Which patient groups at risk for type 2 diabetes benefit from lifestyle interventions: a regression discontinuity design

Date of Approval: 
2020-10-06 00:00:00
Lay Summary: 
Health behaviours such as having a poor diet, smoking, and physical inactivity play a key role in the development of type 2 diabetes. It is estimated that over 90 percent of instances of type 2 diabetes are preventable by maintaining a healthy lifestyle. Thus, so-called lifestyle intervention programs have been developed to help people who are at risk of diabetes type 2 to change their health behaviours. While clinical trials (that is, a study where patients are randomly assigned to receive a lifestyle intervention or not, often under highly controlled conditions) have pointed towards beneficial effects of lifestyle intervention programs, they may not fully capture real-life effects occurring during routine care. It is also important to determine whether different patient groups (e.g., by sex, age, or underlying health conditions) profit from lifestyle intervention programs to the same extent. This study therefore uses differences in treatment decisions induced by public health guidelines in the UK (that is, that people just above a given cut-off receive the treatment while people just below the cut-off do not receive the treatment) to test the real-life effectiveness of lifestyle intervention programs. Thanks to the large number of patients included in CPRD and the extensive time horizon during which these patients were observed, we are not only able to study immediate benefits but also long-run effects for both men and women. Given the promising use of lifestyle intervention, the results of this study have direct implications for clinical care and population health.
Technical Summary: 
Previous studies investigating the health effects of lifestyle intervention administered to people at risk of diabetes type 2 are largely limited to controlled clinical trials. While these studies have pointed towards the efficacy of lifestyle intervention in improving key health outcomes, they might not be able to fully capture treatment effectiveness during routine care and frequently lack the necessary time horizon to study long-run benefits and risks. Non-experimental studies, in contrast, may fail to establish causality due to insufficient control of confounding factors. This study seeks to measure the effect of lifestyle intervention on short-, mid-, and long-term clinical outcomes (progression to type 2 diabetes, mortality, emergency hospitalizations, major adverse health events) in a routine care set-up for adult men and adult women. To establish causality, we make use of a regression discontinuity (RD) design taking advantage of public health guidelines recommending intensive lifestyle intervention based on threshold rules related to patients’ HbA1c and fasting plasma glucose levels. Because physicians base their treatment decisions on additional considerations besides public health guidelines, and lifestyle intervention programs may not always be available, we use an instrumental variable approach that is robust to partial compliance. We evaluate the robustness of results by gradually narrowing down the bandwidth around the treatment threshold and thus only including patients with HbA1c or fasting plasma glucose levels increasingly close to the treatment threshold level. In addition, we will test for heterogenous treatment effects by stratifying our sample by sex, age, ethnicity, socioeconomic status, urban vs rural place of residence, and comorbidities. Sensitivity analyses will focus on potential time effects (i.e. introduction of revised guidelines) and different operationalizations of lifestyle interventions. The findings of this study are expected to provide novel insights into the effectiveness of lifestyle intervention in a real-life setting and can directly inform clinical practice.
Health Outcomes to be Measured: 
Primary outcomes (all to be measured over time horizons of six months, one year, three years, and five years): Type 2 diabetes diagnosis (binary); time to type 2 diabetes diagnosis; number of all-cause emergency hospitalizations; number of diabetes-related and cardiovascular disease-related emergency hospitalizations; number of severe adverse health events (diabetic coma, stroke, heart attack [myocardial infarction] – each event type evaluated separately); all-cause mortality; change in HbA1c level; change in fasting plasma glucose level; dyslipidaemia (levels of cholesterol and triglycerides); levels of overweight (with BMI cut-offs of BMI>=25, BMI>=30) and change in BMI, hypertension (with blood pressure cut-offs of >=140/90 mm Hg, >=160/100 mm Hg) Secondary outcomes (all to be measured over time horizons of six months, one year, three years, and five years): Probability of at least one all-cause emergency hospitalization; probability of at least one diabetes-related and cardiovascular disease-related emergency hospitalization; number of all-cause hospitalizations; probability of at least one all-cause hospitalization; number of diabetes-related and cardiovascular disease-related hospitalizations; probability of at least one all-cause hospitalization; probability of at least one diabetes-related and cardiovascular disease-related hospitalizations; probability of at least one severe adverse health event; diabetic complications (opthalmic, neurological and renal complications of diabetes); presence or absence of osteoarthritis, osteoporosis, injuries induced by falls, and erectile dysfunction Behavioral variables, i.e. change in smoking status, presence or absence of smoking, and documentation/presence or absence of hazardous alcohol consumption, will be explored as potential intermediate outcomes.
Application Number: 
20_000052
Collaborators: 

Till Bärnighausen - Chief Investigator - University of Heidelberg
Julia Lemp - Corresponding Applicant - University of Heidelberg
Anant Jani - Collaborator - University of Oxford
Christian Bommer - Collaborator - University of Heidelberg
Duy Do - Collaborator - University of Heidelberg
Justine Davies - Collaborator - University of Birmingham
Michaela Theilmann - Collaborator - University of Heidelberg
Pascal Geldsetzer - Collaborator - University of Heidelberg

Linkages: 
2011 Rural-Urban Classification at LSOA level;HES Admitted Patient Care;Patient Level Index of Multiple Deprivation