Regression of state-level change in cause-specific mortality on COVID-19 mortality, standardized coefficients.

Noncommunicable diseases and mental health conditions (referred to collectively as NMHs) are the greatest cause of preventable death, illness, and disability in South America and negatively affect countries’ economic performance through their detrimental impacts on labor supply and capital investments. Sound, evidence-based policy-making requires a deep understanding of the macroeconomic costs of NMHs and of their distribution across countries and diseases. The paper estimates and projects the macroeconomic burden of NMHs over the period 2020–2050 in 10 South American countries. We estimate the impact of NMHs on gross domestic product (GDP) through a human capital-augmented production function approach, accounting for mortality and morbidity effects of NMHs on labor supply, for the impact of treatment costs on physical capital accumulation, and for variations in human capital by age. Our central estimates suggest that the overall burden of NMHs in these countries amounts to $7.3 trillion (2022 international $, 3% discount rate, 95% confidence interval: $6.8–$7.8 trillion). Overall, the macroeconomic burden of NMHs is around 4% of total GDP over 2020–2050, with little variation across countries (from 3.2% in Peru to 4.5% in Brazil). In other words, without NMHs, annual GDP over 2020–2050 would be about 4% larger. In most countries, the largest macroeconomic burden is associated with cancers. Results from the paper point to a significant macroeconomic burden of NMHs in South America and provide a strong justification for investment in NMH prevention, early detection, treatment, and formal and informal care.

This dataset represents preliminary estimates of cumulative U.S. COVID-19 disease burden for the 2024-2025 period, including illnesses, outpatient visits, hospitalizations, and deaths. The weekly COVID-19-associated burden estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. The data come from the Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET), a surveillance platform that captures data from hospitals that serve about 10% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of COVID-19 -associated burden that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent COVID-19-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

References

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Regression of state-level change in cause-specific mortality on COVID-19 mortality.

All-cause, COVID-19, and non-COVID-19 ASDR for ages 25+ by state and time period.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcanc-ultrasound-class.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-objdetect.

BackgroundA recent report by the Institute for Health Metrics and Evaluation (IHME) highlights that mental health receives little attention despite being a major cause of disease burden. This paper extends previous assessments of development assistance for mental health (DAMH) in two significant ways; first by contrasting DAMH against that for other disease categories, and second by benchmarking allocated development assistance against the core disease burden metric (disability-adjusted life year) as estimated by the Global Burden of Disease Studies.MethodsIn order to track DAH, IHME collates information from audited financial records, project level data, and budget information from the primary global health channels. The diverse set of data were standardised and put into a single inflation adjusted currency (2015 US dollars) and each dollar disbursed was assigned up to one health focus areas from 1990 through 2015. We tied these health financing estimates to disease burden estimates (DALYs) produced by the Global Burden of Disease 2015 Study to calculated a standardised measure across health focus areas—development assistance for health (in US Dollars) per DALY.FindingsDAMH increased from USD 18 million in 1995 to USD 132 million in 2015, which equates to 0.4% of total DAH in 2015. Over 1990 to 2015, private philanthropy was the most significant source (USD 435 million, 30% of DAMH), while the United States government provided USD 270 million of total DAMH. South and Southeast Asia received the largest proportion of funding for mental health in 2013 (34%). DAMH available per DALY in 2013 ranged from USD 0.27 in East Asia and the Pacific to USD 1.18 in the Middle East and North Africa. HIV/AIDS received the largest ratio of funds to burden—approximately USD150 per DALY in 2013. Mental and substance use disorders and its broader category of non-communicable disease received less than USD1 of DAH per DALY.InterpretationCombining estimates of disease burden and development assistance for health provides a valuable perspective on DAH resource allocation. The findings from this research point to several patterns of unproportioned distribution of DAH, none more apparent than the low levels of international investment in non-communicable diseases, and in particular, mental health. However, burden of disease estimates are only one input by which DAH should be determined.

breastcanc-ultrasound-class

  Background

Cancer is the second leading cause of death worldwide, according to IHME - Global Burden of Disease, with 10.7 mln casualties in 2019.

Amongst the various types of cancer, a huge role is played by breast cancer, which stands in 4th position among the deadliest tumors, with more than 700.000 deaths during 2019 (IHME - Global Burden of Disease).

Moreover, breast cancer has the highest share of number of cases/100 people worldwide… See the full description on the dataset page: https://huggingface.co/datasets/as-cle-bert/breastcancer-auto-segmentation.

BackgroundCardiovascular diseases (CVDs) are not only the primary cause of mortality in China but also represent a significant financial burden. The World Health Organization highlight that as China undergoes rapid socioeconomic development, its disease spectrum is gradually shifting towards that of developed countries, with increasing prevalence of lifestyle-related diseases such as ischemic heart disease and stroke. We reviewed the rates and trends of CVDs incidence, mortality and disability-adjusted life years (DALYs) burden in China and compared them with those in the United States (US) and Japan for formulating CVDs control policies.MethodsData on CVDs incidence, death and DALYs in China, the US and Japan were obtained from the GBD 2019 database. The Joinpoint regression model was used to analyze the trends in CVDs incidence and mortality in China, the US and Japan, calculate the annual percentage change and determine the best-fitting inflection points.ResultsIn 2019, there were approximately 12,341,074 new diagnosed cases of CVDs in China, with 4,584,273 CVDs related deaths, causing 91,933,122 DALYs. The CVDs age-standardized incidence rate (ASIR) in China (538.10/100,000) was lower than that in the US and globally, while age-standardized death rate (ASDR) (276.9/100,000) and age-standardized DALY rate (6,463.47/100,000) were higher than those in the two regions. Compared with the US and Japan, from 1990 to 2019, the CVDs incidence rate in China showed an increasing trend, with a lower annual decrease in ASDR and a younger age structure of disease burden. Furthermore, the disease spectrum in China changed minimally, with stroke, ischemic heart disease, and hypertensive heart disease being the top three leading CVDs diseases in terms of incidence and disease burden, also being the major causes of CVDs in the US and Japan.ConclusionThe prevention and control of CVDs is a global issue. The aging population and increasing unhealthy lifestyles will continue to increase the burden in China. Therefore, relevant departments in China should reference the established practices for CVDs control in developed countries while considering the diversity of CVDs in different regions when adjusting national CVDs control programs.

