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.

Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.

BACKGROUND: The global research on pandemics or epidemics and mental health has been growing exponentially recently, which cannot be integrated through traditional systematic review. Our study aims to systematically synthesize the evidence using natural language processing (NLP) techniques. METHODS: Multiple databases were searched using titles, abstracts, and keywords. We systematically identified relevant literature published prior to Dec 31, 2023, using NLP techniques such as text classification, topic modelling and geoparsing methods. Relevant articles were categorized by content, date, and geographic location, outputting evidence heat maps, geographical maps, and narrative synthesis of trends in related publications. RESULTS: Our NLP analysis identified 77,915 studies in the area of pandemics or epidemics and mental health published before Dec 31, 2023. The Covid pandemic was the most common, followed by SARS and HIV/AIDS; Anxiety and stress were the most frequently studied mental health outcomes; Social support and healthcare were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries, with scant evidence from low-income counties. Co-occurrence of pandemics or epidemics and fear, depression, stress was common. Anxiety was one of the three most common topics in all continents except North America. CONCLUSION: Our findings suggest the importance and feasibility of using NLP to comprehensively map pandemics or epidemics and mental health in the age of big literature. The review identifies clear themes for future clinical and public health research, and is critical for designing evidence-based approaches to reduce the negative mental health impacts of pandemics or epidemics.

Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.

Digital epidemiology is the process of investigating the dynamics of disease-related patterns, both social and clinical, as well as the causes of these trends in epidemiology. Digital epidemiology, utilising big data from a variety of digital sources, has emerged as a viable method for early detection and monitoring of viral outbreaks. The present review gives an overview of digital epidemiology, emphasising its importance in the timely detection of infectious disease outbreaks. Researchers may discover and track outbreaks in real time using digital data sources such as search engine queries, social media trends, and digital health records. However, data quality, concerns about privacy, and data interoperability must be addressed to maximise the effectiveness of digital epidemiology. As the global landscape of infectious diseases evolves, integrating digital epidemiology becomes critical to improving pandemic preparedness and response efforts. Integrating digital epidemiology into routine monitoring systems has the potential to improve global health outcomes and save lives in the event of viral outbreaks.

Epidemic control by social distancing and vaccination: Optimal strategies and remarks on the COVID-19 Italian response policy.

After the many failures in the control of the COVID-19 pandemic, identifying robust principles of epidemic control will be key in future preparedness. In this work, we propose an optimal control model of an age-of-infection transmission model under a two-phase control regime where social distancing is the only available control tool in the first phase, while the second phase also benefits from the arrival of vaccines. We analyzed the problem by an ad-hoc numerical algorithm under a strong hypothesis implying a high degree of prioritization to the protection of health from the epidemic attack, which we termed the "low attack rate" hypothesis. The outputs of the model were also compared with the data from the Italian COVID-19 experience to provide a crude assessment of the goodness of the enacted interventions prior to the onset of the Omicron variant.

Enhancing COVID-19 forecasting precision through the integration of compartmental models, machine learning and variants.

Predicting epidemic evolution is essential for making informed decisions and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from several European and U.S. states. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms conventional data-centric approaches, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants.

Surveillance of SARS-CoV-2 prevalence from repeated pooled testing: application to Swiss routine data.

Surveillance of SARS-CoV-2 through reported positive RT-PCR tests is biased due to non-random testing. Prevalence estimation in population-based samples corrects for this bias. Within this context, the pooled testing design offers many advantages, but several challenges remain with regards to the analysis of such data. We developed a Bayesian model aimed at estimating the prevalence of infection from repeated pooled testing data while (i) correcting for test sensitivity; (ii) propagating the uncertainty in test sensitivity; and (iii) including correlation over time and space. We validated the model in simulated scenarios, showing that the model is reliable when the sample size is at least 500, the pool size below 20, and the true prevalence below 5%. We applied the model to 1.49 million pooled tests collected in Switzerland in 2021-2022 in schools, care centres, and workplaces. We identified similar dynamics in all three settings, with prevalence peaking at 4-5% during winter 2022. We also identified differences across regions. Prevalence estimates in schools were correlated with reported cases, hospitalizations, and deaths (coefficient 0.84 to 0.90). We conclude that in many practical situations, the pooled test design is a reliable and affordable alternative for the surveillance of SARS-CoV-2 and other viruses.

