Background: Projections of the future burden of ischemic stroke (IS) has not been extensively reported for the Australian population; the availability of such data would assist in health policy planning, clinical guideline updates, and public health. Methods: First, we estimated the lifetime risk of IS (from age 40 to 100 years) using a multistate life table model. Second, a dynamic multistate model was constructed to project the burden of IS for the whole Australian population aged between 40 and 100 years over a 20-year period (2019–2038). Data for the study were primarily sourced from a large, representative Victorian linked dataset based on the Victorian Admitted Episode Dataset and National Death Index. The model projected prevalent and incident cases of nonfatal IS, fatal IS, and years of life lived (YLL) with and without IS. The YLL outcome was discounted by 5% annually; we varied the discounting rate in scenario analyses. Results: The lifetime risk of IS from age 40 years was estimated as 15.5% for males and 14.0% for females in 2018. From 2019 to 2038, 644,208 Australians were projected to develop incident IS (564,922 nonfatal and 79,287 fatal). By 2038, the model projected there would be 358,534 people with prevalent IS, 35,554 people with incident nonfatal IS and 5,338 people with fatal IS, a 14.2% (44,535), 72.9% (14,988), and 106.3% (2,751) increase compared to 2019 estimations, respectively. Projected YLL (with a 5% discount rate) accrued by the Australian population were 174,782,672 (84,251,360 in males and 90,531,312 in females), with 4,053,794 YLL among people with IS (2,320,513 in males, 1,733,281 in females). Conclusion: The burden of IS was projected to increase between 2019 and 2038 in Australia. The outcomes of the model provide important information for decision-makers to design strategies to reduce stroke burden.

Stroke is one of the leading causes of disease burden worldwide, accounting for 11.6% of deaths globally in 2019 [1]. Further, as life expectancy increases, more people are expected to survive to ages where stroke risk is highest [2‒4]. Indeed, a European study projected an overall 3% increase in incident stroke (40,000 additional cases), a 27% increase in prevalent stroke (2.6 million additional prevalent cases), and a 17% decline in mortality associated with stroke (80,000 fewer deaths) in Europe by 2047 when compared with 2017 estimates [5]. In the USA, prevalent cases of stroke are expected to increase by nearly 4% (3.4 million additional people) in 2030 relative to 2012 [6]. In Australia, there were approximately 450,000 prevalent cases of stroke, 68,000 hospitalizations (0.6% of total hospitalizations) and 8,700 deaths (5.4% of total deaths) in 2020 [7, 8]. However, it is unclear how the burden in Australia is expected to change in the future.

Therefore, we have developed a model to estimate the lifetime risk of developing ischemic stroke (IS) [9] and projected the incidence, prevalence, and years of life lived (YLL) with and without IS. The findings of this study will provide crucial information about the expected impact of IS in the future to relevant stakeholders, facilitating informed decision making, and prioritization of strategies for disease prevention and management.

Model Overview

A dynamic multistate model (shown in Fig. 1) was designed to project the burden of IS for the Australian population aged 40–100 years from 2019 to 2038 in yearly cycles. Model outcomes included the prevalence of IS, the incidence of IS (fatal and nonfatal), and YLL with and without IS (discounted at 5% per year). Discounting was applied to consider society’s preference in consuming a product or benefit (in this example: YLL) at present rather than delaying the same consumption until sometime in the future [10, 11]. Hence, present benefits were given more value than future benefits using a technique known as discounting [10, 11]. The formula depicted below was applied to calculate the discounted YLL [11]. A static version of this multistate life table model was also used to estimate the lifetime risk of developing IS for the population of Victoria, Australia, in 2018.
Presentvalue=Futurevalue1+rt
where r is the discount rate (e.g., 5%, 3%, or 0%) and t is the number of years into the future the future value (i.e., YLL) occurs.
Fig. 1.

Structure of the multistate life table model and associated transition probabilities. IS, ischemic stroke; ℓ, 1-year period; λ, ischemic stroke incidence rate; µother, mortality rate for people without ischemic stroke (i.e., other mortality); µpost-IS, post-IS all-cause mortality rate; pfatal, proportion of fatal ischemic stroke; ratesum, λ+ µother; tpsum, 1-exp(-ratesum* ℓ); ratesum, the sum of incidence rate of ischemic stroke and mortality rate for people without ischemic stroke (i.e., other mortality); tpsum, transition probability of ratesum.

Fig. 1.

Structure of the multistate life table model and associated transition probabilities. IS, ischemic stroke; ℓ, 1-year period; λ, ischemic stroke incidence rate; µother, mortality rate for people without ischemic stroke (i.e., other mortality); µpost-IS, post-IS all-cause mortality rate; pfatal, proportion of fatal ischemic stroke; ratesum, λ+ µother; tpsum, 1-exp(-ratesum* ℓ); ratesum, the sum of incidence rate of ischemic stroke and mortality rate for people without ischemic stroke (i.e., other mortality); tpsum, transition probability of ratesum.

