A Public Health Primer
Public health seeks to improve human lives through the prevention and management of disease and other health-related conditions. Epidemiology is crucial to the practice of public health, providing the basis for changes in medical practice and public policy that can benefit entire populations.
Epidemiology examines patterns of health and illness in a population. Its goal is to discover associations between disease and exposures that will determine optimal approaches to disease prevention (policy measures and/or preventive medicine) and treatment (clinical practice). A cornerstone of public health research, epidemiologists also use biology, biostatistics and social science disciplines for the most in-depth analysis.
Causal relationships are associations gathered from rigorous scientific method. Epidemiologists gather data and employ a broad range of biomedical and psychosocial theories to generate or expand theory, test hypotheses, and make informed assertions about which relationships are causal. The most well-known causal relationship is that between tobacco exposure and lung cancer development. However, most diseases and outcomes are the result of a very complex web of component causes, not a “one cause-one effect” model.
To examine relationships between disease and exposures, epidemiologists employ different types of study design.
- Case-control studies (like our cancer studies) select participants based on disease status (for example, those with cancer and those without). Study participants should be closely matched in terms of age, socioeconomic status, and other similar population characteristics. These studies retrospectively analyze potential exposures in both groups and measure any associations based on the odds of exposure and disease development (called an odds ratio). Case-control studies are usually more fast and effective than prospective studies but are sensitive to biases and identification of an appropriate control group.
- Cohort studies (like our ALI/ARDS study) select participants based on exposure status. All participants in the cohort are at risk for a selected outcome and are followed over time to assess their rates of disease. An example of this would be selecting participants based on the risk factors associated with ARDS development, then following them to see if they develop ARDS. The relative risk is then calculated to determine the probability of disease based on exposure. This risk assessment is a more powerful effect measure than odds ratios used in case-control studies, however cohort studies are more costly.
Genes are heredity units that provide the building blocks for all life-maintaining functions. Genes are encoded in long strands of DNA that instruct for specific traits, some of which may be visible (such as eye or hair color) and some that are not, like blood type or increased risk of disease. Genetic variation may be caused by a single different letter of genetic code, a single nucleotide polymorphism (SNP).
Environmental interactions combined with genes determine an individual’s health outcomes. Disease and illness are rarely the sole result of genetics or environment. The environment can play a dramatic role in disease development. Environmental interactions can include exposure to hazardous chemicals, poor nutrition, and other factors that influence how genes process information. Changes (mutations) in DNA as a result of environmental insult can change the body’s ability to heal and/or fend off disease.
Molecular Epidemiology and Genetic Research focus at the molecular level on the contribution of genetic and environmental risk factors to disease development across populations. This field improves understanding of specific pathways, genes and molecules that influence susceptibility to disease.
Genome-wide association studies (GWAS) seek different variations among the human genome that may have an impact on disease development. These studies are done by examining single nucleotide polymorphisms (SNPs)–single DNA mutations that influence individual variation and susceptibility to illness. About 800 SNP associations have been found for 150 diseases and traits, but these associations are mostly useful for identifying molecular pathways of disease, not in finding genes that predict risk. It is hoped that successful GWAS will accelerate drug and diagnostic development.