Every time someone quits smoking, takes their child for a vaccination or has their blood pressure checked, they’re taking part in preventive health measures designed to reduce the chances of getting sick.

But how do doctors, public health departments and governments know which disease to help prevent next? And how do they know which people to target?

One of their most powerful weapons is the long-term population study or “longitudinal cohort study”. This type of study follows a large group of people, often for years or decades, collecting data to build up a comprehensive picture of health at a population level over time.

By including information from surveys – linked to other routinely collected data like hospital admissions, prescription medications and cancer registrations, and increasingly genetic information – these studies offer us a window to the health of the population.

They allow us to better understand the relationship between risk factors and disease, and how people are managing their health over time. They also help us make better, informed decisions about changes likely to result in the best health outcomes - for individuals, the community and our health system.

Long-term studies are powerful as they allow researchers and public health officials to investigate and understand a wide range of issues, comparing the exposures and habits of people who have fallen sick with people who remain healthy.

Deaths from heart disease have dropped by around 80% since 1968, mainly because we have been tackling many of the key risk factors, such as smoking, high blood pressure and high cholesterol thanks to data from longitudinal studies such as the British Doctors Study and the Framingham Heart Study.

Findings from long-term studies also improve preventive health care, in everything from helping to inform legislation about smoking, to implementing new practices and policies on managing chronic lung disease, or helping to identify unanticipated side effects of popular and effective medicines, like those prescribed for heartburn and reflux.

A key piece in the puzzle

Long-term studies allow us to gather data on many different chronic diseases simultaneously. What this does, among other things, is allow decision makers to predict risk. If we can better do this, we can more easily determine where our stretched health care resources can be most effectively spent. This is critical to policymakers tasked with designing health systems that do the best job for the greatest amount of people.

For example, the US Nurses’ Health Study, which has been running for 40 years and involves more than 275,000 participants, has had a major impact on public health. These include regulations on dietary trans fats, because of their links to heart disease, and establishing the positive impact of physical activity on cancer recurrence rates.

Here in Australia, the Australian Longitudinal Study on Women’s Health has been tracking the health of 58,000 Australian women in three different age groups for just over two decades. Its findings have been key to a series of government health policies and guidelines, such as on physical activity and managing urinary incontinence.

These studies are also valuable tools to quantify the potential impact of preventive health measures. For instance, colleagues have calculated the costs of hospital admissions in Australia that are related to being overweight and obese at A$4 billion. That’s A$1 in every A$6 spent in hospitals for people aged 45 and over. Having access to such data provides a powerful financial incentive for governments and health funders to support preventive health programs to reduce the risk of obesity.

Long-term studies can also tell us about the potential impact, say on hospital admissions, of preventive health measures.from shutterstock

Another area key to government efforts in keeping populations healthy is better targeting health services and addressing the gaps between what we know about treating or managing ill health and what happens in practice.

Take the example of how to best prevent the recurrence of breast cancer. We know from clinical trials that using hormone-blocking therapies to treat early-stage breast cancer substantially reduces the risk of cancer returning, if medication is taken continuously for at least five years.

But what we haven’t known until now is whether women were sticking to the full five-year treatment. By drawing on data from the 45 and Up Study, researchers have been able to establish that nearly six in every ten women quit their hormone-blocking medication early, putting them at greater risk of their cancer returning.

This sort of information would be difficult, if not impossible, to gather by any other means than through a long-term study. This provides decision makers with essential real-world data, allowing them to design improved treatment plans for the 17,000 Australians diagnosed with breast cancer each year.

Evidence from long-term population studies needs to be considered alongside good quality evidence and data on what works in practice (such as randomised controlled trials or other intervention studies), cost effectiveness, community acceptability, and feasibility.

New targets

Data from long-term studies help us design and deliver preventive health strategies; they can help us target preventive health measures in ways we may not have known about otherwise.

For example, one area researchers and policy makers are trying to better understand is the interplay between mental health, disability and lifestyle-related illnesses. A recent paper using longitudinal data found adults with type 2 diabetes are five times more likely to have a lower quality of life and less social interaction in the five years after diagnosis compared to those without the condition.

Looking to the future, long-term studies could help us answer the big questions for which we currently don’t have data. What are the health impacts of e-cigarettes? Does excessive screen time affect cognitive function and how does it interplay with lifestyle-related illnesses such as obesity and type 2 diabetes?