Disparity Metrics Research Articles

Standardizing fossil disparity metrics using sample coverage

Standardizing fossil disparity metrics using sample coverage

Identifying groups at-risk to extreme heat: Intersections of age, race/ethnicity, and socioeconomic status

Anthropogenic climate change has resulted in a significant rise in extreme heat events, exerting considerable but unequal impacts on morbidity and mortality. Numerous studies have identified inequities in heat exposure across different groups, but social identities have often been viewed in isolation from each other. Children (5 and under) and older adults (65 and older) also face elevated risks of heat-related health impacts. We employ an intersectional cross-classificatory approach to analyze the distribution of heat exposure between sociodemographic categories split into age groups in the contiguous US. We utilize high-resolution daily air temperature data to establish three census tract-level heat metrics (i.e., average summer temperature, heat waves, and heat island days). We pair those metrics with American Community Survey estimates on racial/ethnic, socioeconomic, and disability status by age to calculate population weighted mean exposures and absolute disparity metrics. Our findings indicate few substantive differences between age groups overall, but more substantial differences between sociodemographic categories within age groups, with children and older adults from socially marginalized backgrounds facing greater exposure than adults from similar backgrounds. When looking at sociodemographic differences by age, people of color of any age and older adults without health insurance emerge as the most exposed groups. This study identifies groups who are most exposed to extreme heat. Policy and program interventions aimed at reducing the impacts of heat should take these disparities in exposure into account to achieve health equity objectives.

A qualitative approach to understanding the drivers of unequal receipt of definitive therapy for Black men with prostate cancer in Massachusetts.

Despite mandated insurance coverage since 2006 and robust health infrastructure in urban settings with high concentrations of minority patients, race-based disparities in prostate cancer (PCa) treatment persist in Massachusetts. In this qualitative study, the authors sought to identify factors driving inequities in PCa treatment in Massachusetts. Four hospitals offering PCa treatment in Massachusetts were selected using a case-mix approach. Purposive sampling was used to conduct semistructured interviews with hospital stakeholders. Additional interviews were conducted with representatives from grassroots organizations providing PCa education. Two study staff coded the interviews to identify major themes and recurrent patterns. Of the 35 informants invited, 25 participated in the study. Although national disparities in PCa outcomes were readily discussed, one half of the informants were unaware that PCa disparities existed in Massachusetts. Informants and grassroots organization representatives acknowledged that patients with PCa are willing to face transportation barriers to receive treatment from trusted and accommodating institutions. Except for chief equity officers, most health care providers lacked knowledge on accessing or using metrics regarding racial disparities in cancer outcomes. Although community outreach was recognized as a potential strategy to reduce treatment disparities and engender trust, informants were often unable to provide a clear implementation plan. This statewide qualitative study builds on existing quantitative data on the nature and extent of disparities. It highlights knowledge gaps in recognizing and addressing racial disparities in PCa treatment in Massachusetts. Improved provider awareness, the use of disparity metrics, and strategic community engagement may ensure equitable access to PCa treatment. Despite mandated insurance and urban health care access, racial disparities in prostate cancer treatment persist in Massachusetts. This qualitative study revealed that, although national disparities were acknowledged, awareness about local disparities are lacking. Stakeholders highlighted the importance of ancillary services, including translators, rideshares, and navigators, in the delivery of care. In addition, whereas hospital stakeholders were aware of collected equity outcomes, they were unsure whether and who is monitoring equity metrics. Furthermore, stakeholders agreed that community outreach showed promise in ensuring equitable access to prostate cancer treatment. Nevertheless, most interviewed stakeholders lacked clear implementation plans.

Health Disparity Metrics for Transplant Centers: Theoretical and Practical Considerations.

Health Disparity Metrics for Transplant Centers: Theoretical and Practical Considerations.

Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images

It has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise models may be learning demographics via shortcut learning and combat this using image augmentation. This study included 44,953 patients who identified as Asian, Black, or White (mean age, 60.68 years±18.21; 23,499 women) for a total of 194,359 chest X-rays (CXRs) from MIMIC-CXR database. The included CheXpert images comprised 45,095 patients (mean age 63.10 years±18.14; 20,437 women) for a total of 134,300 CXRs were used for external validation. We also collected 1195 3D brain magnetic resonance imaging (MRI) data from the ADNI database, which included 273 participants with an average age of 76.97 years±14.22, and 142 females. DL models were trained on either non-augmented or augmented images and assessed using disparity metrics. The features learned by the models were analysed using task transfer experiments and model visualisation techniques. In the detection of radiological findings, training a model using augmented CXR images was shown to reduce disparities in error rate among racial groups (-5.45%), age groups (-13.94%), and sex (-22.22%). For AD detection, the model trained with augmented MRI images was shown 53.11% and 31.01% reduction of disparities in error rate among age and sex groups, respectively. Image augmentation led to a reduction in the model's ability to identify demographic attributes and resulted in the model trained for clinical purposes incorporating fewer demographic features. The model trained using the augmented images was less likely to be influenced by demographic information in detecting image labels. These results demonstrate that the proposed augmentation scheme could enhance the fairness of interpretations by DL models when dealing with data from patients with different demographic backgrounds. National Science and Technology Council (Taiwan), National Institutes of Health.

Debiasing Education Algorithms

AbstractThis systematic literature review investigates the fairness of machine learning algorithms in educational settings, focusing on recent studies and their proposed solutions to address biases. Applications analyzed include student dropout prediction, performance prediction, forum post classification, and recommender systems. We identify common strategies, such as adjusting sample weights, bias attenuation methods, fairness through un/awareness, and adversarial learning. Commonly used metrics for fairness assessment include ABROCA, group difference in performance, and disparity metrics. The review underscores the need for context-specific approaches to ensure equitable treatment and reveals that most studies found no strict tradeoff between fairness and accuracy. We recommend evaluating fairness of data and features before algorithmic fairness to prevent algorithms from receiving discriminatory inputs, expanding the scope of education fairness studies beyond gender and race to include other demographic attributes, and assessing the impact of fair algorithms on end users, as human perceptions may not align with algorithmic fairness measures.

Patterns of diversity in cancer clinical trial enrollment at a New York City academic health system, 2018-2022.

e18630 Background: Mitigation of cancer clinical trial (CT) enrollment disparities is increasingly prioritized in healthcare, but disparity metrics are inconsistently published by individual health systems. We aimed to assess health-system-level trends in CT enrollment by race, ethnicity, sex, and treatment location (network vs main hospital) during the COVID-19 pandemic, as well as to evaluate county-level disparities in CT accrual fraction (AF) over a five-year period. Methods: This was a cross-sectional study of the population enrolled on therapeutic cancer CTs in a New York City academic health system (AHS) from 2018-2022. Demographics were descriptively analyzed and compared with most recent public NCI-reported therapeutic trial accrual data. New York County 5-year cancer incidence was estimated for each demographic subgroup using age-adjusted cancer incidence rates from public SEER/NPCR data and US Census data. Primary endpoint was AF, defined as proportion of patients accrued relative to cancer incidence. Results: Of 2,533 patients enrolled on CTs over the 5-year period, 51% were White, 18% Black, 17% Hispanic, 9.8% Asian, 0.08% American Indian/AK Native, 0% Pacific Islander, and 3.9% Multiple/unknown race; 47% were female. Minority accrual proportion was higher than that of NCI accrual data (45% vs 25%, p =< 0.001). Annual enrollment fell from maximum 571 patients in 2019 to 457 in 2020, then increased to 499 by 2022. Meanwhile, the proportion of minority enrollment increased to a peak of 49.2% in 2020 and fell to 45% in 2022. Female enrollment of 49% in 2018 decreased to 42% in 2021, increasing sharply to 51% in 2022. On multivariate analysis, Black and Asian patients were more likely to be enrolled after the start of the pandemic than White patients (ORs 1.3, 95% CI 1.1-1.6; 1.4, 1.03-1.8). Black, Asian, and female patients were more likely to be enrolled at network hospital locations than White or male patients, respectively (ORs 1.9, 95% CI 1.3-2.7; 2, 1.2-3.1; 14, 8.6-21). Relative to county-level estimated cancer incidence, Black patients had a higher AF than White patients (0.96% vs 0.77%, OR 1.3, 95% CI 1.1-1.4), while Hispanic patients had a lower AF (0.55%, OR 0.72, 95% CI 0.64-0.8). Women had a lower AF than men (0.66% vs 0.74%, OR 0.9, 95% CI 0.83-0.97). Conclusions: Minority accrual at this AHS was higher than nationally reported. Despite decreased total accrual numbers in 2020 due to the COVID-19 pandemic, high accrual diversity was maintained. County-level AF disparities among women and Hispanic patients suggest opportunities for improvement in community outreach, and greater diversity of clinical trial enrollees at network hospitals may reflect successful outreach to the communities they serve.

