The T1D Index: Implications of Initial Results, Data Limitations, and Future Development

There is an urgent need for improved type 1 diabetes (T1D) epidemiology data across the world, in order to guide and inform resource allocation and health professional training and thereby improve outcomes. Data on T1D incidence, prevalence, or mortality is absent in many countries, and, even when there are data, they are often dated [1•, 2, 3]. Furthermore, although onset of clinical T1D can occur throughout the lifespan, epidemiological data is concentrated on T1D in children and youth and is scarce for adults [2, 4].

This problem of missing data is most pronounced in low- and middle-income countries where access to care is limited, and complications and mortality rates are thought to be high due to marked differences in diabetes diagnosis rates and treatment [2, 5‐7].

The T1D Index project of the JDRF, Life for a Child, International Society for Pediatric and Adolescent Diabetes (ISPAD) and the International Diabetes Federation (IDF) was designed to produce just such an understanding by providing estimates of T1D prevalence and mortality in all countries, both now and into the future [8•, 9•]. It is an evolving, open-source project aimed at addressing data shortages through an innovative simulation-based approach, which will be updated regularly as new data become available. The initial findings have been published recently [8•] and were used in a 2022 update for the IDF Atlas [10]. These results emphasise the need for timely diagnosis of T1D and improved access to higher levels of care, which could together save millions of lives in coming decades.

At the heart of the T1D Index is a Markov model that estimates the total number of people who have been diagnosed with or died from T1D in any given year based on incidence, mortality, and population size. In simple terms, historical data are used to follow a birth cohort of individuals throughout their simulated lives, with particular proportions of a given cohort developing symptomatic T1D, dying from T1D, or dying from other causes each year, based on the incidence and mortality data available for that year.

Whilst the concept of a Markov model is mathematically sound and frequently used in epidemiologic studies [8•, 11], the outputs of such a model (namely, estimates of prevalence and life-expectancy, and quantities derived from counter-factual scenarios such as “missing prevalence”) can only be as accurate as the model’s inputs allow. In particular, to provide a full set of prevalence estimates in a given year for individuals under the age of 80 years (y), the Markov model requires estimates of population, incidence, and mortality in each country, in each year, and at each age going back at least 80 y prior to the year in question. In addition, to project future prevalence estimates, the model requires projections of population, incidence, and mortality as inputs.

Determining estimates of incidence with this degree of granularity requires access to detailed data on incidence and population denominators in narrow age bands. Some data on diagnosed incidence (under age 20 y) for approximately half of the world’s countries are available from the literature. Data on adult incidence in 32 countries were obtained from Harding and colleagues [4]. However, although T1D incidence has been changing over time [3], data on long-term historical incidence are only available for relatively few countries. This first edition of the T1D Index used ratio-based imputation and stratified regression to extrapolate estimates based on these limited available data.

Producing mortality estimates poses similar challenges in terms of data limitations, requiring estimates of both background and diabetes-related mortality in each country, for each decrement of age and year. Whilst estimates of background population mortality are readily accessible from the UN World Population Prospects [12], it is much less clear how to model mortality arising from T1D, particularly historically. The approach taken in the T1D Index separates T1D-associated mortality into two categories: death soon after onset of symptomatic T1D from non-diagnosis and death in individuals living with a T1D diagnosis. Deaths from non-diagnosis were estimated based on a survey conducted by JDRF and ISPAD in 2020. These were modelled for those younger than 25 y in all countries except for high-income countries (where such deaths were assumed to be zero). Death in individuals living with T1D was estimated as a standardised mortality ratio (SMR) relative to the population mortality. These estimates were derived from a machine-learning method that, for countries and years without published data, predicted SMR based on region, country income class, infant mortality rate, doctors per capita, gross domestic product (GDP) per capita, mortality <5 years, and urbanisation percentage..

Not only is this approach novel but separating the two types of T1D-related deaths allows a quantification of the relative impacts of interventions targeting accurate diagnosis as compared to improving the quality of chronic care. Furthermore, modelling diabetes-related mortality in diagnosed patients based on an SMR allows the index to be integrated with previous work that has estimated SMRs for a given mean HbA1c [2]. The T1D Index can then be used to model the impact of certain T1D interventions such as the provision of insulin and blood glucose test strips on any of the model outputs (e.g., prevalence, life expectancy) by estimating the impact of these interventions on mean HbA1c and thereby on SMR.

