Universal health coverage and intersectoral action for health: key messages from Disease Control Priorities, 3rd edition.

The World Bank is publishing nine volumes of Disease Control Priorities, 3rd edition (DCP3) between 2015 and 2018. Volume 9, Improving Health and Reducing Poverty, summarises the main messages from all the volumes and contains cross-cutting analyses. This Review draws on all nine volumes to convey conclusions. The analysis in DCP3 is built around 21 essential packages that were developed in the nine volumes. Each essential package addresses the concerns of a major professional community (eg, child health or surgery) and contains a mix of intersectoral policies and health-sector interventions. 71 intersectoral prevention policies were identified in total, 29 of which are priorities for early introduction. Interventions within the health sector were grouped onto five platforms (population based, community level, health centre, first-level hospital, and referral hospital). DCP3 defines a model concept of essential universal health coverage (EUHC) with 218 interventions that provides a starting point for country-specific analysis of priorities. Assuming steady-state implementation by 2030, EUHC in lower-middle-income countries would reduce premature deaths by an estimated 4·2 million per year. Estimated total costs prove substantial: about 9·1% of (current) gross national income (GNI) in low-income countries and 5·2% of GNI in lower-middle-income countries. Financing provision of continuing intervention against chronic conditions accounts for about half of estimated incremental costs. For lower-middle-income countries, the mortality reduction from implementing the EUHC can only reach about half the mortality reduction in non-communicable diseases called for by the Sustainable Development Goals. Full achievement will require increased investment or sustained intersectoral action, and actions by finance ministries to tax smoking and polluting emissions and to reduce or eliminate (often large) subsidies on fossil fuels appear of central importance. DCP3 is intended to be a model starting point for analyses at the country level, but country-specific cost structures, epidemiological needs, and national priorities will generally lead to definitions of EUHC that differ from country to country and from the model in this Review. DCP3 is particularly relevant as achievement of EUHC relies increasingly on greater domestic finance, with global developmental assistance in health focusing more on global public goods. In addition to assessing effects on mortality, DCP3 looked at outcomes of EUHC not encompassed by the disability-adjusted life-year metric and related cost-effectiveness analyses. The other objectives included financial protection (potentially better provided upstream by keeping people out of the hospital rather than downstream by paying their hospital bills for them), stillbirths averted, palliative care, contraception, and child physical and intellectual growth. The first 1000 days after conception are highly important for child development, but the next 7000 days are likewise important and often neglected.

Historical trends demonstrate a pattern of increasingly frequent and severe spillover events of high-consequence zoonotic viruses.

The COVID-19 pandemic has focused attention on patterns of infectious disease spillover. Climate and land-use changes are predicted to increase the frequency of zoonotic spillover events, which have been the cause of most modern epidemics. Characterising historical trends in zoonotic spillover can provide insights into the expected frequency and severity of future epidemics, but historical epidemiological data remains largely fragmented and difficult to analyse. We utilised our extensive epidemiological database to analyse a specific subset of high-consequence zoonotic spillover events for trends in the annual frequency and severity of outbreaks. Our analysis, which excludes the ongoing SARS-CoV-2 pandemic, shows that the number of spillover events and reported deaths have been increasing by 4.98% (confidence interval [CI]95% [3.22%; 6.76%]) and 8.7% (CI 95% [4.06%; 13.62%]) annually, respectively. This trend can be altered by concerted global efforts to improve our capacity to prevent and contain outbreaks. Such efforts are needed to address this large and growing risk to global health.

Infectious Disease Underreporting Is Predicted by Country-Level Preparedness, Politics, and Pathogen Severity.

Underreporting of infectious diseases is a pervasive challenge in public health that has emerged as a central issue in characterizing the dynamics of the COVID-19 pandemic. Infectious diseases are underreported for a range of reasons, including mild or asymptomatic infections, weak public health infrastructure, and government censorship. In this study, we investigated factors associated with cross-country and cross-pathogen variation in reporting. We performed a literature search to collect estimates of empirical reporting rates, calculated as the number of cases reported divided by the estimated number of true cases. This literature search yielded a dataset of reporting rates for 32 pathogens, representing 52 countries. We combined epidemiological and social science theory to identify factors specific to pathogens, country health systems, and politics that could influence empirical reporting rates. We performed generalized linear regression to test the relationship between the pathogen- and country-specific factors that we hypothesized could influence reporting rates, and the reporting rate estimates that we collected in our literature search. Pathogen- and country-specific factors were predictive of reporting rates. Deadlier pathogens and sexually transmitted diseases were more likely to be reported. Country epidemic preparedness was positively associated with reporting completeness, while countries with high levels of media bias in favor of incumbent governments were less likely to report infectious disease cases. Underreporting is a complex phenomenon that is driven by factors specific to pathogens, country health systems, and politics. In this study, we identified specific and measurable components of these broader factors that influence pathogen- and country-specific reporting rates and used model selection techniques to build a model that can guide efforts to diagnose, characterize, and reduce underreporting. Furthermore, this model can characterize uncertainty and correct for bias in reported infectious disease statistics, particularly when outbreak-specific empirical estimates of underreporting are unavailable. More precise estimates can inform control policies and improve the accuracy of infectious disease models.

