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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 27% (95%CI[16%,36%]) 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

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.

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.

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.

The availability of scientific knowledge and the strength of supporting statistical evidence for efficacy of available treatments varies considerably across clinical settings. Nonetheless, the goal of clinicians remains largely unchanged: to recommend patients the most beneficial course of treatment available. In this dissertation, we examine the problem of estimating treatment efficacy for heterogeneous individuals in a target population with varied levels of abstraction and with an eye toward varied study designs.In Chapter 2, we restrict ourselves to RCT data and frame the problem of identifying individual characteristics that affect treatment response as a global hypothesis test for qualitative interaction using a convex optimization problem that is solved either with or without the constraint of qualitative interaction under limited modeling assumptions. We also present a permutation-based testing procedure that yields a p-value or false discovery rate.In Chapter 3, we move away from the RCT setting and instead focus on observational study designs that introduce the significant complication of treatment not being assigned independently of patient characteristics. At the core of Chapter 3 is a principled framework and user-friendly R implementation in the DevTreatRules package that allow practitioners to develop a function (known as a treatment rule) to recommend treatment based on individual characteristics, while also obtaining a trustworthy estimate of the treatment rule's benefit in the target population. We also introduces a four-category classification of characteristics collected in a given observational study based on whether each variable might influence treatment assignment and whether it is expected to be observed in independent clinical settings. Our framework and R implementation emphasize the distinct roles these variable types should play in a principled analysis to ensure that an estimated treatment rule is applicable in clinical settings and that the estimate of the rule benefit is reliable.We begin Chapter 4 by exploring the popular outcome-weighted learning (OWL) method that takes a \"direct'' approach to estimating a treatment rule rather than the \"indirect\" approach taken by the split-regression procedure in Chapter 3. We present a simple Bayesian interpretation of OWL that offers a clear equivalence with split-regression when the outcome is binary and a more nuanced connection when the outcome is continuous. We show how OWL fits into the principled framework of Chapter 3 and we accordingly expand the R package DevTreatRules to accommodate OWL. We then conduct a simulation study that uses DevTreatRules to develop and compare the performance of treatment rules from OWL and split-regression under a range of scenarios. We also implement another promising direct approach to estimating treatment rules, referred to as direct-interactions, in DevTreatRules and include it in the simulation study. We share our proposed remedies for a few subtle but critical computational issues we encountered during our simulation study that have a substantial impact on the performance of OWL and direct-interactions in practice.

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In this paper, we outline a principled approach to estimate an individualized treatment rule that is appropriate for data from observational studies where, in addition to treatment assignment not being independent of individual characteristics, some characteristics may affect treatment assignment in the current study but not be available in future clinical settings where the estimated rule would be applied. The estimation framework is quite flexible and accommodates any prediction method that uses observation weights, where the observation weights themselves are a ratio of two flexibly estimated propensity scores. We also discuss how to obtain a trustworthy estimate of the rule's population benefit based on simple propensity-score-based estimators of average treatment effect. We implement our approach in the R package DevTreatRules and share the code needed to reproduce our results on GitHub.

The U.S. secretary of state traveled to Israel to press theneed to capitalize on Hamas' leader Yahya Sinwar's death to bringhostages home and end war in Gaza. Russian President Vladimir Putin ishosting leaders from so-called emerging economies. Harris is leadingin Latino support but increasingly moving to the right mostly due toeconomic reasons more than ethnicity. New York prosecutors accusedformer CEO Mike Jeffries and two others of exploiting young men withpromises of modeling and career opportunities. GUESTS: Alon Pinkas, Mike Madrid

U.S. President Donald Trump threatens Greenland as the next target after Venezuela as he presses for acquisition, some lawmakers have opposed his move. At the Coalition of the Willing summit in Paris, the security protocols for Ukraine are now largely finished, and will provide military assets and a multinational peacekeeping force in the event of a ceasefire. A monkey has gone wild and wreaking havoc in a pawn shop in Tennessee, as caught on camera, eventually the monkey went back to its rightful owner and the shop returned to normal after an encounter happened. GUESTS: Wesley Tabor