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1.
Natl Vital Stat Rep ; 69(10): 1-12, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33054922

RESUMO

Objectives-This report describes the methodology used in the preparation of the 2009-2011 decennial life tables for the United States by race, Hispanic origin, and sex based on the age-specific death rates for the period 2009-2011, appearing in the report, "U.S. Decennial Life Tables for 2009-2011, United States Life Tables" (1). Methods-Data used to prepare these life tables include population data by age on the census date April 1, 2010; deaths occurring in the 3-year period 2009-2011 classified by age at death; births for each of the years 2007-2011; and Medicare data for ages 66-99 for the 3 years 2009-2011. The methods used differ from those applied to the 1999-2001 decennial life tables in the estimation of mortality for ages 66 and over. For the total, white, black, non-Hispanic white, and non-Hispanic black populations, the method developed for the U.S. annual life tables beginning with data year 2008 was used. It consists of the application of the Kannisto logistic model to smooth death rates in the age range 85-99 and predict death rates for ages 100-120 (2,3). For the Hispanic population, which is added to the decennial series for the first time with the 2009-2011 set, the method developed for the U.S. annual life tables beginning with data year 2006 was used. This method consists of using the Brass relational logit model to estimate mortality for ages 80-120 (4).


Assuntos
Tábuas de Vida , Afro-Americanos/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Censos , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Feminino , Hispano-Americanos/estatística & dados numéricos , Humanos , Masculino , Medicare , Estados Unidos/epidemiologia
2.
Natl Vital Stat Rep ; 69(8): 1-73, 2020 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33054929

RESUMO

Objectives-This report presents period life tables for the United States, based on age-specific death rates for the period 2009-2011. These tables are the most recent in a 110-year series of decennial life tables for the United States. Methods-This report presents complete life tables for the United States by race, Hispanic origin, and sex, based on age- specific death rates during 2009-2011. This is the first set of life tables by Hispanic origin presented in the U.S. decennial life table series. Data used to prepare these life tables include population estimates based on the 2010 decennial census; deaths occurring in the United States to U.S. residents in the 3 years 2009 through 2011; counts of U.S. resident births in the years 2007 through 2011; and population and death counts from the Medicare program for years 2009 through 2011. The methodology used to estimate life tables for the Hispanic population is based on the method first implemented with the 2006 annual U.S. life tables by Hispanic origin. The methodology used to estimate the life tables for all other groups is based on the method first implemented with the 2008 annual U.S. life tables. Results-During 2009-2011, life expectancy at birth was 78.60 years for the total U.S. population, representing an increase of 29.36 years from a life expectancy of 49.24 years in 1900. Between 1900 and 2010, life expectancy increased by 42.88 years for black females (from 35.04 to 77.92), by 39.21 years for black males (from 32.54 to 71.75), by 30.15 years for white females (from 51.08 to 81.23), and by 28.26 years for white males (from 48.23 to 76.49). During 2009-2011, Hispanic females had the highest life expectancy at birth (84.05), followed by non-Hispanic white females (81.06), Hispanic males (78.83), non-Hispanic black females (77.62), non-Hispanic white males (76.30), and non-Hispanic black males (71.41).


Assuntos
Expectativa de Vida/etnologia , Tábuas de Vida , Afro-Americanos/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Censos , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Feminino , Hispano-Americanos/estatística & dados numéricos , Humanos , Masculino , Medicare , Estados Unidos/epidemiologia
6.
Value Health ; 23(9): 1210-1217, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32940239

RESUMO

OBJECTIVES: Significant literature exists on the effects of medication adherence on reducing healthcare costs, but less is known about the effect of medication adherence among Medicare low-income subsidy (LIS) recipients. This study examined the effects of medication adherence on healthcare costs among LIS recipients with diabetes, hypertension, and/or heart failure. METHODS: This retrospective study analyzed Medicare claims data (2012-2013) linked to the Area Health Resources Files. Using measures developed by the Pharmacy Quality Alliance, adherence to 11 medication classes was studied among patients with 7 possible combinations of the diseases mentioned. Adherence was measured in 8 categories of proportion of days covered (PDC): ≥95%, 90% to <95%, 85% to <90%, 80% to <85%, 75% to <80%, 50% to <75%, 25% to <50%, and <25%. Annual Medicare costs were compared across adherence categories. A generalized linear model was used to control for patient/community characteristics. RESULTS: Among patients with only one disease, such as diabetes, patients with the lowest adherence (PDC < 25%) had $3152/year higher Medicare costs than patients with the highest adherence (PDC ≥ 95%; $11 101 vs $7949; P < .05). The adjusted costs among patients with PDC < 25% was $1893 higher than patients with PDC ≥ 95% ($9919 vs $8026; P < .05). Among patients with multiple chronic conditions, patients' adherence to medications for fewer diseases had higher costs. CONCLUSIONS: Greater medication adherence is associated with lower Medicare costs in the Medicare LIS population. Future policy affecting the LIS program should encourage better medication adherence among patients with chronic diseases.


