Development and Validation of the Summary Elixhauser Comorbidity Score for Use With ICD-10-CM-Coded Data Among Older Adults.

BACKGROUND: Older adults have many comorbidities contributing to mortality. OBJECTIVE: To develop a summary Elixhauser (S-Elixhauser) comorbidity score to predict 30-day, in-hospital, and 1-year mortality in older adults using the 38 comorbidities operationalized by the Agency for Healthcare Research and Quality (AHRQ). DESIGN: Retrospective cohort study. SETTING: Medicare beneficiaries from 2017 to 2019. PATIENTS: Persons hospitalized in 2018 (n = 899 844) and 3 disease-specific hospitalized cohorts. MEASUREMENTS: Weights were derived for 38 comorbidities to predict 30-day, in-hospital, and 1-year mortality. The S-Elixhauser score was internally validated and calibrated. Individual Elixhauser comorbidity indicators (38 comorbidities), the modified application of the AHRQ-derived Elixhauser summary score, the Charlson comorbidity indicators (17 comorbidities), and the Charlson summary score were externally validated. The c-statistic was used to evaluate discrimination of a comorbidity score model. RESULTS: The S-Elixhauser score was well calibrated and internally validated, with a c-statistic of 0.705 (95% CI, 0.703 to 0.707) in predicting 30-day mortality, 0.654 (CI, 0.651 to 0.657) for in-hospital mortality, and 0.743 (CI, 0.741 to 0.744) for 1-year mortality. In external validation of other comorbidity indices for 30-day mortality, the c-statistic was 0.711 (CI, 0.709 to 0.713) for the individual Elixhauser comorbidity indicators, 0.688 (CI, 0.686 to 0.690) for the AHRQ Elixhauser score, 0.696 (CI, 0.694 to 0.698) for the Charlson comorbidity indicators, and 0.690 (CI, 0.688 to 0.693) for the Charlson summary score. In 3 disease-specific populations, the discrimination of the S-Elixhauser score in predicting 30-day mortality ranged from 0.657 to 0.732. LIMITATION: Validation of the S-Elixhauser comorbidity score and head-to-head comparison with other comorbidity scores in an external population are needed to evaluate comparative performance. CONCLUSION: The S-Elixhauser comorbidity score is well calibrated and internally validated but its advantage over the AHRQ Elixhauser and Charlson summary scores is unclear. PRIMARY FUNDING SOURCE: National Institute on Aging.

Trends and variation in benzodiazepine use in nursing homes in the USA.

PURPOSE: Because of toxicities, benzodiazepines are not usually recommended in older adults. We therefore sought to describe the trends in benzodiazepine use in long-term care and examine the variation in benzodiazepine use among nursing homes. METHODS: In this retrospective repeated cross-sectional analysis of Medicare Parts A, B, and D claims data linked to the Minimum Data Set from 2013 to 2018, we included long-term residents who stayed in a nursing home for at least one entire quarter of a calendar year in 2013-2018. The outcome was whether residents were prescribed a benzodiazepine drug for at least 30 days during each quarter stay. We use mixed effects logistic regression models to assess the variation in benzodiazepine use among nursing homes, adjusting for patient and nursing home characteristics. RESULTS: The cohort for the time trend analysis included 270,566 unique residents and 1,843,580 quarter stays for 2013-2018. Prescribing rates for short-acting benzodiazepines were stable over 2013-2016, then declined from 12.1% in 2016 to 10.6% in 2018. The rate of long-acting benzodiazepine use remained relatively steady at around 4% over 2013-2018. During 2017-2018, the variation among nursing homes in benzodiazepine use was 7.2% for short-acting vs. 9.3% for long-acting benzodiazepines, after controlling for resident characteristics. CONCLUSION: Prescribing for short-acting benzodiazepines in long-term care declined after 2016, while long-acting benzodiazepine use did not change. The variation in benzodiazepine use among nursing homes is substantial. Identifying factors that explain this variation may help in developing strategies for deprescribing benzodiazepines in nursing home residents.

