Health and non-health benefits and equity impacts of individual-level economic relief programs during epidemics/pandemics in high income settings: a scoping review.

BACKGROUND: Economic relief programs are strategies designed to sustain societal welfare and population health during a regional or global scale infectious disease outbreak. While economic relief programmes are considered essential during a regional or global health crisis, there is no clear consensus in the literature about their health and non-health benefits and their impact on promoting equity. METHODS: We conducted a scoping review, searching eight electronic databases from January 01, 2001, to April 3, 2023, using text words and subject headings for recent pathogens (coronavirus (COVID-19), Ebola, Influenza, Middle East Respiratory Syndrome (MERS), severe acute respiratory syndrome (SARS), HIV, West Nile, and Zika), and economic relief programs; but restricted eligibility to high-income countries and selected diseases due to volume. Title and abstract screening were conducted by trained reviewers and Distiller AI software. Data were extracted in duplicates by two trained reviewers using a pretested form, and key findings were charted using a narrative approach. RESULTS: We identified 27,263 de-duplicated records, of which 50 were eligible. Included studies were on COVID-19 and Influenza, published between 2014 and 2023. Zero eligible studies were on MERS, SARS, Zika, Ebola, or West Nile Virus. We identified seven program types of which cash transfer (n = 12) and vaccination or testing incentive (n = 9) were most common. Individual-level economic relief programs were reported to have varying degrees of impact on public health measures, and sometimes affected population health outcomes. Expanding paid sick leave programs had the highest number of studies reporting health-related outcomes and positively impacted public health measures (isolation, vaccination uptake) and health outcomes (case counts and the utilization of healthcare services). Equity impact was most often reported for cash transfer programs and incentive for vaccination programs. Positive effects on general well-being and non-health outcomes included improved mental well-being and quality of life, food security, financial resilience, and job security. CONCLUSIONS: Our findings suggest that individual-level economic relief programs can have significant impacts on public health measures, population health outcomes and equity. As countries prepare for future pandemics, our findings provide evidence to stakeholders to recognize health equity as a fundamental public health goal when designing pandemic preparedness policies.

Fallacy of attributing the U.S. firearm mortality epidemic to mental health.

BACKGROUND: Annual global data on mental disorders prevalence and firearm death rates for 2000-2019, enables the U.S. to be compared with comparable counties for these metrics. METHODS: The Institute for Health Metrics and Evaluation (IHME) Global Health Burden data were used to compare the prevalence of mental disorders with overall, homicide and suicide firearm death rates including homicides and suicides, in high sociodemographic (SDI) countries. RESULTS: Overall and in none of the nine major categories of mental disorders did the U.S. have a statistically-significant higher rate than any of 40 other high SDI countries during 2019, the last year of available data. During the same year, the U.S. had a statistically-significant higher rate of all deaths, homicides, and suicides by firearm (all p<<0.001) than all other 40 high SDI countries. Suicides accounted for most of the firearm death rate differences between the U.S. and other high SDI countries, and yet the prevalence of mental health disorders associated with suicide were not significantly difference between the U.S. and other high SDI countries. CONCLUSION: Mental disorder prevalence in the U.S. is similar in all major categories to its 40 comparable sociodemographic countries, including mental health disorders primarily associated with suicide. It cannot therefore explain the country's strikingly higher firearm death rate, including suicide. Reducing firearm prevalence, which is correlated with the country's firearm death rate, is a logical solution that has been applied by other countries.

Microevolutionary hypothesis of the obesity epidemic.

The obesity epidemic represents potentially the largest phenotypic change in Homo sapiens since the origin of the species. Despite obesity's high heritability, it is generally presumed a change in the gene pool could not have caused the obesity epidemic. Here we advance the hypothesis that a rapid change in the obesogenic gene pool has occurred second to the introduction of modern obstetrics dramatically altering evolutionary pressures on obesity-the microevolutionary hypothesis of the obesity epidemic. Obesity is known to increase childbirth-related mortality several fold. Prior to modern obstetrics, childbirth-related mortality occurred in over 10% of women in their lifetime. After modern obstetrics, this mortality reduced to a fraction of a percent, thereby lifting a strong negative selection pressure. Regression analysis of data for ~ 190 countries was carried out to examine associations between 1990 lifetime maternal death rates (LMDR) and current obesity rates. Multivariate regression showed LMDR correlated more strongly with national obesity rates than GDP, calorie intake and physical inactivity. Analyses controlling for confounders via partial correlation show that LMDR explains approximately 11% of the variability of obesity rate between nations. For nations with LMDR above the median (>0.45%), LMDR explains 33% of obesity variance, while calorie intake, GDP and physical inactivity show no association with obesity in these nations. The microevolutionary hypothesis offers a parsimonious explanation of the global nature of the obesity epidemic.

