US health care spending has continued to increase and now accounts for 18% of the US economy, although little is known about how spending on each health condition varies by payer, and how these ...amounts have changed over time.
To estimate US spending on health care according to 3 types of payers (public insurance including Medicare, Medicaid, and other government programs, private insurance, or out-of-pocket payments) and by health condition, age group, sex, and type of care for 1996 through 2016.
Government budgets, insurance claims, facility records, household surveys, and official US records from 1996 through 2016 were collected to estimate spending for 154 health conditions. Spending growth rates (standardized by population size and age group) were calculated for each type of payer and health condition.
Ambulatory care, inpatient care, nursing care facility stay, emergency department care, dental care, and purchase of prescribed pharmaceuticals in a retail setting.
National spending estimates stratified by health condition, age group, sex, type of care, and type of payer and modeled for each year from 1996 through 2016.
Total health care spending increased from an estimated $1.4 trillion in 1996 (13.3% of gross domestic product GDP; $5259 per person) to an estimated $3.1 trillion in 2016 (17.9% of GDP; $9655 per person); 85.2% of that spending was included in this study. In 2016, an estimated 48.0% (95% CI, 48.0%-48.0%) of health care spending was paid by private insurance, 42.6% (95% CI, 42.5%-42.6%) by public insurance, and 9.4% (95% CI, 9.4%-9.4%) by out-of-pocket payments. In 2016, among the 154 conditions, low back and neck pain had the highest amount of health care spending with an estimated $134.5 billion (95% CI, $122.4-$146.9 billion) in spending, of which 57.2% (95% CI, 52.2%-61.2%) was paid by private insurance, 33.7% (95% CI, 30.0%-38.4%) by public insurance, and 9.2% (95% CI, 8.3%-10.4%) by out-of-pocket payments. Other musculoskeletal disorders accounted for the second highest amount of health care spending (estimated at $129.8 billion 95% CI, $116.3-$149.7 billion) and most had private insurance (56.4% 95% CI, 52.6%-59.3%). Diabetes accounted for the third highest amount of the health care spending (estimated at $111.2 billion 95% CI, $105.7-$115.9 billion) and most had public insurance (49.8% 95% CI, 44.4%-56.0%). Other conditions estimated to have substantial health care spending in 2016 were ischemic heart disease ($89.3 billion 95% CI, $81.1-$95.5 billion), falls ($87.4 billion 95% CI, $75.0-$100.1 billion), urinary diseases ($86.0 billion 95% CI, $76.3-$95.9 billion), skin and subcutaneous diseases ($85.0 billion 95% CI, $80.5-$90.2 billion), osteoarthritis ($80.0 billion 95% CI, $72.2-$86.1 billion), dementias ($79.2 billion 95% CI, $67.6-$90.8 billion), and hypertension ($79.0 billion 95% CI, $72.6-$86.8 billion). The conditions with the highest spending varied by type of payer, age, sex, type of care, and year. After adjusting for changes in inflation, population size, and age groups, public insurance spending was estimated to have increased at an annualized rate of 2.9% (95% CI, 2.9%-2.9%); private insurance, 2.6% (95% CI, 2.6%-2.6%); and out-of-pocket payments, 1.1% (95% CI, 1.0%-1.1%).
Estimates of US spending on health care showed substantial increases from 1996 through 2016, with the highest increases in population-adjusted spending by public insurance. Although spending on low back and neck pain, other musculoskeletal disorders, and diabetes accounted for the highest amounts of spending, the payers and the rates of change in annual spending growth rates varied considerably.