Context-Based Patterns in Machine Learning Bias and Fairness Metrics: A Sensitive Attributes-Based Approach

The majority of current approaches for bias and fairness identification or mitigation in machine learning models are applications for a particular issue that fails to account for the connection between the application context and its associated sensitive attributes, which contributes to the recognition of consistent patterns in the application of bias and fairness metrics. This can be used to drive the development of future models, with the sensitive attribute acting as a connecting element to these metrics. Hence, this study aims to analyze patterns in several metrics for identifying bias and fairness, applying the gender-sensitive attribute as a case study, for three different areas of applications in machine learning models: computer vision, natural language processing, and recommendation systems. The gender attribute case study has been used in computer vision, natural language processing, and recommendation systems. The method entailed creating use cases for facial recognition in the FairFace dataset, message toxicity in the Jigsaw dataset, and movie recommendations in the MovieLens100K dataset, then developing models based on the VGG19, BERT, and Wide Deep architectures and evaluating them using the accuracy, precision, recall, and F1-score classification metrics, as well as assessing their outcomes using fourteen fairness metrics. Certain metrics disclosed bias and fairness, while others did not, revealing a consistent pattern for the same sensitive attribute across different application domains, and similarities for the statistical parity, PPR disparity, and error disparity metrics across domains, indicating fairness related to the studied sensitive attribute. Some attributes, on the other hand, did not follow this pattern. As a result, we conclude that the sensitive attribute may play a crucial role in defining the fairness metrics for a specific context.

Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies.

Monitoring progress toward population health equity goals requires developing robust disparity indicators. However, surveillance data gaps that result in undercounting racial and ethnic minority groups might influence the observed disparity measures. This study aimed to assess the impact of missing race and ethnicity data in surveillance systems on disparity measures. We explored variations in missing race and ethnicity information in reported annual chlamydia and gonorrhea diagnoses in the United States from 2007 to 2018 by state, year, reported sex, and infection. For diagnoses with incomplete demographic information in 2018, we estimated disparity measures (relative rate ratio and rate difference) with 5 imputation scenarios compared with the base case (no adjustments). The 5 scenarios used the racial and ethnic distribution of chlamydia or gonorrhea diagnoses in the same state, chlamydia or gonorrhea diagnoses in neighboring states, chlamydia or gonorrhea diagnoses within the geographic region, HIV diagnoses, and syphilis diagnoses. In 2018, a total of 31.93% (560,551/1,755,510) of chlamydia and 22.11% (128,790/582,475) of gonorrhea diagnoses had missing race and ethnicity information. Missingness differed by infection type but not by reported sex. Missing race and ethnicity information varied widely across states and times (range across state-years: from 0.0% to 96.2%). The rate ratio remained similar in the imputation scenarios, although the rate difference differed nationally and in some states. We found that missing race and ethnicity information affects measured disparities, which is important to consider when interpreting disparity metrics. Addressing missing information in surveillance systems requires system-level solutions, such as collecting more complete laboratory data, improving the linkage of data systems, and designing more efficient data collection procedures. As a short-term solution, local public health agencies can adapt these imputation scenarios to their aggregate data to adjust surveillance data for use in population indicators of health equity.

Rapid Initial Morphospace Expansion and Delayed Morphological Disparity Peak in the First 100 Million Years of the Archosauromorph Evolutionary Radiation