The model estimated that there were 8.4 million prevalent cases of T1D in 2021, with 1.5 million (18%) of these in people <20 y [8••]. The majority of all-age prevalent cases lived in just 10 countries (USA, India, Brazil, China, Germany, UK, Russia, Canada, Saudi Arabia, and Spain).

In 2021, there were an estimated 510,000 incident cases, with 194,000 <20 y [8••].

The results showed that exclusive focus on T1D in the paediatric and young adult population overlooks a significant portion of the T1D global burden. Adult-onset T1D is, in fact, more common than childhood-onset. The median age at diagnosis is 29 y, and the median age of a person living with T1D is 39 y. Although there is an early peak in the age of onset around age 10–14 y, incidence in adults remains substantial with even a potential trend towards an increasing incidence after age 50 y [8••].

Prevalence per 100,000 population in 2021 <20 y varied significantly around the world, from 1.5 in Papua New Guinea to 534 in Finland. Table 1 gives 2021 prevalence estimates for all countries for <20 y and all ages, and Fig. 1 shows prevalence by world region and income group. Table 1 in the Supplementary Materials provides prevalence/100,000 for various age groups for all countries.

Diagnosed incidence <15 y ranged from 0.02/100,000 in parts of Melanesia to >50/100,000 in parts of Europe and the Middle East (see Table 1).

Table 1 in the Supplementary Materials provides prevalence and incidence per 100,000 <15 y, 15–19 y, <20 y, and all ages.

In 2021, there were an estimated 175,000 deaths due to T1D, 35,000 (20%) due to non-diagnosis, and 140,000 (80%) related to excess mortality in diagnosed individuals. However, the relative impact of these two types of mortality is reversed in the younger population. Of the 52,600 deaths occurring in individuals aged <25 y, the majority (67%) of deaths were attributed to non-diagnosis. Most of these occurred in Sub-Saharan Africa (14,500 deaths) and South Asia (8,700) [8••].

Estimated life expectancy of a 10-year old who develops new-onset symptomatic T1D varied from 7 years in a few African countries to up to 70 years in high-income countries [8••].

Using the T1D Index prevalence data and the IDF Atlas type 2 diabetes (T2D) prevalence data, T1D as a percentage of all diabetes in the population aged 0–79 years is 1.6% globally (this calculation excludes T2D <20 y as there are no current global estimates of this number). As the incidence of both T1D and T2D vary widely around the world, this percentage varies from 0.01% in Papua New Guinea to 17.8% in Norway. Figure 2 shows the range of T1D as a percentage of all cases of diabetes aged 0–79 y by world region and income group, and Table 1 provides this number for all countries.

This work aimed to quantify health-adjusted life years. HALYs, also known as disability-free life-expectancy, is the number of remaining years a person is expected to live without disability. HALYs were quantified by a two-stage procedure. In the first stage, a level of care is assigned to a particular region [2]. A given level of care is assumed to be associated with a particular level of glycaemic control, reflected in the mean achieved HbA1c, and in turn with a particular SMR for diabetes-associated mortality. An iteration of the T1D Index model is run to produce estimates of incidence, prevalence, and mortality for that region. In particular, such an iteration allows estimation of the number of individuals in a particular region who have been living with diabetes for any duration of time.

In the second stage, the proportions of individuals living with specific complications of diabetes are determined using methodology from the childhood-onset cohort of the University of Pittsburgh Epidemiology of Diabetes Complications (EDC) study [25•], followed out to 30 years after diagnosis. This methodology provides estimates of the prevalence of several complications at each duration of diabetes, as a function of mean HbA1c. The modelled complications are distal symmetric polyneuropathy, ulcer or amputation, hypertension or microalbuminuria, overt nephropathy, proliferative retinopathy, blindness, non-fatal myocardial infarction, and non-fatal cerebrovascular disease. Each complication type is modelled as a separate occurrence, conditionally independent of one another at a given level of glycaemic control (mean Hba1c). The exception to this principle is an adjustment made to disallow the de novo occurrence of proliferative retinopathy in an already blind individual, and likewise of hypertension/microalbuminuria in an individual with overt nephropathy (such situations were termed ‘impermissible journeys’). Figure 3 is an example of the Gregory et al. [25•] study results, showing the modelled frequency of selected complications at 30 years after diagnosis. Finally, these proportions are combined with population data and standard disability adjusted life years (DALY) costs for each complication to produce an overall estimate of DALYs, reframed as HALYs lost.

