National rates of COVID-19 infection and fatality have varied dramatically since the onset of the pandemic. Understanding the conditions associated with this cross-country variation is essential to ...guiding investment in more effective preparedness and response for future pandemics.
Daily SARS-CoV-2 infections and COVID-19 deaths for 177 countries and territories and 181 subnational locations were extracted from the Institute for Health Metrics and Evaluation's modelling database. Cumulative infection rate and infection-fatality ratio (IFR) were estimated and standardised for environmental, demographic, biological, and economic factors. For infections, we included factors associated with environmental seasonality (measured as the relative risk of pneumonia), population density, gross domestic product (GDP) per capita, proportion of the population living below 100 m, and a proxy for previous exposure to other betacoronaviruses. For IFR, factors were age distribution of the population, mean body-mass index (BMI), exposure to air pollution, smoking rates, the proxy for previous exposure to other betacoronaviruses, population density, age-standardised prevalence of chronic obstructive pulmonary disease and cancer, and GDP per capita. These were standardised using indirect age standardisation and multivariate linear models. Standardised national cumulative infection rates and IFRs were tested for associations with 12 pandemic preparedness indices, seven health-care capacity indicators, and ten other demographic, social, and political conditions using linear regression. To investigate pathways by which important factors might affect infections with SARS-CoV-2, we also assessed the relationship between interpersonal and governmental trust and corruption and changes in mobility patterns and COVID-19 vaccination rates.
The factors that explained the most variation in cumulative rates of SARS-CoV-2 infection between Jan 1, 2020, and Sept 30, 2021, included the proportion of the population living below 100 m (5·4% 4·0–7·9 of variation), GDP per capita (4·2% 1·8–6·6 of variation), and the proportion of infections attributable to seasonality (2·1% 95% uncertainty interval 1·7–2·7 of variation). Most cross-country variation in cumulative infection rates could not be explained. The factors that explained the most variation in COVID-19 IFR over the same period were the age profile of the country (46·7% 18·4–67·6 of variation), GDP per capita (3·1% 0·3–8·6 of variation), and national mean BMI (1·1% 0·2–2·6 of variation). 44·4% (29·2–61·7) of cross-national variation in IFR could not be explained. Pandemic-preparedness indices, which aim to measure health security capacity, were not meaningfully associated with standardised infection rates or IFRs. Measures of trust in the government and interpersonal trust, as well as less government corruption, had larger, statistically significant associations with lower standardised infection rates. High levels of government and interpersonal trust, as well as less government corruption, were also associated with higher COVID-19 vaccine coverage among middle-income and high-income countries where vaccine availability was more widespread, and lower corruption was associated with greater reductions in mobility. If these modelled associations were to be causal, an increase in trust of governments such that all countries had societies that attained at least the amount of trust in government or interpersonal trust measured in Denmark, which is in the 75th percentile across these spectrums, might have reduced global infections by 12·9% (5·7–17·8) for government trust and 40·3% (24·3–51·4) for interpersonal trust. Similarly, if all countries had a national BMI equal to or less than that of the 25th percentile, our analysis suggests global standardised IFR would be reduced by 11·1%.
Efforts to improve pandemic preparedness and response for the next pandemic might benefit from greater investment in risk communication and community engagement strategies to boost the confidence that individuals have in public health guidance. Our results suggest that increasing health promotion for key modifiable risks is associated with a reduction of fatalities in such a scenario.
Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.
Timely, accurate, and comprehensive estimates of SARS-CoV-2 daily infection rates, cumulative infections, the proportion of the population that has been infected at least once, and the effective ...reproductive number (Reffective) are essential for understanding the determinants of past infection, current transmission patterns, and a population's susceptibility to future infection with the same variant. Although several studies have estimated cumulative SARS-CoV-2 infections in select locations at specific points in time, all of these analyses have relied on biased data inputs that were not adequately corrected for. In this study, we aimed to provide a novel approach to estimating past SARS-CoV-2 daily infections, cumulative infections, and the proportion of the population infected, for 190 countries and territories from the start of the pandemic to Nov 14, 2021. This approach combines data from reported cases, reported deaths, excess deaths attributable to COVID-19, hospitalisations, and seroprevalence surveys to produce more robust estimates that minimise constituent biases.