Adaptive radiations have played a major role in generating modern and deep-time biodiversity. The Triassic radiation of the Archosauromorpha was one of the most spectacular vertebrate radiations, giving rise to many highly ecomorphologically varied lineages—including the dinosaurs, pterosaurs, and stem-crocodylians—that dominated the larger-bodied land fauna for the following 150 Ma, and ultimately gave rise to today’s > 10,000 species of birds and crocodylians. This radiation provides an outstanding testbed for hypotheses relating to adaptive radiations more broadly. Recent studies have started to characterize the tempo and mode of the archosauromorph early adaptive radiation, indicating very high initial rates of evolution, non-competitive niche-filling processes, and previously unrecognized morphological disparity even among non-crown taxa. However, these analyses rested primarily either on discrete characters or on geometric morphometrics of the cranium only, or even failed to fully include phylogenetic information. Here we expand previous 2D geometric morphometric cranial datasets to include new taxa and reconstructions, and create an analogous dataset of the pelvis, thereby allowing comparison of anatomical regions and the transition from “sprawling” to “upright” posture to be examined. We estimated morphological disparity and evolutionary rates through time. All sampled clades showed a delayed disparity peak for sum of variances and average nearest neighbor distances in both the cranium and pelvis, with disparity likely not saturated by the end of the studied time span (Late Jurassic); this contrasts with smaller radiations, but lends weight to similar results for large, ecomorphologically-varied groups. We find lower variations in pelvic than cranial disparity among Triassic-Jurassic archosaurs, which may be related to greater morphofunctional constraints on the pelvis. Contrasting with some previous work, but also confirming some previous findings during adaptive radiations, we find relatively widespread evidence of correlation between sampled diversity and disparity, especially at the largest phylogenetic scales and using average displacement rather than sum of variances as disparity metric; this also demonstrates the importance of comparing disparity metrics, and the importance of phylogenetic scale. Stem and crown archosauromorphs show a morphological diversification of both the cranium and pelvis with higher initial rates (Permian–Middle Triassic and at the base of major clades) followed by lower rates once diversification into niches has occurred (Late Triassic–Jurassic), indicating an “early burst” patternsensu lato. Our results provide a more detailed and comprehensive picture of the early archosauromorph radiation and have significant bearing on the understanding of deep-time adaptive radiations more broadly, indicating widespread patterns of delayed disparity peaks, initial correlation of diversity and disparity, and evolutionary early bursts.

The Problem of Fairness in Synthetic Healthcare Data.

Access to healthcare data such as electronic health records (EHR) is often restricted by laws established to protect patient privacy. These restrictions hinder the reproducibility of existing results based on private healthcare data and also limit new research. Synthetically-generated healthcare data solve this problem by preserving privacy and enabling researchers and policymakers to drive decisions and methods based on realistic data. Healthcare data can include information about multiple in- and out- patient visits of patients, making it a time-series dataset which is often influenced by protected attributes like age, gender, race etc. The COVID-19 pandemic has exacerbated health inequities, with certain subgroups experiencing poorer outcomes and less access to healthcare. To combat these inequities, synthetic data must “fairly” represent diverse minority subgroups such that the conclusions drawn on synthetic data are correct and the results can be generalized to real data. In this article, we develop two fairness metrics for synthetic data, and analyze all subgroups defined by protected attributes to analyze the bias in three published synthetic research datasets. These covariate-level disparity metrics revealed that synthetic data may not be representative at the univariate and multivariate subgroup-levels and thus, fairness should be addressed when developing data generation methods. We discuss the need for measuring fairness in synthetic healthcare data to enable the development of robust machine learning models to create more equitable synthetic healthcare datasets.

Quality improvement approaches to reduce racial/ethnic disparities in the neonatal intensive care unit

Inequities in neonatal care quality and outcomes persist. Current models of neonatal quality improvement (QI) typically involve implementation of standardized approaches to clinical care that seek to provide consistent care to all infants and their families, which may neglect to account for the unique needs of diverse patient populations. Current approaches often fail to track outcome and process measures by important social disparity metrics, such as race/ethnicity and primary language. Despite these shortcomings, use of a QI structure has tremendous potential to address disparities in neonatal care. Crucial components of a QI approach to achieve health equity include: (1) Identifying equity goals from the inception of a project; (2) Inclusion of diverse family members on multidisciplinary teams; (3) Tracking outcome and process measures according to disparity metrics; and (4) Conducting interventions that preferentially address barriers of high-risk social groups. Hospital-system commitment to diversity and inclusion in the healthcare work force, recognition of the impact of unconscious provider bias and advocacy in the greater public health setting are needed to address underlying social inequities that impact neonatal care quality.

Explaining monotonic ranking functions

Ranking functions are commonly used to assist in decision-making in a wide variety of applications. As the general public realizes the significant societal impacts of the widespread use of algorithms in decision-making, there has been a push towards explainability and transparency in decision processes and results, as well as demands to justify the fairness of the processes. In this paper, we focus on providing metrics towards explainability and transparency of ranking functions, with a focus towards making the ranking process understandable, a priori , so that decision-makers can make informed choices when designing their ranking selection process. We propose transparent participation metrics to clarify the ranking process, by assessing the contribution of each parameter used in the ranking function in the creation of the final ranked outcome, using information about the ranking functions themselves, as well as observations of the underlying distributions of the parameter values involved in the ranking. To evaluate the outcome of the ranking process, we propose diversity and disparity metrics to measure how similar the selected objects are to each other, and to the underlying data distribution. We evaluate the behavior of our metrics on synthetic data, as well as on data and ranking functions on two real-world scenarios: high school admissions and decathlon scoring.