Analysis of the Brucella melitensis epidemic in Xinjiang: genotyping, polymorphism, antibiotic resistance and tracing.

Brucella spp. are facultative intracellular pathogens that cause zoonosis- brucellosis worldwide. There has been a trend of the re-emergence of brucellosis worldwide in recent years. The epidemic situation of brucellosis is serious in Xinjiang. To analyze the epidemic situation of Brucella spp. in Xinjiang among humans and animals, this study identified 144 Brucella isolates from Xinjiang using classical identification and 16 S rRNA sequencing. MLVA, drug resistance testing, and wgSNP detection were also performed. At the same time, analysis was conducted based on the published data of Brucella isolates worldwide. The results showed that the dominant species was B. melitensis biovar 3, which belonged to GT42 (MLVA-8 typing) and the East Mediterranean lineage. The correlation among isolates was high both in humans or animals. The isolates in Xinjiang exhibited higher polymorphism compared to other locations in China, with polymorphism increasing each year since 2010. No amikacin/kanamycin-resistant strains were detected, but six rifampicin-intermediate isolates were identified without rpoB gene variation. The NJ tree of the wgSNP results indicated that there were three main complexes of the B. melitensis epidemic in Xinjiang. Based on the results of this study, the prevention and control of brucellosis in Xinjiang should focus on B. melitensis, particularly strains belonging to B. melitensis bv.3 GT42 (MLVA-8 typing) and East Mediterranean lineage. Additionally, the rifampicin- and trimethoprim-sulfamethoxazole- resistance of isolates in Xinjiang should be closely monitored to avoid compromising the therapeutic efficacy and causing greater losses. These results provide essential data for the prevention and control of brucellosis in Xinjiang and China. Although the isolates from Xinjiang have significant characteristics among Chinese isolates and can reflect the epidemiological situation of brucellosis in China to some extent, this study cannot represent the characteristics of isolates from other regions.

Complete Blood Count Values Over Time in Young Children During the Dengue Virus Epidemic in the Dominican Republic From 2018 to 2020.

Background: Dengue fever (DF) is a mosquito-borne illness with substantial economic and societal impact. Understanding laboratory trends of hospitalized Dominican Republic (DR) pediatric patients could help develop screening procedures in low-resourced settings. We sought to describe laboratory findings over time in DR children with DF and DF severity from 2018 to 2020. Methods: Clinical information was obtained prospectively from recruited children with DF. Complete blood count (CBC) laboratory measures were assessed across Days 1-10 of fever. Participants were classified as DF-negative and DF-positive and grouped by severity. We assessed associations of DF severity with demographics, clinical characteristics, and peripheral blood studies. Using linear mixed-models, we assessed if hematologic values/trajectories differed by DF status/severity. Results: A total of 597 of 1101 with a DF clinical diagnosis were serologically evaluated, and 574 (471 DF-positive) met inclusion criteria. In DF, platelet count and hemoglobin were higher on earlier days of fever (p < = 0.0017). Eighty had severe DF. Severe DF risk was associated with thrombocytopenia, intraillness anemia, and leukocytosis, differing by fever day (p < = 0.001). Conclusions: In a pediatric hospitalized DR cohort, we found marked anemia in late stages of severe DF, unlike the typically seen hemoconcentration. These findings, paired with clinical symptom changes over time, may help guide risk-stratified screenings for resource-limited settings.

Paradoxes in the coevolution of contagions and institutions.