Healthcare spending in the emergency department (ED) setting has received intense focus from policymakers in the United States (U.S.). Relatively few studies have systematically evaluated ED spending ...over time or disaggregated ED spending by policy-relevant groups, including health condition, age, sex, and payer to inform these discussions. This study's objective is to estimate ED spending trends in the U.S. from 2006 to 2016, by age, sex, payer, and across 154 health conditions and assess ED spending per visit over time. This observational study utilized the National Emergency Department Sample, a nationally representative sample of hospital-based ED visits in the U.S. to measure healthcare spending for ED care. All spending estimates were adjusted for inflation and presented in 2016 U.S. Dollars. Overall ED spending was $79.2 billion (CI, $79.2 billion-$79.2 billion) in 2006 and grew to $136.6 billion (CI, $136.6 billion-$136.6 billion) in 2016, representing a population-adjusted annualized rate of change of 4.4% (CI, 4.4%-4.5%) as compared to total healthcare spending (1.4% CI, 1.4%-1.4%) during that same ten-year period. The percentage of U.S. health spending attributable to the ED has increased from 3.9% (CI, 3.9%-3.9%) in 2006 to 5.0% (CI, 5.0%-5.0%) in 2016. Nearly equal parts of ED spending in 2016 was paid by private payers (49.3% CI, 49.3%-49.3%) and public payers (46.9% CI, 46.9%-46.9%), with the remainder attributable to out-of-pocket spending (3.9% CI, 3.9%-3.9%). In terms of key groups, the majority of ED spending was allocated among females (versus males) and treat-and-release patients (versus those hospitalized); those between age 20-44 accounted for a plurality of ED spending. Road injuries, falls, and urinary diseases witnessed the highest levels of ED spending, accounting for 14.1% (CI, 13.1%-15.1%) of total ED spending in 2016. ED spending per visit also increased over time from $660.0 (CI, $655.1-$665.2) in 2006 to $943.2 (CI, $934.3-$951.6) in 2016, or at an annualized rate of 3.4% (CI, 3.3%-3.4%). Though ED spending accounts for a relatively small portion of total health system spending in the U.S., ED spending is sizable and growing. Understanding which diseases are driving this spending is helpful for informing value-based reforms that can impact overall health care costs.
Although geographically specific data can help target HIV prevention and treatment strategies, Nigeria relies on national- and state-level estimates for policymaking and intervention planning. We ...calculated sub-state estimates along the HIV continuum of care in Nigeria.
Using data from the Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS) (July-December 2018), we conducted a geospatial analysis estimating three key programmatic indicators: prevalence of HIV infection among adults (aged 15-64 years); antiretroviral therapy (ART) coverage among adults living with HIV; and viral load suppression (VLS) rate among adults living with HIV.
We used an ensemble modeling method called stacked generalization to analyze available covariates and a geostatistical model to incorporate the output from stacking as well as spatial autocorrelation in the modeled outcomes. Separate models were fitted for each indicator. Finally, we produced raster estimates of each indicator on an approximately 5×5-km grid and estimates at the sub-state/local government area (LGA) and state level.
Estimates for all three indicators varied both within and between states. While state-level HIV prevalence ranged from 0.3% (95% uncertainty interval UI: 0.3%-0.5%) to 4.3% (95% UI: 3.7%-4.9%), LGA prevalence ranged from 0.2% (95% UI: 0.1%-0.5%) to 8.5% (95% UI: 5.8%-12.2%). Although the range in ART coverage did not substantially differ at state level (25.6%-76.9%) and LGA level (21.9%-81.9%), the mean absolute difference in ART coverage between LGAs within states was 16.7 percentage points (range, 3.5-38.5 percentage points). States with large differences in ART coverage between LGAs also showed large differences in VLS-regardless of level of effective treatment coverage-indicating that state-level geographic targeting may be insufficient to address coverage gaps.
Geospatial analysis across the HIV continuum of care can effectively highlight sub-state variation and identify areas that require further attention in order to achieve epidemic control. By generating local estimates, governments, donors, and other implementing partners will be better positioned to conduct targeted interventions and prioritize resource distribution.
Because of a soaring number of opioid-related deaths during the past decade, opioid use disorder has become a prominent issue in both the scientific literature and lay press. Although most of the ...focus within the emergency medicine community has been on opioid prescribing—specifically, on reducing the incidence of opioid prescribing and examining alternative pain treatment—interest is heightening in identifying and managing patients with opioid use disorder in an effective and evidence-based manner. In this clinical review article, we examine current strategies for identifying patients with opioid use disorder, the treatment of patients with acute opioid withdrawal syndrome, approaches to medication-assisted therapy, and the transition of patients with opioid use disorder from the emergency department to outpatient services.
