Estimating the health effects of COVID-19-related immunisation disruptions in 112 countries during 2020-30: a modelling study.

BACKGROUND: There have been declines in global immunisation coverage due to the COVID-19 pandemic. Recovery has begun but is geographically variable. This disruption has led to under-immunised cohorts and interrupted progress in reducing vaccine-preventable disease burden. There have, so far, been few studies of the effects of coverage disruption on vaccine effects. We aimed to quantify the effects of vaccine-coverage disruption on routine and campaign immunisation services, identify cohorts and regions that could particularly benefit from catch-up activities, and establish if losses in effect could be recovered. METHODS: For this modelling study, we used modelling groups from the Vaccine Impact Modelling Consortium from 112 low-income and middle-income countries to estimate vaccine effect for 14 pathogens. One set of modelling estimates used vaccine-coverage data from 1937 to 2021 for a subset of vaccine-preventable, outbreak-prone or priority diseases (ie, measles, rubella, hepatitis B, human papillomavirus [HPV], meningitis A, and yellow fever) to examine mitigation measures, hereafter referred to as recovery runs. The second set of estimates were conducted with vaccine-coverage data from 1937 to 2020, used to calculate effect ratios (ie, the burden averted per dose) for all 14 included vaccines and diseases, hereafter referred to as full runs. Both runs were modelled from Jan 1, 2000, to Dec 31, 2100. Countries were included if they were in the Gavi, the Vaccine Alliance portfolio; had notable burden; or had notable strategic vaccination activities. These countries represented the majority of global vaccine-preventable disease burden. Vaccine coverage was informed by historical estimates from WHO-UNICEF Estimates of National Immunization Coverage and the immunisation repository of WHO for data up to and including 2021. From 2022 onwards, we estimated coverage on the basis of guidance about campaign frequency, non-linear assumptions about the recovery of routine immunisation to pre-disruption magnitude, and 2030 endpoints informed by the WHO Immunization Agenda 2030 aims and expert consultation. We examined three main scenarios: no disruption, baseline recovery, and baseline recovery and catch-up. FINDINGS: We estimated that disruption to measles, rubella, HPV, hepatitis B, meningitis A, and yellow fever vaccination could lead to 49 119 additional deaths (95% credible interval [CrI] 17 248-134 941) during calendar years 2020-30, largely due to measles. For years of vaccination 2020-30 for all 14 pathogens, disruption could lead to a 2·66% (95% CrI 2·52-2·81) reduction in long-term effect from 37 378 194 deaths averted (34 450 249-40 241 202) to 36 410 559 deaths averted (33 515 397-39 241 799). We estimated that catch-up activities could avert 78·9% (40·4-151·4) of excess deaths between calendar years 2023 and 2030 (ie, 18 900 [7037-60 223] of 25 356 [9859-75 073]). INTERPRETATION: Our results highlight the importance of the timing of catch-up activities, considering estimated burden to improve vaccine coverage in affected cohorts. We estimated that mitigation measures for measles and yellow fever were particularly effective at reducing excess burden in the short term. Additionally, the high long-term effect of HPV vaccine as an important cervical-cancer prevention tool warrants continued immunisation efforts after disruption. FUNDING: The Vaccine Impact Modelling Consortium, funded by Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation. TRANSLATIONS: For the Arabic, Chinese, French, Portguese and Spanish translations of the abstract see Supplementary Materials section.

Exploring the subnational inequality and heterogeneity of the impact of routine measles immunisation in Africa.

Despite vaccination being one of the most effective public health interventions, there are persisting inequalities and inequities in immunisation. Understanding the differences in subnational vaccine impact can help improve delivery mechanisms and policy. We analyse subnational vaccination coverage of measles first-dose (MCV1) and estimate patterns of inequalities in impact, represented as deaths averted, across 45 countries in Africa. We also evaluate how much this impact would improve under more equitable vaccination coverage scenarios. Using coverage data for MCV1 from 2000-2019, we estimate the number of deaths averted at the first administrative level. We use the ratio of deaths averted per vaccination from two mathematical models to extrapolate the impact at a subnational level. Next, we calculate inequality for each country, measuring the spread of deaths averted across its regions, accounting for differences in population. Finally, using three more equitable vaccination coverage scenarios, we evaluate how much impact of MCV1 immunisation could improve by (1) assuming all regions in a country have at least national coverage, (2) assuming all regions have the observed maximum coverage; and (3) assuming all regions have at least 80% coverage. Our results show that progress in coverage and reducing inequality has slowed in the last decade in many African countries. Under the three scenarios, a significant number of additional deaths in children could be prevented each year; for example, under the observed maximum coverage scenario, global MCV1 coverage would improve from 76% to 90%, resulting in a further 363(95%CrI:299-482) deaths averted per 100,000 live births. This paper illustrates that estimates of the impact of MCV1 immunisation at a national level can mask subnational heterogeneity. We further show that a considerable number of deaths could be prevented by maximising equitable access in countries with high inequality when increasing the global coverage of MCV1 vaccination.