Epidemic models study the spread of undesired agents through populations, be it infectious diseases through a country, misinformation in social media or pests infesting a region. In combating these epidemics, we rely neither on global top-down interventions, nor solely on individual adaptations. Instead, interventions commonly come from local institutions such as public health departments, moderation teams on social media platforms or other forms of group governance. Classic models, which are often individual or agent-based, are ill-suited to capture local adaptations. We leverage developments of institutional dynamics based on cultural group selection to study how groups attempt local control of an epidemic by taking inspiration from the successes and failures of other groups. Incorporating institutional changes into epidemic dynamics reveals paradoxes: a higher transmission rate can result in smaller outbreaks as does decreasing the speed of institutional adaptation. When groups perceive a contagion as more worrisome, they can invest in improved policies and, if they maintain these policies long enough to have impact, lead to a reduction in endemicity. By looking at the interplay between the speed of institutions and the transmission rate of the contagions, we find rich coevolutionary dynamics that reflect the complexity of known biological and social contagions.

Deciphering the enigma of Lassa virus transmission dynamics and strategies for effective epidemic control through awareness campaigns and rodenticides.

This study aims to formulate a mathematical framework to examine how the Lassa virus spreads in humans of opposite genders. The stability of the model is analyzed at an equilibrium point in the absence of the Lassa fever. The model's effectiveness is evaluated using real-life data, and all the parameters needed to determine the basic reproduction number are estimated. Sensitivity analysis is performed to pinpoint the crucial parameters significantly influencing the spread of the infection. The interaction between threshold parameters and the basic reproduction number is simulated. Control theory is employed to devise and evaluate strategies, such as awareness campaigns, advocating condom usage, and deploying rodenticides to reduce the possibility of virus transmission efficiently.

Unmasking the silent epidemic: a comprehensive systematic review and meta-analysis of undiagnosed diabetes in Ethiopian adults.

Background: Undiagnosed diabetes mellitus poses a significant global public health concern, exerting a substantial impact on the well-being of individuals, their families, and societies at large. Those individuals with undiagnosed diabetes miss opportunities to maintain quality of life and prevent diabetes-related complications. Even if there are ample primary studies on undiagnosed diabetes in Ethiopia, the results reveal conflicting results. Therefore, a comprehensive national picture of undiagnosed diabetes is essential for designing effective strategies at the national level. Methods: This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for prevalence studies (PROSPERO ID: CRD42021266676). PubMed, Web of Science and the World Health Organization's Hinari portal were searched using a strategy developed in collaboration with Liberians. The inclusion criteria comprised studies reporting undiagnosed diabetes in Ethiopia. Two independent reviewers conducted a quality assessment using a 10-item appraisal tool. Meta-analysis and meta-regression were performed using a random-effects model. Result: Twenty-five studies with 22,193 participants met the inclusion criteria. The pooled prevalence of undiagnosed diabetes among the Ethiopian adult population was 5.68% (95% CI: 4.53 - 6.83, I2 = 75.4). Factors significantly associated with undiagnosed diabetes include age, waist circumference, overweight, family history of diabetes, and a history of hypertension. Conclusion: Our systematic review found a noteworthy prevalence of undiagnosed diabetes in Ethiopia. The majority of factors linked with undiagnosed diabetes in this review were modifiable. This underscores the importance of targeted factors and public health interventions to improve early detection and reduce the burden of undiagnosed diabetes and its complications in Ethiopia. Systematic review registration: https://www.crd.york.ac.uk/prospero, identifier CRD42021266676.

Computation of random time-shift distributions for stochastic population models.

Even in large systems, the effect of noise arising from when populations are initially small can persist to be measurable on the macroscale. A deterministic approximation to a stochastic model will fail to capture this effect, but it can be accurately approximated by including an additional random time-shift to the initial conditions. We present a efficient numerical method to compute this time-shift distribution for a large class of stochastic models. The method relies on differentiation of certain functional equations, which we show can be effectively automated by deriving rules for different types of model rates that arise commonly when mass-action mixing is assumed. Explicit computation of the time-shift distribution can be used to build a practical tool for the efficient generation of macroscopic trajectories of stochastic population models, without the need for costly stochastic simulations. Full code is provided to implement the calculations and we demonstrate the method on an epidemic model and a model of within-host viral dynamics.

Analysis of seasonal H3N2 influenza virus epidemic characteristics and whole genome features in Jining City from 2018 to 2023.