Close modal

Model Structure

The model was constructed with four health states: “alive no-IS,” “alive with new-IS,” “alive after surviving IS (i.e., post-IS),” and “death.” The death health state could either be due to causes not related to IS (i.e., “Death other”), due to IS (“fatal-IS”), or death after surviving IS (“Death Post-IS”). From the “alive no-IS” health state, people could move to either “alive with new-IS” health state, “Dead other” health state (absorbing state), or remain in the “alive no-IS” health state. The “alive new-IS” health state was transient only – people moved out of this health state within the same cycle depending on whether the IS was fatal or non-fatal. For nonfatal IS, people moved immediately to the “post-IS” health state; for fatal IS, people moved immediately to the “fatal IS” health state (absorbing state). Once people reached the “post-IS” health state, they could either die and move to the “Death Post-IS” health state (absorbing state) or remain in the “post-IS” health state.

Population

The dynamic model population was based on the Australian population in 2018 (see online suppl. Table s1; for all online suppl. material, see https://doi.org/10.1159/000538800). Age- and sex-specific Australian population data were derived from the Australian Bureau of Statistics (ABS) [12]. This population was then aged in yearly cycles from 2019 to 2038. Migrants were added to the model at the beginning of each cycle [13], as were people who reached the model age range (i.e., the cohort aged from 39 to 40 years each year).

Data Sources

Data for the model transition probabilities were primarily sourced from a Victorian linked dataset, which has been previously described [14, 15]. Briefly, this linked dataset was based on the Victorian Admitted Episode Dataset (VAED), which records all hospital admissions in Victoria. The VAED was linked to the National Death Index (NDI), which records all deaths that occur in Australia. The dataset contained all admissions for IS (defined by the presence of ICD-10 codes I63–I64 as the primary diagnosis for an admission) among people aged ≥40 years in the state of Victoria between July 1, 2012, and June 30, 2017 (with follow-up data until June 30, 2018). In addition to the linked dataset, age-group and sex-specific prevalence of IS and all-cause mortality data in the general population were sourced from the Australian Institute of Health and Welfare (AIHW) [16] and ABS [17], respectively, for the year 2018 (online suppl. Table s2).

Estimating Transition Probabilities in the Model

Stroke prevalence data were only available in broad age groups; thus, we estimated the single-year age- and sex-specific prevalence of stroke in 2018 using beta regression [18, 19]. The model outcome was the prevalence of stroke (expressed as a proportion), the predictor a log-linear effect of age (where age was the mid-point of each age group available), and used a log link function. The predicted prevalence of stroke was then multiplied by 0.87, assuming 87% of strokes were ischemic [9]. Then, these values were used to derive the prevalence of IS in 2018 for the initial model population. Alternative sources of data for IS were implemented in the scenario analysis.

All-cause mortality, incidence of IS, and post-IS mortality data were also only available in age groups; therefore, we estimated the single-year age- and sex-specific rate of each of these outcomes for their respective data in 2018 using Poisson regression [20]. A separate model was fit for each outcome, the predictor for all models was log-linear effect of age (where age was the mid-point of each age group available), used a log link function, and the offset (all log) was general population of Victoria (for all-cause mortality), people without IS (for incidence of IS) and person-years of follow-up of people who survived IS (for estimation of post-IS mortality).

To estimate the proportion of nonfatal IS and fatal IS by single-year age and sex, a beta regression model was fitted [18, 19]. The model included the mid-point of each age group as a log-linear predictor and used a log link function. The estimated values derived from these models were then used to calculate the number of people who did not survive IS.

The number of “other deaths” (i.e., deaths in people who had not experienced IS, which was used to estimate the mortality rate in people without IS) was calculated using the estimated incidence rate of IS, all-cause mortality rate, post-IS mortality rate and proportion of fatal IS from the above models for each sex and single-year age, using the formula depicted below [21, 22].
Deathother=μpop*PopIS+PopwithoutISμpostIS*PopIS+pfatalIs*λ*PopwithoutIS
where Deathother is the number of deaths without developing IS, µpop: the estimated all-cause mortality rate, PopIS: people with IS; Popwithout-IS: people without IS; µpost-IS: estimated post-IS mortality rate; pfatal-IS: estimated proportion of fatal IS; λ: estimated incidence rate of IS.

Once the number of “other deaths” was calculated, Poisson regression was used to estimate the mortality rate for people without IS in 2018, with the predictor a log-linear effect of age, people without IS the offset, and a log link function [20]. A separate model was fit for each sex.

Model Outcomes

These transition probabilities populated both the static and dynamic models described above (Fig. 1). The outcomes captured for the static model was lifetime risk of IS from ages 40 to 100 years. Outcomes from the dynamic model included projected prevalent IS, nonfatal IS, fatal IS, and YLL. To understand the impact of stroke in the population, the main outcomes such as YLL were captured for two cohorts, YLL was calculated for people without IS (YLL without IS) and for people with IS (YLL with IS), with half cycle correction.

