Hearing Loss Prevalence, Years Lived With Disability, and Hearing Aid Use in the United States From 1990 to 2019: Findings From the Global Burden of Disease Study.

OBJECTIVES: This article describes key data sources and methods used to estimate hearing loss in the United States, in the Global Burden of Disease study. Then, trends in hearing loss are described for 2019, including temporal trends from 1990 to 2019, changing prevalence over age, severity patterns, and utilization of hearing aids. DESIGN: We utilized population-representative surveys from the United States to estimate hearing loss prevalence for the Global Burden of Disease study. A key input data source in modeled estimates are the National Health and Nutrition Examination Surveys (NHANES), years 1988 to 2010. We ran hierarchical severity-specific models to estimate hearing loss prevalence. We then scaled severity-specific models to sum to total hearing impairment prevalence, adjusted estimates for hearing aid coverage, and split estimates by etiology and tinnitus status. We computed years lived with disability (YLDs), which quantifies the amount of health loss associated with a condition depending on severity and creates a common metric to compare the burden of disparate diseases. This was done by multiplying the prevalence of severity-specific hearing loss by corresponding disability weights, with additional weighting for tinnitus comorbidity. RESULTS: An estimated 72.88 million (95% uncertainty interval (UI) 68.53 to 77.30) people in the United States had hearing loss in 2019, accounting for 22.2% (20.9 to 23.6) of the total population. Hearing loss was responsible for 2.24 million (1.56 to 3.11) YLDs (3.6% (2.8 to 4.7) of total US YLDs). Age-standardized prevalence was higher in males (17.7% [16.7 to 18.8]) compared with females (11.9%, [11.2 to 12.5]). While most cases of hearing loss were mild (64.3%, 95% UI 61.0 to 67.6), disability was concentrated in cases that were moderate or more severe. The all-age prevalence of hearing loss in the United States was 28.1% (25.7 to 30.8) higher in 2019 than in 1990, despite stable age-standardized prevalence. An estimated 9.7% (8.6 to 11.0) of individuals with mild to profound hearing loss utilized a hearing aid, while 32.5% (31.9 to 33.2) of individuals with hearing loss experienced tinnitus. Occupational noise exposure was responsible for 11.2% (10.2 to 12.4) of hearing loss YLDs. CONCLUSIONS: Results indicate large burden of hearing loss in the United States, with an estimated 1 in 5 people experiencing this condition. While many cases of hearing loss in the United States were mild, growing prevalence, low usage of hearing aids, and aging populations indicate the rising impact of this condition in future years and the increasing importance of domestic access to hearing healthcare services. Large-scale audiometric surveys such as NHANES are needed to regularly assess hearing loss burden and access to healthcare, improving our understanding of who is impacted by hearing loss and what groups are most amenable to intervention.

Socioeconomic inequalities in physical, psychological, and cognitive multimorbidity in middle-aged and older adults in 33 countries: a cross-sectional study.

BACKGROUND: Many physical, psychological, and cognitive disorders are highly clustered among populations with low socioeconomic status. However, the extent to which socioeconomic status is associated with different combinations of these disorders is unclear, particularly outside high-income countries. We aimed to evaluate these associations in 33 countries including high-income countries, upper-middle-income countries, and one lower-middle-income country. METHODS: This cross-sectional multi-region study pooled individual-level data from seven studies on ageing between 2017 and 2020. Education and total household wealth were used to measure socioeconomic status. Physical disorder was defined as having one or more of the self-reported chronic conditions. Psychological and cognitive disorders were measured by study-specific instruments. The outcome included eight categories: no disorders, physical disorder, psychological disorder, cognitive disorder, and their four combinations. Multivariable-adjusted logistic regression models were used to estimate odds ratios (ORs) and 95% CIs for the associations of socioeconomic status with these outcomes separately for high-income countries, upper-middle-income countries, and the lower-middle-income country. FINDINGS: Among 167 376 individuals aged 45 years and older, the prevalence of multimorbidity was 24·5% in high-income countries, 33·9% in upper-middle-income countries, and 8·1% in the lower-middle-income country (India). Lower levels of education, household wealth, and a combined socioeconomic status score were strongly associated with physical, psychological, and cognitive multimorbidity in high-income countries and upper-middle-income countries, with ORs (low vs high socioeconomic status) for physical-psychological-cognitive multimorbidity of 12·36 (95% CI 10·29-14·85; p<0·0001) in high-income countries and of 23·84 (18·85-30·14; p<0·0001) in upper-middle-income countries. The associations in the lower-middle-income country were mixed. Participants with both a low level of education and low household wealth had the highest odds of multimorbidity (eg, OR for physical-psychological-cognitive multimorbidity 21·21 [15·95-28·19; p<0·0001] in high-income countries, 37·07 [25·66-53·56; p<0·0001] in upper-middle-income countries, and 54·96 [7·66-394·38; p<0·0001] in the lower-middle-income country). INTERPRETATION: In study populations from high-income countries, upper-middle-income countries, and the lower-middle-income country, the odds of multimorbidity, which included physical, psychological, and cognitive disorders, were more than ten times greater in individuals with low socioeconomic status. Equity-oriented policies and programmes that reduce social inequalities in multimorbidity are urgently needed to achieve Sustainable Development Goals. FUNDING: Zhejiang University, Fundamental Research Funds for the Central Universities, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Wellcome Trust, Medical Research Council, National Institute on Aging, and Academy of Finland. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis.