Assuntos
Diabetes Mellitus Tipo 2/epidemiologia , Custos de Cuidados de Saúde/estatística & dados numéricos , Insuficiência Cardíaca/epidemiologia , Hipertensão/epidemiologia , Medicare/estatística & dados numéricos , Adesão à Medicação/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Comorbidade , Diabetes Mellitus Tipo 2/tratamento farmacológico , Insuficiência Cardíaca/tratamento farmacológico , Humanos , Hipertensão/tratamento farmacológico , Medicare/economia , Estudos Retrospectivos , Estados Unidos
7.
Tex Med ; 116(8): 43-44, 2020 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-32866277

RESUMO

First, the bad news: Physicians need to take some serious time between now and Jan 1, 2021, to study changes that are coming to Medicare outpatient evaluation and management (E&M) codes - changes most private insurers likely will follow. Now the good news: The changes should reduce the amount of documentation needed with each patient.


Assuntos
Medicare/normas , Pacientes Ambulatoriais , Padrões de Prática Médica/economia , Avaliação de Sintomas/normas , Documentação , Controle de Formulários e Registros , Humanos , Seguro Saúde , Visita a Consultório Médico/economia , Reembolso de Incentivo , Estados Unidos
10.
Medicine (Baltimore) ; 99(38): e22245, 2020 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-32957371

RESUMO

BACKGROUND: CMS recently decided to produce private "healthcare disparities reports" that include dual eligibility (DE) as the sole stratifying variable used to assess pneumonia readmission disparities. RESEARCH DESIGN: We measure the relationship between DE status and readmissions, both with and without conceptually relevant social risk factors, including air pollution, severe housing problems, and food insecurity, using data from county- and hospital-level readmission rates, DE status, and social risk factors. RESULTS: At the county level, the relationship between DE status and readmissions is partially confounded by at least three social risk factors. DE populations vary widely across hospitals, creating unequal between-hospital comparisons. CONCLUSIONS: Because of differences in the DE population, between-hospital comparisons could be misleading using a methodology that stratifies by DE only. We suggest viable alternatives to sole-factor stratification to properly account for social risk factors and better isolate quality differences that might yield readmission rate inequities. IMPLICATIONS: CMS's healthcare disparities reports provided to hospitals are limited by relying exclusively on DE proportion as the measure of social risk, undercutting the power of quality measurement and its related incentives to close or minimize healthcare inequities.


Assuntos
Definição da Elegibilidade , Disparidades em Assistência à Saúde , Medicaid/organização & administração , Medicare/organização & administração , Determinantes Sociais da Saúde , Poluição do Ar/efeitos adversos , Abastecimento de Alimentos , Habitação , Humanos , Readmissão do Paciente , Pneumonia/terapia , Fatores de Risco , Estados Unidos
11.
Phys Ther ; 100(10): 1862-1871, 2020 09 28.
Artigo em Inglês | MEDLINE | ID: mdl-32949237

RESUMO

OBJECTIVE: Although Medicare assessment files will include Standardized Patient Assessment Data Elements from 2016 forward, lack of uniformity of functional data prior to 2016 impedes longitudinal research. The purpose of this study was to create crosswalks for postacute care assessment measures and the basic mobility and daily activities scales of the Activity Measure for Post-Acute Care (AM-PAC) and to test their accuracy and validity in development and validation datasets. METHODS: This cross-sectional study is a secondary analysis of AM-PAC, the Inpatient Rehabilitation Facility Patient Assessment Instrument, the Minimum Data Set, and the Outcome and Assessment Information Set data from 300 adults receiving rehabilitation recruited from 6 health care networks in 1 metropolitan area. Rasch analysis was used to co-calibrate items from the 3 measures onto the AM-PAC metric and to create look-up tables to create estimated AM-PAC (eAM-PAC) scores. Mean scores and correlation and agreement between actual and estimated scores were examined in the development dataset. Scores were estimated in a cohort of Medicare beneficiaries with hip, humerus and radius fractures. Correlations between eAM-PAC and Functional Independence Measure motor scores were examined. Differences in mean eAM-PAC scores were evaluated across groups of known differences (age, fracture type, dementia). RESULTS: Strong correlations were found between actual and eAM-PAC scores in the development dataset. Moderate to strong correlations were found between the eAM-PAC basic mobility and Functional Independence Measure motor scores in the validation dataset. Differences in basic mobility scores across known groups were statistically significant and appeared to be clinically important. Differences between mean daily activities scores were statistically significant but appeared not to be clinically important. CONCLUSION: Although further testing is warranted, the basic mobility crosswalk appears to provide valid scores for aggregate analysis of Medicare postacute care data. IMPACT: This study reports on a method to take data from different Medicare administrative data sources and estimate scores on 1 scale. This approach was applied separately for data related to basic mobility and to daily activities. This may allow researchers to overcome challenges with using Medicare administrative data from different sources.