Twenty-five years of sentinel laboratory-based surveillance of shigellosis in a high-income country endemic for the disease, Israel, 1998 to 2022.

BackgroundShigella is a leading cause of moderate-to-severe diarrhoea worldwide and diarrhoeal deaths in children in low- and-middle-income countries.AimWe investigated trends and characteristics of shigellosis and antimicrobial resistance of Shigella sonnei in Israel.MethodsWe analysed data generated by the Sentinel Laboratory-Based Surveillance Network for Enteric Pathogens that systematically collects data on detection of Shigella at sentinel laboratories, along with the characterisation of the isolates at the Shigella National Reference Laboratory. Trends in the shigellosis incidence were assessed using Joinpoint regression and interrupted time-series analyses.ResultsThe average incidence of culture-confirmed shigellosis in Israel declined from 114 per 100,000 population (95% confidence interval (CI): 112-115) 1998-2004 to 80 per 100,000 population (95% CI: 79-82) 2005-2011. This rate remained stable 2012-2019, being 18-32 times higher than that reported from the United States or European high-income countries. After decreasing to its lowest values during the COVID-19 pandemic years (19/100,000 in 2020 and 5/100,000 in 2021), the incidence of culture-confirmed shigellosis increased to 39 per 100,000 population in 2022. Shigella sonnei is the most common serogroup, responsible for a cyclic occurrence of propagated epidemics, and the proportion of Shigella flexneri has decreased. Simultaneous resistance of S. sonnei to ceftriaxone, ampicillin and sulphamethoxazole-trimethoprim increased from 8.5% (34/402) in 2020 to 92.0% (801/876) in 2022.ConclusionsThese findings reinforce the need for continuous laboratory-based surveillance and inform the primary and secondary prevention strategies for shigellosis in Israel and other endemic high-income countries or communities.

Genome characterization of Rift Valley fever virus isolated from cattle, goats and sheep during interepidemic periods in Kenya.

Rift Valley fever virus (RVFV) is a mosquito-borne RNA virus of the Phlebovirus genus in the phenuviridae family. Its genome is trisegmented with small (S), medium (M) and large (L) fragments. In nature, the virus exists as a single serotype that is responsible for outbreaks of Rift Valley fever (RVF), a zoonotic disease that often occurs in Africa and the Middle East. RVFV genomes are thought to undergo both recombination and reassortment and investigations of these events is important for monitoring the emergence of virulent strains and understanding the evolutionary characteristics of this virus. The aim of this study was to characterize the genomes of RVFV isolates from cattle, sheep, and goats collected during an interepidemic period in Kenya between June 2016 and November 2021. A total of 691 serum samples from cattle (n = 144), goats (n = 185) and sheep (n = 362) were analysed at the Central Veterinary Laboratories. The competitive IgM-capture ELISA, was used to screen the samples; 205 samples (29.67%) tested positive for RVFV. Of the 205 positive samples, 42 (20.5%) were from cattle, 57 (27.8%) from goats, and 106 (51.7%) from sheep. All the IgM-positive samples were further analyzed by qPCR, and 24 (11.71%) tested positive with Ct values ranging from 14.788 to 38.286. Two samples, 201808HABDVS from sheep and 201810CML3DVS from cattle, had Ct values of less than 20.0 and yielded whole genome sequences with 96.8 and 96.4 coverage, respectively. There was no statistically significant evidence of recombination in any of the three segments and also phylogenetic analysis showed no evidence of reassortment in the two isolated RVFV segments when compared with other isolates of different lineages from previous outbreaks whose genomes are deposited in the GenBank. No evidence of reassortment leaves room for other factors to be the most probable contributors of change in virulence, pathogenicity and emergence of highly virulent strains of the RVFV.

Wheat genetic resources have avoided disease pandemics, improved food security, and reduced environmental footprints: A review of historical impacts and future opportunities.

