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Why Epidemiology Rules

Learning by Observing

Girl looking through telescope

Previously, I discussed how Hazard Ratios (HR), often for the risk of premature death, are the best metric for measuring the effectiveness of interventions. Now, let’s look at epidemiological studies (ES), one of the best sources for HR. ES use observational data to find correlations between actions and health outcomes.

Benefits of Epidemiological Studies

Big Data

Premature death, or all-cause mortality, is the primary outcome of interest for HR. Fortunately, even older adults don’t die often. For example, a Norwegian HIIT study, which looked at 70-77-year-olds, despite having an effect size of nearly a 50% hazard ratio and over 8,000 person-years of data, did not reach significance for the HR of premature death.1 Reaching significance, even for a strong effect with older participants, generally requires over ten thousand person-years of data.

Ten thousand years of data is challenging for a randomized, controlled trial, but many extensive surveys can be searched for correlations for ES. The USA has the National Health and Nutrition Examination Survey with over 100,000 person-years of data, while the UK Biobank has nearly ten million person-years. These resources are sufficient to detect significant results, even for minor effects.

Ethical Approach to Risky Interventions

Perhaps the most famous use of ES is the association between smoking and lung cancer. Given strong indications that tobacco causes lung cancer, conducting a Randomized Controlled Trial (RCT) where people smoke until death would be unethical. Instead, scientists used ES to show the correlation between smoking and lung cancer.

Girl jumping out of plane with parachute
A randomized, controlled test on the efficacy of parachutes

Ability to Examine Long-Term Effects

Because ES requires large data to find statistically significant correlations, they typically span years, not just weeks or months, like RCTs. This is especially important when dealing with the impacts of diet and exercise. It may take months for the body to adjust to changes in diet or activity. For instance, the immediate response to skipping breakfast is weight loss, as the body gets fewer calories than it is used to. RCTs on skipping breakfast confirm this, with an average loss of half a kilogram (a pound) over nine weeks.2 In the long term, though, the body adapts by reducing metabolism or increasing hunger, and ES show that breakfast skippers have a nearly a 50% higher risk of overweight or obesity.3

See also  Choosing Healthy Interventions

Similarly, according to the book Burn4, metabolic adaptations to changes in weekly exercise volume take three to five months. Most RCT durations last for weeks and miss these metabolic effects.

Epidemiological Challenges

Reliability of Self-reported Data

A frequent challenge to the findings of epidemiological studies based on surveys is that people may not answer truthfully. It is true that if you estimate how many calories someone eats in a day by asking them what foods they ate and how much of each, you’ll end up with much less calories than expected. People don’t eat meals with scales, and estimating the weight or amount of servings of food is not a common task. That said, asking someone, “How many servings of fish do you eat weekly?” yields reasonable numbers. Scientists have verified this by comparing food diaries—record the food you ate immediately after eating—with food frequency-type questionnaires (FFQ), and they find some differences. The FFQ responses are close to the food diary responses.5

Angry chicken
Log your chicken consumption correctly!

Confounding Confounders

Some people question whether epidemiological studies can be trusted because perhaps food, exercise, or habit doesn’t affect longevity, but rather, people who do those things are “healthy users” (or “unhealthy users”) who distort the data with other habits that do affect longevity. An oft-cited example of health user bias is flu vaccine studies, which can show reduced premature death before flu season starts. Having reviewed those studies, the issue there is not “healthy user” but “financial funding bias”—they’re funded by flu vaccine makers. Independent Cochrane studies don’t show this issue.6 Disregard studies funded by vendors, folks!

Unintended correlations with the study objective are called confounders, and epidemiological studies control for the effects of confounders by adjusting for factors that impact health, such as age, gender, socio-economic status, BMI, or physical activity. These factors are added as a separate variable as the study objective, and their influence is removed from the analysis. Once these factors are controlled, the test results will unlikely change from something correlated with the study control and outcome but not with the confounders.

It’s unlikely, but not impossible. It’s educational to look at the one factor I have seen affect the results of epidemiological studies, even when the researchers tried to control its impact as a confounder: smoking. Smoking is correlated with coffee drinking and also reduces the effect of caffeine. Although coffee studies controlled for a yes/no smoking confounder, the increased smoking correlation with increased coffee consumption made it appear that drinking too much coffee was unhealthy. It’s not, but smoking too much certainly is!

See also  Choosing Healthy Interventions

The Bottom Line

Epidemiological studies offer valuable insights into the relationships between lifestyle factors and health outcomes despite the challenges of self-reported data and potential confounders. These studies have advantages over randomized controlled trials, such as studying long-term effects and utilizing large datasets. By giving access to HR correlations, epidemiological studies provide essential guidance for public health recommendations and future research, helping us make informed decisions about our health and well-being.

Footnotes

  1. Effect of exercise training for five years on all cause mortality in older adults—the Generation 100 study: randomised controlled trial
  2. Breakfast Skipping, Body Composition, and Cardiometabolic Risk: A Systematic Review and Meta-Analysis of Randomized Trials
  3. Skipping breakfast is associated with overweight and obesity: A systematic review and meta-analysis
  4. Burn on Amazon, non-affiliate
  5. Validity of the food frequency questionnaire for adults in nutritional epidemiological studies: A systematic review and meta-analysis
  6. Vaccines to prevent influenza in healthy adults

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Unlocking The Physiology of Marathon Training, VO2 Max, Health, and Longevity