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The Sustainable Development Goals include a target on eliminating child marriage, a human rights abuse. Yet, the indicator used in the SDG framework is a summary statistic and does not provide a full picture of the incidence of marriage at different ages. This paper aims to address this limitation by providing an alternative method of measuring child marriage. The paper reviews recent data on nuptiality and captures evidence of changes in the proportion married and in the age at marriage, in 98 low- and middle-income countries (LMICs). Using data collected from nationally representative Demographic and Health Surveys and Multiple Indicator Cluster Surveys, survival analysis is applied to estimate (a) age-specific marriage hazard rates among girls before age 18; and (b) the number of girls that were married before age 18 in 2020. Results show that the vast majority of girls remain unmarried until age 10. Child marriage rates increase gradually until age 14 and accelerate significantly thereafter at ages 15–17. By accounting for both single-year-age-specific child marriage hazard rates and the age structure of the population with a survival analysis approach, lower estimates in countries with a rapid decrease in child marriage and higher estimates in countries with constant or slightly rising child marriage rates relative to the direct approach are obtained.

The number of COVID-19 infections is key for accurately monitoring the pandemics. However, due to differential testing policies, asymptomatic individuals and limited large-scale testing availability, it is challenging to detect all cases. Seroprevalence studies aim to address this gap by retrospectively assessing the number of infections, but they can be expensive and time-intensive, limiting their use to specific population subgroups. In this paper, we propose a complementary approach that combines estimated (1) infection fatality rates (IFR) using a Bayesian melding SEIR model with (2) reported case-fatality rates (CFR) in order to indirectly estimate the fraction of people ever infected (from the total population) and detected (from the ever infected). We apply the technique to the U.S. due to their remarkable regional diversity and because they count with almost a quarter of all global confirmed cases and deaths. We obtain that the IFR varies from 1.25% (0.39–2.16%, 90% CI) in Florida, the most aged population, to 0.69% in Utah (0.21–1.30%, 90% CI), the youngest population. By September 8, 2020, we estimate that at least five states have already a fraction of people ever infected between 10% and 20% (New Jersey, New York, Massachussets, Connecticut, and District of Columbia). The state with the highest estimated fraction of people ever infected is New Jersey with 17.3% (10.0, 55.8, 90% CI). Moreover, our results indicate that with a probability of 90 percent the fraction of detected people among the ever infected since the beginning of the epidemic has been less than 50% in 15 out of the 20 states analyzed in this paper. Our approach can be a valuable tool that complements seroprevalence studies and indicates how efficient have testing policies been since the beginning of the outbreak.

Objective The Spanish health system is made up of seventeen regional health systems. Through the official reporting systems, some inconsistencies and differences in case fatality rates between Autonomous Communities (CC.AA.) have been observed. Therefore the objective of this paper is to compare COVID-19 case fatality rates across the Spanish CC.AA. Material and methods Observational descriptive study. The COVID-19 case fatality rate (CFR) was estimated according to the official records (CFR-PCR+), the daily mortality monitory system (MoMo) record (CFR-Mo), and the seroprevalence study ENE-COVID-19 (Estudio Nacional de sero Epidemiologia Covid-19) according to sex, age group and CC.AA. between March and June 2020. The main objective is to detect whether there are any differences in CFR between Spanish Regions using two different register systems, i. e., the official register of the Ministry of Health and the MoMo. Results Overall, the CFR-Mo was higher than the CFR-PCR+, 1.59% vs 0.98%. The differences in case fatality rate between both methods were significantly higher in Castilla La Mancha, Castilla y León, Cataluña, and Madrid. The difference between both methods was higher in persons over 74 years of age (CFR-PCR+ 7.5% vs 13.0% for the CFR-Mo) but without statistical significance. There was no correlation of the estimated prevalence of infection with CFR-PCR+, but there was with CFR-Mo (R2 = 0.33). Andalucía presented a SCFR below 1 with both methods, and Asturias had a SCFR higher than 1. Cataluña and Castilla La Mancha presented a SCFR greater than 1 in any scenario of SARS-CoV-2 infection calculated with SCFR-Mo. Conclusions The PCR+ case fatality rate underestimates the case fatality rate of the SARS-CoV- 2 virus pandemic. It is therefore preferable to consider the MoMo case fatality rate. Significant differences have been observed in the information and registration systems and in the severity of the pandemic between the Spanish CC.AA. Although the infection prevalence correlates with case fatality rate, other factors such as age, comorbidities, and the policies adopted to address the pandemic can explain the differences observed between CC.AA.

