|
1
|
Ledesma JR, Basting A, Chu HT, Ma J, Zhang M, Vongpradith A, Novotney A, Dalos J, Zheng P, Murray CJL, Kyu HH. Global-, Regional-, and National-Level Impacts of the COVID-19 Pandemic on Tuberculosis Diagnoses, 2020-2021. Microorganisms 2023; 11:2191. [PMID: 37764035 PMCID: PMC10536333 DOI: 10.3390/microorganisms11092191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Evaluating cross-country variability on the impact of the COVID-19 pandemic on tuberculosis (TB) may provide urgent inputs to control programs as countries recover from the pandemic. We compared expected TB notifications, modeled using trends in annual TB notifications from 2013-2019, with observed TB notifications to compute the observed to expected (OE) ratios for 170 countries. We applied the least absolute shrinkage and selection operator (LASSO) method to identify the covariates, out of 27 pandemic- and tuberculosis-relevant variables, that had the strongest explanatory power for log OE ratios. The COVID-19 pandemic was associated with a 1.55 million (95% CI: 1.26-1.85, 21.0% [17.5-24.6%]) decrease in TB diagnoses in 2020 and a 1.28 million (0.90-1.76, 16.6% [12.1-21.2%]) decrease in 2021 at a global level. India, Indonesia, the Philippines, and China contributed the most to the global declines for both years, while sub-Saharan Africa achieved pre-pandemic levels by 2021 (OE ratio = 1.02 [0.99-1.05]). Age-stratified analyses revealed that the ≥ 65-year-old age group experienced greater relative declines in TB diagnoses compared with the under 65-year-old age group in 2020 (RR = 0.88 [0.81-0.96]) and 2021 (RR = 0.88 [0.79-0.98]) globally. Covariates found to be associated with all-age OE ratios in 2020 were age-standardized smoking prevalence in 2019 (β = 0.973 [0.957-990]), school closures (β = 0.988 [0.977-0.998]), stay-at-home orders (β = 0.993 [0.985-1.00]), SARS-CoV-2 infection rate (β = 0.991 [0.987-0.996]), and proportion of population ≥65 years (β = 0.971 [0.944-0.999]). Further research is needed to clarify the extent to which the observed declines in TB diagnoses were attributable to disruptions in health services, decreases in TB transmission, and COVID-19 mortality among TB patients.
Collapse
Affiliation(s)
- Jorge R. Ledesma
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Epidemiology, Brown University School of Public Health, 121 S Main St, Providence, RI 02912, USA
| | - Ann Basting
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Huong T. Chu
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Jianing Ma
- Center for Biostatistics, Department of Biomedical Informatics, The Ohio State University, 1800 Cannon Drive, Columbus, OH 43210, USA;
| | - Meixin Zhang
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Avina Vongpradith
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Amanda Novotney
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Jeremy Dalos
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Christopher J. L. Murray
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| | - Hmwe H. Kyu
- Institute for Health Metrics and Evaluation, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA; (J.R.L.); (A.B.); (H.T.C.); (M.Z.); (A.V.); (A.N.); (J.D.); (P.Z.); (C.J.L.M.)
- Department of Health Metrics Sciences, University of Washington, 3980 15th Ave. NE, Seattle, WA 98195, USA
| |
Collapse
|
|
2
|
Bollyky TJ, Castro E, Aravkin AY, Bhangdia K, Dalos J, Hulland EN, Kiernan S, Lastuka A, McHugh TA, Ostroff SM, Zheng P, Chaudhry HT, Ruggiero E, Turilli I, Adolph C, Amlag JO, Bang-Jensen B, Barber RM, Carter A, Chang C, Cogen RM, Collins JK, Dai X, Dangel WJ, Dapper C, Deen A, Eastus A, Erickson M, Fedosseeva T, Flaxman AD, Fullman N, Giles JR, Guo G, Hay SI, He J, Helak M, Huntley BM, Iannucci VC, Kinzel KE, LeGrand KE, Magistro B, Mokdad AH, Nassereldine H, Ozten Y, Pasovic M, Pigott DM, Reiner RC, Reinke G, Schumacher AE, Serieux E, Spurlock EE, Troeger CE, Vo AT, Vos T, Walcott R, Yazdani S, Murray CJL, Dieleman JL. 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. Lancet 2023; 401:1341-1360. [PMID: 36966780 PMCID: PMC10036128 DOI: 10.1016/s0140-6736(23)00461-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/14/2023] [Accepted: 02/21/2023] [Indexed: 03/25/2023]
Abstract
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.