Assuntos
Pessoas com Deficiência/reabilitação , Cuidados Semi-Intensivos/métodos , Inquéritos e Questionários/normas , Atividades Cotidianas , Adulto , Estudos Transversais , Avaliação da Deficiência , Feminino , Humanos , Masculino , Medicare , Pessoa de Meia-Idade , Avaliação de Resultados em Cuidados de Saúde , Psicometria/estatística & dados numéricos , Recuperação de Função Fisiológica , Estados Unidos , Caminhada
16.
JAMA Netw Open ; 3(9): e2015470, 2020 09 01.
Artigo em Inglês | MEDLINE | ID: mdl-32876682

RESUMO

Importance: Home health care is one of the fastest growing postacute services in the US and is increasingly important in the era of coronavirus disease 2019 and payment reform, yet it is unknown whether patients who need home health care are receiving it. Objective: To examine how often patients referred to home health care at hospital discharge receive it and whether there is evidence of disparities. Design, Setting, and Participants: This cross-sectional study used Medicare data regarding the postacute home health care setting from October 1, 2015, through September 30, 2016. The participants were Medicare fee-for-service and Medicare Advantage beneficiaries who were discharged alive from a hospital with a referral to home health care (2 379 506 discharges). Statistical analysis was performed from July 2019 to June 2020. Exposures: Hospital referral to home health care. Main Outcomes and Measures: Primary outcomes included whether discharges received their first home health care visit within 14 days of hospital discharge and the number of days between hospital discharge and the first home health visit. Differences in the likelihood of receiving home health care across patient, zip code, and hospital characteristics were also examined. Results: Among 2 379 506 discharges from the hospital with a home health care referral, 1 358 697 patients (57.1%) were female, 468 762 (19.7%) were non-White, and 466 383 (19.6%) were dually enrolled in Medicare and Medicaid; patients had a mean (SD) age of 73.9 (11.9) years and 4.1 (2.1) Elixhauser comorbidities. Only 1 284 300 patients (54.0%) discharged from the hospital with a home health referral received home health care services within 14 days of discharge. Of the remaining 1 095 206 patients (46.0%) discharged, 37.7% (896 660 discharges) never received any home health care, while 8.3% (198 546 discharges) were institutionalized or died within 14 days without a preceding home health care visit. Patients who were Black or Hispanic received home health at lower rates than did patients who were White (48.0% [95% CI, 47.8%-48.1%] of Black and 46.1% [95% CI, 45.7%-46.5%] of Hispanic discharges received home health within 14 days compared with 55.3% [95% CI, 55.2%-55.4%] of White discharges). In addition, disadvantaged patients waited longer for their first home health care visit. For example, patients living in high-unemployment zip codes waited a mean of 2.0 days (95% CI, 2.0-2.0 days), whereas those living in low-unemployment zip codes waited 1.8 days (95% CI, 1.8-1.8 days). Conclusions and Relevance: Disparities in the use of home health care remain an issue in the US. As home health care is increasingly presented as a safer alternative to institutional postacute care during coronavirus disease 2019, and payment reforms continue to pressure hospitals to discharge patients home, ensuring the availability of safe and equitable care will be crucial to maintaining high-quality care.


Assuntos
Assistência ao Convalescente/estatística & dados numéricos , Acesso aos Serviços de Saúde , Disparidades em Assistência à Saúde/etnologia , Serviços de Assistência Domiciliar/estatística & dados numéricos , Encaminhamento e Consulta , Afro-Americanos/estatística & dados numéricos , Idoso , Idoso de 80 Anos ou mais , Estudos Transversais , Grupo com Ancestrais do Continente Europeu/estatística & dados numéricos , Planos de Pagamento por Serviço Prestado , Feminino , Disparidades em Assistência à Saúde/estatística & dados numéricos , Hispano-Americanos/estatística & dados numéricos , Humanos , Masculino , Medicaid/estatística & dados numéricos , Medicare , Medicare Part C , Alta do Paciente , Pobreza/estatística & dados numéricos , Características de Residência/estatística & dados numéricos , Desemprego/estatística & dados numéricos , Estados Unidos
17.
JAMA ; 324(5): 481-487, 2020 08 04.
Artigo em Inglês | MEDLINE | ID: mdl-32749490