Sensitivity and Scenario Analyses

To derive 95% uncertainty intervals (UI) for key outputs, we performed one thousand iterations of a Monte Carlo simulation. Inputs for the simulation were drawn from the distributions appropriate for each parameter (online suppl. Table s3). We also performed scenario analyses, where – (1) we varied the discount rate (0% and 3%), (2) changed the proportion of prevalent IS to 79.1% to accommodate the AIHW estimation [23], and (3) changed the duration of projection to 10 years. All the analyses were performed using Stata version 17.0 (StataCorp, USA).

Lifetime Risk and Life Expectancy

The lifetime risk of developing IS was estimated to be 15.5% (95% UI: 14.9%, 16.1%) and 14.0% (95% UI: 13.4%, 14.6%) for males and females at age 40 years in 2018, respectively (shown in Fig. 2 and online suppl. Table s4).

Fig. 2.

Cumulative incidence of ischemic stroke for people aged between 40 and 100 years in Victoria, Australia, in 2018.

Fig. 2.

Cumulative incidence of ischemic stroke for people aged between 40 and 100 years in Victoria, Australia, in 2018.

Close modal

Prevalence and Incidence of IS from 2019 to 2038

Overall, the model projected 644,208 (95% UI: 615,723, 672,177) incident IS, comprising 564,922 (95% UI: 540,119, 589,785) nonfatal IS and 79,287 (95% UI: 75,011, 83,596) fatal IS for the Australian population aged 40–100 years between 2019 and 2038 (Tables 1, 2). By 2038, 358,534 (95% UI: 343,262, 373,590) people would be expected to live with IS in Australia compared to 313,999 (95% UI: 311,225, 316,720) people in 2019 (online suppl. Table s5–s8). The age of developing IS was different for males and females – there was higher IS in the male population between the fifth decade and the eighth decade of life while females experience more IS after the eighth decade of life (Tables 1, 2).

Table 1.

Projected prevalent and incident IS cases by age group for the Australian population aged 40–100 years over the 20-year period (2019–2038)

Projected prevalent IS cases
age groupmalefemaletotal
prevalent ISLBUBprevalent ISLBUBprevalent ISLBUB
40–49 344,218 338,756 349,498 230,040 226,563 233,625 574,258 565,319 583,123 
50–59 506,651 492,703 519,689 326,765 317,154 336,425 833,416 809,857 856,114 
60–69 795,471 773,928 815,518 533,751 517,178 550,452 1,329,222 1,291,106 1,365,970 
70–79 1,023,221 995,181 1,050,971 729,921 706,432 753,170 1,753,142 1,701,613 1,804,141 
80–89 810,685 782,278 838,688 660,605 637,367 684,141 1,471,290 1,419,645 1,522,829 
90–100 281,422 264,195 299,841 312,554 295,368 329,744 593,976 559,563 629,585 
Projected prevalent IS cases
age groupmalefemaletotal
prevalent ISLBUBprevalent ISLBUBprevalent ISLBUB
40–49 344,218 338,756 349,498 230,040 226,563 233,625 574,258 565,319 583,123 
50–59 506,651 492,703 519,689 326,765 317,154 336,425 833,416 809,857 856,114 
60–69 795,471 773,928 815,518 533,751 517,178 550,452 1,329,222 1,291,106 1,365,970 
70–79 1,023,221 995,181 1,050,971 729,921 706,432 753,170 1,753,142 1,701,613 1,804,141 
80–89 810,685 782,278 838,688 660,605 637,367 684,141 1,471,290 1,419,645 1,522,829 
90–100 281,422 264,195 299,841 312,554 295,368 329,744 593,976 559,563 629,585 
Projected incident IS cases
age groupmalefemaletotal
incident ISLBUBincident ISLBUBincident ISLBUB
40–49 16,752 15,540 17,957 8,794 8,049 9,624 25,546 23,589 27,581 
50–59 31,430 29,730 33,088 18,605 17,411 19,910 50,035 47,141 52,998 
60–69 58,909 56,538 61,142 39,905 38,064 41,877 98,814 94,602 103,019 
70–79 93,683 90,319 96,786 73,121 70,532 75,876 166,804 160,851 172,662 
80–89 98,217 93,987 102,113 93,173 89,698 96,863 191,390 183,685 198,976 
90–100 48,246 45,574 50,896 63,372 60,271 66,666 111,618 105,845 117,562 
Total 347,238 331,594 361,446 296,970 284,129 310,731 644,208 615,723 672,177 
Projected incident IS cases
age groupmalefemaletotal
incident ISLBUBincident ISLBUBincident ISLBUB
40–49 16,752 15,540 17,957 8,794 8,049 9,624 25,546 23,589 27,581 
50–59 31,430 29,730 33,088 18,605 17,411 19,910 50,035 47,141 52,998 
60–69 58,909 56,538 61,142 39,905 38,064 41,877 98,814 94,602 103,019 
70–79 93,683 90,319 96,786 73,121 70,532 75,876 166,804 160,851 172,662 
80–89 98,217 93,987 102,113 93,173 89,698 96,863 191,390 183,685 198,976 
90–100 48,246 45,574 50,896 63,372 60,271 66,666 111,618 105,845 117,562 
Total 347,238 331,594 361,446 296,970 284,129 310,731 644,208 615,723 672,177 

IS, ischemic stroke; LB, lower bound of the uncertainty interval (2.5%); UB, upper bound of the uncertainty interval (97.5%).