This paper quantifies the net impact (direct and indirect effects) of the pandemic on the United States population in 2020 using three metrics: excess deaths, life expectancy, and total years of life lost. The findings indicate there were 375,235 excess deaths, with 83% attributable to direct, and 17% attributable to indirect effects of COVID-19. The decrease in life expectancy was 1.67 years, translating to a reversion of 14 years in historical life expectancy gains. Total years of life lost in 2020 was 7,362,555 across the USA (73% directly attributable, 27% indirectly attributable to COVID-19), with considerable heterogeneity at the individual state level.

BackgroundThe objective of this paper is to model lost Quality Adjusted Life Years (QALYs) from symptoms arising from COVID-19 in the UK population, including symptoms of ‘long-COVID’. The scope includes QALYs lost to symptoms, but not deaths, due to acute COVID-19 and long COVID.MethodsThe prevalence of symptomatic COVID-19, encompassing acute symptoms and long-COVID symptoms, was modelled using a decay function. Permanent injury as a result of COVID-19 infection, was modelled as a fixed prevalence. Both parts are combined to calculate QALY loss due to COVID-19 symptoms.ResultsAssuming a 60% final attack rate for SARS-CoV-2 infection in the population, we modelled 299,719 QALYs lost within 1 year of infection (90% due to symptomatic COVID-19 and 10% permanent injury) and 557,754 QALYs lost within 10 years of infection (49% due to symptomatic COVID-19 and 51% due to permanent injury). The UK Government willingness-to-pay to avoid these QALY losses would be £17.9 billion and £32.2 billion, respectively. Additionally, 90,143 people were subject to permanent injury from COVID-19 (0.14% of the population).ConclusionGiven the ongoing development in information in this area, we present a model framework for calculating the health economic impacts of symptoms following SARS-CoV-2 infection. This model framework can aid in quantifying the adverse health impact of COVID-19, long COVID and permanent injury following COVID-19 in society and assist the proactive management of risk posed to health. Further research is needed using standardised measures of patient reported outcomes relevant to long COVID and applied at a population level.

Objective Accumulating evidence suggests the possibility that early life exposures may contribute to risk of Alzheimer’s Disease (AD). This paper explores geographic disparities in AD mortality based on both state of residence in older age as well as state of birth measures in order to assess the relative importance of these factors. Methods We use a subset of a large survey, the NIH-AARP Diet and Health Study, of over 150,000 individuals aged 65–70 with 15 years of mortality follow-up, allowing us to study over 1050 cases of AD mortality. We use multi-level logistic regression, where individuals are nested within states of residence and/or states of birth, to assess the contributions of place to AD mortality variation. Results We show that state of birth explains a modest amount of variation in AD mortality, approximately 4%, which is consistent with life course theories that suggest that early life conditions can produce old age health disparities. However, we also show that nearly all of the variation from state of birth is explained by state of residence in old age. Conclusions These results suggest that later life factors are potentially more consequential targets for intervention in reducing AD mortality and provide some evidence against the importance of macro-level environmental exposures at birth as a core determinant of later AD.

There is evidence that adoption of non-pharmaceutical containment measures (NPMs) may have had a major impact on Covid-19 epidemic dynamics, and mitigated its effect on healthcare system. Optimal timing of implementation of these measures however is not known. In Italy, a national lockdown was decided on March 11th 2020 and ended 4th of May. At that time, cumulative incidence (CI) was different in Italian regions which ranged from 11 cases/100,000 inhabitants. In this paper, we aim to evaluate how level of incidence in different regions at the time of implementation of NPMs affected CI and had an impact on the healthcare system in terms of ICU bed occupancy and mortality rates. We used regional daily new COVID-19 diagnosed cases as well number of people hospitalized in ICU and number of deaths for period February 24-May 11 from all the 19 Italian regions and two autonomous provinces. For each region we calculated: temporal daily trend of cumulative cases of Covid-19/100,000 inhabitants, daily trend of ICU bed occupancy and mortality rate at the end of period. We found that the epidemic curves show similar trends for all regions and all tend to flatten between 11–32 days. However, after 2 months, regions with lower CI at lockdown remained at substantially lower CI (

Background The COVID-19 pandemic has caused a significant number of deaths worldwide. If the prevalence of the virus infection continues to rise, it can potentially have an impact on life expectancy. This paper provides first estimates of the potential impact of the COVID-19 pandemic on period life expectancy. Methods From the estimates of bias-adjusted age-specific case fatality rates in Hubei (China) and a range of six assumptions of prevalence rates ranging from 1% to 70%, we built a discrete-time microsimulation model that simulates the number of infected by COVID-19, the number of dying from it and the number of dying from all causes week by week for a period of one year. We applied our simulation to four broad regions: North America and Europe, Latin America and the Caribbean, South Eastern Asia, and Sub-Saharan African. For each region, 100,000 individuals per each 5-year age group are simulated. Results At 10% prevalence rate, the loss in life expectancy at birth is likely above 1 year in North America and Europe and in Latin America and the Caribbean. In South Eastern Asia and in Sub-Saharan Africa, one year lost in life expectancy corresponds to a prevalence of infection of about 15% and 25%, respectively. Given the uncertainty in fatality rates, with a prevalence of COVID-19 infections of 50% under 95% prediction intervals, life expectancy would drop by 3 to 9 years in North America and Europe, by 3 to 8 years in Latin America and the Caribbean, by 2 to 7 years in South Eastern Asia and by 1 to 4 in Sub-Saharan Africa. In all prevalence scenarios, as long as the prevalence rate of COVID-19 infection remains below 1 or 2%, COVID-19 would not affect life expectancy in a substantial manner. Interpretation In the regions with relatively high life expectancy, for a prevalence of infection threshold above 1 or 2%, the COVID-19 pandemic will break the secular trend of increasing life expectancy resulting in a decline in period life expectancy. With life expectancy being a key indicator of human development, mortality increase, especially among the vulnerable subgroups of populations would set the country back on their path of human development.