Relationship Between Nursing Home Compare Improvement in Function Quality Measure and Physical Recovery After Hip Replacement.

OBJECTIVE: To determine whether patients with a total or partial hip replacement admitted to a skilled nursing facility (SNF) after the improvement in function quality measure was added to Nursing Home Compare in July 2016 have greater physical recovery than patients admitted before July 2016. DESIGN: Pre (January 1, 2015-June 30, 2016) vs post (July 1, 2016-December 31, 2017) design. SETTING: Skilled nursing facilities (n=12,829). PARTICIPANTS: Medicare fee-for-service beneficiaries (N=106,832) discharged from acute hospitals to SNF after hip replacement between January 1, 2015 and December 31, 2017. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The 5- and 14-day minimum data set assessments were used to calculate total scores for the quality measure, self-care, mobility, and balance. We calculated the average adjusted change per 10 days and any improvement between the 5- and 14-day assessments. RESULTS: The average adjusted change per 10 days for the quality measure total score for patients admitted before July 2016 and after July 2016 was 1.00 points (standard error, 0010) and 1.06 points (standard error, 0.010), respectively (P<.01). This was a relative increase of 6.0%. Among patients admitted to a SNF before July 2016, 44.4% (standard error, 0.06) had any improvement in the quality measure total score compared with 45.5% (standard error, 0.23) of patients admitted after July 2016 (P<.01). This was a relative increase of 2.5%. The adjusted change per 10 days and percentage of patients who had any improvement in the total scores for self-care, mobility, and balance were all significantly higher after July 2016. CONCLUSIONS: Patients admitted to a SNF after a hip replacement after July 2016 had greater physical recovery than patients admitted before the improvement in function quality measure was added to Nursing Home Compare.

Variation among hospitals in the continuity of care for older hospitalized patients: a cross-sectional cohort study.

BACKGROUND: Little is known about how continuity of care for hospitalized patients varies among hospitals. We describe the number of different general internal medicine physicians seeing hospitalized patients during a medical admission and how that varies by hospital. METHODS: We conducted a retrospective study of a national 20% sample of Medicare inpatients from 01/01/16 to 12/31/18. In patients with routine medical admissions (length of stay of 3-6 days, no Intensive Care Unit stay, and seen by only one generalist per day), we assessed odds of receiving all generalist care from one generalist. We calculated rates for each hospital, adjusting for patient and hospital characteristics in a multi-level logistic regression model. RESULTS: Among routine medical admissions with 3- to 6-day stays, only 43.1% received all their generalist care from the same physician. In those with a 3-day stay, 50.1% had one generalist providing care vs. 30.8% in those with a 6-day stay. In a two-level (admission and hospital) logistic regression model controlling for patient characteristics and length of stay, the odds of seeing just one generalist did not vary greatly by patient characteristics such as age, race/ethnicity, comorbidity or reason for admission. There were large variations in continuity of care among different hospitals and geographic areas. In the highest decile of hospitals, the adjusted mean percentage of patients receiving all generalist care from one physician was > 84.1%, vs. < 24.1% in the lowest decile. This large degree of variation persisted when hospitals were stratified by size, ownership, location or teaching status. CONCLUSIONS: Continuity of care provided by generalist physicians to medical inpatients varies widely among hospitals. The impact of this variation on quality of care is unknown.

Opioid Prescribing by Primary Care Providers: a Cross-Sectional Analysis of Nurse Practitioner, Physician Assistant, and Physician Prescribing Patterns.