COVID-19 impact on routine immunisations for vaccine-preventable diseases: Projecting the effect of different routes to recovery.

Over the past two decades, vaccination programmes for vaccine-preventable diseases (VPDs) have expanded across low- and middle-income countries (LMICs). However, the rise of COVID-19 resulted in global disruption to routine immunisation activities. Such disruptions could have a detrimental effect on public health, leading to more deaths from VPDs, particularly without mitigation efforts. Hence, as routine immunisation activities resume, it is important to estimate the effectiveness of different approaches for recovery. We apply an impact extrapolation method developed by the Vaccine Impact Modelling Consortium to estimate the impact of COVID-19-related disruptions with different recovery scenarios for ten VPDs across 112 LMICs. We focus on deaths averted due to routine immunisations occurring in the years 2020-2030 and investigate two recovery scenarios relative to a no-COVID-19 scenario. In the recovery scenarios, we assume a 10% COVID-19-related drop in routine immunisation coverage in the year 2020. We then linearly interpolate coverage to the year 2030 to investigate two routes to recovery, whereby the immunization agenda (IA2030) targets are reached by 2030 or fall short by 10%. We estimate that falling short of the IA2030 targets by 10% leads to 11.26% fewer fully vaccinated persons (FVPs) and 11.34% more deaths over the years 2020-2030 relative to the no-COVID-19 scenario, whereas, reaching the IA2030 targets reduces these proportions to 5% fewer FVPs and 5.22% more deaths. The impact of the disruption varies across the VPDs with diseases where coverage expands drastically in future years facing a smaller detrimental effect. Overall, our results show that drops in routine immunisation coverage could result in more deaths due to VPDs. As the impact of COVID-19-related disruptions is dependent on the vaccination coverage that is achieved over the coming years, the continued efforts of building up coverage and addressing gaps in immunity are vital in the road to recovery.

Vaccine equity in low and middle income countries: a systematic review and meta-analysis.

BACKGROUND: Evidence to date has shown that inequality in health, and vaccination coverage in particular, can have ramifications to wider society. However, whilst individual studies have sought to characterise these heterogeneities in immunisation coverage at national level, few have taken a broad and quantitative view of the contributing factors to heterogeneity in immunisation coverage and impact, i.e. the number of cases, deaths, and disability-adjusted life years averted. This systematic review aims to highlight these geographic, demographic, and sociodemographic characteristics through a qualitative and quantitative approach, vital to prioritise and optimise vaccination policies. METHODS: A systematic review of two databases (PubMed and Web of Science) was undertaken using search terms and keywords to identify studies examining factors on immunisation inequality and heterogeneity in vaccination coverage. Inclusion criteria were applied independently by two researchers. Studies including data on key characteristics of interest were further analysed through a meta-analysis to produce a pooled estimate of the risk ratio using a random effects model for that characteristic. RESULTS: One hundred and eight studies were included in this review. We found that inequalities in wealth, education, and geographic access can affect vaccine impact and vaccination dropout. We estimated those living in rural areas were not significantly different in terms of full vaccination status compared to urban areas but noted considerable heterogeneity between countries. We found that females were 3% (95%CI[1%, 5%]) less likely to be fully vaccinated than males. Additionally, we estimated that children whose mothers had no formal education were 28% (95%CI[18%,47%]) less likely to be fully vaccinated than those whose mother had primary level, or above, education. Finally, we found that individuals in the poorest wealth quintile were 27% (95%CI [16%,37%]) less likely to be fully vaccinated than those in the richest. CONCLUSIONS: We found a nuanced picture of inequality in vaccination coverage and access with wealth disparity dominating, and likely driving, other disparities. This review highlights the complex landscape of inequity and further need to design vaccination strategies targeting missed subgroups to improve and recover vaccination coverage following the COVID-19 pandemic. TRIAL REGISTRATION: Prospero, CRD42021261927.