Objective How disability manifests itself in an individual is a highly complex process influenced by a wide range of individual and environmental factors. Its complexity makes the search for generalizable characteristics of the disablement process a challenging task. Consequentially, little is known about how the effect on other health outcomes such as life expectancy are modified after the onset of chronic ailments. In this paper we posit an alternative approach to generalize health trajectories of older people with disability and then analyze how socioeconomic conditions affect the longevity within these trajectory groups., Methods Individual level information about the first three successive onsets of chronic disability after age 50 is transformed into state-sequences. We extract trajectory groups based on onset time and the time spent in a certain state. Mortality hazards are then estimated with a Gompertz proportional hazards model to compare effects of different socioeconomic measures within the trajectory groups., Results Three distinct trajectory groups are identified, the mild (1), the early severe (2), and late severe (3) pathway. Estimates of the mortality analysis suggest that social inequalities in longevity are less pronounced after onset of old-age disability. We found a consistent survival prolonging effect for individuals who engage in daily activities (such as meeting with friends, walking) that ranged between 33.2% and 77.3%. The importance of other variables varies between trajectory groups., Discussion This study shows how health trajectories of individuals with disability can be generalized when information on the onset and severity of single conditions is available. Such an approach may help us to better predict health and care expenditures and help families and individuals with their personal care planning. The findings from the subsequent survival analysis suggest a substantial reduction of socioeconomic mortality differences after onset of old-age disability, which appears to be independent of its nature., This work was supported by the LONGPOP project (http://longpop-itn.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 676060 and the project LONGHEALTH CSO2015-69834-R funded by the Spanish Ministry of Economy and Competitiveness. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

A major outbreak of the Ebola virus occurred in 2014 in Sierra Leone. We investigate the spatial heterogeneity of the outbreak among districts in Sierra Leone. The stochastic discrete-time susceptible-exposed-infectious-removed (SEIR) model is used, allowing for probabilistic movements from one compartment to another. Our model accounts for heterogeneity among districts by making use of a hierarchical approach. The transmission rates are considered time-varying. It is investigated whether or not incubation period, infectious period and transmission rates are different among districts. Estimation is done using the Bayesian formalism. The posterior estimates of the effective reproductive number were substantially different across the districts, with pronounced variability in districts with few cases of Ebola. The posterior estimates of the reproductive number at the district level varied between below 1.0 and 4.5, whereas at nationwide level it varied between below 1.0 and 2.5. The posterior estimate of the effective reproductive number reached a value below 1.0 around December. In some districts, the effective reproductive number pointed out for the persistence of the outbreak or for a likely resurgence of new cases of Ebola virus disease (EVD). The posterior estimates have shown to be highly sensitive to prior elicitation, mainly the incubation period and infectious period. The present study received financial support from Universidade Eduardo Mondlane The authors would like to express their gratitude to the editor and to the two anonymous reviewers for their valuable constructive comments and suggestions, which considerably improved the presentation of the paper.

Given recent slowing of declines in national all-cause, heart disease, and stroke mortality, examining spatiotemporal distributions of coronary heart disease (CHD) death rates and trends can provide data critical to improving the cardiovascular health of populations. This paper documents county-level CHD death rates and trends by age group, race, and gender from 1979 through 2017. Using data from the National Vital Statistics System and a Bayesian multivariate space-time conditional autoregressive model, we estimated county-level age-standardized annual CHD death rates for 1979 through 2017 by age group (35-64 years, 65 years and older), race (white, black, other), and gender (men, women). We then estimated county-level total percent change in CHD death rates during four intervals (1979-1990, 1990-2000, 2000-2010, 2010-2017) using log-linear regression models. For all intervals, national CHD death rates declined for all groups. Prior to 2010, although most counties across age, race, and gender experienced declines, pockets of increasing CHD death rates were observed in the Mississippi Delta, Oklahoma, East Texas, and New Mexico across age groups and gender, and were more prominent among non-white populations than whites. Since 2010, across age, race, and gender, county-level declines in CHD death rates have slowed, with a marked increase in the percent of counties with increasing CHD death rates (e.g. 4.4% and 19.9% for ages 35 and older during 1979-1990 and 2010-2017, respectively). Recent increases were especially prevalent and geographically widespread among ages 35-64 years, with 40.5% of counties (95% CI: 38.4, 43.1) experiencing increases. Spatiotemporal differences in these long term, county-level results can inform responses by the public health community, medical providers, researchers, and communities to address troubling recent trends.