We produced a comprehensive set of global and location-specific estimates of daily and cumulative SARS-CoV-2 infections through Nov 14, 2021, using data largely from Johns Hopkins University (Baltimore, MD, USA) and national databases for reported cases, hospital admissions, and reported deaths, as well as seroprevalence surveys identified through previous reviews, SeroTracker, and governmental organisations. We corrected these data for known biases such as lags in reporting, accounted for under-reporting of deaths by use of a statistical model of the proportion of excess mortality attributable to SARS-CoV-2, and adjusted seroprevalence surveys for waning antibody sensitivity, vaccinations, and reinfection from SARS-CoV-2 escape variants. We then created an empirical database of infection–detection ratios (IDRs), infection–hospitalisation ratios (IHRs), and infection–fatality ratios (IFRs). To estimate a complete time series for each location, we developed statistical models to predict the IDR, IHR, and IFR by location and day, testing a set of predictors justified through published systematic reviews. Next, we combined three series of estimates of daily infections (cases divided by IDR, hospitalisations divided by IHR, and deaths divided by IFR), into a more robust estimate of daily infections. We then used daily infections to estimate cumulative infections and the cumulative proportion of the population with one or more infections, and we then calculated posterior estimates of cumulative IDR, IHR, and IFR using cumulative infections and the corrected data on reported cases, hospitalisations, and deaths. Finally, we converted daily infections into a historical time series of Reffective by location and day based on assumptions of duration from infection to infectiousness and time an individual spent being infectious. For each of these quantities, we estimated a distribution based on an ensemble framework that captured uncertainty in data sources, model design, and parameter assumptions.
Global daily SARS-CoV-2 infections fluctuated between 3 million and 17 million new infections per day between April, 2020, and October, 2021, peaking in mid-April, 2021, primarily as a result of surges in India. Between the start of the pandemic and Nov 14, 2021, there were an estimated 3·80 billion (95% uncertainty interval 3·44–4·08) total SARS-CoV-2 infections and reinfections combined, and an estimated 3·39 billion (3·08–3·63) individuals, or 43·9% (39·9–46·9) of the global population, had been infected one or more times. 1·34 billion (1·20–1·49) of these infections occurred in south Asia, the highest among the seven super-regions, although the sub-Saharan Africa super-region had the highest infection rate (79·3 per 100 population 69·0–86·4). The high-income super-region had the fewest infections (239 million 226–252), and southeast Asia, east Asia, and Oceania had the lowest infection rate (13·0 per 100 population 8·4–17·7). The cumulative proportion of the population ever infected varied greatly between countries and territories, with rates higher than 70% in 40 countries and lower than 20% in 39 countries. There was no discernible relationship between Reffective and total immunity, and even at total immunity levels of 80%, we observed no indication of an abrupt drop in Reffective, indicating that there is not a clear herd immunity threshold observed in the data.
COVID-19 has already had a staggering impact on the world up to the beginning of the omicron (B.1.1.529) wave, with over 40% of the global population infected at least once by Nov 14, 2021. The vast differences in cumulative proportion of the population infected across locations could help policy makers identify the transmission-prevention strategies that have been most effective, as well as the populations at greatest risk for future infection. This information might also be useful for targeted transmission-prevention interventions, including vaccine prioritisation. Our statistical approach to estimating SARS-CoV-2 infection allows estimates to be updated and disseminated rapidly on the basis of newly available data, which has and will be crucially important for timely COVID-19 research, science, and policy responses.
Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.
Previous efforts to report estimates of cancer incidence and mortality in India and its different parts include the National Cancer Registry Programme Reports, Sample Registration System cause of ...death findings, Cancer Incidence in Five Continents Series, and GLOBOCAN. We present a comprehensive picture of the patterns and time trends of the burden of total cancer and specific cancer types in each state of India estimated as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 because such a systematic compilation is not readily available.
We used all accessible data from multiple sources, including 42 population-based cancer registries and the nationwide Sample Registration System of India, to estimate the incidence of 28 types of cancer in every state of India from 1990 to 2016 and the deaths and disability-adjusted life-years (DALYs) caused by them, as part of GBD 2016. We present incidence, DALYs, and death rates for all cancers together, and the trends of all types of cancers, highlighting the heterogeneity in the burden of specific types of cancers across the states of India. We also present the contribution of major risk factors to cancer DALYs in India.