Developing Biomarker Panels to Predict Progression of Acute Kidney Injury After Cardiac Surgery.

INTRODUCTION: Acute kidney injury (AKI) is a frequent complication of cardiac surgery, but only a fraction of cardiac surgery patients that experience postoperative AKI have progression to more severe stages. Biomarkers that can distinguish patients that will experience progression of AKI are potentially useful for clinical care and/or the development of therapies. METHODS: Data come from a prospective cohort study of cardiac surgery patients; the analytic dataset contained data from 354 cardiac surgery patients meeting criteria for AKI following surgery. Candidate predictors were 38 biomarkers of kidney function, insult, or injury measured at the time of AKI diagnosis. The outcome was AKI progression, defined as worsening of AKI Network stage. We investigated combining biomarkers with Bayesian model averaging (BMA) and random forests of classification trees, with and without center transformation. For both approaches, we used resampling-based methods to avoid optimistic bias in our assessment of model performance. RESULTS: BMA yielded a combination of 3 biomarkers and an optimism-corrected estimated area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.82). The random forests approach, which nominally uses all biomarkers, had an estimated AUC of 0.74 (95% CI: 0.66, 0.82). A second application of random forests applied to biomarker values after a center-specific transformation had an estimated AUC of 0.80 (95% CI: 0.72, 0.88). CONCLUSION: These findings suggest that the application of advanced statistical techniques to combine biomarkers offers only modest improvements over use of single biomarkers alone. This exemplifies a common experience in biomarker research: combinations of modestly performing biomarkers often also have modest performance.

A framework for estimating and testing qualitative interactions with applications to predictive biomarkers.

An effective treatment may only benefit a subset of patients enrolled in a clinical trial. We translate the search for patient characteristics that predict treatment benefit to a search for qualitative interactions, which occur when the estimated response-curve under treatment crosses the estimated response-curve under control. We propose a regression-based framework that tests for qualitative interactions without assuming linearity or requiring pre-specified risk strata; this flexibility is useful in settings where there is limited a priori scientific knowledge about the relationship between features and the response. Simulations suggest that our method controls Type I error while offering an improvement in power over a procedure based on linear regression or a procedure that pre-specifies evenly spaced risk strata. We apply our method to a publicly available dataset to search for a subset of HER2+ breast cancer patients who benefit from adjuvant chemotherapy. We implement our method in Python and share the code/data used to produce our results on GitHub (https://github.com/jhroth/data-example).

Evaluating biomarkers for prognostic enrichment of clinical trials.

BACKGROUND/AIMS: A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. METHODS: We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. RESULTS: We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. CONCLUSION: In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Interleukin-8 and Tumor Necrosis Factor Predict Acute Kidney Injury After Pediatric Cardiac Surgery.

BACKGROUND: Inflammation is a key component of both acute kidney injury (AKI) and response to cardiopulmonary bypass. Because AKI poses risks to children after cardiac surgery, we investigated the value of inflammatory biomarkers interleukin-8 (IL-8) and tumor necrosis factor alpha (TNFα) for predicting AKI and other complications. METHODS: We enrolled 412 children between the ages of 1 month and 18 years undergoing cardiopulmonary bypass for cardiac surgery. We collected blood both preoperatively and postoperatively (within 6 hours post-surgery) and measured plasma IL-8 and TNFα. RESULTS: IL-8 and TNFα did not predict AKI in children <2 years, but were strongly associated with AKI in children ≥2 years. There were significant associations between biomarker levels and age (<2 or ≥2 years). In children ≥2 years, patients in the highest tertile of preoperative IL-8 and postoperative TNFα had 4.9-fold (95% CI: 1.8-13.2) and 3.3-fold (95% CI: 1.2-9.0) higher odds of AKI compared with those in the lowest tertile. Children <2 years with higher biomarker levels also had higher odds of AKI, but the difference was not significant. We also found that postoperative TNFα levels were significantly higher in patients with longer hospital stays, and that both postoperative IL-8 and TNFα levels were significantly higher in patients with longer ventilation lengths. There was no evidence that biomarker levels mediated the association between AKI and length of ventilation; they appear to be independent predictors. CONCLUSIONS: Preoperative IL-8 and postoperative TNFα are significantly associated with higher odds of AKI and greater lengths of hospital stays and ventilator use in children 2 years and older.