BACKGROUND: The USA struggled in responding to the COVID-19 pandemic, but not all states struggled equally. Identifying the factors associated with cross-state variation in infection and mortality rates could help to improve responses to this and future pandemics. We sought to answer five key policy-relevant questions regarding the following: 1) what roles social, economic, and racial inequities had in interstate variation in COVID-19 outcomes; 2) whether states with greater health-care and public health capacity had better outcomes; 3) how politics influenced the results; 4) whether states that imposed more policy mandates and sustained them longer had better outcomes; and 5) whether there were trade-offs between a state having fewer cumulative SARS-CoV-2 infections and total COVID-19 deaths and its economic and educational outcomes. METHODS: Data disaggregated by US state were extracted from public databases, including COVID-19 infection and mortality estimates from the Institute for Health Metrics and Evaluation's (IHME) COVID-19 database; Bureau of Economic Analysis data on state gross domestic product (GDP); Federal Reserve economic data on employment rates; National Center for Education Statistics data on student standardised test scores; and US Census Bureau data on race and ethnicity by state. We standardised infection rates for population density and death rates for age and the prevalence of major comorbidities to facilitate comparison of states' successes in mitigating the effects of COVID-19. We regressed these health outcomes on prepandemic state characteristics (such as educational attainment and health spending per capita), policies adopted by states during the pandemic (such as mask mandates and business closures), and population-level behavioural responses (such as vaccine coverage and mobility). We explored potential mechanisms connecting state-level factors to individual-level behaviours using linear regression. We quantified reductions in state GDP, employment, and student test scores during the pandemic to identify policy and behavioural responses associated with these outcomes and to assess trade-offs between these outcomes and COVID-19 outcomes. Significance was defined as p<0·05. FINDINGS: Standardised cumulative COVID-19 death rates for the period from Jan 1, 2020, to July 31, 2022 varied across the USA (national rate 372 deaths per 100 000 population [95% uncertainty interval [UI] 364-379]), with the lowest standardised rates in Hawaii (147 deaths per 100 000 [127-196]) and New Hampshire (215 per 100 000 [183-271]) and the highest in Arizona (581 per 100 000 [509-672]) and Washington, DC (526 per 100 000 [425-631]). A lower poverty rate, higher mean number of years of education, and a greater proportion of people expressing interpersonal trust were statistically associated with lower infection and death rates, and states where larger percentages of the population identify as Black (non-Hispanic) or Hispanic were associated with higher cumulative death rates. Access to quality health care (measured by the IHME's Healthcare Access and Quality Index) was associated with fewer total COVID-19 deaths and SARS-CoV-2 infections, but higher public health spending and more public health personnel per capita were not, at the state level. The political affiliation of the state governor was not associated with lower SARS-CoV-2 infection or COVID-19 death rates, but worse COVID-19 outcomes were associated with the proportion of a state's voters who voted for the 2020 Republican presidential candidate. State governments' uses of protective mandates were associated with lower infection rates, as were mask use, lower mobility, and higher vaccination rate, while vaccination rates were associated with lower death rates. State GDP and student reading test scores were not associated with state COVD-19 policy responses, infection rates, or death rates. Employment, however, had a statistically significant relationship with restaurant closures and greater infections and deaths: on average, 1574 (95% UI 884-7107) additional infections per 10 000 population were associated in states with a one percentage point increase in employment rate. Several policy mandates and protective behaviours were associated with lower fourth-grade mathematics test scores, but our study results did not find a link to state-level estimates of school closures. INTERPRETATION: COVID-19 magnified the polarisation and persistent social, economic, and racial inequities that already existed across US society, but the next pandemic threat need not do the same. US states that mitigated those structural inequalities, deployed science-based interventions such as vaccination and targeted vaccine mandates, and promoted their adoption across society were able to match the best-performing nations in minimising COVID-19 death rates. These findings could contribute to the design and targeting of clinical and policy interventions to facilitate better health outcomes in future crises. FUNDING: Bill & Melinda Gates Foundation, J Stanton, T Gillespie, J and E Nordstrom, and Bloomberg Philanthropies.