8·3% (95% uncertainty interval UI 7·9–8·6) of the total deaths and 5·0% (4·6–5·5) of the total DALYs in India in 2016 were due to cancer, which was double the contribution of cancer in 1990. However, the age-standardised incidence rate of cancer did not change substantially during this period. The age-standardised cancer DALY rate had a 2·6 times variation across the states of India in 2016. The ten cancers responsible for the highest proportion of cancer DALYs in India in 2016 were stomach (9·0% of the total cancer DALYs), breast (8·2%), lung (7·5%), lip and oral cavity (7·2%), pharynx other than nasopharynx (6·8%), colon and rectum (5·8%), leukaemia (5·2%), cervical (5·2%), oesophageal (4·3%), and brain and nervous system (3·5%) cancer. Among these cancers, the age-standardised incidence rate of breast cancer increased significantly by 40·7% (95% UI 7·0–85·6) from 1990 to 2016, whereas it decreased for stomach (39·7%; 34·3–44·0), lip and oral cavity (6·4%; 0·4–18·6), cervical (39·7%; 26·5–57·3), and oesophageal cancer (31·2%; 27·9–34·9), and leukaemia (16·1%; 4·3–24·2). We found substantial inter-state heterogeneity in the age-standardised incidence rate of the different types of cancers in 2016, with a 3·3 times to 11·6 times variation for the four most frequent cancers (lip and oral, breast, lung, and stomach). Tobacco use was the leading risk factor for cancers in India to which the highest proportion (10·9%) of cancer DALYs could be attributed in 2016.
The substantial heterogeneity in the state-level incidence rate and health loss trends of the different types of cancer in India over this 26-year period should be taken into account to strengthen infrastructure and human resources for cancer prevention and control at both the national and state levels. These efforts should focus on the ten cancers contributing the highest DALYs in India, including cancers of the stomach, lung, pharynx other than nasopharynx, colon and rectum, leukaemia, oesophageal, and brain and nervous system, in addition to breast, lip and oral cavity, and cervical cancer, which are currently the focus of screening and early detection programmes.
Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.
India has 18% of the global population and an increasing burden of chronic respiratory diseases. However, a systematic understanding of the distribution of chronic respiratory diseases and their ...trends over time is not readily available for all of the states of India. Our aim was to report the trends in the burden of chronic respiratory diseases and the heterogeneity in their distribution in all states of India between 1990 and 2016.
Using all accessible data from multiple sources, we estimated the prevalence of major chronic respiratory diseases and the deaths and disability-adjusted life-years (DALYs) caused by them for every state of India from 1990 to 2016 as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016. We assessed heterogeneity in the burden of chronic obstructive pulmonary disease (COPD) and asthma across the states of India. The states were categorised into four groups based on their epidemiological transition level (ETL). ETL was defined as the ratio of DALYs from communicable diseases to those from non-communicable diseases and injuries combined, with a low ratio denoting high ETL and vice versa. We also assessed the contribution of risk factors to DALYs due to COPD. We compared the burden of chronic respiratory diseases in India against the global average in GBD 2016. We calculated 95% uncertainty intervals (UIs) for the point estimates.
The contribution of chronic respiratory diseases to the total DALYs in India increased from 4·5% (95% UI 4·0–4·9) in 1990 to 6·4% (5·8–7·0) in 2016. Of the total global DALYs due to chronic respiratory diseases in 2016, 32·0% occurred in India. COPD and asthma were responsible for 75·6% and 20·0% of the chronic respiratory disease DALYs, respectively, in India in 2016. The number of cases of COPD in India increased from 28·1 million (27·0–29·2) in 1990 to 55·3 million (53·1–57·6) in 2016, an increase in prevalence from 3·3% (3·1–3·4) to 4·2% (4·0–4·4). The age-standardised COPD prevalence and DALY rates in 2016 were highest in the less developed low ETL state group. There were 37·9 million (35·7–40·2) cases of asthma in India in 2016, with similar prevalence in the four ETL state groups, but the highest DALY rate was in the low ETL state group. The highest DALY rates for both COPD and asthma in 2016 were in the low ETL states of Rajasthan and Uttar Pradesh. The DALYs per case of COPD and asthma were 1·7 and 2·4 times higher in India than the global average in 2016, respectively; most states had higher rates compared with other locations worldwide at similar levels of Socio-demographic Index. Of the DALYs due to COPD in India in 2016, 53·7% (43·1–65·0) were attributable to air pollution, 25·4% (19·5–31·7) to tobacco use, and 16·5% (14·1–19·2) to occupational risks, making these the leading risk factors for COPD.