Seasonal H3N2 influenza virus, known for its rapid evolution, poses a serious threat to human health. This study focuses on analyzing the influenza virus trends in Jining City (2018-2023) and understanding the evolving nature of H3N2 strains. Data on influenza-like cases were gathered from Jining City's sentinel hospitals: Jining First People's Hospital and Rencheng Maternal and Child Health Hospital, using the Chinese Influenza Surveillance Information System. Over the period from 2018 to 2023, 7844 throat swab specimens were assessed using real-time fluorescence quantitative PCR for influenza virus nucleic acid detection. For cases positive for seasonal H3N2 influenza virus, virus isolation was followed by whole genome sequencing. Evolutionary trees were built for the eight gene segments, and protein variation analysis was performed. From 2018 to 2023, influenza-like cases in Jining City represented 6.99% (237 299/3 397 247) of outpatient visits, peaking in December and January. Influenza virus was detected in 15.67% (1229/7844) of cases, primarily from December to February. Notably, no cases were found in the 2020-2021 season. Full genome sequencing was conducted on 70 seasonal H3N2 strains, revealing distinct evolutionary branches across seasons. Significant antigenic site variations in the HA protein were noted. No resistance mutations to inhibitors were found, but some strains exhibited mutations in PA, NS1, PA-X, and PB1-F2. Influenza trends in Jining City saw significant shifts in the 2020-2021 and 2022-2023 seasons. Seasonal H3N2 exhibited rapid evolution. Sustained vigilance is imperative for vaccine updates and antiviral selection.

Biological, social, and healthcare factors for death due to influenza A(H1N1) during the 2009 epidemic in Brazil.

OBJECTIVE: To identify risk factors for death from influenza A(H1N1), including the effectiveness of the vaccine against influenza A(H1N1) concerning mortality. METHODS: A case-control of incident cases of influenza A(H1N1) reported in the epidemiological information systems of the states of São Paulo, Paraná, Pará, Amazonas, and Rio Grande do Sul was conducted. RESULTS: 305 participants were included, 70 of them cases and 235 controls, distributed as follows: Amazonas, 9 cases/10 controls; Pará, 22 cases/77 controls, São Paulo, 19 cases/49 controls; Paraná, 10 cases/54 controls; Rio Grande do Sul, 10 cases/45 controls. These participants had a mean age of 30 years, with 33 years among cases and 25 years among controls. There was a predominance of females both among the cases and controls. Biological (age), pre-existing diseases (congestive heart failure, respiratory disease, and diabetes mellitus), and care factors (ICU admission) associated with death from influenza A(H1N1) were identified. CONCLUSION: The risk factors identified in this investigation not only allowed subsidizing the elaboration of clinical conducts but also indicate important aspects for facing "new" influenza epidemics that are likely to occur in our country.

Exploring the seasonality and optimal control strategy of HIV/AIDS epidemic in China: The impact of seasonal testing.

In this work, we investigate how the seasonal variation in the number of individuals who are tested for an HIV antibody in outpatient clinics affects the HIV transmission patterns in China, which has not been well studied. Based on the characteristics of outpatient testing data and reported cases, we establish a periodic infectious disease model to study the impact of seasonal testing on HIV transmission. The results indicate that the seasonal testing is a driving factor for the seasonality of new cases. We demonstrate the feasibility of ending the HIV/AIDS epidemic. We find that the diagnostic rates related to testing play a crucial role in controlling the size of the epidemic. Specifically, when considering minimizing both infected individuals and diagnostic rates, the level of attention paid to undiagnosed infected individuals is always positively correlated with the optimal diagnostic rates, while the optimal diagnostic rates are negatively correlated with the size of the epidemic at the terminal time.

Estimating the potential value of MSM-focused evidence-based implementation interventions in three Ending the HIV Epidemic jurisdictions in the United States: a model-based analysis.