Childhood socioeconomic disadvantage and risk of physical multimorbidity in later life: The mediating role of depression.

OBJECTIVE: To assess the impact of childhood socioeconomic disadvantage (SED) on the risks of depression and physical multimorbidity in later life and to explore whether depression mediates the association between childhood SED and physical multimorbidity. METHODS: Data on 8214 adults from the China Health and Retirement Longitudinal Study were analyzed. The mean (SD) age of the study population was 57.0 (8.0) years at baseline (2011) and 51.9 % were females. Multivariable logistic regressions were used to examine the associations of childhood SED (indexed by food insecurity, highest education level of parents, and self-perceived household financial situation, and scored 0-3) with later-life depression (scored ≥10 on the 10-item Center for Epidemiologic Studies Depression Scale) and physical multimorbidity (having two or more doctor-diagnosed chronic conditions) assessed in the 2018 follow-up survey. Mediation analysis was conducted in the overall sample and further stratified by sex to estimate the degree to which the association between childhood SED and physical multimorbidity could be explained by baseline depression. RESULTS: Participants with a childhood SED score of 3 (i.e., the most disadvantaged) had 2.63 (95 % confidence interval [CI]: 1.91-3.63) times and 2.08 (95 % CI: 1.56-2.77) times higher odds of depression and physical multimorbidity respectively compared with those who had a score of 0 (i.e., the least disadvantaged). Depression mediated 20 % of the association between childhood SED and physical multimorbidity (36 % in females and 5 % in males). CONCLUSIONS: Childhood SED was associated with higher risks of depression and physical multimorbidity in later life, and the association of childhood SED with physical multimorbidity was mediated by depression, especially among females.

Disease burden of ageing, sex and regional disparities and health resources allocation: a longitudinal analysis of 31 provinces in Mainland China.

OBJECTIVES: To measure the disease burden of ageing based on age-related diseases (ARDs), the sex and regional disparities and the impact of health resources allocation on the burden in China. DESIGN: A national comparative study based on Global Burden of Diseases Study estimates and China's routine official statistics. SETTING AND PARTICIPANTS: Thirty-one provinces of Mainland China were included for analysis in the study. No individuals were involved. METHODS: We first identified the ARDs and calculated the disability-adjusted life years (DALYs) of ARDs in 2016. We assessed the ARD burden disparities by province and sex and calculated the provincial ARD burden-adjusted age. We assessed historical changes between 1990 and 2016. Fixed effects regression models were adopted to evaluate the impact of health expenditures and health workforce indicators on the ARD burden in 2010-2016. RESULTS: In 2016, China's total burden of ARDs was 15 703.7 DALYs (95% uncertainty intervals: 12 628.5, 18 406.2) per 100 000 population. Non-communicable diseases accounted for 91.9% of the burden. There were significant regional disparities. The leading five youngest provinces were Beijing, Guangdong, Shanghai, Zhejiang and Fujian, located on the east coast of China with an ARD burden-adjusted age below 40 years. After standardising the age structure, western provinces, including Tibet, Qinghai, Guizhou and Xinjiang, had the highest burden of ARDs. Males were disproportionately affected by ARDs. China's overall age-standardised ARD burden has decreased since 1990, and females and eastern provinces experienced the largest decline. Regression results showed that the urban-rural gap in health workforce density was positively associated with the ARD burdens. CONCLUSION: Chronological age alone does not provide a strong enough basis for appropriate ageing resource planning or policymaking. In China, concerted efforts should be made to reduce the ARDs burden and its disparities. Health resources should be deliberately allocated to western provinces facing the greatest health challenges due to future ageing.

