NISAN - the National Institute for Stroke and Applied Neurosciences

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https://hdl.handle.net/10292/10244

Institute Director: Professor Valery Feigin

Deputy Director: Associate Professor Alice Theadom



The National Institute for Stroke and Applied Neurosciences (NISAN) conducts epidemiological studies and clinical trials to improve health and outcomes in people with major neurological disorders. Current research programmes focus on:
  • Stroke
  • Traumatic brain injury
  • Neuromuscular disorders
Research with this aim is unique in New Zealand and NISAN is a hub for information sharing and developing a cohesive network between existing research and clinical groups with interests in:
  • Neuroepidemiology
  • Public health
  • Neurorehabilitation
  • Neuropsychology
  • Biostatistics

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Browsing NISAN - the National Institute for Stroke and Applied Neurosciences by Author "Abate, YH"

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  • Item
    Global Burden of 288 Causes of Death and Life Expectancy Decomposition in 204 Countries and Territories and 811 Subnational Locations, 1990–2021: A Systematic Analysis for the Global Burden of Disease Study 2021
    (Elsevier BV, 2024) Naghavi, M; Ong, KL; Aali, A; Ababneh, HS; Abate, YH; Abbafati, C; Abbasgholizadeh, R; Abbasian, M; Abbasi-Kangevari, M; Abbastabar, H; Abd ElHafeez, S; Abdelmasseh, M; Abd-Elsalam, S; Abdelwahab, A; Abdollahi, M; Abdollahifar, MA; Abdoun, M; Abdulah, DM; Abdullahi, A; Abebe, M; Abebe, SS; Abedi, A; Abegaz, KH; Abhilash, ES; Abidi, H; Abiodun, O; Aboagye, RG; Abolhassani, H; Abolmaali, M; Abouzid, M; Aboye, GB; Abreu, LG; Abrha, WA; Abtahi, D; Abu Rumeileh, S; Abualruz, H; Abubakar, B; Abu-Gharbieh, E; Abu-Rmeileh, NM; Aburuz, S; Abu-Zaid, A; Accrombessi, MMK; Adal, TG; Adamu, AA; Addo, IY; Addolorato, G; Adebiyi, AO; Adekanmbi, V; Adepoju, AV; Adetunji, CO; Adetunji, JB; Adeyeoluwa, TE; Adeyinka, DA; Adeyomoye, OI; Admass, BAA; Adnani, QES; Adra, S; Afolabi, AA; Afzal, MS; Afzal, S; Agampodi, SB; Agasthi, P; Aggarwal, M; Aghamiri, S; Agide, FD; Agodi, A; Agrawal, A; Agyemang-Duah, W; Ahinkorah, BO; Ahmad, A; Ahmad, D; Ahmad, F; Ahmad, MM; Ahmad, S; Ahmad, S; Ahmad, T; Ahmadi, K; Ahmadzade, AM; Ahmed, A; Ahmed, A; Ahmed, H; Ahmed, LA; Ahmed, MS; Ahmed, MS; Ahmed, MB; Ahmed, SA; Ajami, M; Aji, B; Akara, EM; Akbarialiabad, H; Akinosoglou, K; Akinyemiju, T; Akkaif, MA; Akyirem, S; Al Hamad, H; Al Hasan, SM; Alahdab, F; Alalalmeh, SO; Alalwan, TA; Al-Aly, Z
    Background: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation.
  • Item
    Global Fertility in 204 Countries and Territories, 1950–2021, With Forecasts to 2100: A Comprehensive Demographic Analysis for the Global Burden of Disease Study 2021
    (Elsevier BV, 2024) Bhattacharjee, NV; Schumacher, AE; Aali, A; Abate, YH; Abbasgholizadeh, R; Abbasian, M; Abbasi-Kangevari, M; Abbastabar, H; Abd ElHafeez, S; Abd-Elsalam, S; Abdollahi, M; Abdollahifar, MA; Abdoun, M; Abdullahi, A; Abebe, M; Abebe, SS; Abiodun, O; Abolhassani, H; Abolmaali, M; Abouzid, M; Aboye, GB; Abreu, LG; Abrha, WA; Abrigo, MRM; Abtahi, D; Abualruz, H; Abubakar, B; Abu-Gharbieh, E; Abu-Rmeileh, NM; Adal, TGG; Adane, MM; Adeagbo, OAA; Adedoyin, RA; Adekanmbi, V; Aden, B; Adepoju, AV; Adetokunboh, OO; Adetunji, JB; Adeyinka, DA; Adeyomoye, OI; Adnani, QES; Adra, S; Afolabi, RF; Afyouni, S; Afzal, MS; Afzal, S; Aghamiri, S; Agodi, A; Agyemang-Duah, W; Ahinkorah, BO; Ahlstrom, AJ; Ahmad, A; Ahmad, D; Ahmad, F; Ahmad, MM; Ahmad, S; Ahmad, T; Ahmed, A; Ahmed, A; Ahmed, H; Ahmed, LA; Ahmed, MS; Ahmed, SA; Ajami, M; Aji, B; Akalu, GT; Akbarialiabad, H; Akinyemi, RO; Akkaif, MA; Akkala, S; Al Hamad, H; Al Hasan, SM; Al Qadire, M; AL-Ahdal, TMA; Alalalmeh, SO; Alalwan, TA; Al-Aly, Z; Alam, K; Al-amer, RM; Alanezi, FM; Alanzi, TM; Albakri, A; Albashtawy, M; AlBataineh, MT; Alemi, H; Alemi, S; Alemu, YM; Al-Eyadhy, A; Al-Gheethi, AAS; Alhabib, KF; Alhajri, N; Alhalaiqa, FAN; Alhassan, RK; Ali, A; Ali, BA; Ali, L; Ali, MU; Ali, R; Ali, SSS; Alif, SM
    Background: Accurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios. Methods: To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline. Findings: During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction. Interpretation: Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world. Funding: Bill & Melinda Gates Foundation.
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