Human trafficking is a significant human rights problem that is often associated with psychological and physical violence. There is no demographic that is spared from human trafficking. Traffickers ...maintain control of victims through physical, sexual, and emotional violence and manipulation. Because victims of trafficking seek medical attention for the medical and psychological consequences of assault and neglected health conditions, emergency clinicians are in a unique position to recognize victims and intervene. Evaluation of possible trafficking victims is challenging because patients who have been exploited rarely self-identify. This article outlines the clinical approach to the identification and treatment of a potential victim of human trafficking in the emergency department. Emergency practitioners should maintain a high index of suspicion when evaluating patients who appear to be at risk for abuse and violence, and assess for specific indicators of trafficking. Potential victims should be evaluated with a multidisciplinary and patient-centered technique. Furthermore, emergency practitioners should be aware of national and local resources to guide the approach to helping identified victims. Having established protocols for victim identification, care, and referrals can greatly facilitate health care providers’ assisting this population.
Health care spending on children in the United States continues to rise, yet little is known about how this spending varies by condition, age and sex group, and type of care, nor how these patterns ...have changed over time.
To provide health care spending estimates for children and adolescents 19 years and younger in the United States from 1996 through 2013, disaggregated by condition, age and sex group, and type of care.
Health care spending estimates were extracted from the Institute for Health Metrics and Evaluation Disease Expenditure 2013 project database. This project, based on 183 sources of data and 2.9 billion patient records, disaggregated health care spending in the United States by condition, age and sex group, and type of care. Annual estimates were produced for each year from 1996 through 2013. Estimates were adjusted for the presence of comorbidities and are reported using inflation-adjusted 2015 US dollars.
From 1996 to 2013, health care spending on children increased from $149.6 (uncertainty interval UI, 144.1-155.5) billion to $233.5 (UI, 226.9-239.8) billion. In 2013, the largest health condition leading to health care spending for children was well-newborn care in the inpatient setting. Attention-deficit/hyperactivity disorder and well-dental care (including dental check-ups and orthodontia) were the second and third largest conditions, respectively. Spending per child was greatest for infants younger than 1 year, at $11 741 (UI, 10 799-12 765) in 2013. Across time, health care spending per child increased from $1915 (UI, 1845-1991) in 1996 to $2777 (UI, 2698-2851) in 2013. The greatest areas of growth in spending in absolute terms were ambulatory care among all types of care and inpatient well-newborn care, attention-deficit/hyperactivity disorder, and asthma among all conditions.
These findings provide health policy makers and health care professionals with evidence to help guide future spending. Some conditions, such as attention-deficit/hyperactivity disorder and inpatient well-newborn care, had larger health care spending growth rates than other conditions.
The HealthRise initiative seeks to implement and evaluate innovative community-based strategies for diabetes, hypertension and hypercholesterolemia along the entire continuum of care (CoC)-from ...awareness and diagnosis, through treatment and control. In this study, we present baseline findings from HealthRise South Africa, identifying gaps in the CoC, as well as key barriers to care for non-communicable diseases (NCDs).
This mixed-methods needs assessment utilized national household data, health facility surveys, focus group discussions, and key informant interviews in Umgungundlovu and Pixley ka Seme districts. Risk factor and disease prevalence were estimated from the South Africa National Health and Nutrition Examination Survey. Health facility surveys were conducted at 86 facilities, focusing on essential intervention, medications and standard treatment guidelines. Quantitative results are presented descriptively, and qualitative data was analyzed using a framework approach.
46.8% of the population in Umgungundlovu and 51.0% in Pixley ka Seme were hypertensive. Diabetes was present in 11.0% and 9.7% of the population in Umgungundlovu and Pixley ka Seme. Hypercholesterolemia was more common in Pixley ka Seme (17.3% vs. 11.1%). Women and those of Indian descent were more likely to have diabetes. More than half of the population was found to be overweight, and binge drinking, inactivity and smoking were all common. More than half of patients with hypertension were unaware of their disease status (51.6% in Pixley ka Seme and 51.3% in Umgungundlovu), while the largest gap in the diabetes CoC occurred between initiation of treatment and achieving disease control. Demand-side barriers included lack of transportation, concerns about confidentiality, perceived discrimination and long wait times. Supply-side barriers included limited availability of testing equipment, inadequate staffing, and pharmaceutical stock outs.