Table 2.

Projected nonfatal and fatal IS cases by age group for the Australian population aged 40–100 years over the 20-year period (2019–2038)

Projected nonfatal IS
age groupmalefemaletotal
nonfatal ISLBUBnonfatal ISLBUBnonfatal ISLBUB
40–49 16,346 15,144 17,535 8,568 7,841 9,374 24,914 22,985 26,909 
50–59 30,218 28,508 31,877 17,827 16,659 19,088 48,045 45,167 50,965 
60–69 55,332 52,815 57,727 37,208 35,288 39,218 92,540 88,103 96,945 
70–79 84,906 80,953 88,566 65,334 62,237 68,462 150,240 143,190 157,028 
80–89 84,432 79,697 88,807 78,028 73,848 82,233 162,460 153,545 171,040 
90–100 38,461 35,785 41,166 48,262 44,902 51,672 86,723 80,687 92,838 
Total 309,695 295,616 322,991 255,227 244,503 266,794 564,922 540,119 589,785 
Projected nonfatal IS
age groupmalefemaletotal
nonfatal ISLBUBnonfatal ISLBUBnonfatal ISLBUB
40–49 16,346 15,144 17,535 8,568 7,841 9,374 24,914 22,985 26,909 
50–59 30,218 28,508 31,877 17,827 16,659 19,088 48,045 45,167 50,965 
60–69 55,332 52,815 57,727 37,208 35,288 39,218 92,540 88,103 96,945 
70–79 84,906 80,953 88,566 65,334 62,237 68,462 150,240 143,190 157,028 
80–89 84,432 79,697 88,807 78,028 73,848 82,233 162,460 153,545 171,040 
90–100 38,461 35,785 41,166 48,262 44,902 51,672 86,723 80,687 92,838 
Total 309,695 295,616 322,991 255,227 244,503 266,794 564,922 540,119 589,785 
Projected fatal IS
age groupmalefemaletotal
fatal ISLBUBfatal ISLBUBfatal ISLBUB
40–49 406 215 658 225 122 365 631 337 1,023 
50–59 1,212 736 1,805 779 482 1,146 1,991 1,218 2,951 
60–69 3,578 2,472 4,934 2,697 1,893 3,653 6,275 4,365 8,587 
70–79 8,777 6,497 11,329 7,787 5,895 9,838 16,564 12,392 21,167 
80–89 13,785 10,863 16,983 15,145 12,205 18,330 28,930 23,068 35,313 
90–100 9,785 8,052 11,644 15,110 12,780 17,687 24,895 20,832 29,331 
Total 37,543 35,592 39,612 41,744 39,419 43,984 79,287 75,011 83,596 
Projected fatal IS
age groupmalefemaletotal
fatal ISLBUBfatal ISLBUBfatal ISLBUB
40–49 406 215 658 225 122 365 631 337 1,023 
50–59 1,212 736 1,805 779 482 1,146 1,991 1,218 2,951 
60–69 3,578 2,472 4,934 2,697 1,893 3,653 6,275 4,365 8,587 
70–79 8,777 6,497 11,329 7,787 5,895 9,838 16,564 12,392 21,167 
80–89 13,785 10,863 16,983 15,145 12,205 18,330 28,930 23,068 35,313 
90–100 9,785 8,052 11,644 15,110 12,780 17,687 24,895 20,832 29,331 
Total 37,543 35,592 39,612 41,744 39,419 43,984 79,287 75,011 83,596 

IS, ischemic stroke; LB, lower bound of the uncertainty interval (2.5%); UB, upper bound of the uncertainty interval (97.5%).

Years of Life Lived with and without IS from 2019 to 2038

Over the 20-year period, the model projected the total YLL accrued by the Australian population would be 174,782,672 (95% UI: 174,500,992, 175,045,144). Of these, 170,728,880 (95% UI: 170,452,920, 171,015,752) YLL would be accrued by people without IS and the remaining 4,053,794 (95% UI: 3,944,326, 4,161,150) YLL would be accrued by people with IS (Table 3). While females were projected to accrue a greater total number of YLL, males were projected to accrue more YLL with IS (Table 3; online suppl. Tables s9–s11).

Table 3.