Background Previous research has focused on the mortality associated with armed conflict as the primary measure of the population health effects of war. However, mortality only demonstrates part of the burden placed on a population by conflict. Injuries and resultant disabilities also have long-term effects on a population and are not accounted for in estimates that focus solely on mortality. Our aim was to demonstrate a new method to describe the effects of both lives lost, and years of disability generated by a given conflict, with data from the US-led 2003 invasion and subsequent occupation of Iraq. Methods and findings Our data come from interviews conducted in 2014 in 900 Baghdad households containing 5,148 persons. The average household size was 5.72 persons. The majority of the population (55.8%) were between the ages of 19 and 60. Household composition was evenly divided between males and females. Household sample collection was based on methodology previously designed for surveying households in war zones. Survey questions were answered by the head of household or senior adult present. The questions included year the injury occurred, the mechanism of injury, the body parts injured, whether injury resulted in disability and, if so, the length of disability. We present this modeling study to offer an innovative methodology for measuring “years lived with disability” (YLDs) and “years of life lost” (YLLs) attributable to conflict-related intentional injuries, using the Global Burden of Disease (GBD) approach. YLDs were calculated with disability weights, and YLLs were calculated by comparing the age at death to the GBD standard life table to calculate remaining life expectancy. Calculations were also performed using Iraq-specific life expectancy for comparison. We calculated a burden of injury of 5.6 million disability-adjusted life years (DALYs) lost due to conflict-related injuries in Baghdad from 2003 to 2014. The majority of DALYs lost were attributable to YLLs, rather than YLDs, 4.99 million YLLs lost (95% uncertainty interval (UI) 3.87 million to 6.13 million) versus 616,000 YLDs lost (95% UI 399,000 to 894,000). Cause-based analysis demonstrated that more DALYs were lost to due to gunshot wounds (57%) than any other cause. Our study has several limitations. Recall bias regarding the reporting and attribution of injuries is possible. Second, we have no data past the time of the interview, so we assumed individuals with ongoing disability at the end of data collection would not recover, possibly counting more disability for injuries occurring later. Additionally, incomplete data could have led to misclassification of deaths, resulting in an underestimation of the total burden of injury. Conclusions In this study, we propose a methodology to perform burden of disease calculations for conflict-related injuries (expressed in DALYs) in Baghdad from 2003 to 2014. We go beyond previous reports of simple mortality to assess long-term population health effects of conflict-related intentional injuries. Ongoing disability is, in cross section, a relatively small 10% of the total burden. Yet, this small proportion creates years of demands on the health system, persistent limitations in earning capacity, and continuing burdens of care provision on family members., In a modelling study, Guy Jensen and colleagues propose a method for calculating burden of injury in conflict using data on conflict-related intentional injuries in Baghdad, Iraq 2003-2014., Author summary Why was this study done? The Global Burden of Disease (GBD) project, based at the University of Washington, publishes the estimates of prevalence, incidence, years of life lost (YLLs), and years lived with disability (YLDs) for a comprehensive list of diseases and injuries and for all countries from 1990 to the present. The World Health Organization has also published YLL and YLD estimates. Previous researchers innovated the population-proportionate to size sampling methodology for household surveys in war zones, which we used in this study. Previous papers have attempted to calculate only Iraq War deaths, as mortality is an important population health indicator and is relatively straightforward to measure. Injuries are, along with mortality, an important factor in conflict settings; they measure a more nuanced toll on public health. Ongoing disability is important, especially when associated with demands on the health system, ongoing limitations in earning capacity, and the burdens created for family caregivers. We aim to lay out our application of the GBD methodology, along with our alteration to it for calculation of burden of injury in conflict. Additionally, we aim to describe specifically how these calculations were applied in hopes of developing a methodology for the study of conflict populations. What did the researchers do and find? This study innovates the application of GBD methodology for calculating YLLs and YLDs to data collected in a war zone. We calculated a burden of injury of 5.6 million DALYs lost due to conflict-related injuries in Baghdad from 2003 to 2014. These calculations encompass both mortality as well as the effects of injury and disability on the population of Baghdad. We performed these calculations using a variation of the GBD methodology to better reflect the dynamic nature of conflict. Media reports tend to focus heavily on the more “spectacular” aspects of conflict, such as explosions, shelling, and air strikes. By contrast, our results demonstrate gunshot wounds were the leading contributor to YLDs and YLLs. What do these findings mean? Our results demonstrate the importance of data-driven, epidemiologically sound assessment of conflicts, especially those spanning multiple years. However, it should be noted that our primary goal was to develop a methodology, demonstrated by our calculations with the city of Baghdad, to allow for its use in other conflicts. With more sophisticated methods, epidemiologists might better prevent the most harmful aspects of conflict for civilian populations, might better predict losses in advance of the onset of conflict, and might contribute to health systems in planning for care delivery. Limitations for this study include the possibility for recall bias among the survey participants as well as the possible undercounting of deaths due to injury due to classifying any injuries that did not definitively result in death as YLDs rather than YLLs. Lastly, we did not adjust for changes in the population of Baghdad over the course of the study.