BACKGROUND: Prescription opioid overprescribing is a focal point for legislators, but little is known about opioid prescribing patterns of primary care nurse practitioners (NPs) and physician assistants (PAs). OBJECTIVE: To identify prescription opioid overprescribers by comparing prescribing patterns of primary care physicians (MDs), nurse practitioners (NPs), and physician assistants (PAs). DESIGN: Retrospective, cross-sectional analysis of Medicare Part D enrollee prescription data. PARTICIPANTS: Twenty percent national sample of 2015 Medicare Part D enrollees. MAIN MEASURES: We identified potential opioid overprescribing as providers who met at least one of the following: (1) prescribed any opioid to > 50% of patients, (2) prescribed ≥ 100 morphine milligram equivalents (MME)/day to > 10% of patients, or (3) prescribed an opioid > 90 days to > 20% of patients. KEY RESULTS: Among 222,689 primary care providers, 3.8% of MDs, 8.0% of NPs, and 9.8% of PAs met at least one definition of overprescribing. 1.3% of MDs, 6.3% of NPs, and 8.8% of PAs prescribed an opioid to at least 50% of patients. NPs/PAs practicing in states with independent prescription authority were > 20 times more likely to overprescribe opioids than NPs/PAs in prescription-restricted states. CONCLUSIONS: Most NPs/PAs prescribed opioids in a pattern similar to MDs, but NPs/PAs had more outliers who prescribed high-frequency, high-dose opioids than did MDs. Efforts to reduce opioid overprescribing should include targeted provider education, risk stratification, and state legislation.

Variation Among Primary Care Physicians in 30-Day Readmissions.

Background: Whether readmission rates vary by primary care physician (PCP) is unknown, although federal policy holds PCPs accountable for reducing readmissions. Objective: To determine whether 30-day readmission rates vary by PCP. Design: Retrospective cohort study using marginal models and multilevel logistic regression with 100% of data on Texas Medicare claims from 2008 to 2015. Setting: Texas. Participants: Patients discharged alive between 1 January 2008 and 30 November 2015 who had a PCP in the prior year and whose PCP had at least 50 admissions in the study period. Measurements: Readmission within 30 days of discharge. Follow-up visits with a PCP within 7 days of discharge were also measured. Results: Between 2012 and 2015, the mean risk-standardized rate of 30-day readmissions was 12.9%. Of 4230 PCPs, 1 had a readmission rate that was significantly higher than the mean and none had a significantly lower rate. The 10th and 90th percentiles of PCP readmission rates were 12.4% and 13.4%, respectively, each only 0.5 percentage point different from the mean. The 99th percentile of PCP readmission rates was 14.0%, 1.1 percentage points higher than the mean. Detecting a 1.1-percentage point difference from the mean adjusted readmission rate would require more than 3500 admissions per PCP per year. Limitations: Only fee-for-service Medicare patients in a single state were included. The authors could not account for confounders not included in Medicare databases or classify readmissions as avoidable. Conclusion: Variation in readmission rates among PCPs is very low. Programs holding PCPs accountable for readmissions may prove ineffective. Primary Funding Source: National Institutes of Health.

Association of Pain on Hospital Discharge with the Risk of 30-Day Readmission in Patients with Total Hip and Knee Replacement.

BACKGROUND: It is not clear if there is a risk of 30-day readmissions following total hip and knee arthroplasty in patients reporting high levels of pain at hospital discharge. We examined the relationship between post-surgical pain on the day of discharge and 30-day readmission in patients who received total knee and hip arthroplasty. METHODS: Retrospective cohort study was conducted of patients who received total knee (n = 155,284) or hip arthroplasty (n = 89,283) from 2011 to 2018 using electronic health records from the Optum database. Four categories of pain at discharge were created, from none to severe. Multivariate logistic regression models to predict 30-day all-cause readmission were adjusted for patient and clinical characteristics and built separately for knee and hip arthroplasty patients. RESULTS: Mean ages for hip and knee patients were 64.4 (standard deviation 11.3) and 65.7 (standard deviation 9.7) years, respectively. The majority of patients were female (hip: 54.4%; knee: 61.5%). The unadjusted rate of 30-day readmission was 3.54% for hip replacement and 3.66% for knee replacement. In models adjusted for patient and clinical characteristics, for patients with total hip replacement, the odds of 30-day readmission for those with severe pain score at discharge vs those with no pain at discharge were 1.60 (95% confidence interval 1.33-1.92). Similarly, readmission likelihood increased as pain at discharge increased (severe pain vs no pain) for patients with total knee arthroplasty (odds ratio 1.38, 95% confidence interval 1.19-1.59). CONCLUSION: Our findings demonstrated that the pain scores on the day of discharge are associated with 30-day hospital readmission.

