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      Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non–Critical Access Hospitals

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          Abstract

          Are differences in risk-adjusted mortality rates between critical access hospitals (CAHs) and non-CAHs related to differences in diagnosis coding practices? In this serial cross-sectional study of 4 094 720 rural Medicare beneficiaries hospitalized from 2007 to 2017, combined in-hospital and 30-day postdischarge mortality rates were higher for CAHs than non-CAHs when risk adjustment included both preexisting conditions and in-hospital comorbidity measures, but were not significantly different for CAHs vs non-CAHs when adjusting only for preexisting conditions. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs when risk adjustment is based on preexisting conditions that are not influenced by coding practices for in-hospital comorbidities. 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. To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. 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. Admission to a CAH vs non-CAH. 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. 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). 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. This study uses Medicare data to estimate mortality differences for common medical conditions (pneumonia, heart failure, chronic obstructive pulmonary disease, urinary tract infection, others) at US critical access vs non–critical access hospitals between 2007 and 2017 with vs without adjustment for discharge diagnosis counts to assess the extent to which coding practices rather than illness severity might account for observed mortality differences.

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          Most cited references 18

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          Widening rural-urban disparities in life expectancy, U.S., 1969-2009.

          There is limited research on rural-urban disparities in U.S. life expectancy. This study examined trends in rural-urban disparities in life expectancy at birth in the U.S. between 1969 and 2009. The 1969-2009 U.S. county-level mortality data linked to a rural-urban continuum measure were analyzed. Life expectancies were calculated by age, gender, and race for 3-year time periods between 1969 and 2004 and for 2005-2009 using standard life-table methodology. Differences in life expectancy were decomposed by age and cause of death. Life expectancy was inversely related to levels of rurality. In 2005-2009, those in large metropolitan areas had a life expectancy of 79.1 years, compared with 76.9 years in small urban towns and 76.7 years in rural areas. When stratified by gender, race, and income, life expectancy ranged from 67.7 years among poor black men in nonmetropolitan areas to 89.6 among poor Asian/Pacific Islander women in metropolitan areas. Rural-urban disparities widened over time. In 1969-1971, life expectancy was 0.4 years longer in metropolitan than in nonmetropolitan areas (70.9 vs 70.5 years). By 2005-2009, the life expectancy difference had increased to 2.0 years (78.8 vs 76.8 years). The rural poor and rural blacks currently experience survival probabilities that urban rich and urban whites enjoyed 4 decades earlier. Causes of death contributing most to the increasing rural-urban disparity and lower life expectancy in rural areas include heart disease, unintentional injuries, COPD, lung cancer, stroke, suicide, and diabetes. Between 1969 and 2009, residents in metropolitan areas experienced larger gains in life expectancy than those in nonmetropolitan areas, contributing to the widening gap. Published by American Journal of Preventive Medicine on behalf of American Journal of Preventive Medicine.
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            Patient Characteristics and Differences in Hospital Readmission Rates.

            Medicare penalizes hospitals with higher than expected readmission rates by up to 3% of annual inpatient payments. Expected rates are adjusted only for patients' age, sex, discharge diagnosis, and recent diagnoses.
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              Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge.

               Kumar Dharmarajan (corresponding) ,  Yongfei Wang,  Zhenqiu Lin (2017)
              The Affordable Care Act has led to US national reductions in hospital 30-day readmission rates for heart failure (HF), acute myocardial infarction (AMI), and pneumonia. Whether readmission reductions have had the unintended consequence of increasing mortality after hospitalization is unknown.
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                Author and article information

                Journal
                JAMA
                JAMA
                American Medical Association (AMA)
                0098-7484
                August 04 2020
                August 04 2020
                : 324
                : 5
                : 481
                Affiliations
                [1 ]Department of Health Services, Policy, and Practice, Brown University, Providence, Rhode Island
                [2 ]Center for Gerontology and Healthcare Research, Brown University, Providence, Rhode Island
                [3 ]Department of Veteran Affairs Medical Center, Providence, Rhode Island
                Article
                10.1001/jama.2020.9935
                7403917
                32749490
                edb5a196-1d75-44a8-a04b-dda2a12dcb15
                © 2020

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