The use of plant genetic resources (PGR)-wild relatives, landraces, and isolated breeding gene pools-has had substantial impacts on wheat breeding for resistance to biotic and abiotic stresses, while increasing nutritional value, end-use quality, and grain yield. In the Global South, post-Green Revolution genetic yield gains are generally achieved with minimal additional inputs. As a result, production has increased, and millions of hectares of natural ecosystems have been spared. Without PGR-derived disease resistance, fungicide use would have easily doubled, massively increasing selection pressure for fungicide resistance. It is estimated that in wheat, a billion liters of fungicide application have been avoided just since 2000. This review presents examples of successful use of PGR including the relentless battle against wheat rust epidemics/pandemics, defending against diseases that jump species barriers like blast, biofortification giving nutrient-dense varieties and the use of novel genetic variation for improving polygenic traits like climate resilience. Crop breeding genepools urgently need to be diversified to increase yields across a range of environments (>200 Mha globally), under less predictable weather and biotic stress pressure, while increasing input use efficiency. Given that the ~0.8 m PGR in wheat collections worldwide are relatively untapped and massive impacts of the tiny fraction studied, larger scale screenings and introgression promise solutions to emerging challenges, facilitated by advanced phenomic and genomic tools. The first translocations in wheat to modify rhizosphere microbiome interaction (reducing biological nitrification, reducing greenhouse gases, and increasing nitrogen use efficiency) is a landmark proof of concept. Phenomics and next-generation sequencing have already elucidated exotic haplotypes associated with biotic and complex abiotic traits now mainstreamed in breeding. Big data from decades of global yield trials can elucidate the benefits of PGR across environments. This kind of impact cannot be achieved without widescale sharing of germplasm and other breeding technologies through networks and public-private partnerships in a pre-competitive space.

Beyond SIRD models: a novel dynamic model for epidemics, relating infected with entries to health care units and application for identification and restraining policy.

Epidemic models of susceptibles, exposed, infected, recovered and deceased (SΕIRD) presume homogeneity, constant rates and fixed, bilinear structure. They produce short-range, single-peak responses, hardly attained under restrictive measures. Tuned via uncertain I,R,D data, they cannot faithfully represent long-range evolution. A robust epidemic model is presented that relates infected with the entry rate to health care units (HCUs) via population averages. Model uncertainty is circumvented by not presuming any specific model structure, or constant rates. The model is tuned via data of low uncertainty, by direct monitoring: (a) of entries to HCUs (accurately known, in contrast to delayed and non-reliable I,R,D data) and (b) of scaled model parameters, representing population averages. The model encompasses random propagation of infections, delayed, randomly distributed entries to HCUs and varying exodus of non-hospitalized, as disease severity subdues. It closely follows multi-pattern growth of epidemics with possible recurrency, viral strains and mutations, varying environmental conditions, immunity levels, control measures and efficacy thereof, including vaccination. The results enable real-time identification of infected and infection rate. They allow design of resilient, cost-effective policy in real time, targeting directly the key variable to be controlled (entries to HCUs) below current HCU capacity. As demonstrated in ex post case studies, the policy can lead to lower overall cost of epidemics, by balancing the trade-off between the social cost of infected and the economic contraction associated with social distancing and mobility restriction measures.

Clostridium perfringens in the Intestine: Innocent Bystander or Serious Threat?

The Clostridium perfringens epidemic threatens biosecurity and causes significant economic losses. C. perfringens infections are linked to more than one hundred million cases of food poisoning annually, and 8-60% of susceptible animals are vulnerable to infection, resulting in an economic loss of more than 6 hundred million USD. The enzymes and toxins (>20 species) produced by C. perfringens play a role in intestinal colonization, immunological evasion, intestinal micro-ecosystem imbalance, and intestinal mucosal disruption, all influencing host health. In recent decades, there has been an increase in drug resistance in C. perfringens due to antibiotic misuse and bacterial evolution. At the same time, traditional control interventions have proven ineffective, highlighting the urgent need to develop and implement new strategies and approaches to improve intervention targeting. Therefore, an in-depth understanding of the spatial and temporal evolutionary characteristics, transmission routes, colonization dynamics, and pathogenic mechanisms of C. perfringens will aid in the development of optimal therapeutic strategies and vaccines for C. perfringens management. Here, we review the global epidemiology of C. perfringens, as well as the molecular features and roles of various virulence factors in C. perfringens pathogenicity. In addition, we emphasize measures to prevent and control this zoonotic disease to reduce the transmission and infection of C. perfringens.