As COVID-19 is rapidly unfolding in the United States, it is important to understand how individuals perceive the health and economic risks of the pandemic. In the absence of a readily available medical treatment, any strategy to contain the virus in the US will depend on the behavioral response of US residents. In this paper, we study individual's perceptions on COVID-19 and social distancing during the week of March 10-16, 2020, a week when COVID-19 was officially declared to be a pandemic by WHO and when new infections in the US were more than doubling every three days. Using a nationally representative sample of 5,414 respondents 18+ years of age from the Understanding America Study (UAS), we find that perceptions about COVID-19 health risks and economic consequences in the US population were largely pessimistic and highly variable by age and education. US residents who are young and do not have a college degree perceived a lower risk of getting infected but a higher probability of running out of money than others. Most individuals reported taking some steps to distance themselves from others but important differences emerge by gender and by source of information on COVID-19. Using state and day fixed-effect regressions, we show that perceptions of the health risks closely followed the number of COVID-19 cases in the country, and perceptions of the economic consequences and the prevalence of social distancing were driven upwards by both national and state-level cases. Unless addressed by effective health communication that reaches individuals across all social strata, variations in perceptions about COVID-19 epidemic raise concerns about the ability of the US to implement and sustain the widespread and restrictive policies that are required to curtail the pandemic.

Predominantly heterosexual HIV-1 epidemics like those in sub-Saharan Africa continue to have high HIV incidence in young people. We used a stochastic, agent-based model for age-disparate networks to test the hypothesis that focusing uptake and retention of ART among youth could enhance the efficiency of treatment as prevention (TasP) campaigns. We used the model to identify strategies that reduce incidence to negligible levels (i.e., < 0.1 cases/100 person-years) 20–25 years after initiation of a targeted TasP campaign. The model was parameterized using behavioral, demographic, and clinical data from published papers and national reports. To keep a focus on the underlying age effects we model a generalized heterosexual population with average risks (i.e., no MSM, no PWIDs, no sex workers) and no entry of HIV+ people from other regions. The model assumes that most people (default 95%, range in variant simulations 60–95%) are “linkable”; i.e., could get linked to effective care given sufficient resources. To simplify the accounting, we assume a rapid jump in the number of people receiving treatment at the start of the TasP campaign, followed by a 2% annual increase that continues until all linkable HIV+ people have been treated. Under historical scenarios of CD4-based targeted ART allocation and current policies of untargeted (random) ART allocation, our model predicts that viral replication would need to be suppressed in 60–85% of infected people at the start of the TasP campaign to drive incidence to negligible levels. Under age-based strategies, by contrast, this percentage dropped by 18–54%, depending on the strength of the epidemic and the age target. For our baseline model, targeting those under age 30 halved the number of people who need to be treated. Age-based targeting also minimized total and time-discounted AIDS deaths over 25 years. Age-based targeting yielded benefits without being highly exclusive; in a model in which 60% of infected people were treated, ~87% and ~58% of those initiating therapy during a campaign targeting those, Author summary We used an agent-based model for heterosexual epidemics to evaluate the effects of focusing treatment-as-prevention (TasP) efforts on young people. To keep the focus on age effects, we modeled populations with average risks (i.e., no MSM, no PWIDs, no sex workers). For our baseline model, we found that a long-term TasP program that focuses on HIV+ people under the age of 30 can halve the number of people who need to be treated in order to drive incidence to zero. Within the context of an overall expansion of care, we were able to identify multiple scenarios in which a successful youth-focused TasP campaign also expanded care for older people. Interestingly, we continued to see strong advantages to age-based TasP in sensitivity tests in which we removed key age-related risks (e.g., the tendency for young people to have shorter relationships). Youth-focused TasP succeeds, in large part, because it protects HIV- adolescents entering the sexually active population; i.e., a population that would otherwise be at high risk not only for getting infected but also for spreading the virus to others. This protection of adolescents leads to a steadily expanding AIDS-free generation that drives the virus to extinction.

