1.4 time scales
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A MThis document discusses three time scales that can affect health and disease: age effects, period effects, and cohort effects. Age effects refer to how biological aging influences health status. Period effects refer to how environmental changes during a time period impact health for all ages. Cohort effects refer to how exposures experienced by people born in the same time period influence their long-term health status regardless of age. Each time scale is depicted graphically and examples are provided.
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- India's elderly population is growing rapidly and will exceed 300 million by 2051, placing stress on families and the health system.
- The elderly experience many health issues like cardiovascular disease, osteoporosis, accidents and falls. Functional and economic dependency is also a major problem.
- Proper nutrition, physical activity, social engagement and managing medications are important for healthy aging. Support is needed through home care, day care centers and institutionalization in some cases.
- The government has introduced policies and programs to support elderly welfare, but more support is
Study Designs Cohort StudiesChapter 7Learning.docx
Study Designs: Cohort
Studies
Chapter 7
Learning Objectives
• Differentiate cohort studies from other
study designs
• List main characteristics, advantages, and
disadvantages of cohort studies
• Describe three research questions that
lend themselves to cohort studies
• Calculate and interpret a relative risk
• Give three examples of published studies
discussed in this chapter
Temporality
• Temporality refers to the timing of
information about cause and effect.
• Did the information about cause and effect
refer to the same point in time?
• Or, was the information about the cause
garnered before or after the information
about the effect?
Limitations of Other Study
Designs
• Demonstrating temporality is a
difficulty of most observational
studies.
Limitations of Other Study
Designs (cont’d)
• Cross-sectional and case-control study
designs are based on exposure and
disease information that is collected at the
same time.
• Advantage: Efficient for generating and
testing hypotheses.
• Disadvantage: Leads to challenges
regarding interpretation of results.
Limitations of Other Study
Designs (cont’d)
• Cross-sectional studies:
– Present difficulties in distinguishing the
exposures from the outcomes of the disease,
especially if the outcome marker is a
biological or physiological parameter.
Limitations of Other Study
Designs (cont’d)
• Case-control studies:
– Raise concerns that recall of past
exposures differs between cases and
controls.
Limitations of Other Study
Designs (cont’d)
• There has been no actual lapse of time
between measurement of exposure and
disease.
• None of the previous study designs is well
suited for uncommon exposures.
What is a cohort?
• A cohort is defined as a population group,
or subset thereof, that is followed over a
period of time.
• The term cohort is said to originate from
the Latin cohors, which referred to one of
ten divisions of an ancient Roman legion.
What is a cohort? (cont’d)
• Cohort group members experience a
common exposure associated with a
specific setting (e.g., an occupational
cohort or a school cohort) or they share a
non-specific exposure associated with a
general classification (e.g., a birth
cohort—being born in the same year or
era).
Cohort Effect
• The influence of membership in a particular
cohort.
• Example: Tobacco use in the U.S.
– Fewer than 5% of population smoked around the
early 1900s.
– Free cigarettes for WWI troops increased
prevalence of smoking in the population.
– During WWI, age of onset varied greatly; then
people began smoking earlier in life.
– One net effect was a shift in the distribution of the
age of onset of lung cancer.
Cohort Analysis
• The tabulation and analysis of morbidity or
mortality rates in relationship to the ages
of a specific group of people (cohort)
identified at a particular period of time and
followed as they pass thro.
Public health problem
This document discusses public health problems and outlines criteria for determining priority public health issues. It defines key terms like health, disease, illness, and ill health. Public health aims to prevent disease through organized community efforts. Demands on health care exceed resources due to factors like population growth, aging, and unhealthy environments. Priority public health problems are determined by prevalence, individual impact, societal impact, and potential for prevention/treatment. The document concludes that governments should implement policies to reduce population growth, increase health sector resources, and encourage individual health maintenance.