Temporal dynamics of the impact of land use on modal disparity in commuting efficiency

Urban land use is known to affect commuting efficiency according to the excess commuting framework. However, most studies do not include temporal dynamics, and those that do, focus on decadal, yearly, or daily temporal resolutions. However, commuting is not a stationary spatial process. Since people leave home and start their jobs at different times of the day and since traffic congestion varies throughout the day, neglecting hourly dynamics can misestimate commuting efficiency in a region and lead to erroneous policy implications. Another important issue often overlooked in the past is the modal disparity in commuting efficiency and how it evolves during the day. To overcome these limitations, this research examines the commuting efficiency variation by car and public transport by six one-hour periods between 5 AM and 11 AM in Warsaw, Poland, using travel survey data and travel times generated from GPS-based big data for cars and from GTFS for public transport. We develop four different groups of modeling scenarios: no disaggregation, disaggregation by time, disaggregation by mode, and disaggregation by time and mode. Therefore, excess commuting and modal disparity metrics are applied for a total of 21 specific time and mode combinations. The results suggest that commuting efficiency is worst during the 8–9 AM period for both modes, and that public transport users are more efficient after 7 AM. Hourly variations in the excess commuting metrics imply that policy makers should examine ways to encourage flexible work hours to distribute work starts and to increase public transport frequencies in the off-peak.

Morphological disparity in theropod jaws: comparing discrete characters and geometric morphometrics

AbstractDisparity, the diversity of form and function of organisms, can be assessed from cladistic or phenetic characters, and from discrete characters or continuous characters such as landmarks, outlines, or ratios. But do these different methods of assessing disparity provide comparable results? Here we provide evidence that all metrics correlate significantly with each other and capture similar patterns of morphological variation. We compare three methods of capturing morphological disparity (discrete characters, geometric morphometric outlines and geometric morphometric landmarks) in coelurosaurian dinosaurs. We standardize our study by focusing all our metrics on the mandible, so avoiding the risk of confounding disparity methods with anatomical coverage of the taxa. The correlation is strongest between the two geometric morphometric methods, and weaker between the morphometric methods and the discrete characters. By using phylogenetic simulations of discrete character and geometric morphometric data sets, we show that the strength of these correlations is significantly greater than expected from the evolution of random data under Brownian motion. All disparity metrics confirm that Maniraptoriformes had the highest disparity of all coelurosaurians, and omnivores and herbivores had higher disparity than carnivores.

Diet variability among insular populations of Podarcis lizards reveals diverse strategies to face resource-limited environments.

Access to resources is a dynamic and multicausal process that determines the success and survival of a population. It is therefore often challenging to disentangle the factors affecting ecological traits like diet. Insular habitats provide a good opportunity to study how variation in diet originates, in particular in populations of mesopredators such as lizards. Indeed, high levels of population density associated with low food abundance and low predation are selection pressures typically observed on islands. In the present study, the diet of eighteen insular populations of two closely related species of lacertid lizards (Podarcis sicula and Podarcis melisellensis) was assessed. Our results reveal that despite dietary variability among populations, diet taxonomic diversity is not impacted by island area. In contrast, however, diet disparity metrics, based on the variability in the physical (hardness) and behavioral (evasiveness) properties of ingested food items, are correlated with island size. These findings suggest that an increase in intraspecific competition for access to resources may induce shifts in functional components of the diet. Additionally, the two species differed in the relation between diet disparity and island area suggesting that different strategies exist to deal with low food abundance in these two species. Finally, sexual dimorphism in diet and head dimensions is not greater on smaller islands, in contrast to our predictions.

Geographic inequity in transplant access.

Scarcity is a defining feature of the modern transplant landscape, and in light of chronic shortages in donor organs, there is cause for concern about geographic inequities in patients' access to lifesaving resources. Recent policy changes designed to ameliorate unequal donor supply and demand have brought new interest to measuring and addressing disparities at all stages of transplant care. The purpose of this review is to describe an overview of recent literature on geographic inequities in transplant access, focusing on kidney, liver, and lung transplantation and the impact of policy changes on organ allocation. Despite a major change to the kidney allocation policy in 2014, geographic inequity in kidney transplant access remains. In liver transplantation, the debate has centered on the median acuity score at transplantation; however, a more thorough examination of disparities in access and survival has emerged. Geographic differences in access and quality of transplant care are undeniable, but existing disparity metrics reflect disparities only among candidates who are waitlisted. Future research should address major gaps in our understanding of geographic inequity in transplant access, including patients who may be transplant-eligible but experience a wide variety of barriers in accessing the transplant waiting list.