Dog rabies has been recognized from ancient times and remains widespread across the developing world with an estimated 59,000 people dying annually from the disease. In 2011 a tri-partite alliance consisting of the OIE, the WHO and the FAO committed to globally eliminating dog-mediated human rabies by 2030. Regardless of global support, the responsibility remains with local program managers to implement successful elimination programs. It is well known that vaccination programs have a high probability of successful elimination if they achieve a population-coverage of 70%. It is often quoted that reducing population turnover (typically through sterilizations) raises the probability for local elimination by maintaining herd immunity for longer. Besides this, other factors that affect rabies elimination are rarely mentioned. This paper investigates the probability for local elimination as it relates to immunity, fecundity, dog population size, infectivity (bite rates), in-migration of immune-naïve dogs, and the initial incidence. To achieve this, an individual-based, stochastic, transmission model was manipulated to create a dataset covering combinations of factors that may affect elimination. The results thereof were analysed using a logistic regression model with elimination as the dependent variable. Our results suggest that smaller dog populations, lower infectivity and lower incidence (such as when epidemics start with single introductions) strongly increased the probability for elimination at wide ranges of vaccination levels. Lower fecundity and lower in-migration had weak effects. We discuss the importance of these findings in terms of their impact and their practical application in the design of dog-mediated rabies control programs., Author summary Most guidelines for rabies control call for at least 70% vaccination coverage of dogs. This level of immunity has a very high probability for the local elimination of rabies, but it is often not an achievable ideal due to resource constraints. Campaign managers can be strategic on how they allocate their resources. Lower infectivity rates are present in areas with more restricted dog movements and have higher probabilities for elimination at lower vaccination rates. Smaller sub-populations have higher probabilities for elimination at the same vaccination coverage levels compared to larger sub-populations. Vaccinating immune corridors can divide meta-populations into smaller sub-populations that are likely to result in elimination either due to their small size or due to the local low infectivity. Areas already free of rabies require lower vaccination levels to maintain freedom compared to endemic areas. Where donors do not specifically require sterilization campaigns, funds meant for rabies control should not be diverted to sterilizations.

Altres ajuts: RYC-2017-22586 Life expectancy in Spain is among the highest in the world. Nevertheless, we do not know if improvements in health conditions at older ages have followed postponements of death. Previous studies in Spain show a stable trend in years lived in ill health in the past. In this paper we investigate changes between 2006, 2012 and 2017 in life expectancy with and without disease at age 65 in Spain and, for the first time, in Spanish regions, which have autonomous powers of health planning, public health and healthcare. Results show that, at the country level, disease-free life expectancy reduced between 2006 and 2017 in Spain. This was explained by an expansion of most diseases except for some cardiovascular and respiratory chronic conditions. However, at the regional level the evolution was different, especially regarding each disease and sex. First, regional differences reduced between 2006 and 2012 but largely widened in 2017, suggesting that not all regions had the same ability to recover after the 2008 financial crisis that caused government cuts to health services. Second, regional analysis also highlighted diverging trends by sex. While men experienced expansion of morbidity in most regions, women experienced a compression in about half of them, ending up with women showing higher disease-free life expectancies than men in 9 out of the 17 regions considered. This study, then, calls attention to the importance of focusing the analysis of health surveillance to more disaggregated levels, more in accordance with the level of health management, as regional trends showed heterogeneity in the prevalence of diseases and different progresses in the relationship between sexes.

IntroductionWithin country inequality in infant mortality poses a big challenge for countries moving towards the internationally agreed upon targets on child mortality by 2030. There is a lack of high-quality evidence on infant mortality measured through different dimensions of social inequality in Angola. Thus, this paper was carried out to address the knowledge gap by conducting in-depth examination of infant mortality rate (IMR) inequality among population subgroups to provide more nuanced evidence to help end IMR disparity in the country.MethodsThe World Health Organization's (WHO) Health Equity Assessment Toolkit (HEAT) was used to analyze IMR inequality. HEAT is a software application that facilitates examination of disparities in reproductive, maternal, neonatal and child health indicators using the WHO Health Equity Monitor (HEM) database. Inequality of IMR was analyzed through disaggregation by five equity stratifiers: education, wealth, gender, subnational region and residence. These were analyzed through three inequality measures: Population Attributable Risk, Ratio and Slope Index of Inequality. A 95% confidence Interval (CI) was built around point estimates to determine statistical significance.ResultsA notable disadvantage was found for children born to poor (Population Attributable Risk (PAR): -27.0; -28.4, -26.0) and uneducated (PAR: -17.0; -17.9, -16.0), women who live in rural areas (PAR: -7.3;-7.8, -6.7) and those residing in certain regions of the country (PAR: -43.0; 45.3, -4). Male infants had a higher risk of death than female infants (PAR: -6.8;-7.5, -6.2). The subnational regional variation of IMR had been the most evident when compared with the disparities in the other equity stratifers.ConclusionsPolicymakers and planners need to address the disproportionately higher clustering of IMR among infants born to disadvantaged subpopulations through interventions that benefit such subgroups.