Use of Medicare Data to Identify Team-based Primary Care: Is it Possible?

BACKGROUND: It is unclear whether Medicare data can be used to identify type and degree of collaboration between primary care providers (PCPs) [medical doctors (MDs), nurse practitioners, and physician assistants] in a team care model. METHODS: We surveyed 63 primary care practices in Texas and linked the survey results to 2015 100% Medicare data. We identified PCP dyads of 2 providers in Medicare data and compared the results to those from our survey. Sensitivity, specificity, and positive predictive value (PPV) of dyads in Medicare data at different threshold numbers of shared patients were reported. We also identified PCPs who work in the same practice by Social Network Analysis (SNA) of Medicare data and compared the results to the surveys. RESULTS: With a cutoff of sharing at least 30 patients, the sensitivity of identifying dyads was 27.8%, specificity was 91.7%, and PPV 72.2%. The PPV was higher for MD-nurse practitioner/physician assistant pairs (84.4%) than for MD-MD pairs (61.5%). At the same cutoff, 90% of PCPs identified in a practice from the survey were also identified by SNA in the corresponding practice. In 5 of 8 surveyed practices with at least 3 PCPs, about ≤20% PCPs identified in the practices by SNA of Medicare data were not identified in the survey. CONCLUSIONS: Medicare data can be used to identify shared care with low sensitivity and high PPV. Community discovery from Medicare data provided good agreement in identifying members of practices. Adapting network analyses in different contexts needs more validation studies.

Preferences for Communicating about Breast Cancer Screening Among Racially/Ethnically Diverse Older Women.

Differences exist across breast cancer screening guidelines regarding frequency of screening and age of discontinuation for older women (≥70 years) at average risk for breast cancer. These differences highlight concerns about the benefits and harms of screening, and may negatively impact older women's ability to make informed screening decisions. This study examined preferences for communicating about screening mammography among racially/ethnically diverse, older women. In-depth interviews were conducted with 59 women with no breast cancer history. Non-proportional quota sampling ensured roughly equal numbers on age (70-74 years, ≥75 years), race/ethnicity (non-Hispanic/Latina White, non-Hispanic/Latina Black, Hispanic/Latina), and education (≤high school diploma, >high school diploma). Interviews were audio-recorded, transcribed, and analyzed using NVivo 10. Thematic analyses revealed that rather than being told to get mammograms, participants wanted to hear about the benefits and harms of screening mammography, including overdiagnosis. Participants recommended that this information be communicated via physicians or other healthcare providers, included in brochures/pamphlets, and presented outside of clinical settings (e.g., in senior groups). Results were consistent regardless of participants' age, race/ethnicity, or education. Findings revealed that older women desire information about the benefits and harms of screening mammography, and would prefer to learn this information through discussions with healthcare providers and multiple other formats.

Adapting the Elixhauser comorbidity index for cancer patients.