India has a disproportionately high burden of chronic respiratory diseases. The increasing contribution of these diseases to the overall disease burden across India and the high rate of health loss from them, especially in the less developed low ETL states, highlights the need for focused policy interventions to address this significant cause of disease burden in India.
Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.
Peer-reviewed literature on health is almost exclusively published in English, limiting the uptake of research for decision making in francophone African countries. We used results from the Global ...Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 to assess the burden of disease in francophone Africa and inform health professionals and their partners in the region.
We assessed the burden of disease in the 21 francophone African countries and compared the results with those for their non-francophone counterparts in three economic communities: the Economic Community of West African States, the Economic Community of Central African States, and the Southern African Development Community. GBD 2017 employed a variety of statistical models to determine the number of deaths from each cause, through the Cause of Death Ensemble model algorithm, using CoDCorrect to ensure that the number of deaths per cause did not exceed the total number of estimated deaths. After producing estimates for the number of deaths from each of the 282 fatal outcomes included in the GBD 2017 list of causes, the years of life lost (YLLs) due to premature death were calculated. Years lived with disability (YLDs) were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae. Disability-adjusted life-years (DALYs) were calculated as the sum of YLLs and YLDs. All calculations are presented with 95% uncertainty intervals (UIs). A sample of 1000 draws was taken from the posterior distribution of each estimation step; aggregation of uncertainty across age, sex, and location was done on each draw, assuming independence of uncertainty. The lower and upper UIs represent the ordinal 25th and 975th draws of each quantity and attempt to describe modelling as well as sampling error.
In 2017, 779 deaths (95% UI 750–809) per 100 000 population occurred in francophone Africa, a decrease of 45·3% since 1990. Malaria, lower respiratory infections, neonatal disorders, diarrhoeal diseases, and tuberculosis were the top five Level 3 causes of death. These five causes were found among the six leading causes of death in most francophone countries. In 2017, francophone Africa experienced 53 570 DALYs (50 164–57 361) per 100 000 population, distributed between 43 708 YLLs (41 673–45 742) and 9862 YLDs (7331–12 749) per 100 000 population. In 2017, YLLs constituted the majority of DALYs in the 21 countries of francophone Africa. Age-specific and cause-specific mortality and population ageing were responsible for most of the reductions in disease burden, whereas population growth was responsible for most of the increases.
Francophone Africa still carries a high burden of communicable and neonatal diseases, probably due to the weakness of health-care systems and services, as evidenced by the almost complete attribution of DALYs to YLLs. To cope with this burden of disease, francophone Africa should define its priorities and invest more resources in health-system strengthening and in the quality and quantity of health-care services, especially in rural and remote areas. The region could also be prioritised in terms of technical and financial assistance focused on achieving these goals, as much as on demographic investments including education and family planning.
Bill & Melinda Gates Foundation.
In the Flexibility in Duty Hour Requirements for Surgical Trainees (FIRST) trial, there were several differences in residents' perceptions of aspects of their education, well-being, and patient care ...that differed between standard and flexible duty hour policies. Our objective was to assess whether these perceptions differed by level of training.
A survey assessed residents participating in the FIRST trial's perceptions of the effect of duty hour policies on aspects of patient safety, continuity of care, resident education, clinical training, and resident well-being. Hierarchical logistic regression models were used to examine the association between residents' perceptions, study arm, and level of training (interns, junior residents, and senior residents).
In the Standard Policy arm, as the PGY level increased, residents more frequently reported that duty hour policies negatively affected patient safety, professionalism, morale, and career choice (all interactions p < 0.001). However, in the Flexible Policy arm, as the PGY level increased, residents less frequently perceived negative effects of duty hour policies on resident health, rest, and time for family and friends and extracurricular activities (all interactions p < 0.001). Overall, there was an increase by PGY level in the proportion of residents expressing a preference for training in programs with flexible duty hour policies, and this preference for flexible duty hour policies was even more apparent among residents who were in the Flexible Policy arm (p < 0.001).
As PGY level increased, residents had increasing concerns about patient care and resident education and training under standard duty hour policies, but they had decreasing concerns about well-being under flexible policies. When given the choice between training under standard or flexible duty hour policies, only 14% of residents expressed a preference for standard policies.
