure of a risk factor, supporting a causal link

Threshold model

model suggesting that an exposure must reach a certain level before an effect occurs

Describe epidemiologic research strategies in regards to hypothesis, method of difference, method of concomitant variation, and operationalization

1. Hypothesis - a testable statement about a potential relationship between exposure and a disease

   1. Ex: exposure to air pollution inc the risk of asthma

2. Method of difference - compares 2 groups that are similar in every way except for the exposure under investigation 

   1. Ex: comparing lung cancer rates in smoker vs nonsmokers

3. Method of concomitant variation - observes whether changes in exposure levels correspond to changes in disease risk

   1. Ex: the dose response relationship between alcohol consumption and liver disease

4. Operalization - the process of defining variables in measurable terms to enable research 

   1. Ex: defining physical activity as “the number of steps taken per day” rather than a vague concept like “active lifestyle”

What is the web of causation used for

1. Web of causation is a conceptual model that illustrates how multiple factors contribute to the development of disease 

   1. Shows that diseases have multiple causes rather than a single cause

   2. Identify modifiable risk factors for prevention

   3. Ex: heart disease is influenced by genetics, diet, exercise, smoking, and socioeconomic status, rather than one factor

How do epidemiologists account for the change in observed associations

1. Bias - errors in study design, data collection, or participant selection

2. Confounding variables - other factors that might influence the observed relationship

3. Random variation - fluctuations due to chance 

4. Advancements in measurements - improvements in diagnostic techniques, or data collection methods

5. Changes in population behavior - shifts in lifestyle, healthcare access, or public health policies

What is the multivariate casualty

Refers to the idea that multiple factors contribute to disease development, rather than a single cause

Describe disease causality from a historical perspective

1. Hippocratic theory - disease was believed to result from an imbalance in 4 humors (blood, phlegm, yellow bile, black bile)

2. Miasma theory (middle ages) - disease were thought to be caused by “bad air” or noxious fumes from decaying matter

3. Germ theory - louis pasteur and robert koch demonstrated that microorganisms cause infectious diseases, shifting the focus to specific pathogens as causes

4. Epidemiologic transition - with the decline in infectious diseases due to vaccines and antibiotics, chronic diseases became the primary focus, leading to multifactorial models of causation

Define and describe deterministic and probabilistic in causality

1. Deterministic causality (necessary and sufficient causes) - follows the idea that a specific cause always produces a specific effect

   1. Limitation: does not explain chronic disease or cases where multiple factors contribute to disease

   2. Ex: microorganism must be present in every case of the disease

2. Probabilistic causality - disease risk increases with exposure, but the outcome is not guaranteed

   1. Ex: smoking inc the risk of lung cancer, but not every smoker develops the disease

Distinguish between necessary and sufficient causes

1. Necessary cause - a factor that MUST be present for a disease to occur, but it may not be enough by itself

   1. Ex: hiv is necessary for aids, but not everyone w hiv develops aids immediately 

2. Sufficient cause - a factor (or combination of factors) that alone can cause disease, but other factors might also cause it

   1. Ex: a lethal dose of cyanide is sufficient to cause death 

3. Combination - some diseases require multiple factors for causation 

   1. Ex: lung cancer - smoking is neither necessary nor sufficient alone, but a strong risk factor

examples of continuous variables

1.  variable that can take an infinite number of values within a given range

   1. Ex: 

      1. BMI

      2. BP

      3. Age 

      4. Cholesterol levels

      5. Air pollution levels

Define the role of chance in associations

1. Real (true association) - a genuine relationship exists 

2. Due to chance (random error) - observed association occurs randomly, not because of a true relationship 

3. How to reduce the effect of chance:

   1. Larger sample size 

   2. Statistical tests (p values, confidence intervals)

   3. Repeating studies can confirm if an association is consistent

Hill’s criteria of causality and describe examples of each

1. Strength of association - the stronger the association, the more likely it is casual 

   1. Ex: smoking and lung cancer have a strong association (high relative risk)

2. Consistency - the association is observed in different populations, settings, and studies

   1. Ex: multiple studies across different countries link obesity to heart disease 

3. Specificity - a single cause leads to a single effect 

   1. Mycobacterium tuberculosis causes tuberculosis 

4. Temporality - the exposure must occur before the disease develops 

   1. Ex: exposure to asbestos occurs years before mesothelioma appears 

5. Biological gradient - higher exposure leads to greater effect

   1. Ex: heavier smoking -> higher risk of lung cancer

6. Plausibility - there is a biological mechanism explaining the association

7. Ex: cholesterol buildup in arteries explains why high fat diets inc heart disease risk 

8. Coherence - the association does not conflict with existing biological and medical knowledge 

   1. Ex: the link between air pollution and lung disease aligns with known respiratory health effects

9. Experiment - interventions that modify exposure lead to changes in disease outcome 

   1. Ex: smoking cessation programs reduce the risk of lung cancer

10. Analogy - if a similar exposure is known to cause disease, it strengthens the case for a new association

    1. Ex: thalidomide caused birth defects, supporting concerns about other drugs affecting fetal development 

Confidence interval estimate

a range of values within which the true population parameter is expected to fall, with a given level of certainly (ex: 95% confidence interval)

Biological gradient

a components of causation where greater exposure leads to a greater effect, similar to a dose response relationship

Operationalization

the process of defining a concept in measurable terms (defining “physical activity” as the number of steps per day)

Latency

the time period between exposure to a risk factor and the onset of disease symptoms (ex: the latency period for lung cancer after smoking)