Trends in the incidence of diagnosed diabetes: a multicountry analysis of aggregate data from 22 million diagnoses in high-income and middle-income settings

Abstract

Background: Diabetes prevalence is increasing in most places in the world, but prevalence is affected by both risk of developing diabetes and survival of those with diabetes. Diabetes incidence is a better metric to understand the trends in population risk of diabetes. Using a multicountry analysis, we aimed to ascertain whether the incidence of clinically diagnosed diabetes has changed over time.

Methods: In this multicountry data analysis, we assembled aggregated data describing trends in diagnosed total or type 2 diabetes incidence from 24 population-based data sources in 21 countries or jurisdictions. Data were from administrative sources, health insurance records, registries, and a health survey. We modelled incidence rates with Poisson regression, using age and calendar time (1995-2018) as variables, describing the effects with restricted cubic splines with six knots for age and calendar time.

Findings: Our data included about 22 million diabetes diagnoses from 5 billion person-years of follow-up. Data were from 19 high-income and two middle-income countries or jurisdictions. 23 data sources had data from 2010 onwards, among which 19 had a downward or stable trend, with an annual estimated change in incidence ranging from -1·1% to -10·8%. Among the four data sources with an increasing trend from 2010 onwards, the annual estimated change ranged from 0·9% to 5·6%. The findings were robust to sensitivity analyses excluding data sources in which the data quality was lower and were consistent in analyses stratified by different diabetes definitions.

Interpretation: The incidence of diagnosed diabetes is stabilising or declining in many high-income countries. The reasons for the declines in the incidence of diagnosed diabetes warrant further investigation with appropriate data sources.

Funding: US Centers for Disease Control and Prevention, Diabetes Australia Research Program, and Victoria State Government Operational Infrastructure Support Program.

Figure 1:. Age-standardised and sex-standardised incidence rates of diagnosed diabetes per 1000 person-years (EU standard population 2010, with equal weights for men and women)

Standardisation is based on annual age-specific incidence rates from age–period–cohort models fitted separately for each data source and sex. Shaded areas represent 95% CIs around incidence trends. CHS=Clalit Health Services. KPNW=Kaiser Permanente Northwest. MHS=Maccabi Healthcare Services. NHIS=National Health Interview Survey.

Figure 2:. Estimated changes in diagnosed diabetes incidence rates before and after the join points at the years 2009, 2010, 2011, and 2012

The diagonal lines indicate equality of pre-join and post-join point changes in rates, in which there is no change in trend. Each coloured circle represents a data source; the area is proportional to the precision (inverse variance) of the sum of the estimated annual changes before and after the join point. Estimates are from a model with common slopes for men and women, controlling for sex. Model fit for the later time period is poor for Israel (MHS), leading to unreliable estimates of the annual trend in incidence in this time period. CHS=Clalit Health Services. KPNW=Kaiser Permanente Northwest. MHS=Maccabi Healthcare Services. NHIS=National Health Interview Survey.

Figure 3:. Trends in the proportion of the population undergoing HbA_1c and blood glucose testing, along with diagnosed diabetes incidence, in data from Israel (Maccabi Healthcare Services)

Incident cases are defined by an algorithm, incorporating blood tests, prescription of antidiabetic medications, and clinical diagnosis by clinical practitioners.

Figure 4:. Trends in the proportion of the population undergoing HbA_1c and blood glucose testing, along with diagnosed diabetes incidence, in data from Ontario, Canada (administrative diabetes database)

Incidence data depicted here are from Ontario, rather than national Canadian incidence data. Incident cases are defined by an algorithm, incorporating at least one hospital admission or at least two physician claims with evidence of diabetes within 2 years.