Multifaceted disparity approach reveals dinosaur herbivory flourished before the end-Cretaceous mass extinction

AbstractUnderstanding temporal patterns in biodiversity is an enduring question in paleontology. Compared with studies of taxonomic diversity, long-term perspectives on ecological diversity are rare, particularly in terrestrial systems. Yet ecological diversity is critical for the maintenance of biodiversity, especially during times of major perturbations. Here, we explore the ecological diversity of Cretaceous herbivorous dinosaurs leading up to the K-Pg extinction, using dental and jaw morphological disparity as a proxy. We test the hypothesis that a decline in ecological diversity could have facilitated their rapid extinction 66 Ma. We apply three disparity metrics that together capture different aspects of morphospace occupation and show how this approach is key to understanding patterns of morphological evolution. We find no evidence of declining disparity in herbivorous dinosaurs as a whole—suggesting that dinosaur ecological diversity remained high during the last 10 Myr of their existence. Clades show different disparity trends through the Cretaceous, but none except sauropods exhibits a long-term decline. Herbivorous dinosaurs show two disparity peaks characterized by different processes; in the Early Cretaceous by expansion in morphospace and in the Campanian by morphospace packing. These trends were only revealed by using a combination of disparity metrics, demonstrating how this approach can offer novel insights into macroevolutionary processes underlying patterns of disparity and ecological diversity.

Racial and Ethnic Disparities in the Incidence of Esophageal Cancer in the United States, 1992-2013.

Racial and ethnic disparities in the incidence of esophageal cancer have not been thoroughly characterized with quantitative health-disparity measures. Using data from 1992-2013 from 13 US cancer registries in the Surveillance, Epidemiology, and End Results database, we assessed such disparities according to histological type, based on a variety of disparity metrics. The age-standardized incidence rate of squamous cell carcinoma (SCC) was highest among black persons, while adenocarcinoma mainly affected white men. The rate of SCC decreased over time in all racial/ethnic groups, and this was most pronounced in black persons (by 5.7% per year among men and 5.0% among women). The adenocarcinoma rate rose among non-Hispanic whites and among black men. Racial/ethnic disparities in the incidence of total esophageal cancer decreased over time, which was due mainly to reduced disparities in SCC. The 2 absolute disparity measures-range difference and between-group variance-for adenocarcinoma rose by 3.2% and 6.8% per year, respectively, in men and by 1.8% and 5.3% per year, respectively, in women. This study demonstrates decreased racial/ethnic disparities in the incidence of esophageal SCC over time in the United States, while disparities increased in adenocarcinoma incidence as measured on the absolute scale.

Trends in Longevity in the Americas: Disparities in Life Expectancy in Women and Men, 1965-2010.

ObjectiveWe describe trends in life expectancy at birth (LE) and between-country LE disparities since 1965, in Latin America and the Caribbean.Methods & FindingsLE trends since 1965 are described for three geographical sub-regions: the Caribbean, Central America, and South America. LE disparities are explored using a suite of absolute and relative disparity metrics, with measurement consensus providing confidence to observed differences. LE has increased throughout Latin America and the Caribbean. Compared to the Caribbean, LE has increased by an additional 6.6 years in Central America and 4.1 years in South America. Since 1965, average reductions in between-country LE disparities were 14% (absolute disparity) and 23% (relative disparity) in the Caribbean, 55% and 51% in Central America, 55% and 52% in South America.ConclusionsLE in Latin America and the Caribbean is exceeding ‘minimum standard’ international targets, and is improving relative to the world region with the highest human longevity. The Caribbean, which had the highest LE and the lowest between-country LE disparities in Latin America and the Caribbean in 1965-70, had the lowest LE and the highest LE disparities by 2005-10. Caribbean Governments have championed a collaborative solution to the growing burden of non-communicable disease, with 15 territories signing on to the Declaration of Port of Spain, signalling regional commitment to a coordinated public-health response. The persistent LE inequity between Caribbean countries suggests that public health interventions should be tailored to individual countries to be most effective. Between- and within-country disparity monitoring for a range of health metrics should be a priority, first to guide country-level policy initiatives, then to contribute to the assessment of policy success.