This paper investigates the profile of COVID-19 cases in Hong Kong, highlighting the unique age structure of confirmed cases compared to other territories. While the majority of cases in most territories around the world have fitted an older age profile, our analysis shows that positive cases in Hong Kong have been concentrated among younger age groups, with the largest incidence of cases reported in the 15-24 age group. This is despite the population's rapidly aging structure and extremely high levels of population density. Using detailed case data from Hong Kong's Centre for Health Department and Immigration Department, we analyze the sex and age distribution of the confirmed cases along with their recent travel histories and immigration flows for the period January to April 2020. Our analysis highlights Hong Kong's high proportion of imported cases and large overseas student population in developing COVID-19 hotspot areas such as the United Kingdom. Combined with community action and targeted and aggressive early policy measures taken to contain the virus, these factors may have contributed to the uniquely younger age structure of COVID-19 cases in the city. Consequently, this young profile of confirmed cases may have prevented fatalities in the territory. Recent research has highlighted the importance of a demographic approach to understanding COVID-19 transmission and fatality rates. The experience in Hong Kong shows that while an older population age structure may be important for understanding COVID-19 fatality, it is not a given. From a social science perspective at least, there is 'no easy answer' to why one area should experience COVID-19 differently from another.

This paper presents a data analysis framework to uncover relationships between health conditions, age and sex for a large population of patients. We study a massive heterogeneous sample of 1.7 million patients in Brazil, containing 47 million of health records with detailed medical conditions for visits to medical facilities for a period of 17 months. The findings suggest that medical conditions can be grouped into clusters that share very distinctive densities in the ages of the patients. For each cluster, we further present the ICD-10 chapters within it. Finally, we relate the findings to comorbidity networks, uncovering the relation of the discovered clusters of age densities to comorbidity networks literature., Author summary Age and sex of a patient can be directly related to susceptibilities to certain medical conditions. We present a method to generate clusters of human phenotype, based on the age of the population. This method helps extract knowledge on age and sex from the data. The age and sex correlations with disease conditions can help in a task of predicting the susceptibility of incoming patients to conditions.

In 2011 a new Investment Framework was proposed that described how the scale-up of key HIV interventions could dramatically reduce new HIV infections and AIDS-related deaths in low and middle income countries by 2015. This framework included ambitious coverage goals for prevention and treatment services for 2015, resulting in a reduction of new HIV infections by more than half, in line with the goals of the declaration of the UN High Level Meeting in June 2011. However, the approach suggested a leveling in the number of new infections at about 1 million annually - far from the UNAIDS goal of ending AIDS by 2030. In response, UNAIDS has developed the Fast-Track approach that is intended to provide a roadmap to the actions required to achieve this goal. The Fast-Track approach is predicated on a rapid scale-up of focused, effective prevention and treatment services over the next 5 years and then maintaining a high level of programme implementation until 2030. Fast-Track aims to reduce new infections and AIDS-related deaths by 90% from 2010 to 2030 and proposes a set of biomedical, behavioral and enabling intervention targets for 2020 and 2030 to achieve that goal, including the rapid scale-up initiative for antiretroviral treatment known as 90-90-90. Compared to a counterfactual scenario of constant coverage for all services at early-2015 levels, the Fast-Track approach would avert 18 million HIV infections and 11 million deaths from 2016 to 2030 globally. This paper describes the analysis that produced these targets and the estimated resources needed to achieve them in low- and middle-income countries. It indicates that it is possible to achieve these goals with a significant push to achieve rapid scale-up of key interventions between now and 2020. The annual resources required from all sources would rise to US$7.4Bn in low-income countries, US$8.2Bn in lower middle-income countries and US$10.5Bn in upper-middle-income-countries by 2020 before declining approximately 9% by 2030.