Use of Open-Source Epidemic Intelligence for Infectious Disease Outbreaks, Ukraine, 2022.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia's 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021-February 23, 2022) and during (February 24-July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

A Networked Meta-Population Epidemic Model with Population Flow and Its Application to the Prediction of the COVID-19 Pandemic.

This article addresses the crucial issues of how asymptomatic individuals and population movements influence the spread of epidemics. Specifically, a discrete-time networked Susceptible-Asymptomatic-Infected-Recovered (SAIR) model that integrates population flow is introduced to investigate the dynamics of epidemic transmission among individuals. In contrast to existing data-driven system identification approaches that identify the network structure or system parameters separately, a joint estimation framework is developed in this study. The joint framework incorporates historical measurements and enables the simultaneous estimation of transmission topology and epidemic factors. The use of the joint estimation scheme reduces the estimation error. The stability of equilibria and convergence behaviors of proposed dynamics are then analyzed. Furthermore, the sensitivity of the proposed model to population movements is evaluated in terms of the basic reproduction number. This article also rigorously investigates the effectiveness of non-pharmaceutical interventions via distributively controlling population flow in curbing virus transmission. It is found that the population flow control strategy reduces the number of infections during the epidemic.

Development of a core outcome set for maternal and perinatal health research and surveillance in light of emerging and ongoing epidemic threats.

Background: Maternal and perinatal health is often directly and indirectly affected during infectious disease epidemics. Yet, a lack of evidence on epidemics' impact on women and their offspring delays informed decision-making for healthcare providers, pregnant women, women in the post-pregnancy period and policy-makers. To rapidly generate evidence in these circumstances, we aim to develop a Core Outcome Set (COS) for maternal and perinatal health research and surveillance in light of emerging and ongoing epidemic threats. Methods: We will conduct a Systematic Review and a four-stage modified Delphi expert consensus. The systematic literature will aim to inform experts on outcomes reported in maternal and perinatal research and surveillance during previous epidemics. The expert consensus will involve two individual, anonymous online surveys to rate outcomes' importance and suggest new ones, one virtual meeting to discuss disagreements, and one in-person meeting to agree on the final COS, outcomes definitions and measurement methods. Four panels will be established to participate in the modified Delphi with expertise in (a) maternal and perinatal health, (b) neonatal health, (c) public health and emergency response, and (d) representation of civil society. We will recruit at least 20 international experts for each stakeholder group, with diverse backgrounds and gender, professional, and geographic balance. Only highly-rated outcomes (with at least 80% of ratings being 7-9 on a 9-point Likert scale) and no more than 10% of low ratings (1-3) will be included in the final COS. Conclusions: Implementing this COS in future maternal and perinatal research and surveillance, especially in the context of emerging and ongoing epidemic threats, will facilitate the rapid and systematic generation of evidence. It will also enhance the ability of policy-makers, healthcare providers, pregnant women and women in the post-pregnancy period and their families to make well-informed choices in challenging circumstances.

Predictability of human mobility during the COVID-19 pandemic in the United States.

Human mobility is fundamental to a range of applications including epidemic control, urban planning, and traffic engineering. While laws governing individual movement trajectories and population flows across locations have been extensively studied, the predictability of population-level mobility during the COVID-19 pandemic driven by specific activities such as work, shopping, and recreation remains elusive. Here we analyze mobility data for six place categories at the US county level from 2020 February 15 to 2021 November 23 and measure how the predictability of these mobility metrics changed during the COVID-19 pandemic. We quantify the time-varying predictability in each place category using an information-theoretic metric, permutation entropy. We find disparate predictability patterns across place categories over the course of the pandemic, suggesting differential behavioral changes in human activities perturbed by disease outbreaks. Notably, predictability change in foot traffic to residential locations is mostly in the opposite direction to other mobility categories. Specifically, visits to residences had the highest predictability during stay-at-home orders in March 2020, while visits to other location types had low predictability during this period. This pattern flipped after the lifting of restrictions during summer 2020. We identify four key factors, including weather conditions, population size, COVID-19 case growth, and government policies, and estimate their nonlinear effects on mobility predictability. Our findings provide insights on how people change their behaviors during public health emergencies and may inform improved interventions in future epidemics.

Inference of epidemic dynamics in the COVID-19 era and beyond.