BACKGROUND: This study was designed to adapt the Elixhauser comorbidity index for 4 cancer-specific populations (breast, prostate, lung, and colorectal) and compare 3 versions of the Elixhauser comorbidity score (individual comorbidities, summary comorbidity score, and cancer-specific summary comorbidity score) with 3 versions of the Charlson comorbidity score for predicting 2-year survival with 4 types of cancer. METHODS: This cohort study used Texas Cancer Registry-linked Medicare data from 2005 to 2011 for older patients diagnosed with breast (n = 19,082), prostate (n = 23,044), lung (n = 26,047), or colorectal cancer (n = 16,693). For each cancer cohort, the data were split into training and validation cohorts. In the training cohort, competing risk regression was used to model the association of Elixhauser comorbidities with 2-year noncancer mortality, and cancer-specific weights were derived for each comorbidity. In the validation cohort, competing risk regression was used to compare 3 versions of the Elixhauser comorbidity score with 3 versions of the Charlson comorbidity score. Model performance was evaluated with c statistics. RESULTS: The 2-year noncancer mortality rates were 14.5% (lung cancer), 11.5% (colorectal cancer), 5.7% (breast cancer), and 4.1% (prostate cancer). Cancer-specific Elixhauser comorbidity scores (c = 0.773 for breast cancer, c = 0.772 for prostate cancer, c = 0.579 for lung cancer, and c = 0.680 for colorectal cancer) performed slightly better than cancer-specific Charlson comorbidity scores (ie, the National Cancer Institute combined index; c = 0.762 for breast cancer, c = 0.767 for prostate cancer, c = 0.578 for lung cancer, and c = 0.674 for colorectal cancer). Individual Elixhauser comorbidities performed best (c = 0.779 for breast cancer, c = 0.783 for prostate cancer, c = 0.587 for lung cancer, and c = 0.687 for colorectal cancer). CONCLUSIONS: The cancer-specific Elixhauser comorbidity score performed as well as or slightly better than the cancer-specific Charlson comorbidity score in predicting 2-year survival. If the sample size permits, using individual Elixhauser comorbidities may be the best way to control for confounding in cancer outcomes research. Cancer 2018;124:2018-25. © 2018 American Cancer Society.

Processes and Outcomes of Congestive Heart Failure Care by Different Types of Primary Care Models.

BACKGROUND: Having nurse practitioners (NPs) as primary care providers for patients with congestive heart failure (CHF) is 1 way to address the growing shortage of primary care physicians (PCPs). METHODS AND RESULTS: We used inverse probability of treatment weighted with propensity score to examine the processes and outcomes of care for patients under 3 care models. Approximately 72.9%, 0.8%, and 26.3% of CHF patients received care under the PCP model, the NP model, and the shared care model, respectively. Patients under the NP or shared care models were more likely than those under the PCP model to be referred to cardiologists (odds ratio 1.35, 95% confidence interval 1.32-1.37; odds ratio 1.32, 95% confidence interval 1.30-1.35) and to get guideline-recommended medications. NPs and PCPs had similar rates of emergency room (ER) visits and Medicare spending after adjusting for processes of care. Patients under the shared care model had a higher burden of comorbidity and experienced a higher rate of ER visits and hospitalizations than those under the PCP model. CONCLUSION: The delivery of CHF care mirrors the severity of comorbidity in these patients. The high rate of hospitalization and ER visits in the shared care model underscores the need to design and implement more effective chronic disease management and integrated care programs.

Decline in opioid prescribing after federal rescheduling of hydrocodone products.

PURPOSE: To examine differences in opioid prescribing by patient characteristics and variation in hydrocodone combination product (HCP) prescribing attributed to states, before and after the 2014 Drug Enforcement Administration's reclassification of HCP from schedule III to the more restrictive schedule II. METHODS: We used 2013 to 2015 data for 9 202 958 patients aged 18 to 64 from a large nationally representative commercial health insurance program to assess the temporal trends in the monthly rate of opioid prescribing. RESULTS: HCP prescribing decreased by 26% from June 2013 to June 2015; the rate of prescriptions for any opioid decreased by 11%. Prescribing of non-hydrocodone schedule III opioids increased slightly while prescribing of non-hydrocodone schedule II opioids and tramadol was stable. Absolute decreases in HCP prescribing rates were larger in patients being treated for cancer (-2.26% vs -0.7% for non-cancer patients, P < 0.0001) and in those with high comorbidities (-2.13% vs -0.55% for those with no comorbidity, P < 0.0001). Differences in the absolute and relative changes in HCP prescribing rates among states were large; for example, a relative decrease of 46.7% in Texas and a 12.7% increase in South Dakota. The variation in HCP prescribing attributable to the state of residence increased from 6.6% in 2013 to 8.7% in 2015. CONCLUSIONS: The 2014 federal policy was associated with a decrease in rates of HCP and total opioid prescribing. The large decrease in the rates of HCP prescribing for patients with actively treated cancer may represent an unintended consequence.