Collapse
Affiliation(s)
| | - Emma Castro
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Aleksandr Y Aravkin
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Applied Mathematics, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Kayleigh Bhangdia
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Jeremy Dalos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Erin N Hulland
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Global Health, University of Washington, Seattle, WA, USA
| | | | - Amy Lastuka
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Theresa A McHugh
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Samuel M Ostroff
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Henry M Jackson School of International Studies, University of Washington, Seattle, WA, USA
| | - Peng Zheng
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Hamza Tariq Chaudhry
- Council on Foreign Relations, Washington, DC, USA; Department of Public Policy, Harvard University, Cambridge, MA, USA
| | | | | | - Christopher Adolph
- Department of Political Science, University of Washington, Seattle, WA, USA; Center for Statistics and the Social Sciences, University of Washington, Seattle, WA, USA
| | - Joanne O Amlag
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Ryan M Barber
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Austin Carter
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Cassidy Chang
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Rebecca M Cogen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - James K Collins
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Xiaochen Dai
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - William James Dangel
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Carolyn Dapper
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Amanda Deen
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Alexandra Eastus
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Health Management and Policy, Drexel University, Philadelphia, PA, USA
| | - Megan Erickson
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Tatiana Fedosseeva
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Abraham D Flaxman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Nancy Fullman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - John R Giles
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Gaorui Guo
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Simon I Hay
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Jiawei He
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - Bethany M Huntley
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Vincent C Iannucci
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kasey E Kinzel
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Kate E LeGrand
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Beatrice Magistro
- Munk School of Global Affairs and Public Policy, University of Toronto, Toronto, ON, Canada
| | - Ali H Mokdad
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Hasan Nassereldine
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Yaz Ozten
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Maja Pasovic
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - David M Pigott
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Robert C Reiner
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Grace Reinke
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Austin E Schumacher
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Elizabeth Serieux
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Emma E Spurlock
- Department of Social and Behavioral Sciences, Yale University, New Haven, CT, USA; Yale School of Public Health-Social and Behavioral Sciences, Yale University, New Haven, CT, USA
| | - Christopher E Troeger
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Anh Truc Vo
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Theo Vos
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Rebecca Walcott
- Evans School of Public Policy & Governance, University of Washington, Seattle, WA, USA
| | - Shafagh Yazdani
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | - Christopher J L Murray
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA
| | - Joseph L Dieleman
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA; Department of Health Metrics Sciences, School of Medicine, University of Washington, Seattle, WA, USA.
| |
Collapse
|
|
3
|
Stein C, Nassereldine H, Sorensen RJD, Amlag JO, Bisignano C, Byrne S, Castro E, Coberly K, Collins JK, Dalos J, Daoud F, Deen A, Gakidou E, Giles JR, Hulland EN, Huntley BM, Kinzel KE, Lozano R, Mokdad AH, Pham T, Pigott DM, Reiner Jr. RC, Vos T, Hay SI, Murray CJL, Lim SS. Past SARS-CoV-2 infection protection against re-infection: a systematic review and meta-analysis. Lancet 2023; 401:833-842. [PMID: 36930674 PMCID: PMC9998097 DOI: 10.1016/s0140-6736(22)02465-5] [Citation(s) in RCA: 113] [Impact Index Per Article: 113.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 02/18/2023]
Abstract
BACKGROUND Understanding the level and characteristics of protection from past SARS-CoV-2 infection against subsequent re-infection, symptomatic COVID-19 disease, and severe disease is essential for predicting future potential disease burden, for designing policies that restrict travel or access to venues where there is a high risk of transmission, and for informing choices about when to receive vaccine doses. We aimed to systematically synthesise studies to estimate protection from past infection by variant, and where data allow, by time since infection. METHODS In this systematic review and meta-analysis, we identified, reviewed, and extracted from the scientific literature retrospective and prospective cohort studies and test-negative case-control studies published from inception up to Sept 31, 2022, that estimated the reduction in risk of COVID-19 among individuals with a past SARS-CoV-2 infection in comparison to those without a previous infection. We meta-analysed the effectiveness of past infection by outcome (infection, symptomatic disease, and severe disease), variant, and time since infection. We ran a Bayesian meta-regression to estimate the pooled estimates of protection. Risk-of-bias assessment was evaluated using the National Institutes of Health quality-assessment tools. The systematic review was PRISMA compliant and was registered with PROSPERO (number CRD42022303850). FINDINGS We identified a total of 65 studies from 19 different countries. Our meta-analyses showed that protection from past infection and any symptomatic disease was high for ancestral, alpha, beta, and delta variants, but was substantially lower for the omicron BA.1 variant. Pooled effectiveness against re-infection by the omicron BA.1 variant was 45·3% (95% uncertainty interval [UI] 17·3-76·1) and 44·0% (26·5-65·0) against omicron BA.1 symptomatic disease. Mean pooled effectiveness was greater than 78% against severe disease (hospitalisation and death) for all variants, including omicron BA.1. Protection from re-infection from ancestral, alpha, and delta variants declined over time but remained at 78·6% (49·8-93·6) at 40 weeks. Protection against re-infection by the omicron BA.1 variant declined more rapidly and was estimated at 36·1% (24·4-51·3) at 40 weeks. On the other hand, protection against severe disease remained high for all variants, with 90·2% (69·7-97·5) for ancestral, alpha, and delta variants, and 88·9% (84·7-90·9) for omicron BA.1 at 40 weeks. INTERPRETATION Protection from past infection against re-infection from pre-omicron variants was very high and remained high even after 40 weeks. Protection was substantially lower for the omicron BA.1 variant and declined more rapidly over time than protection against previous variants. Protection from severe disease was high for all variants. The immunity conferred by past infection should be weighed alongside protection from vaccination when assessing future disease burden from COVID-19, providing guidance on when individuals should be vaccinated, and designing policies that mandate vaccination for workers or restrict access, on the basis of immune status, to settings where the risk of transmission is high, such as travel and high-occupancy indoor settings. FUNDING Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom.
Collapse
|