Projected YLL, overall and with and without IS, by age group for the Australian population aged 40–100 years over the 20-year period (2019–2038)

Discounting (5%)
age groupmalefemaletotal
YLLLBUBYLLLBUBYLLLBUB
Projected YLL without IS 
 40–49 23,624,345 23,618,901 23,630,286 24,291,366 24,287,724 24,294,775 47,915,711 47,906,625 47,925,061 
 50–59 20,611,251 20,597,053 20,626,802 21,588,320 21,577,556 21,598,440 42,199,571 42,174,609 42,225,242 
 60–69 17,676,705 17,654,297 17,701,522 19,101,545 19,081,329 19,120,948 36,778,250 36,735,626 36,822,470 
 70–79 12,740,220 12,709,302 12,774,919 14,290,735 14,260,379 14,320,054 27,030,955 26,969,681 27,094,973 
 80–89 5,968,727 5,930,566 6,011,375 7,428,561 7,390,447 7,465,098 13,397,288 13,321,013 13,476,473 
 90–100 1,309,598 1,282,626 1,339,546 2,097,508 2,061,443 2,131,424 3,407,106 3,344,069 3,470,970 
 Total 81,930,848 81,792,792 82,084,520 88,798,032 88,660,128 88,931,232 170,728,880 170,452,920 171,015,752 
Projected YLL with IS 
 40–49 214,493 211,628 217,221 144,776 142,923 146,667 359,269 354,551 363,888 
 50–59 325,823 318,452 332,690 214,540 209,348 219,691 540,363 527,800 552,381 
 60–69 507,932 496,447 518,729 345,987 336,951 355,012 853,919 833,398 873,741 
 70–79 635,832 620,926 650,798 454,407 441,500 466,923 1,090,239 1,062,426 1,117,721 
 80–89 479,246 464,363 494,186 392,987 380,476 405,501 872,233 844,839 899,687 
 90–100 157,186 148,021 167,002 180,584 171,138 189,934 337,770 319,159 356,936 
 Total 2,320,513 2,261,371 2,378,310 1,733,281 1,682,955 1,782,840 4,053,794 3,944,326 4,161,150 
Projected total YLL 
 40–49 23,838,838 23,833,831 23,843,712 24,436,142 24,432,846 24,438,900 48,274,980 48,266,677 48,282,612 
 50–59 20,937,075 20,923,648 20,950,179 21,802,860 21,792,927 21,811,292 42,739,935 42,716,575 42,761,471 
 60–69 18,184,637 18,162,076 18,206,473 19,447,532 19,428,536 19,464,366 37,632,169 37,590,612 37,670,839 
 70–79 13,376,052 13,344,252 13,408,021 14,745,142 14,715,570 14,772,272 28,121,194 28,059,822 28,180,293 
 80–89 6,447,973 6,407,824 6,488,128 7,821,548 7,783,592 7,856,585 14,269,521 14,191,416 14,344,713 
 90–100 1,466,785 1,437,332 1,496,308 2,278,092 2,241,365 2,312,259 3,744,877 3,678,697 3,808,567 
 Total 84,251,360 84,106,160 84,391,184 90,531,312 90,394,832 90,653,960 174,782,672 174,500,992 175,045,144 
Discounting (5%)
age groupmalefemaletotal
YLLLBUBYLLLBUBYLLLBUB
Projected YLL without IS 
 40–49 23,624,345 23,618,901 23,630,286 24,291,366 24,287,724 24,294,775 47,915,711 47,906,625 47,925,061 
 50–59 20,611,251 20,597,053 20,626,802 21,588,320 21,577,556 21,598,440 42,199,571 42,174,609 42,225,242 
 60–69 17,676,705 17,654,297 17,701,522 19,101,545 19,081,329 19,120,948 36,778,250 36,735,626 36,822,470 
 70–79 12,740,220 12,709,302 12,774,919 14,290,735 14,260,379 14,320,054 27,030,955 26,969,681 27,094,973 
 80–89 5,968,727 5,930,566 6,011,375 7,428,561 7,390,447 7,465,098 13,397,288 13,321,013 13,476,473 
 90–100 1,309,598 1,282,626 1,339,546 2,097,508 2,061,443 2,131,424 3,407,106 3,344,069 3,470,970 
 Total 81,930,848 81,792,792 82,084,520 88,798,032 88,660,128 88,931,232 170,728,880 170,452,920 171,015,752 
Projected YLL with IS 
 40–49 214,493 211,628 217,221 144,776 142,923 146,667 359,269 354,551 363,888 
 50–59 325,823 318,452 332,690 214,540 209,348 219,691 540,363 527,800 552,381 
 60–69 507,932 496,447 518,729 345,987 336,951 355,012 853,919 833,398 873,741 
 70–79 635,832 620,926 650,798 454,407 441,500 466,923 1,090,239 1,062,426 1,117,721 
 80–89 479,246 464,363 494,186 392,987 380,476 405,501 872,233 844,839 899,687 
 90–100 157,186 148,021 167,002 180,584 171,138 189,934 337,770 319,159 356,936 
 Total 2,320,513 2,261,371 2,378,310 1,733,281 1,682,955 1,782,840 4,053,794 3,944,326 4,161,150 
Projected total YLL 
 40–49 23,838,838 23,833,831 23,843,712 24,436,142 24,432,846 24,438,900 48,274,980 48,266,677 48,282,612 
 50–59 20,937,075 20,923,648 20,950,179 21,802,860 21,792,927 21,811,292 42,739,935 42,716,575 42,761,471 
 60–69 18,184,637 18,162,076 18,206,473 19,447,532 19,428,536 19,464,366 37,632,169 37,590,612 37,670,839 
 70–79 13,376,052 13,344,252 13,408,021 14,745,142 14,715,570 14,772,272 28,121,194 28,059,822 28,180,293 
 80–89 6,447,973 6,407,824 6,488,128 7,821,548 7,783,592 7,856,585 14,269,521 14,191,416 14,344,713 
 90–100 1,466,785 1,437,332 1,496,308 2,278,092 2,241,365 2,312,259 3,744,877 3,678,697 3,808,567 
 Total 84,251,360 84,106,160 84,391,184 90,531,312 90,394,832 90,653,960 174,782,672 174,500,992 175,045,144 