Challenges in reported COVID-19 data: best practices and recommendations for future epidemics.

The proliferation of composite data sources tracking the COVID-19 pandemic emphasises the need for such databases during large-scale infectious disease events as well as the potential pitfalls due to the challenges of combining disparate data sources. Multiple organisations have attempted to standardise the compilation of disparate data from multiple sources during the COVID-19 pandemic. However, each composite data source can use a different approach to compile data and address data issues with varying results.We discuss some best practices for researchers endeavouring to create such compilations while discussing three key categories of challenges: (1) data dissemination, which includes discrepant estimates and varying data structures due to multiple agencies and reporting sources generating public health statistics on the same event; (2) data elements, such as date formats and location names, lack standardisation, and differing spatial and temporal resolutions often create challenges when combining sources; and (3) epidemiological factors, including missing data, reporting lags, retrospective data corrections and changes to case definitions that cannot easily be addressed by the data compiler but must be kept in mind when reviewing the data.Efforts to reform the global health data ecosystem should bear such challenges in mind. Standards and best practices should be developed and incorporated to yield more robust, transparent and interoperable data. Since no standards exist yet, we have highlighted key challenges in creating a comprehensive spatiotemporal view of outbreaks from multiple, often discrepant, reporting sources and provided guidelines to address them. In general, we caution against an over-reliance on fully automated systems for integrating surveillance data and strongly advise that epidemiological experts remain engaged in the process of data assessment, integration, validation and interpretation to identify, diagnose and resolve data challenges.

Using social media to estimate Zika's impact on tourism: #babymoon, 2014-2017.

Zika virus infection during pregnancy can cause microcephaly and other birth defects. We hypothesized that the Latin America Zika epidemic resulted in pregnant women and their partners adopting behavioral changes to limit risk, leading them to forego travel to Zika-affected locations. We evaluated this hypothesis by studying travelers' intent and behavior through Twitter data related to babymoon: a holiday taken by parents-to-be before their baby is born. We found the odds of mentioning representative Zika-affected locations in #babymoon tweets dropped significantly (Odds ratio: 0.29, 95% CI: 0.20-0.40) after the Zika-microcephaly association became well-known. This result was further corroborated through a content analysis of #babymoon tweets mentioning Zika-affected locations, which identified if the Twitter user was physically present in the Zika-affected locations. Conversely, we found a small but statistically insignificant increase in the odds of mentioning Zika-free locations from #babymoon tweets (Odds Ratio: 1.11, 95% CI: 0.97-1.27) after the Zika-microcephaly association became well-known.

Assessing global preparedness for the next pandemic: development and application of an Epidemic Preparedness Index.

INTRODUCTION: Robust metrics for national-level preparedness are critical for assessing global resilience to epidemic and pandemic outbreaks. However, existing preparedness assessments focus primarily on public health systems or specific legislative frameworks, and do not measure other essential capacities that enable and support public health preparedness and response. METHODS: We developed an Epidemic Preparedness Index (EPI) to assess national-level preparedness. The EPI is global, covering 188 countries. It consists of five subindices measuring each country's economic resources, public health communications, infrastructure, public health systems and institutional capacity. To evaluate the construct validity of the EPI, we tested its correlation with proxy measures for preparedness and response capacity, including the timeliness of outbreak detection and reporting, as well as vaccination rates during the 2009 H1N1 influenza pandemic. RESULTS: The most prepared countries were concentrated in Europe and North America, while the least prepared countries clustered in Central and West Africa and Southeast Asia. Better prepared countries were found to report infectious disease outbreaks more quickly and to have vaccinated a larger proportion of their population during the 2009 pandemic. CONCLUSION: The EPI measures a country's capacity to detect and respond to infectious disease events. Existing tools, such as the Joint External Evaluation (JEE), have been designed to measure preparedness within a country over time. The EPI complements the JEE by providing a holistic view of preparedness and is constructed to support comparative risk assessment between countries. The index can be updated rapidly to generate global estimates of pandemic preparedness that can inform strategy and resource allocation.