Descriptive epidemiology
Descriptive epidemiology is the first phase of an epidemiological investigation concerned with observing disease distribution in a population and identifying associated characteristics. It involves defining the population and disease, describing disease occurrence in terms of time, place and person, measuring disease burden, comparing data to known indices, and formulating hypotheses about disease etiology. The steps include defining the population and disease under study, describing patterns of occurrence by time, location and personal attributes, measuring disease incidence and prevalence, and developing hypotheses to explain observed patterns and suggest preventive measures.
Chapter15 HDEV
1. Physical changes in middle adulthood include declines in reaction time, lung capacity, muscle mass, bone density, and metabolism. Regular exercise can help maintain physical functioning.
2. Cognitive abilities like crystallized intelligence and practical problem-solving increase due to life experience, while fluid intelligence and information processing speed tend to decline.
3. Major health risks in middle adulthood include cancer, heart disease, and accidents. Screening and lifestyle factors can help reduce risks. Physical and sexual changes also occur.
Ch01 outline
This document provides an overview of community health from the past, present, and future. It discusses the major public health achievements of the 20th century, definitions of key terms, factors that influence community health, and a brief history of community health efforts dating back to ancient civilizations. It outlines the goals of Healthy People 2020 to attain high quality, longer lives for all Americans by creating healthy environments and promoting healthy behaviors across all populations.
ageing ppt.pdf
This document provides an overview of the biology of aging, covering general concepts, theories of aging, aging at the cellular and molecular level, and interventions in the aging process. It discusses topics such as the universality and rates of senescence across species, primary aging processes, theories including evolutionary, genetic/developmental, and oxidative damage theories. It covers cellular aging concepts such as replicative senescence, telomere shortening, and the role of telomerase. It also discusses pro-longevity factors seen in long-lived human populations and exceptions to characteristics typically associated with aging.
HEALTH & FACTORS.pptx
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Health status of the community
The document discusses indicators used to assess the health status of a community. It provides definitions of key terms like health, illness, disease, and wellness. It also defines indicators and discusses characteristics of good indicators. The document categorizes indicators as quantitative and qualitative. It describes hierarchies of indicators and provides examples of common indicators used to measure mortality, morbidity, disability rates, nutritional status, health care delivery, utilization rates, social and mental health, the environment, socioeconomics, health policies, and quality of life.
Health Indicators.pptx
The document discusses health indicators and statistics. It defines key terms like statistics, indicators, variables, and characteristics of health indicators. It then provides examples of different types of health indicators including fertility, mortality, and morbidity indicators. Specific indicators are defined such as crude birth rate, general fertility rate, age-specific fertility rate, total fertility rate, crude death rate, infant mortality rate, and others. The sources and uses of health statistics are also mentioned.
Occurrence of disease morbidity
The prevalence rate as of June 30, 2012 is the number of active TB cases on that date (264) divided by the population on March 30, 2012 (183,000) and multiplied by 100,000.
264/183,000 = 144 per 100,000
The answer is c.
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Nutrition during the 19-60 year old age and related disorders
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GCE Sociology Revision (AQA)- Unit 1 Demography (4)
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This document contains slides from a lecture on public health given by Alasdair Cohen at UC Berkeley on October 17, 2016. The slides discuss various topics related to research quality including publication bias, p-hacking, negligent science, Cochrane reviews, transparent reporting, and the CONSORT checklist. The CONSORT checklist is explained in detail over several slides, outlining the key elements that should be reported in randomized controlled trials to improve transparency and replication.
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Transparency3
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Transparency2
Alasdair Cohen gave a lecture on replication science at UC Berkeley on October 17, 2016 for a public health class. The lecture introduced the concept of replication by discussing two examples: a story about computer worms and how an Excel error impacted austerity policies. The remainder of the lecture covered the topic of replication more broadly.
Transparency1
This presentation outline covers issues with scientific research including publication bias, substandard science, p-hacking, and scientific misconduct. It then discusses approaches to address these problems such as replication science, pre-registration of study protocols and analysis plans, transparent reporting, and responsible data management. Tools for organizing research and ensuring ethical standards are also reviewed. The presentation aims to improve the reliability and integrity of scientific research.