YLL, years of life lived; LB, lower bound of the uncertainty interval (2.5%); UB, upper bound of the uncertainty interval (97.5%).

Scenario Analyses

Results from the scenario analyses varying the discounting rate, the proportion of IS (of all stroke forms) and the projection period are shown in Table 4. Decreasing the discounting rate increased the total YLL by 18% when the discounting rate was 3% and 57% when the discounting rate was 0%. Changing the proportion of IS from 87% to 79.1% had a negligible effect on the total YLL (reduced the total YLL by 0.05%).

Table 4.

Results of scenario analyses for projected YLL by the Australian population aged 40–100 years

OutcomeMaleFemaleTotal
estimateLBUBestimateLBUBestimateLBUB
20-year projection 
 Discount rate 5% 
  YLL w/o IS 81,930,848 81,792,792 82,084,520 88,798,032 88,660,128 88,931,232 170,728,880 170,452,920 171,015,752 
  YLL with IS 2,320,513 2,261,371 2,378,310 1,733,281 1,682,955 1,782,840 4,053,794 3,944,326 4,161,150 
  Total YLL 84,251,360 84,106,160 84,391,184 90,531,312 90,394,832 90,653,960 174,782,672 174,500,992 175,045,144 
 Discount rate 3% 
  YLL w/o IS 96,954,336 96,782,608 97,145,504 105,133,672 104,962,336 105,299,312 202,088,008 201,744,944 202,444,816 
  YLL with IS 2,731,027 2,657,980 2,802,101 2,033,432 1,971,926 2,094,133 4,764,459 4,629,906 4,896,234 
  Total YLL 99,685,368 99,505,016 99,859,520 107,167,104 106,998,032 107,319,320 206,852,472 206,503,048 207,178,840 
 Discount rate 0% 
  YLL w/o IS 128,611,952 128,367,264 128,884,384 139,570,896 139,327,136 139,806,816 268,182,848 267,694,400 268,691,200 
  YLL with IS 3,593,116 3,490,459 3,692,708 2,662,685 2,576,842 2,747,726 6,255,801 6,067,301 6,440,434 
  Total YLL 132,205,064 131,948,632 132,453,496 142,233,584 141,994,064 142,449,792 274,438,648 273,942,696 274,903,288 
 Proportion of IS (79.1%)a 
  YLL w/o IS 82,016,280 81,878,080 82,170,208 88,849,976 88,711,960 88,983,096 170,866,256 170,590,040 171,153,304 
  YLL with IS 2,192,133 2,134,778 2,247,746 1,637,722 1,589,301 1,685,815 3,829,855 3,724,079 3,933,561 
  Total YLL 84,208,416 84,065,008 84,348,384 90,487,696 90,352,032 90,609,864 174,696,112 174,417,040 174,958,248 
10-year projection 
 Discount rate 5% 
  YLL w/o IS 47,493,200 47,440,248 47,552,072 51,290,880 51,237,096 51,342,248 98,784,080 98,677,344 98,894,320 
  YLL with IS 1,392,480 1,367,502 1,417,562 1,060,352 1,037,785 1,082,850 2,452,832 2,405,287 2,500,412 
  Total YLL 48,885,680 48,829,720 48,939,172 52,351,236 52,297,244 52,399,592 101,236,916 101,126,964 101,338,764 
OutcomeMaleFemaleTotal
estimateLBUBestimateLBUBestimateLBUB
20-year projection 
 Discount rate 5% 
  YLL w/o IS 81,930,848 81,792,792 82,084,520 88,798,032 88,660,128 88,931,232 170,728,880 170,452,920 171,015,752 
  YLL with IS 2,320,513 2,261,371 2,378,310 1,733,281 1,682,955 1,782,840 4,053,794 3,944,326 4,161,150 
  Total YLL 84,251,360 84,106,160 84,391,184 90,531,312 90,394,832 90,653,960 174,782,672 174,500,992 175,045,144 
 Discount rate 3% 
  YLL w/o IS 96,954,336 96,782,608 97,145,504 105,133,672 104,962,336 105,299,312 202,088,008 201,744,944 202,444,816 
  YLL with IS 2,731,027 2,657,980 2,802,101 2,033,432 1,971,926 2,094,133 4,764,459 4,629,906 4,896,234 
  Total YLL 99,685,368 99,505,016 99,859,520 107,167,104 106,998,032 107,319,320 206,852,472 206,503,048 207,178,840 
 Discount rate 0% 
  YLL w/o IS 128,611,952 128,367,264 128,884,384 139,570,896 139,327,136 139,806,816 268,182,848 267,694,400 268,691,200 
  YLL with IS 3,593,116 3,490,459 3,692,708 2,662,685 2,576,842 2,747,726 6,255,801 6,067,301 6,440,434 
  Total YLL 132,205,064 131,948,632 132,453,496 142,233,584 141,994,064 142,449,792 274,438,648 273,942,696 274,903,288 
 Proportion of IS (79.1%)a 
  YLL w/o IS 82,016,280 81,878,080 82,170,208 88,849,976 88,711,960 88,983,096 170,866,256 170,590,040 171,153,304 
  YLL with IS 2,192,133 2,134,778 2,247,746 1,637,722 1,589,301 1,685,815 3,829,855 3,724,079 3,933,561 
  Total YLL 84,208,416 84,065,008 84,348,384 90,487,696 90,352,032 90,609,864 174,696,112 174,417,040 174,958,248 
10-year projection 
 Discount rate 5% 
  YLL w/o IS 47,493,200 47,440,248 47,552,072 51,290,880 51,237,096 51,342,248 98,784,080 98,677,344 98,894,320 
  YLL with IS 1,392,480 1,367,502 1,417,562 1,060,352 1,037,785 1,082,850 2,452,832 2,405,287 2,500,412 
  Total YLL 48,885,680 48,829,720 48,939,172 52,351,236 52,297,244 52,399,592 101,236,916 101,126,964 101,338,764 

