There are few data available on the incidence of T1D; hence, the methods for the T1D Index rely on extrapolation [8••]. For example, only 97 of the 205 countries with more than 50,000 inhabitants have published data for any year at all, and very few countries have any data prior to 1975. Assumptions made about incidence prior to this time naturally affect any estimates made for the population over the age of 45 y. In some countries where data are available, observed incidence rates fluctuate from year to year. In order to use these data to estimate incidence over time in other countries in the same region, these estimates are smoothed. This may partially mask true incidence trends, although it reduces the risk of over-fitting.

The work necessitated modelling of changes in incidence over time to populate most data points for most countries. This was done by fitting a line of best-fit to year-on-year annual percentage incidence change data for all years for which two or more countries had data (1985–2015). A global curve was developed, as well as curves for 10 geographic regions where there was sufficient data to model this, which were used in place of the global curves for years that had relevant data.

Table 1 shows the 2021 index incidence per 100,000 population <15 years for all countries, and compares these to the IDF Atlas 10th Edition figures [1•, 13], in which incidence for a particular country was kept stable at the level of the last published data. The median within-country difference between these estimates is an IDF Atlas estimate that was 27.5% lower than the T1D Index estimate. These differences will be greater in low- and middle-income countries if estimated non-diagnosis cases were included as the T1D Index incidence figure would be higher.

These index data show that, whilst estimated incidences have increased in nearly all countries (commensurate with global trends), they have increased three- to five-fold in some countries in Eastern Europe and the Middle East. This is due to the age of some of the studies which date from 1990 to 1999 (including the landmark DIAMOND studies [14]), or even earlier, as well as the rapid year-on-year increases seen in the published data from these regions. Whilst it is reasonable to assume that incidence has risen markedly in these countries with only dated studies countries since the mid-1990s, the 2021 index predictions for countries like Kuwait, Ukraine, and Poland are most likely over-estimated as the projected incidences are now approaching or exceeding incidence in Finland, which has the highest reported incidence. We believe that it is more likely that after incidence has increased steadily for a period, the increase in incidence will then plateau as has been seen in some high-risk European-origin populations [3, 9•]. Further variation would then occur between countries depending on where they are in the timescale depending on the varying temporal impacts of the incompletely understood environmental factors which are causing the increase in incidence. Nonetheless, there is marked inter-country variation in incidence over time even in European-origin populations [3, 9•].

Adult T1D incidence data are only available from 32 countries. The peak age-of-onset appears to be later in sub-Saharan Africa (from a pool of four studies), and so a separate T1D onset pattern was modelled for this region. Moreover, the diagnosis of adult-onset T1D is complicated by the high incidence of T2D in this age group, with a disease progression that is slower and less likely to present with diabetic ketoacidosis, or for insulin treatment to be initiated at onset irrespective of probable type [15]. Misclassification rates of T1D versus T2D are therefore likely to be high [16, 17], and methods of diagnosis vary, depending on the use or availability of biomarkers such as C-peptide and autoantibodies [4].

The potential for misclassification can also occur in childhood and adolescent populations as well, due to the heterogeneity of T1D [18, 19], which appears to be more pronounced in sub-Saharan Africa and South Asia [5, 20].

It is not possible to obtain data on the death rate from non-diagnosis since unidentified cases by their very nature cannot be reliably enumerated. The rates of death from non-diagnosis used in the index were therefore derived from the JDRF/ISPAD 2020 survey of health professionals, who were asked to estimate, in the region in which they live, the percentage of all children and youth who develop symptomatic T1D who die soon after onset without ever being diagnosed [8••]. They were requested to estimate this rate in three time periods: before 2000, 2000–2010, and after 2010. For example, in Sub-Saharan Africa prior to 2010, the mean survey response was a non-diagnosis rate of 60%.

These numbers may well be underestimates of the number of cases missed. Data from case studies following the initiation of systematic care and increased diabetes awareness can help quantify this issue. For example, in Mali [6], the observed incidence of T1D in the <25-year age group in 2007 was 0.12/100,000, increasing to 0.74/100,000 in 2016, an observed incidence ratio of 6.2. The ratio of observed incidences is equal to the ratio of true incidences multiplied by the ratio of diagnosis rates. Therefore, an increase of this magnitude in the observed incidence can only be due to a substantial increase in true incidence or a substantial increase in diagnosis rate. To explain this increase by true incidence increase alone would require assuming an average annual year-to-year increase in incidence of 22.4% (1.2246 = 6.2), far higher than that observed in other countries. A more modest but still rapid increase in true incidence of 7% per year would only give a true incidence ratio of 1.8 (1.076). To explain the observed ratio of 6.2 would require a diagnosis rate in 2016 that was 3.4 times higher than the 2007 rate (6.2/1.8). Even if no cases were missed in 2016 (diagnosis rate of 100%), these figures would imply a diagnosis rate of just 30% (100%/3.4) in 2007. Similar rapid increases in incidence have been observed in Rwanda [21], Gabon [22], and Burkina Faso [23]. See also the Supplementary Material in [8••].

Recently, Ward et al. [24], using a different modelling approach, have also drawn attention to this large number of estimated deaths from non-diagnosis.

Estimates of mortality in individuals living with T1D were based on an SMR relative to background mortality. The SMRs used in the index are based on the outputs of a machine learning model trained on a very sparse dataset consisting of 71 data points representing the SMR for a person with T1D in a particular year, country, and age group. This extrapolation relied on assumptions including that the overall pattern of SMR variation across an individual’s life-span was constant across all countries and years. In addition, one of the inputs on which the model was trained (aside from readily available population statistics such as infant mortality rate, doctors per capita or GDP) was an estimate of the proportion of individuals in a particular region and at a particular time who were receiving a “minimal” versus a more than minimal (“non-minimal”) level of care (minimal care is defined as a simple insulin regimen with minimal or no self-blood glucose monitoring, HbA1c testing, and diabetes education) [2, 7]). These estimates were made based on expert opinion alone (Table 2).

Projections of future prevalence and associated statistics rely on underlying assumptions about ongoing trends in incidence and mortality which cannot be known with certainty. To account for this, the index modelled two scenarios. In one case (conservative estimate), incidence, mortality, and diagnosis rates were held constant going forward. In the second case (momentum estimate), they continued to change at the average rate of change between 2012 and 2021. Projections suggest that there will be a substantial increase in T1D prevalence between 2020 and 2040, of between 66 and 116% depending on the scenario, due to projected reductions in mortality and, in some regions, increase in incidence. Although the figures are not precise, it is clear that the global burden of T1D will markedly increase in the medium-term future.