Effect of Home- and Community-Based Physical Activity Interventions on Physical Function Among Cancer Survivors: A Systematic Review and Meta-Analysis.

OBJECTIVE: To examine the effect of home- and community-based physical activity interventions on physical functioning among cancer survivors based on the most prevalent physical function measures, randomized trials were reviewed. DATA SOURCES: Five electronic databases-Medline Ovid, PubMed, CINAHL, Web of Science, and PsycINFO-were searched from inception to March 2016 for relevant articles. STUDY SELECTION: Search terms included community-based interventions, physical functioning, and cancer survivors. A reference librarian trained in systematic reviews conducted the final search. DATA EXTRACTION: Four reviewers evaluated eligibility and 2 reviewers evaluated methodological quality. Data were abstracted from studies that used the most prevalent physical function measurement tools-Medical Outcomes Study 36-Item Short-Form Health Survey, Late-Life Function and Disability Instrument, European Organisation for the Research and Treatment of Cancer Quality-of-Life Questionnaire, and 6-minute walk test. Random- or fixed-effects models were conducted to obtain overall effect size per physical function measure. DATA SYNTHESIS: Fourteen studies met inclusion criteria and were used to compute standardized mean differences using the inverse variance statistical method. The median sample size was 83 participants. Most of the studies (n=7) were conducted among breast cancer survivors. The interventions produced short-term positive effects on physical functioning, with overall effect sizes ranging from small (.17; 95% confidence interval [CI], .07-.27) to medium (.45; 95% CI, .23-.67). Community-based interventions that met in groups and used behavioral change strategies produced the largest effect sizes. CONCLUSIONS: Home and community-based physical activity interventions may be a potential tool to combat functional deterioration among aging cancer survivors. More studies are needed among other cancer types using clinically relevant objective functional measures (eg, gait speed) to accelerate translation into the community and clinical practice.

Patient and physician views of shared decision making in cancer.

CONTEXT: Engaging patients in shared decision making involves patient knowledge of treatment options and physician elicitation of patient preferences. OBJECTIVE: Our aim was to explore patient and physician perceptions of shared decision making in clinical encounters for cancer care. DESIGN: Patients and physicians were asked open-ended questions regarding their perceptions of shared decision making throughout their cancer care. Transcripts of interviews were coded and analysed for shared decision-making themes. SETTING AND PARTICIPANTS: At an academic medical centre, 20 cancer patients with a range of cancer diagnoses, stages of cancer and time from diagnosis, and eight physicians involved in cancer care were individually interviewed. DISCUSSION AND CONCLUSIONS: Most physicians reported providing patients with written information. However, most patients reported that written information was too detailed and felt that the physicians did not assess the level of information they wished to receive. Most patients wanted to play an active role in the treatment decision, but also wanted the physician's recommendation, such as what their physician would choose for him/herself or a family member in a similar situation. While physicians stated that they incorporated patient autonomy in decision making, most provided data without making treatment recommendations in the format preferred by most patients. We identified several communication gaps in cancer care. While patients want to be involved in the decision-making process, they also want physicians to provide evidence-based recommendations in the context of their individual preferences. However, physicians often are reluctant to provide a recommendation that will bias the patient.