YLL, years of life lived; w/o, without; LB, lower bound of the uncertainty interval (2.5%); UB, upper bound of the uncertainty interval (97.5%).

aThe proportion of IS is the percentage of IS of all forms for stroke. YLL are reported for the annual 5% discount rate.

The lifetime risk of IS from age 40 to 100 years was estimated to be 15.5% for males and 14.0% for females in Victoria in 2018 (i.e., approximately 1 in 7 people were expected to develop IS over their lifetime). Our dynamic model projected that, over the 20 years from 2019 to 2038, 0.64 million Australians aged 40 years or older would experience an incident IS and approximately 4 million (2% of total) YLL would accrue among people with IS.

The lifetime risk of IS in our study is lower than the reported lifetime risk from the Framingham study (around 1 in 6 for men and 1 in 5 for women) [24], Rotterdam study (around 1 in 5 for both men and women) [25] and a Japanese study (around 1 in 5 for both men and women) [26]. The difference in the estimation of the lifetime risk of IS partly attributable to the fact that our study mainly focused on the lifetime risk of IS, while in contrast, the previous studies primarily reported on the lifetime risk of stroke in general [24‒26].

Our study presented the projected health burden of IS by age and sex stratification. In keeping with existing knowledge [27], we projected that a higher number of males would experience the burden of IS in early years of life while females experience the burden in later years of life. However, the overall fatal IS throughout the projection period was higher for females than males. Recent studies also showed that fatal stroke in females exceeded that of males [27‒30]. Multiple factors could contribute to the exceeding stroke case fatality in females than males, the leading on likely being higher life expectancy [28, 31] although other female-specific risk factors that increase the risk of developing stroke could include use of menopausal hormone therapy [28, 32, 33], combined oral contraceptives [28, 34], time from menarche to menopause with elevated stroke risk in premature (<40 years) and early (40–44 years) menopause [28, 34, 35]. Pregnancy related adverse outcomes, including preeclampsia, gestational hypertension, and preterm delivery and fetal growth restriction have been associated with long-term risk of stroke [28, 36‒38]. The living condition at the onset of stroke also contributes to the case fatality of stroke in female population [28, 39]. Females, due to longer life expectancy, in general tend to be widowed, unmarried, or living alone and more likely affected by disability curbing their activity of daily living compared to males at onset of stroke [28, 39]. With increasing life expectancy and aging population [31, 40], and given predominance of females in the ≥85 years age group [40], the future burden of stroke would disproportionately impact females.

Current research in biological sex differences in stroke provides better understanding of the risk factors specific to each sex and could help tailor preventive treatment and recovery strategies accordingly [28, 41]. With increasing research interest on gender (i.e., the social construct) in addition to biological sex difference in stroke, there is an opportunity to capture more information on individuals’ traits beyond biological characteristics including gender identity, expression, roles, and stereotypes [28, 42]. Furthermore, gender is also relating to individual’s economic resources and healthcare access which could affect health [28, 41]. This could unmask more knowledge to stroke burden not addressed by biological sex stratification [28, 41] and could modify sex differences in the projected IS burden in the coming decades.