5.3.5 causal inference in research
1. The document outlines steps for conducting causal inference research including defining a hypothesis, study design, drawing a directed acyclic graph (DAG) to identify confounders, analyzing data while controlling for confounders, and making scientific inferences.
2. An example is provided on estimating associations between deworming, hygienic latrines, finished floors, and soil-transmitted helminth infections using a cross-sectional survey design.
3. Results show the adjusted prevalence ratios for single and combined exposures on deworming and household characteristics on infection, including any additive interaction effects.
5.3.4 reporting em
Causal inference involves using directed acyclic graphs (DAGs) and the potential outcomes model to assess effect modification (EM) from a causal perspective. DAGs can help identify confounding and selection bias. The potential outcomes model distinguishes statistical from causal interaction and allows estimating average risk or risk differences to determine if causal interaction is present on the additive scale. Studies should minimize bias, control confounding, and quantify uncertainty to best assess causal EM.
5.3.2 sufficient cause em
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5.2.3 dags for selection bias
This document discusses causal inference and outlines key concepts including:
1) Using directed acyclic graphs (DAGs) to identify confounding and selection bias
2) Different types of interaction and effect modification that can be modeled causally
3) Integrating causal concepts like DAGs, effect modification, and addressing biases into research by defining hypotheses, study design, analysis, and scientific inferences
5.1.2 counterfactual framework
1. The document discusses approaches for identifying confounding using directed acyclic graphs (DAGs). It outlines two main approaches: (1) removing direct effects and checking for common causes, and (2) checking for open back-door paths and blocking them.
2. An example applies these approaches to assess whether adjusting for prenatal care is sufficient to control for confounding between vitamin use and birth defects. It determines that additional adjustment is needed based on remaining connections in the DAG.
3. Traditional criteria for confounding may sometimes disagree with the DAG approach, and the DAG approach is more reliable as it can identify situations like conditioning on a collider that may bias results.
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5.2.1 dags
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4.4. effect modification
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4.5. logistic regression
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Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
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1.4 time scales
- 1. Time scales • Disease occurrence at a population level affected by different time scales – age, period and cohort effects
- 2. Time scales • People are conceived, born, and then move through time until death with a variety of health states and events along the way • Time (and exposures in time) can affect health/disease in three main ways – Age effects: biological age of individuals – Period effects: calendar time – Cohort effects: year of birth
- 3. Time scales • Age effect • Definition: variation in health status arising from social or biological consequences of aging • What it looks like when graphed – Rate (of disease) changes with age – Irrespective of birth cohort and calendar time
- 4. Time scales • Age effect • Example: rate of heart disease increases with age regardless of whether you examine a population born in 1900 or 1950; at the age of 50 the rate of heart disease is higher than at age 30
- 5. Time scales • Age effect - depiction
- 6. Time scales • Period effect • Definition: Variation in health status arising from changes in physical, ecological, or social environment during a time period • What it looks like – Change in rate (of disease) affecting an entire population at some point in time – Irrespective of age and birth cohort
- 7. Time scales • Period effect • Example: DDT spraying in 1950s led to increased risk of certain cancers for anyone living in affected areas in the 1950s, regardless of how old they were or when they were born
- 8. Time scales • Period effect - depiction
- 9. Time scales • Cohort effect • Definition: Variation in health status arising from exposures that vary by cohort • What it looks like: – Change in the rate (of disease) according to membership in some cohort – Birth cohort is established by year of birth • Note that one can examine cohorts defined by any life event which places a person permanently in a group – Irrespective of age and calendar time
- 10. Time scales • Cohort effect • Example: Women exposed to DES in utero have increased risk of vaginal and cervical cancer at all ages and over all time periods
- 11. Time scales • Cohort effect - depiction
- 12. Time scales A real example • Peptic ulcer mortality (Susser 1982, reprinted 2001) – Cohort effects – for those born after 1900 age-specific mortality from peptic ulcer was continually declining