RESUMO

Importance: Critical access hospitals (CAHs) provide care to rural communities. Increasing mortality rates have been reported for CAHs relative to non-CAHs. Because Medicare reimburses CAHs at cost, CAHs may report fewer diagnoses than non-CAHs, which may affect risk-adjusted comparisons of outcomes. Objective: To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. Design, Setting, and Participants: Serial cross-sectional study of rural Medicare Fee-for-Service beneficiaries admitted to US CAHs and non-CAHs for pneumonia, heart failure, chronic obstructive pulmonary disease, arrhythmia, urinary tract infection, septicemia, and stroke from 2007 to 2017. The final date of follow-up was December 31, 2017. Exposure: Admission to a CAH vs non-CAH. Main Outcomes and Measures: Discharge diagnosis count including trends from 2010 to 2011 when Medicare expanded the allowable number of billing codes for hospitalizations, and combined in-hospital and 30-day postdischarge mortality adjusted for demographics, primary diagnosis, preexisting conditions, and with vs without further adjustment for Hierarchical Condition Category (HCC) score to understand the contribution of in-hospital secondary diagnoses. Results: There were 4 094 720 hospitalizations (17% CAH) for 2 850 194 unique Medicare beneficiaries (mean [SD] age, 76.3 [11.7] years; 55.5% women). Patients in CAHs were older (median age, 80.1 vs 76.8 years) and more likely to be female (58% vs 55%). In 2010, the adjusted mean discharge diagnosis count was 7.52 for CAHs vs 8.53 for non-CAHs (difference, -0.99 [95% CI, -1.08 to -0.90]; P < .001). In 2011, the CAH vs non-CAH difference in diagnoses coded increased (P < .001 for interaction between CAH and year) to 9.27 vs 12.23 (difference, -2.96 [95% CI, -3.19 to -2.73]; P < .001). Adjusted mortality rates from the model with HCC were 13.52% for CAHs vs 11.44% for non-CAHs (percentage point difference, 2.08 [95% CI, 1.74 to 2.42]; P < .001) in 2007 and increased to 15.97% vs 12.46% (difference, 3.52 [95% CI, 3.09 to 3.94]; P < .001) in 2017 (P < .001 for interaction). Adjusted mortality rates from the model without HCC were not significantly different between CAHs and non-CAHs in all years except 2007 (12.19% vs 11.74%; difference, 0.45 [95% CI, 0.12 to 0.79]; P = .008) and 2010 (12.71% vs 12.28%; difference, 0.42 [95% CI, 0.07 to 0.77]; P = .02). Conclusions and Relevance: For rural Medicare beneficiaries hospitalized from 2007 to 2017, CAHs submitted significantly fewer hospital diagnosis codes than non-CAHs, and short-term mortality rates adjusted for preexisting conditions but not in-hospital comorbidity measures were not significantly different by hospital type in most years. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs after accounting for different coding practices for in-hospital comorbidities.


Assuntos
Doença Crônica/mortalidade , Codificação Clínica , Mortalidade Hospitalar , Hospitais Rurais , Idoso , Idoso de 80 Anos ou mais , Doença Crônica/classificação , Estudos Transversais , Planos de Pagamento por Serviço Prestado , Feminino , Hospitalização/estatística & dados numéricos , Humanos , Masculino , Medicare , Sumários de Alta do Paciente Hospitalar , Risco Ajustado , Estados Unidos/epidemiologia
20.
PLoS One ; 15(8): e0237082, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32776954

RESUMO

To understand the cost burden of medical care it is essential to partition medical spending into conditions. Two broad strategies have been used to measure disease-specific spending. The first attributes each medical claim to the condition that physicians list as its cause. The second decomposes total spending for a person over a year to their cumulative set of health conditions. Traditionally, this has been done through regression analysis. This paper has two contributions. First, we develop a new cost attribution method to attribute spending to conditions using a more flexible attribution approach, based on propensity score analysis. Second, we compare the propensity score approach to the claims-based approach and the regression approach in a common set of beneficiaries age 65 and older in the 2009 Medicare Current Beneficiary Survey. Our estimates show that the three methods have important differences in spending allocation and that the propensity score model likely offers the best theoretical and empirical combination.


Assuntos
Efeitos Psicossociais da Doença , Custos e Análise de Custo/métodos , Idoso , Feminino , Humanos , Revisão da Utilização de Seguros/estatística & dados numéricos , Masculino , Medicare/estatística & dados numéricos , Pontuação de Propensão , Análise de Regressão , Estados Unidos
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