Delayed density-dependent prevalence of Sin Nombre virus antibody in Montana deer mice (Peromyscus maniculatus) and implications for human disease risk.

American hantaviruses cause a severe respiratory disease known as hantavirus pulmonary syndrome (HPS). In the United States, Sin Nombre virus (SNV), carried by the deer mouse ( Peromyscus maniculatus), is the etiologic agent in the majority of HPS cases. The relationship between deer mouse population density and SNV infection prevalence in deer mice is poorly understood. Our purpose was to clarify this relationship by demonstrating the existence of delayed-density-dependent prevalence of SNV infection in populations of wild deer mice. We also explored the relationship between SNV infection in deer mouse populations and the incidence of human HPS. The study population was 3,616 deer mice captured on 10 mark-recapture grids in Montana during May and September, 1994-2004. Using multivariate logistic regression analysis, we found a strong association between deer mouse population density in fall (September) and SNV antibody prevalence in deer mice the following spring (May). Other characteristics associated with SNV infection in deer mice in spring were: (1) presence of at least one infected deer mouse in the population the previous fall, (2) male gender, (3) adult age class, (4) presence of scars, (5) grassland and logged habitats, and (6) elevations below 1,300 m. There was a strong association between concurrently measured SNV antibody prevalence in deer mice and probable exposure of human HPS cases during the same time period. Human cases were more likely to occur during seasons when SNV antibody prevalence was at least 10% in deer mouse populations. These findings suggest that fall rodent population parameters could be used to help guide prevention efforts the following spring.

Brush mouse (Peromyscus boylii) population dynamics and hantavirus infection during a warm, drought period in southern Arizona.

We monitored Limestone Canyon hantavirus (LSCV) antibody prevalence, host (brush mouse, Peromyscus boylii) abundance, and environmental variables (temperature and rainfall) in brush mice captured on three trapping webs in southern Arizona for 5 yr. Although seasonal patterns were subtle, we observed large multiyear variation in population abundance and antibody prevalence. Limestone Canyon hantavirus infection in brush mouse populations varied over time with prevalence ranging from 0% to 33%. At all trapping webs, evidence of infection disappeared completely for an extended period (up to 2 yr) and eventually reappeared, suggesting that dispersal may play a role in maintaining infection in brush mouse metapopulations. Weather during the study period was drier and warmer than average and these conditions, especially during spring through fall, may have contributed to low brush mouse population density and the local extinction of LSCV during the second year of the study. Nevertheless, population growth was associated with relatively warm, dry conditions during winter periods and a cool, wet spring and summer period in the fifth year of the study. After prolonged absence, LSCV infection was consistently detected only when brush mouse population abundance reached relatively high levels during that fifth year. Comparison of our results to similar studies suggests that stochastic events resulting in the loss or survival of a few infected mice in low-density host populations may result in local extinction of virus; reestablishment of infection may occur via immigration of infected individuals from adjacent populations, but may be successful only when populations are of sufficient density to support frequent rodent-to-rodent interactions and virus transmission.

A dispersal model for the range expansion of blacklegged tick (Acari: Ixodidae).

The blacklegged tick, Ixodes scapularis Say, a vector for the agents of Lyme borreliosis and other diseases, has expanded its range dramatically over the past 20 yr. However, the relative contributions of different vertebrate host species to this expansion have remained largely unexplored. To address this issue, we simulated the expansion of a theoretical tick population across a simple landscape by using a deterministic, spatially explicit, cellular automata model. The model incorporates the ecology of ticks and three vertebrate hosts: white-tailed deer, Odocoileus virginianus Zimmermann; white-footed mouse, Peromyscus leucopus Rafinesque; and American robin, Turdus migratorius L. Host contribution to tick dispersal is modeled as a function of tick burden, home range size, and population density. These parameters were determined using published and unpublished data. Our results suggest that 1) hosts with high tick burdens and large home ranges (e.g., deer) play a critical role in I. scapularis range expansion; 2) hosts with small home ranges (e.g., mice) can limit range expansion if they divert a sufficient number of ticks from feeding on more mobile hosts; and (3) birds that migrate annually (e.g., robins) can play a crucial role in tick range expansion.