Comparison of methods to identify long term care nursing home residence with administrative data.

BACKGROUND: To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013. METHODS: We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility. RESULTS: Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%). CONCLUSIONS: Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators.

Impact of Laws Regulating Pain Clinics on Opioid Prescribing and Opioid-Related Toxicity Among Texas Medicare Part D Beneficiaries.

BACKGROUND: Pain management clinics are major sources of prescription opioids. Texas government passed several laws regulating pain clinics between 2009 and 2011 to reduce opioid-related toxicity. Understanding the impact of these laws can inform policy geared toward making the laws more effective in curbing the growing epidemic of opioid overdose, especially among the elderly population. OBJECTIVES: To examine the longitudinal association of laws regulating pain clinics on opioid-prescribing and opioid-related toxicity among Texas Medicare recipients. METHODS: The 2007 to 2012 claims data for Texas Medicare Part D recipients were used to assess temporal trends in the percentage of patients filling any schedule II or schedule III opioid prescription, hospitalization for opioid toxicity, and their relationships to the 2009 to 2011 Texas laws regulating pain clinics. We excluded those with a cancer diagnosis. Join-point trend analysis with Bayesian Information Criterion selection methods were used to evaluate the change in monthly percentages of patients filling opioid prescriptions and hospitalization over time. RESULTS: There was a short-lived decline in the monthly percentages of patients who filled a schedule II or schedule III opioid prescription after the 2009 laws regulating pain clinics. The decline lasted about 3 months. Subsequent new laws had no effect on the percentages of patients who filled any opioid prescription or were hospitalized for potential opioid toxicity. Hospitalizations for opioid toxicity were highest in the winter and lowest in the summer. CONCLUSIONS: Changes in the percentages of opioid-prescribing or opioid-related hospitalizations over time were not associated with laws regulating pain clinics.

Receipt of Cancer Screening Is a Predictor of Life Expectancy.

BACKGROUND: Obtaining cancer screening on patients with limited life expectancy has been proposed as a measure for low quality care for primary care physicians (PCPs). However, administrative data may underestimate life expectancy in patients who undergo screening. OBJECTIVE: To determine the association between receipt of screening mammography or PSA and overall survival. DESIGN: Retrospective cohort study from 1/1/1999 to 12/31/2012. Receipt of screening was assessed for 2001-2002 and survival from 1/1/2003 to 12/31/2012. Life expectancy was estimated as of 1/1/03 using a validated algorithm, and was compared to actual survival for men and women, stratified by receipt of cancer screening. PARTICIPANTS: A 5 % sample of Medicare beneficiaries aged 69-90 years as of 1/1/2003 (n = 906,723). INTERVENTIONS: Receipt of screening mammography in 2001-2002 for women, or a screening PSA test in 2002 for men. MAIN MEASURES: Survival from 1/1/2003 through 12/31/2012. KEY RESULTS: Subjects were stratified by life expectancy based on age and comorbidity. Within each stratum, the subjects with prior cancer screening had actual median survivals higher than those who were not screened, with differences ranging from 1.7 to 2.1 years for women and 0.9 to 1.1 years for men. In a Cox model, non-receipt of screening in women had an impact on survival (HR = 1.52; 95 % CI = 1.51, 1.54) similar in magnitude to a diagnosis of complicated diabetes or heart failure, and was comparable to uncomplicated diabetes or liver disease in men (HR = 1.23; 1.22, 1.25). CONCLUSIONS: Receipt of cancer screening is a powerful marker of health status that is not captured by comorbidity measures in administrative data. Because life expectancy algorithms using administrative data underestimate the life expectancy of patients who undergo screening, they can overestimate the problem of cancer screening in patients with limited life expectancy.

Variation among Primary Care Physicians in the Use of Imaging for Older Patients with Acute Low Back Pain.