In this baseline assessment of two South African health districts we found high rates of undiagnosed hypercholesterolemia and hypertension, and poor control of hypercholesterolemia, hypertension, and diabetes. The HealthRise Initiative will need to address key supply- and demand-side barriers in an effort to improve important NCD outcomes.
Patients with mental illness have been shown to receive lower quality of care and experience worse cardiovascular (CV) outcomes compared to those without mental illness. This present study examined ...mental health-related disparities in CV outcomes after an Emergency Department (ED) visit for chest pain.
This retrospective cohort included adult Medicaid beneficiaries in Washington state discharged from the ED with a primary diagnosis of unspecified chest pain in 2010–2017. Outcomes for patients with any mental illness (any mental health diagnosis or mental-health specific service use within 1 year of an index ED visit) and serious mental illness (at least two claims (on different dates of service) within 1 year of an index ED visit with a diagnosis of schizophrenia, other psychotic disorder, or major mood disorder) were compared to those of patients without mental illness. Our outcomes of interest were the incidence of major adverse cardiac events (MACE) within 30 days and 6 months of discharge of their ED visit, defined as a composite of death, acute myocardial infarction (AMI), CV rehospitalization, or revascularization. Secondary outcomes included cardiovascular diagnostic testing (diagnostic angiography, stress testing, echocardiography, and coronary computed tomography (CT) angiography) rates within 30 days of ED discharge. Only treat-and-release visits were included for outcomes assessment. Hierarchical logistic random effects regression models assessed the association between mental illness and the outcomes of interest, controlling for age, gender, race, ethnicity, Elixhauser comorbidities, and health care use in the past year, as well as fixed year effects.
There were 98,812 treat-and-release ED visits in our dataset. At 30 days, enrollees with any mental illness had no differences in rates of MACE (AOR 0.96; 95% CI, 0.72–1.27) or any of the individual components. At 6 months, enrollees with any mental illness (AOR 1.86; 95% CI, 1.11–3.09) and serious mental illness (AOR 2.60; 95% CI 1.33–5.13) were significantly more likely to be hospitalized for a CV condition compared to those without mental illness. Individuals with any mental illness had higher rates of testing at 30 days (AOR 1.16; 95% CI 1.07–1.27).
Patients with mental illness have similar rates of MACE, but higher rates of certain CV outcomes, such as CV hospitalization and diagnostic testing, after an ED visit for chest pain.
This cross-sectional study evaluates the proportion of patients tested for coronavirus disease 2019 (COVID-19) and the proportion of positive cases, using language as a surrogate for immigrant status.
Antiretroviral therapy (ART) guidelines were significantly changed by the World Health Organization in 2010. It is largely unknown to what extent these guidelines were adopted into clinical practice.
...This was a retrospective observational analysis of first-line ART regimens in a sample of health facilities providing ART in Kenya, Uganda, and Zambia between 2007-2008 and 2011-2012. Data were analyzed for changes in regimen over time and assessed for key patient- and facility-level determinants of tenofovir (TDF) utilization in Kenya and Uganda using a mixed effects model.
Data were obtained from 29,507 patients from 146 facilities. The overall percentage of patients initiated on TDF-based therapy increased between 2007-2008 and 2011-2012 from 3% to 37% in Kenya, 2% to 34% in Uganda, and 64% to 87% in Zambia. A simultaneous decrease in stavudine (d4T) utilization was also noted, but its use was not eliminated, and there remained significant variation in facility prescribing patterns. For patients initiating ART in 2011-2012, we found increased odds of TDF use with more advanced disease at initiation in both Kenya (odds ratio OR: 2.78; 95% confidence interval CI: 1.73-4.48) and Uganda (OR: 2.15; 95% CI: 1.46-3.17). Having a CD4 test performed at initiation was also a significant predictor in Uganda (OR: 1.43; 95% CI: 1.16-1.76). No facility-level determinants of TDF utilization were seen in Kenya, but private facilities (OR: 2.86; 95% CI: 1.45-5.66) and those employing a doctor (OR: 2.86; 95% CI: 1.48-5.51) were more likely to initiate patients on TDF in Uganda.
d4T-based ART has largely been phased out over the study period. However, significant in-country and cross-country variation exists. Among the most recently initiated patients, those with more advanced disease at initiation were most likely to start TDF-based treatment. No facility-level determinants were consistent across countries to explain the observed facility-level variation.
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