Evolution of the global smoking epidemic over the past half century: strengthening the evidence base for policy action.

BACKGROUND: Despite compelling evidence on the health hazards of tobacco products accumulated over the past 70 years, smoking remains a leading cause of death worldwide. Policy action to control smoking requires timely, comprehensive, and comparable evidence on smoking levels within and across countries. This study provides a recent assessment of that evidence based on the methods used in the Global Burden of Disease (GBD) Study. METHODS: We estimated annual prevalence of, and mortality attributable to smoking any form of tobacco from 1970 to 2020 and 1990-2020, respectively, using the methods and data sources (including 3431 surveys and studies) from the GBD collaboration. We modelled annual prevalence of current and former smoking, distributions of cigarette-equivalents per smoker per day, pack-years for current smoking, years since cessation for former smokers and estimated population-attributable fractions due to smoking. RESULTS: Globally, adult smoking prevalence in 2020 was 32.6% (32.2% to 33.1%) and 6.5% (6.3% to 6.7%) among men and women, respectively. 1.18 (0.94 to 1.47) billion people regularly smoke tobacco, causing 7.0 (2.0 to 11.2) million deaths in 2020. Smoking prevalence has declined by 27.2% (26.0% to 28.3%) for men since 1990, and by 37.9% (35.3% to 40.1%) for women. Declines have been largest in the higher sociodemographic countries, falling by more than 40% in some high-income countries, and also in several Latin American countries, notably Brazil, where prevalence has fallen by 70% since 1990. Smoking prevalence for women has declined substantially in some countries, including Nepal, the Netherlands and Denmark, and remains low throughout Asia and Africa. Conversely, there has been little decline in smoking in most low- and middle-income countries (LMICs) with over half of all men continuing to smoke in large populations in Asia (China, Indonesia), as well as the Pacific Islands. IMPLICATIONS: While global smoking prevalence has fallen, smoking is still common and causes a significant health burden worldwide. The unequal pace of declines across the globe is shifting the epidemic progressively to LMICs. Smoking is likely to remain a leading cause of preventable death throughout this century unless smoking cessation efforts can significantly and rapidly reduce the number of smokers, particularly in Asia. FUNDING: XD and EG received funding through grant projects from Bloomberg Philanthropies (funding no. 66-9468) and the Bill & Melinda Gates Foundation (funding no. 63-3452).

Comparing quality of primary healthcare between public and private providers in China: study protocol of a cross-sectional study using unannounced standardised patients in seven provinces of China.

INTRODUCTION: The Chinese government has encouraged the development of private sector in delivering healthcare, including primary healthcare (PHC) in the new round of national health reform since 2009. However, the debate about the role of the private sector in achieving universal health coverage continues with poor support from theories and empirical evidence. This study intends to compare the quality of PHC services between the private and public providers in seven provinces in China, using unannounced standardised patients (USPs). METHODS: We are developing and validating 13 USP cases most commonly observed in the PHC setting. Six domains of quality will be assessed by the USP: effectiveness, safety, patient centredness, efficiency, timeliness and equity. The USP will make 2200 visits to 705 public and 521 private PHC institutions across seven provinces, following a multistage clustered sample design. Using each USP-provider encounter as the analytical unit, we will first descriptively compare the raw differences in quality between the private and public providers and then analyse the association of ownership types and quality, using propensity score weighting. ETHICS AND DISSEMINATION: The study was primarily funded by the National Natural Science Foundation of China (#71974211, #71874116 and # 72074163) and was also supported by the China Medical Board (#16-260, #18-300 and #18-301), and have received ethical approval from Sun Yat-sen University (#2019-024). The validated USP tool and the data collected in this study will be freely available for the public after the primary analysis of the study. TRIAL REGISTRATION NUMBER: Chinese Clinical Trial Registry: #ChiCTR2000032773.