Objective This study analysed clinical and sociodemographic aspects and follow-up for notified cases of tuberculosis (TB) and explored inequalities in incidence rates and outcome by colour or race and the geographic macro-regions of Brazil. Methods This paper reports the results of a population-based descriptive epidemiological study of all notified cases of TB in Brazil during the period from 01/01/2008 to 31/12/2011. We analysed sociodemographic and clinical variables according to colour or race (white, black, Asian, mixed, and indigenous) and geographic macro-regions of the country (North, Northeast, Central-West, South, and Southeast). Results During the study period, the average incidence of TB in Brazil was 36.7 cases per 100,000 inhabitants, with the highest rates occurring in the North and Southeast regions. The analysis of TB notifications by colour or race revealed that the indigenous population presented the highest incidence rates in all macro-regions except the South, where higher rates were reported in black patients. ‘Cured’ was the most frequently reported treatment outcome for all skin colour categories. The highest cure rate occurred among the indigenous population (76.8%), while the lowest cure rate occurred among the black population (70.7%). Rates of treatment default were highest among blacks (10.5%) and lowest among the indigenous population (6.9%). However, the fatality rate was similar across race categories, varying between 2.8% and 3.8% for whites and the indigenous population, respectively. The lowest cure rates were observed when follow-up was inadequate (58.3%), and the highest was observed when the follow-up was classified as excellent (96.8%). Conclusions This study revealed that—apart from the heterogeneous distribution of TB among the Brazilian macro-regions—ethnic-racial inequalities exist in terms of clinical-epidemiological characteristics and incidence rates as well as follow-up for cases undergoing treatment. The highest rates of TB occurred among the indigenous people.

Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying ) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in , suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission., Author Summary The goal of this paper is to identify epidemiological factors that correlate with either an increased or decreased risk of transmitting a particular disease. We are particularly interested in identifying such factors for diseases that are self-limited (meaning that infections tend to occur in isolated clusters), because targeted control of these diseases can facilitate public health goals for minimizing the risk of disease emergence or promoting disease elimination. For example, we show that there is a significant difference in the transmission of measles between the United States and Canada. In contrast, we find that an observed decrease in the transmission of Middle East respiratory syndrome coronavirus during the latter half of 2013 cannot be ascertained with sufficient confidence. We then quantify the degree to which control was effective in eradicating smallpox in Europe. We also consider how the transmission of monkeypox in humans depends on whether the infection source is an animal or a human. Finally, we demonstrate how our approach can be used by surveillance programs to detect changes in transmission that may occur over time.

Background Indoor spraying of walls and ceilings with residual insecticide remains a primary method of malaria control. Insecticide resistance in malaria vectors is a growing problem. Novel insecticides for indoor residual spraying (IRS) which can improve the control of pyrethroid resistant malaria vectors are urgently needed. Insecticide mixtures have the potential to improve efficacy or even to manage resistance in some situations but this possibility remains underexplored experimentally. Chlorfenapyr is a novel pyrrole insecticide which has shown potential to improve the control of mosquitoes which are resistant to current WHO-approved insecticides. Method The efficacy of IRS with chlorfenapyr applied alone or as a mixture with alpha-cypermeththrin (a pyrethroid) was evaluated in experimental huts in Cove, Southern Benin against wild free flying pyrethroid resistant Anopheles gambiae sl. Comparison was made with IRS with alpha-cypermethrin alone. Fortnightly 30-minute in situ cone bioassays were performed to assess the residual efficacy of the insecticides on the treated hut walls. Results Survival rates of wild An gambiae from the Cove hut site in WHO resistance bioassays performed during the trial were >90% with permethrin and deltamethrin treated papers. Mortality of free-flying mosquitoes entering the experimental huts was 4% in the control hut. Mortality with alpha-cypermethrin IRS did not differ from the control (5%, P>0.656). The highest mortality was achieved with chlorfenapyr alone (63%). The alpha-cypermethrin + chlorfenapyr mixture killed fewer mosquitoes than chlorfenapyr alone (43% vs. 63%, P

Introduction Socioeconomic inequalities in health represent unfairness in the health distribution of a population. Efforts to produce information on mortality distributions in many low and middle income countries (LMICs) are mostly hampered by lack of data disaggregated by socioeconomic groups. In this paper we describe how mortality statistics obtained from multiple data sources were combined to provide an evaluation of the socioeconomic distribution of mortality in Malaysia, a LMIC located in the Asia Pacific region. Methods This study has an ecological design. As a measure of socioeconomic status, we used principal component analysis to construct a socioeconomic index using census data. Districts were ranked according to the standardised median index of households and assigned to each individual in the 5-year mortality data. The mortality indicators of interest were potential years of life lost (PYLL), standardised mortality ratio (SMR), infant mortality rate (IMR) and under-5 mortality rate (U5MR). Both socioeconomic status and mortality outcomes were used compute the concentration index which provided the summary measure of the magnitude of inequality. Results Socially disadvantaged districts were found to have worse mortality outcomes compared to more advantaged districts. The values of the concentration index for the overall population of the Peninsula are C = -0.1334 (95% CI: -0.1605 to -0.1063) for the PYLL, C = -0.0685 (95% CI: -0.0928 to -0.0441) for the SMR, C = -0.0997 (95% CI: -0.1343 to -0.0652) for the IMR and C = -0.1207 (95% CI: -0.1523 to -0.0891) for the U5MR. Mortality outcomes within ethnic groups were also found to be less favourable among the poor. Conclusion The findings of this study suggest that socioeconomic inequalities disfavouring the poor exist in Malaysia.