INTRODUCTION: Improving the delivery of existing evidence-based interventions to prevent and diagnose HIV is key to Ending the HIV Epidemic in the United States. Structural barriers in the access and delivery of related health services require municipal or state-level policy changes; however, suboptimal implementation can be addressed directly through interventions designed to improve the reach, effectiveness, adoption or maintenance of available interventions. Our objective was to estimate the cost-effectiveness and potential epidemiological impact of six real-world implementation interventions designed to address these barriers and increase the scale of delivery of interventions for HIV testing and pre-exposure prophylaxis (PrEP) in three US metropolitan areas. METHODS: We used a dynamic HIV transmission model calibrated to replicate HIV microepidemics in Atlanta, Los Angeles (LA) and Miami. We identified six implementation interventions designed to improve HIV testing uptake ("Academic detailing for HIV testing," "CyBER/testing," "All About Me") and PrEP uptake/persistence ("Project SLIP," "PrEPmate," "PrEP patient navigation"). Our comparator scenario reflected a scale-up of interventions with no additional efforts to mitigate implementation and structural barriers. We accounted for potential heterogeneity in population-level effectiveness across jurisdictions. We sustained implementation interventions over a 10-year period and evaluated HIV acquisitions averted, costs, quality-adjusted life years and incremental cost-effectiveness ratios over a 20-year time horizon (2023-2042). RESULTS: Across jurisdictions, implementation interventions to improve the scale of HIV testing were most cost-effective in Atlanta and LA (CyBER/testing cost-saving and All About Me cost-effective), while interventions for PrEP were most cost-effective in Miami (two of three were cost-saving). We estimated that the most impactful HIV testing intervention, CyBER/testing, was projected to avert 111 (95% credible interval: 110-111), 230 (228-233) and 101 (101-103) acquisitions over 20 years in Atlanta, LA and Miami, respectively. The most impactful implementation intervention to improve PrEP engagement, PrEPmate, averted an estimated 936 (929-943), 860 (853-867) and 2152 (2127-2178) acquisitions over 20 years, in Atlanta, LA and Miami, respectively. CONCLUSIONS: Our results highlight the potential impact of interventions to enhance the implementation of existing evidence-based interventions for the prevention and diagnosis of HIV.

Instabilities and self-organization in spatiotemporal epidemic dynamics driven by nonlinearity and noise.

Theoretical analysis of epidemic dynamics has attracted significant attention in the aftermath of the COVID-19 pandemic. In this article, we study dynamic instabilities in a spatiotemporal compartmental epidemic model represented by a stochastic system of coupled partial differential equations (SPDE). Saturation effects in infection spread-anchored in physical considerations-lead to strong nonlinearities in the SPDE. Our goal is to study the onset of dynamic, Turing-type instabilities, and the concomitant emergence of steady-state patterns under the interplay between three critical model parameters-the saturation parameter, the noise intensity, and the transmission rate. Employing a second-order perturbation analysis to investigate stability, we uncover both diffusion-driven and noise-induced instabilities and corresponding self-organized distinct patterns of infection spread in the steady state. We also analyze the effects of the saturation parameter and the transmission rate on the instabilities and the pattern formation. In summary, our results indicate that the nuanced interplay between the three parameters considered has a profound effect on the emergence of dynamical instabilities and therefore on pattern formation in the steady state. Moreover, due to the central role played by the Turing phenomenon in pattern formation in a variety of biological dynamic systems, the results are expected to have broader significance beyond epidemic dynamics.

Impact of community structure on the spread of epidemics on time-varying multiplex networks.

Community structure plays a crucial role in realistic networks and different communities can be created by groups of interest and activity events, and exploring the impact of community properties on collective dynamics is an active topic in the field of network science. Here, we propose a new coupled model with different time scales for online social networks and offline epidemic spreading networks, in which community structure is added into online social networks to investigate its role in the interacting dynamics between information diffusion and epidemic spreading. We obtain the analytical equations of epidemic threshold by MMC (Microscopic Markov Chain) method and conduct a large quantities of numerical simulations using Monte Carlo simulations in order to verify the accuracy of the MMC method, and more valuable insights are also obtained. The results indicate that an increase in the probability of the mobility of an individual can delay the spread of epidemic-related information in the network, as well as delaying the time of the peak of the infection density in the network. However, an increase in the contact ability of mobile individuals produces a facilitating effect on the spread of epidemics. Finally, it is also found that the stronger the acceptance of an individual to information coming from a different community, the lower the infection density in the network, which suggests that it has an inhibitory effect on the disease spreading.

[The history of the fight against cholera epidemics in the Krasnodar territory in 1892 and 1910 (based on archival data)].