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Impact of climate change on vector-borne diseases: Exploring hotspots, recent trends and future outlooks in Bangladesh.

Climate change is a significant risk multiplier and profoundly influences the transmission dynamics, geographical distribution, and resurgence of vector-borne diseases (VBDs). Bangladesh has a noticeable rise in VBDs attributed to climate change. Despite the severity of this issue, the interconnections between climate change and VBDs in Bangladesh have yet to be thoroughly explored. To address this research gap, our review meticulously examined existing literature on the relationship between climate change and VBDs in Bangladesh. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we identified 3849 records from SCOPUS, Web of Science, and Google Scholar databases. Ultimately, 22 research articles meeting specific criteria were included. We identified that the literature on the subject matter of this study is non-contemporaneous, with 68% of studies investing datasets before 2014, despite studies on climate change and dengue nexus having increased recently. We pinpointed Dhaka and Chittagong Hill Tracts as the dengue and malaria research hotspots, respectively. We highlighted that the 2023 dengue outbreak illustrates a possible shift in dengue-endemic areas in Bangladesh. Moreover, dengue cases surged by 317% in 2023 compared to 2019 records, with a corresponding 607% increase in mortality compared to 2022. A weak connection was observed between dengue incidents and climate drivers, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). However, no compelling evidence supported an association between malaria cases, and Sea Surface Temperature (SST) in the Bay of Bengal, along with the NINO3 phenomenon. We observed minimal microclimatic and non-climatic data inclusion in selected studies. Our review holds implications for policymakers, urging the prioritization of mitigation measures such as year-round surveillance and early warning systems. Ultimately, it calls for resource allocation to empower researchers in advancing the understanding of VBD dynamics amidst changing climates.

Wastewater-borne viruses and bacteria, surveillance and biosensors at the interface of academia and field deployment.

Wastewater is a complex, but an ideal, matrix for disease monitoring and surveillance as it represents the entire load of enteric pathogens from a local catchment area. It captures both clinical and community disease burdens. Global interest in wastewater surveillance has been growing rapidly for infectious diseases monitoring and for providing an early warning of potential outbreaks. Although molecular detection methods show high sensitivity and specificity in pathogen monitoring from wastewater, they are strongly limited by challenges, including expensive laboratory settings and prolonged sample processing and analysis. Alternatively, biosensors exhibit a wide range of practical utility in real-time monitoring of biological and chemical markers. However, field deployment of biosensors is primarily challenged by prolonged sample processing and pathogen concentration steps due to complex wastewater matrices. This review summarizes the role of wastewater surveillance and provides an overview of infectious viral and bacterial pathogens with cutting-edge technologies for their detection. It emphasizes the practical utility of biosensors in pathogen monitoring and the major bottlenecks for wastewater surveillance of pathogens, and overcoming approaches to field deployment of biosensors for real-time pathogen detection. Furthermore, the promising potential of novel machine learning algorithms to resolve uncertainties in wastewater data is discussed.

[The venereal morbidity in Russia in 1914-1924].

It is accepted to explain increasing of venereal diseases during years of the Revolution by degradation of morality and general disorder of system of state administration and sanitary services in Russia. The cross-verification of information presented in scientific publications and primary information sources makes it possible to look into following issues: degree of venereal (syphilitic) contamination of population of pre-revolutionary Russia; influence on sanitary statistics by erroneous diagnostics and convictions of Zemstvo medicine about predominantly non-sexual path of transmission of syphilis pathogen in Russian countryside; dynamics and sources of venereal morbidity in wartime. The high indicators of pre-revolutionary statistics of venereal infections could be affected by diagnostic errors. The "village syphilis" encountered in public milieu could be completely different disease not sexually transmitted and not chronic form of disease. The primary documents allow to discuss increasing of the number of venereal patients during war years, that however, does not reach catastrophic numbers that can be found even in scientific publications. This is also confirmed by data of Chief Military Sanitary Board of the Red Army for 1920s and statistical materials of People's Commissariat of Health Care of the RSFSR. The high morbidity was demonstrated by same Gubernias that were problematic before the Revolution and only later by those ones through which during the war years passed army masses. In Russia, total level of syphilis morbidity after the end of Civil War occurred to be more than twice lower than in pre-war 1913 and continued to decrease under impact of sanitary measures of Soviet public health.