We use COVID-19 case and mortality data from 1 February 2020 to 21 September 2020 and a deterministic SEIR (susceptible, exposed, infectious and recovered) compartmental framework to model possible ...trajectories of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the effects of non-pharmaceutical interventions in the United States at the state level from 22 September 2020 through 28 February 2021. Using this SEIR model, and projections of critical driving covariates (pneumonia seasonality, mobility, testing rates and mask use per capita), we assessed scenarios of social distancing mandates and levels of mask use. Projections of current non-pharmaceutical intervention strategies by state-with social distancing mandates reinstated when a threshold of 8 deaths per million population is exceeded (reference scenario)-suggest that, cumulatively, 511,373 (469,578-578,347) lives could be lost to COVID-19 across the United States by 28 February 2021. We find that achieving universal mask use (95% mask use in public) could be sufficient to ameliorate the worst effects of epidemic resurgences in many states. Universal mask use could save an additional 129,574 (85,284-170,867) lives from September 22, 2020 through the end of February 2021, or an additional 95,814 (60,731-133,077) lives assuming a lesser adoption of mask wearing (85%), when compared to the reference scenario.
Pneumonia is the most common intensive care unit-acquired infection in the trauma and emergency general surgery population. Despite guidelines urging rapid antibiotic use, data supporting immediate ...antibiotic initiation in cases of suspected infection are limited. Our hypothesis was that a protocol of specimen-initiated antibiotic initiation would have similar compliance and outcomes to an immediate initiation protocol.
We devised a pragmatic cluster-randomized crossover pilot trial. Four surgical and trauma intensive care units were randomized to either an immediate initiation or specimen-initiated antibiotic protocol for intubated patients with suspected pneumonia and bronchoscopically obtained cultures who did not require vasopressors. In the immediate initiation arm, antibiotics were started immediately after the culture regardless of patient status. In the specimen-initiated arm, antibiotics were delayed until objective Gram stain or culture results suggested infection. Each site participated in both arms after a washout period and crossover. Outcomes were protocol compliance, all-cause 30-day mortality, and ventilator-free alive days at 30 days. Standard statistical techniques were applied.
A total of 186 patients had 244 total cultures, of which only the first was analyzed. Ninety-three patients (50%) were enrolled in each arm, and 94.6% were trauma patients (84.4% blunt trauma). The median age was 50.5 years, and 21% of the cohort was female. There were no differences in demographics, comorbidities, sequential organ failure assessment, Acute Physiology and Chronic Health Evaluation II, or Injury Severity Scores. Antibiotics were started significantly later in the specimen-initiated arm (0 vs. 9.3 hours; p < 0.0001) with 19.4% avoiding antibiotics completely for that episode. There were no differences in the rate of protocol adherence, 30-day mortality, or ventilator-free alive days at 30 days.
In this cluster-randomized crossover trial, we found similar compliance rates between immediate and specimen-initiated antibiotic strategies. Specimen-initiated antibiotic protocol in patients with a suspected hospital-acquired pneumonia did not result in worse clinical outcomes compared with immediate initiation.
Therapeutic/Care Management; Level II.
Mental disorders are among the leading causes of non-fatal disease burden in India, but a systematic understanding of their prevalence, disease burden, and risk factors is not readily available for ...each state of India. In this report, we describe the prevalence and disease burden of each mental disorder for the states of India, from 1990 to 2017.
We used all accessible data from multiple sources to estimate the prevalence of mental disorders, years lived with disability (YLDs), and disability-adjusted life-years (DALYs) caused by these disorders for all the states of India from 1990 to 2017, as part of the Global Burden of Diseases, Injuries, and Risk Factors Study. We assessed the heterogeneity and time trends of mental disorders across the states of India. We grouped states on the basis of their Socio-demographic Index (SDI), which is a composite measure of per-capita income, mean education, and fertility rate in women younger than 25 years. We also assessed the association of major mental disorders with suicide deaths. We calculated 95% uncertainty intervals (UIs) for the point estimates.