Epidemics of a wide variety of infectious diseases were constantly recorded in Russia. Asian cholera occupied a special place among epidemic diseases. In 1892, cholera was imported into the Russian Empire through the Black Sea ports, which reached the territory of the Kuban region by the summer of the same year. In 1892, about 300 thousand people died of cholera in Russia. They still did not know how to treat this terrible disease, did not know its mechanism of spread, as well as the peculiarities of its course. The article, prepared on the basis of an analysis of documentary data from the Archive Department of the administration of the municipal formation of the city of Novorossiysk and the archival Department of the Administration of the municipal formation of the Mostovsky district, examines the cholera epidemic that swept the territories of the Kuban region in 1892 and 1910. The authors characterize the main factors that contributed to the rapid spread of infection and assess the measures that were taken to combat the disease: the implementation of anti-cholera measures organized by the authorities was greatly hampered by the lack of education, prejudices and superstitions of the vast majority of the population; representatives of the nonresident population not only did not comply with basic standards of personal hygiene, but also expressed distrust, and sometimes and hostility towards doctors. Using archival data, the authors investigate the impact of infectious diseases on the demographic indicators of these settlements.

Surveillance of respiratory viruses by aerosol screening in indoor air as an early warning system for epidemics.

The development of effective methods for the surveillance of seasonal respiratory viruses is required for the timely management of outbreaks. We aimed to survey Influenza-A, Influenza-B, RSV-A, Rhinovirus and SARS-CoV-2 surveillance in a tertiary hospital and a campus over 5 months. The effectiveness of air screening as an early warning system for respiratory viruses was evaluated in correlation with respiratory tract panel test results. The overall viral positivity was higher on the campus than in the hospital (55.0% vs. 38.0%). Influenza A was the most prevalent pathogen in both locations. There were two influenza peaks (42nd and 49th weeks) in the hospital air, and a delayed peak was detected on campus in the 1st-week of January. Panel tests indicated a high rate of Influenza A in late December. RSV-A-positivity was higher on the campus than the hospital (21.6% vs. 7.4%). Moreover, we detected two RSV-A peaks in the campus air (48th and 51st weeks) but only one peak in the hospital and panel tests (week 49). Although rhinovirus was the most common pathogen in panel tests, rhinovirus positivity was low in air samples. The air screening for Influenza-B and SARS-Cov-2 revealed comparable positivity rates with panel tests. Air screening can be integrated into surveillance programs to support infection control programs for potential epidemics of respiratory virus infections except for rhinoviruses.

A sub-national HIV epidemic appraisal in Kenya: a new approach for identifying priority geographies, populations and programmes for optimizing coverage for HIV prevention.

INTRODUCTION: The HIV Prevention 2025 Roadmap, developed by UNAIDS, recommends the adoption of a precision prevention approach focused on priority populations and geographies. With reduction in new HIV acquisitions in many countries, designing a differentiated HIV prevention response, using a Programme Science approach, based on the understanding of the epidemic and transmission dynamics at a sub-national level, is critical. METHODS: To support strategic planning, an epidemic appraisal at the sub-national level across 47 counties, with the 2019 population ranging from 0.14 million in Lamu to 4.40 million in Nairobi City, was conducted in Kenya using several existing data sources. Using 2021 Spectrum/EPP/Naomi model estimates of national and sub-national HIV incidence and prevalence, counties with high HIV incidence and prevalence were identified for geographic prioritization. The size of local key population (KP) networks and HIV prevalence in key and general populations were used to define epidemic typology and prioritize populations for HIV prevention programmes. Analysis of routine programme monitoring data for 2021 was used to assess coverage gaps in HIV prevention programmes, including prevention of vertical transmission, anti-retroviral therapy, KP programmes, adolescent girls and young women programme, and voluntary male medical circumcision programme. RESULTS: Ten counties with more than 1000 incident acquisitions in 2021 accounted for 57% of new acquisitions. Twenty-four counties were grouped into the concentrated epidemic type-due to their low prevalence in the general population, high prevalence in KPs and relatively higher density of female sex workers and men who have sex with men populations. Four counties reflected a generalized epidemic, where HIV prevalence was more than 10% and 30%, respectively, among the general and key populations. The remaining 19 counties were classified as having mixed epidemics. Gaps in programmes were identified and counties where these gaps need to be addressed were also prioritized. CONCLUSIONS: The HIV burden in Kenya is unevenly distributed and hence the mix of prevention strategies may vary according to the epidemic typology of the county. Prioritization of programmes based not only on disease burden and epidemic typology, but also on the prevailing gaps in coverage for reducing inequities is a key aspect of this appraisal.