BackgroundThe rise in health spending in the United States and the prevalence of multimorbidity—having more than one chronic condition—are interlinked but not well understood. Multimorbidity is believed to have an impact on an individual’s health spending, but how having one specific additional condition impacts spending is not well established. Moreover, most studies estimating spending for single diseases rarely adjust for multimorbidity. Having more accurate estimates of spending associated with each disease and different combinations could aid policymakers in designing prevention policies to more effectively reduce national health spending. This study explores the relationship between multimorbidity and spending from two distinct perspectives: (1) quantifying spending on different disease combinations; and (2) assessing how spending on a single diseases changes when we consider the contribution of multimorbidity (i.e., additional/reduced spending that could be attributed in the presence of other chronic conditions).Methods and findingsWe used data on private claims from Truven Health MarketScan Research Database, with 16,288,894 unique enrollees ages 18 to 64 from the US, and their annual inpatient and outpatient diagnoses and spending from 2018. We selected conditions that have an average duration of greater than one year among all Global Burden of Disease causes. We used penalized linear regression with stochastic gradient descent approach to assess relationship between spending and multimorbidity, including all possible disease combinations with two or three different conditions (dyads and triads) and for each condition after multimorbidity adjustment. We decomposed the change in multimorbidity-adjusted spending by the type of combination (single, dyads, and triads) and multimorbidity disease category.We defined 63 chronic conditions and observed that 56.2% of the study population had at least two chronic conditions. Approximately 60.1% of disease combinations had super-additive spending (e.g., spending for the combination was significantly greater than the sum of the individual diseases), 15.7% had additive spending, and 23.6% had sub-additive spending (e.g., spending for the combination was significantly less than the sum of the individual diseases). Relatively frequent disease combinations (higher observed prevalence) with high estimated spending were combinations that included endocrine, metabolic, blood, and immune disorders (EMBI disorders), chronic kidney disease, anemias, and blood cancers. When looking at multimorbidity-adjusted spending for single diseases, the following had the highest spending per treated patient and were among those with high observed prevalence: chronic kidney disease ($14,376 [12,291,16,670]), cirrhosis ($6,465 [6,090,6,930]), ischemic heart disease (IHD)-related heart conditions ($6,029 [5,529,6,529]), and inflammatory bowel disease ($4,697 [4,594,4,813]). Relative to unadjusted single-disease spending estimates, 50 conditions had higher spending after adjusting for multimorbidity, 7 had less than 5% difference, and 6 had lower spending after adjustment.ConclusionsWe consistently found chronic kidney disease and IHD to be associated with high spending per treated case, high observed prevalence, and contributing the most to spending when in combination with other chronic conditions. In the midst of a surging health spending globally, and especially in the US, pinpointing high-prevalence, high-spending conditions and disease combinations, as especially conditions that are associated with larger super-additive spending, could help policymakers, insurers, and providers prioritize and design interventions to improve treatment effectiveness and reduce spending.

Costs associated with chronic low back pain (n = 2957).

Non-typhoidal Salmonella enterica infections cause a high disease burden in the United States with an estimated 1.2 million illnesses annually. The state of Florida consistently has a relatively high incidence compared to other states in the United States. Nevertheless, studies regarding the epidemiology of nontyphoidal salmonellosis and its spatial and temporal patterns in Florida were rarely reported. We examined the spatial and temporal patterns of 62,947 salmonellosis cases reported to FL Health Charts between 2009 and 2018. Dominant serotypes circulating in Florida were also explored using whole genome sequencing (WGS) based serotype-prediction for 2,507 Salmonella isolates sequenced by the Florida Department of Health during 2017 and 2018. The representativeness of laboratory-sequenced isolates for reported cases was determined by regression modeling. The annual incidence rate of salmonellosis decreased from 36.0 per 100,000 population in 2009 to 27.8 per 100,000 in 2016, and gradually increased in 2017 and 2018. Increased use of culture-independent testing did not fully explain this increase. The highest incidence rate was observed in children, contributing 40.9% of total reported cases during this period. A seasonal pattern was observed with the incidence peaking in September and October, later than the national average pattern. Over these 10 years, the Northeast and Northwest regions of the state had higher reported incidence rates, while reported rates in the Southeast and South were gradually increasing over time. Serotypes were predicted based on WGS data in the EnteroBase platform. The top-five most prevalent serotypes in Florida during 2017–2018 were Enteritidis, Newport, Javiana, Sandiego and Braenderup. The highest percentage of isolates was from children under 5 years of age (41.4%), and stool (84.7%) was the major source of samples. A zero-inflated negative binomial regression model showed that the reported case number was a strong predictor for the number of lab-sequenced isolates in individual counties, and the geospatial distribution of sequenced isolates was not biased by other factors such as age group. The spatial and temporal patterns identified in this study along with the prevalence of different serotypes will be helpful for the development of efficient prevention and control strategies for salmonellosis in Florida.