The analysis showed marked disparities in predicted outcomes for people living with T1D depending on geography. The estimated HLY lost for an individual diagnosed with T1D at age 10 y in 2021 varied from 14 to 55 years (Fig. 4). Figure 5 shows, for 12 representative countries, that the HALY lost in high-income countries are mostly due to disability, whilst in lower-income countries, a shorter lifespan is the major contributor to total HLY lost.

This analysis has a number of limitations. In addition to those noted above regarding the modelling of diabetes-related mortality with SMRs, key assumptions of this modelling are (a) that a particular level of care is able to achieve a given mean HbA1c and (b) that mortality and complication rates can be reliably estimated based on this metric. In practice, the HbA1c achieved by a particular level of care will vary both across individuals receiving that care level, and within patients throughout the duration of their diabetes. For example, the level of glycaemic control tends to vary with age and likely varies across time.

In addition, the EDC study only estimated complication rates up to a diabetes duration of 30 years, and it is not clear what the best assumptions should be for extrapolating ongoing rates of complications beyond 30-year duration. It was also a study of complication rates in a higher-income setting, which may underestimate the complication rates truly experienced by people with diabetes with higher mean HbA1c’s in lower income settings.

The T1D Index model’s limitations are not static, but rather serve as catalysts for continuous refinement. This improvement is a dual-pronged process involving the integration of new data and the enhancement of the model’s components.

New T1D-specific data will be assimilated into the model at each revision. This includes not just incidence, prevalence, and mortality data but also information on the proportions in each country receiving specific levels of care, and attendant outcomes. These epidemiological and clinical data are becoming more abundant due to the establishment of regional and national registries, the continuous expansion of trans-national initiatives such as the SWEET Database [26], the fostering of epidemiological studies in countries where there are no data or it is quite dated [1•, 27], and data resulting from the operations and attendant research, in partnership with local stakeholders, of programs such as Life for a Child [28], Changing Diabetes in Children [29], and Action4Diabetes [30]. Data collection is becoming easier through digital tools that streamline data collection with in-clinic workflows, using mobile phones and the web. However, there is a recognition [27] that guidelines and tools are needed, such as for standardising the definition of T1D, with resources such as the IDF Guide for Diabetes Epidemiology Studies being potentially helpful in this regard [31].

We will also incorporate changes in underlying country characteristics such as infant mortality, doctor-population ratio, urbanisation rates, and economic changes.

This generation of new data should progressively enhance the accuracy of the index’s predictions. Ideally, as data quality improves and ascertainment becomes complete, reliance on predictions will diminish.

In the interim and beyond integrating new data, we are also committed to refining the model's components. Our strategy involves implementing our own improvements and modifications, which will be open for public comment before integration.

Key areas of methodological improvement we are exploring for future versions of the T1D Index include the following:

Prevalence: Integrating actual prevalence data (e.g., from registries) into the model. Discrepancies between observed prevalence and the model’s predictions will inform adjustments to our overall estimates as well as the inputs and algorithms which shaped them. A “tuning” algorithm of this kind will also mean that including prevalence data from one country will help to refine estimates for similar countries.

Incidence: Through increased access to registry data, as well as new studies and refinements in modelling, we aim to improve our capacity for more accurately projecting out-of-sample data, particularly in settings where a lower incidence (from an older study) could still imply a significantly higher future incidence. We also aim to examine regional differences within large countries such as India.

Diagnosis Rates: Our estimates of non-diagnosis mortality rates described above suggest that the model may currently be under-estimating these rates. We are interested in exploring other estimation methods as well as encouraging and supporting new interventions aimed at improving diagnosis, such as those described in Mali [6].

Mortality and Complication Rates: Recent models predict these rates using published mortality rates and either HbA1C [25•] and/or country characteristics [8••]. Future model revisions will integrate these approaches and include data from registries and administrative data under various monitoring and treatment regimens. This will allow us to improve our estimates of mortality and complication rates, and also improve projections, by leveraging data on the adoption rates of different types and models of care in each country.

We welcome feedback and suggestions for improvement, which can be submitted directly to the authors or to the Index staff at hello@t1dindex.org.

The first version of the T1D Index has produced robust estimates of T1D incidence, prevalence, and mortality for all countries. The various limitations of the model will be reduced with progressive new versions as more data become available and the modelling is refined.

The results demonstrate great disparity in outcomes around the world. Initiatives are needed to reduce death from non-diagnosis, and improve the level of care, especially in low- and middle-income countries.