The development of a virtual 3D model of the renal corpuscle from serial histological sections for E-learning environments.

Histology is a core subject in the anatomical sciences where learners are challenged to interpret two-dimensional (2D) information (gained from histological sections) to extrapolate and understand the three-dimensional (3D) morphology of cells, tissues, and organs. In gross anatomical education 3D models and learning tools have been associated with improved learning outcomes, but similar tools have not been created for histology education to visualize complex cellular structure-function relationships. This study outlines steps in creating a virtual 3D model of the renal corpuscle from serial, semi-thin, histological sections obtained from epoxy resin-embedded kidney tissue. The virtual renal corpuscle model was generated by digital segmentation to identify: Bowman's capsule, nuclei of epithelial cells in the parietal capsule, afferent arteriole, efferent arteriole, proximal convoluted tubule, distal convoluted tubule, glomerular capillaries, podocyte nuclei, nuclei of extraglomerular mesangial cells, nuclei of epithelial cells of the macula densa in the distal convoluted tubule. In addition to the imported images of the original sections the software generates, and allows for visualization of, images of virtual sections generated in any desired orientation, thus serving as a "virtual microtome". These sections can be viewed separately or with the 3D model in transparency. This approach allows for the development of interactive e-learning tools designed to enhance histology education of microscopic structures with complex cellular interrelationships. Future studies will focus on testing the efficacy of interactive virtual 3D models for histology education.

Trends in Prescription Drug Spending Leading Up to Health Reform.

Over the past decade, prescription drug expenditures grew faster than any other service category and comprised an increasing share of per capita health spending. Using the 2005 and 2009 Medical Expenditure Panel Surveys, this analysis identifies the sources of spending growth for prescription drugs among the nonelderly population. We find that prescription drug expenditures among the nonelderly increased by $14.9 billion (9.2%) from 2005 to 2009 and expenditures increased in 12 out of the 16 therapeutic classes. Changes in the number of users and expenditures per fill were the drivers of spending fluctuations in these categories. The main results also provide insight into generic entry, the price gap between brand and generic drugs, and from a health reform evaluation perspective, the importance of separating prepolicy secular trends in expenditures from changes attributable to specific forces, such as shifts toward generic versions of blockbuster drugs.

Trends in health care financial burdens, 2001 to 2009.

CONTEXT: Over the past decade, health care spending increased faster than GDP and income, and decreasing affordability is cited as contributing to personal bankruptcies and as a reason that some of the nonelderly population is uninsured. We examined the trends in health care affordability over the past decade, measuring the financial burdens associated with health insurance premiums and out-of-pocket costs and highlighting implications of the Affordable Care Act for the future financial burdens of particular populations. METHODS: We used cross sections of the Medical Expenditure Panel Survey Household Component (MEPS-HC) from 2001 to 2009. We defined financial burden at the health insurance unit (HIU) level and calculated it as the ratio of expenditures on health care-employer-sponsored insurance coverage (ESI) and private nongroup premiums and out-of-pocket payments-to modified adjusted gross income. FINDINGS: The median health care financial burden grew on average by 2.7% annually and by 21.9% over the period. Using a range of definitions, the fraction of households facing high financial burdens increased significantly. For example, the share of HIUs with health care expenses exceeding 10% of income increased from 35.9% to 44.8%, a 24.8% relative increase. The share of the population in HIUs with health care financial burdens between 2% and 10% fell, and the share with burdens between 10% and 44% rose. CONCLUSIONS: We found a clear trend over the past decade toward an increasing share of household income devoted to health care. The ACA will affect health care spending for subgroups of the population differently. Several groups' burdens will likely decrease, including those becoming eligible for Medicaid or subsidized private insurance and those with expensive medical conditions. Those newly obtaining coverage might increase their health spending relative to income, but they will gain access to care and the ability to spread their expenditures over time, both of which have demonstrable economic value.