In 2017, 197·3 million (95% UI 178·4–216·4) people had mental disorders in India, including 45·7 million (42·4–49·8) with depressive disorders and 44·9 million (41·2–48·9) with anxiety disorders. We found a significant, but modest, correlation between the prevalence of depressive disorders and suicide death rate at the state level for females (r2=0·33, p=0·0009) and males (r2=0·19, p=0·015). The contribution of mental disorders to the total DALYs in India increased from 2·5% (2·0–3·1) in 1990 to 4·7% (3·7–5·6) in 2017. In 2017, depressive disorders contributed the most to the total mental disorders DALYs (33·8%, 29·5–38·5), followed by anxiety disorders (19·0%, 15·9–22·4), idiopathic developmental intellectual disability (IDID; 10·8%, 6·3–15·9), schizophrenia (9·8%, 7·7–12·4), bipolar disorder (6·9%, 4·9–9·6), conduct disorder (5·9%, 4·0–8·1), autism spectrum disorders (3·2%, 2·7–3·8), eating disorders (2·2%, 1·7–2·8), and attention-deficit hyperactivity disorder (ADHD; 0·3%, 0·2–0·5); other mental disorders comprised 8·0% (6·1–10·1) of DALYs. Almost all (>99·9%) of these DALYs were made up of YLDs. The DALY rate point estimates of mental disorders with onset predominantly in childhood and adolescence (IDID, conduct disorder, autism spectrum disorders, and ADHD) were higher in low SDI states than in middle SDI and high SDI states in 2017, whereas the trend was reversed for mental disorders that manifest predominantly during adulthood. Although the prevalence of mental disorders with onset in childhood and adolescence decreased in India from 1990 to 2017, with a stronger decrease in high SDI and middle SDI states than in low SDI states, the prevalence of mental disorders that manifest predominantly during adulthood increased during this period.
One in seven Indians were affected by mental disorders of varying severity in 2017. The proportional contribution of mental disorders to the total disease burden in India has almost doubled since 1990. Substantial variations exist between states in the burden from different mental disorders and in their trends over time. These state-specific trends of each mental disorder reported here could guide appropriate policies and health system response to more effectively address the burden of mental disorders in India.
Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.
Accurate and up-to-date estimates on incidence, prevalence, mortality, and disability-adjusted life-years (burden) of neurological disorders are the backbone of evidence-based health care planning ...and resource allocation for these disorders. It appears that no such estimates have been reported at the state level for the US.
To present burden estimates of major neurological disorders in the US states by age and sex from 1990 to 2017.
This is a systematic analysis of the Global Burden of Disease (GBD) 2017 study. Data on incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) of major neurological disorders were derived from the GBD 2017 study of the 48 contiguous US states, Alaska, and Hawaii. Fourteen major neurological disorders were analyzed: stroke, Alzheimer disease and other dementias, Parkinson disease, epilepsy, multiple sclerosis, motor neuron disease, migraine, tension-type headache, traumatic brain injury, spinal cord injuries, brain and other nervous system cancers, meningitis, encephalitis, and tetanus.
Any of the 14 listed neurological diseases.
Absolute numbers in detail by age and sex and age-standardized rates (with 95% uncertainty intervals) were calculated.
The 3 most burdensome neurological disorders in the US in terms of absolute number of DALYs were stroke (3.58 95% uncertainty interval UI, 3.25-3.92 million DALYs), Alzheimer disease and other dementias (2.55 95% UI, 2.43-2.68 million DALYs), and migraine (2.40 95% UI, 1.53-3.44 million DALYs). The burden of almost all neurological disorders (in terms of absolute number of incident, prevalent, and fatal cases, as well as DALYs) increased from 1990 to 2017, largely because of the aging of the population. Exceptions for this trend included traumatic brain injury incidence (-29.1% 95% UI, -32.4% to -25.8%); spinal cord injury prevalence (-38.5% 95% UI, -43.1% to -34.0%); meningitis prevalence (-44.8% 95% UI, -47.3% to -42.3%), deaths (-64.4% 95% UI, -67.7% to -50.3%), and DALYs (-66.9% 95% UI, -70.1% to -55.9%); and encephalitis DALYs (-25.8% 95% UI, -30.7% to -5.8%). The different metrics of age-standardized rates varied between the US states from a 1.2-fold difference for tension-type headache to 7.5-fold for tetanus; southeastern states and Arkansas had a relatively higher burden for stroke, while northern states had a relatively higher burden of multiple sclerosis and eastern states had higher rates of Parkinson disease, idiopathic epilepsy, migraine and tension-type headache, and meningitis, encephalitis, and tetanus.
There is a large and increasing burden of noncommunicable neurological disorders in the US, with up to a 5-fold variation in the burden of and trends in particular neurological disorders across the US states. The information reported in this article can be used by health care professionals and policy makers at the national and state levels to advance their health care planning and resource allocation to prevent and reduce the burden of neurological disorders.
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