The projected increase in the health burden of IS underscores the importance of primary preventive strategies. Reprioritising primary prevention strategies could have a considerable impact on the economy [43] – every US$1 invested in the prevention of stroke and cardiovascular disease is estimated to return US$10 [43]. However, priorities for high-risk population screening as a primary prevention strategy for stroke left out many people without appropriate interventions [44]. Resetting the priorities toward population-wide strategies that reduce exposure to risk factors across a lifespan of the whole population is needed [44, 45]. Integrating healthy lifestyle with interventions (e.g., polypill) targeting risk factors for stroke has been proposed as an effective way of implementing population-wide preventive strategies [45, 46]. Policies should be in place to make sure sustainable funding available for implementing population-wide strategies, training and deployment of community health workers to facilitate implementation of prevention strategies and monitoring the effectiveness of such strategies through research [44‒46].

Strengthening secondary prevention strategies is also needed to reduce the recurrence of IS [45]. A recent report from Australia showed that the proportion of people with IS discharged from hospitals on antihypertensive medications, antithrombotic medications or lipid-lowering medications were below the achievable benchmarks [47]. For instance, 77% of people with IS discharged on antihypertensive medications compared to the achievable benchmark of 94% [47].

Strengths and Limitations

Our study projects outcomes over a 20-years period (2019–2038) specifically for IS, using a large, representative dataset as the data source. Victoria state account for the second largest population in Australia [12] and projecting data sourced from the Victorian linked dataset to the whole Australian population would represent the health burden of IS. The dynamic nature of the model allows us to account temporal changes in population demography into the model while projecting these outcomes.

Lifetime risk estimates are only applicable to the entire population, and we did not have access to clinical data allowing us to stratify by important clinical characteristics. Despite this caveat, lifetime risk estimations are useful for public education and awareness campaigns as they are intuitive to comprehend [48]. For instance, presenting the lifetime risk of IS for the male population aged ≥40 years in Australia as “1 in 7” will have a better impact on awareness creation than providing other measures of risk.

Another limitation is that the post-IS mortality data were derived from a short follow-up period (up to 6 years); thus, we likely overestimated post-IS mortality rates. Our model depended on estimates from overt IS without the consideration of the additional burden of covert (silent) IS; moreover, deaths not recorded in the NDI were not considered which will encompass people who died overseas or died before being admitted to the hospital. These limitations will have led to underestimation of the true burden of IS. Furthermore, the rates and proportions estimated held constant throughout the projection time. Thus, our estimates should be considered projections, and not predictions, as the underlying rates and proportions used in the models will likely change from 2019 to 2038.

The study also did not consider socioeconomic characteristics in the model – people with greater socioeconomic disadvantage accumulate a greater burden of CVD [49]. Finally, although our study is likely generalizable to the Australian population, it is unlikely to be representative globally, as demonstrated by the regional differences in lifetime risk of stroke [50] and stroke-related mortality rates [1] in different parts of the world.

Our model projected the burden of IS in Australia from 2019 to 2038. The findings of this study provide a multitude benefit for various stakeholders – policy-makers may utilize the outcomes to develop and implement targeted prevention and treatment strategies for IS in Australia; clinicians may use the findings for future guideline updates related to IS; and outcomes such as lifetime risk can be used to as an effective way of disseminating the burden of IS information across the community.

The authors would like to thank the Victorian Department of Health as the source of VAED data; the Center for Victorian Data Linkage (Victorian Department of Health) for the provision of data linkage, and the Australian Institute of Health and Welfare provided access to the NDI.

This study was approved by the Australian Institute of Health and Welfare Ethics Committee (EO2018/4/468) and Monash University Human Research Ethics Committee (14339). We have received a waiver for informed consent to access data.

The authors have stated explicitly that there are no conflicts of interest in connection with this article.

T.B.A. is supported by Monash Graduate Research Scholarship and Monash International Tuition Fee Scholarship. J.I.M. and Z.A. were supported by the National Health and Medical Research Council Ideas Grants Application ID: 2012582. The funder had no input into the design of the study or decision to submit for publication. J.I. has received funding from AstraZeneca and Amgen for projects unrelated to this study.

T.B.A.: conceptualization (support); formal analysis (lead); methodology (equal); software (lead); visualization (lead); writing-original draft (lead); and writing-review and editing (equal). J.I.M.: conceptualization (equal); methodology (equal); software (support); visualization (support); writing – review and editing (equal); and supervision (equal). J.I.: conceptualization (support); writing – review and editing (equal); and supervision (support). Z.A.: conceptualization (lead); methodology (lead); visualization (support); writing – review and editing (equal); supervision (lead); and resources (lead).

The data that support the findings of this study are available in the supplementary material of this article.

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