BACKGROUND: Diagnostic imaging is not recommended in the evaluation and management of non-specific acute low back pain. OBJECTIVE: To estimate the variation among primary care providers (PCPs) in the use of diagnostic imaging for older patients with non-specific acute low back pain. DESIGN AND PARTICIPANTS: Retrospective cohort study using 100 % Texas Medicare claims data. We identified 145,320 patients aged 66 years and older with non-specific acute low back pain during the period January 1, 2007, through November 30, 2011, cared for by 3297 PCPs. MAIN MEASURES: We tracked whether each patient received lumbar imaging (radiography, computed tomography [CT], or magnetic resonance imaging [MRI]) within 4 weeks of the initial visit. Multilevel logistic regression models were used to estimate physician-level variation in imaging use. KEY RESULTS: Among patients, 27.2 % received radiography and 11.1 % received CT or MRI within 4 weeks of the initial visit for low back pain. PCPs varied substantially in the use of imaging. The average rate of radiography within 4 weeks was 53.9 % for PCPs in the highest decile, compared to 6.1 % for PCPs in the lowest decile. The average rates of CT/MRI within 4 weeks were 18.5 % vs. 3.2 % for PCPs in the highest and lowest deciles, respectively. The specific physician seen by a patient accounted for 25 % of the variability in whether imaging was performed, while only 0.44 % of the variance was due to measured patient characteristics and 1.4 % to known physician characteristics. Use of imaging by individual physicians was stable over time. CONCLUSIONS: PCPs vary substantially in the use of imaging for non-specific acute low back pain. Provider-level measures can be employed to provide feedback to physicians in an effort to modify imaging use.

Motor and Cognitive Functional Status Are Associated with 30-day Unplanned Rehospitalization Following Post-Acute Care in Medicare Fee-for-Service Beneficiaries.

BACKGROUND: The Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014 stipulates that standardized functional status (self-care and mobility) and cognitive function data will be used for quality reporting in post-acute care settings. Thirty-day post-discharge unplanned rehospitalization is an established quality metric that has recently been extended to post-acute settings. The relationships between the functional domains in the IMPACT Act and 30-day unplanned rehospitalization are poorly understood. OBJECTIVE: To determine the degree to which discharge mobility, self-care, and cognitive function are associated with 30-day unplanned rehospitalization following discharge from post-acute care. DESIGN: This was a retrospective cohort study. SETTING: Inpatient rehabilitation facilities submitting claims and assessment data to the Centers for Medicare and Medicaid Services in 2012-2013. PARTICIPANTS: Medicare fee-for-service enrollees discharged from post-acute rehabilitation in 2012-2013. The sample included community-dwelling adults admitted for rehabilitation following an acute care stay who survived for 32 days following discharge (N = 252,406). INTERVENTIONS: Not applicable. MAIN MEASURES: Thirty-day unplanned rehospitalization following post-acute rehabilitation. KEY RESULTS: The unadjusted 30-day unplanned rehospitalization rate was 12.0 % (n = 30,179). Overall, patients dependent at discharge for mobility had a 50 % increased odds of rehospitalization (OR = 1.50, 95 % CI: 1.42-1.59), patients dependent for self-care a 36 % increased odds (OR = 1.36, 95 % CI: 1.27-1.47), and patients dependent for cognition a 19 % increased odds (OR = 1.19, 95 % CI: 1.09-1.29). Patients dependent for both self-care and mobility at discharge (n = 8312, 3.3 %) had a 16.1 % (95 % CI: 15.3-17.0 %) adjusted rehospitalization rate versus 8.5 % (95 % CI: 8.3-8.8 %) for those independent for both (n = 74,641; 29.6 %). CONCLUSIONS: The functional domains identified in the IMPACT Act were associated with 30-day unplanned rehospitalization following post-acute care in this large national sample. Further research is needed to better understand and improve the functional measures, and to determine if their association with rehospitalizations varies across post-acute settings, patient populations, or episodes of care.