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The American Journal of Managed Care March 2017
Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions
Weiyi Ni, PhD; Danielle Colayco, PharmD, MS; Jonathan Hashimoto, PharmD; Kevin Komoto, PharmD, MBA; Chandrakala Gowda, MD, MBA; Bruce Wearda, RPh; and Jeffrey McCombs, PhD
Applying Organizational Behavior Theory to Primary Care
Samyukta Mullangi, MD, MBA, and Sanjay Saint, MD, MPH
Private Sector Accountable Care Organization Development: A Qualitative Study
Ann Scheck McAlearney, ScD; Brian Hilligoss, PhD; and Paula H. Song, PhD
Scaling Lean in Primary Care: Impacts on System Performance
Dorothy Y. Hung, PhD; Michael I. Harrison, PhD; Meghan C. Martinez, MPH; and Harold S. Luft, PhD
Patient Experience Midway Through a Large Primary Care Practice Transformation Initiative
Kaylyn E. Swankoski, MA; Deborah N. Peikes, PhD, MPA; Stacy B. Dale, MPA; Nancy A. Clusen, MS; Nikkilyn Morrison, MPPA; John J. Holland, BS; Timothy J. Day, MSPH; and Randall S. Brown, PhD
A Better Way: Leveraging a Proven and Utilized System for Improving Current Medication Reconciliation Processes
Ajit A. Dhavle, PharmD, MBA; Seth Joseph, MBA; Yuze Yang, PharmD; Chris DiBlasi, MBA; and Ken Whittemore, RPh, MBA
Effects of an Enhanced Primary Care Program on Diabetes Outcomes
Sarah L. Goff, MD; Lorna Murphy, MA, MPH; Alexander B. Knee, MS; Haley Guhn-Knight, BA; Audrey Guhn, MD; and Peter K. Lindenauer, MD, MSc
Consumer-Directed Health Plans: Do Doctors and Nurses Buy In?
Lucinda B. Leung, MD, MPH, and José J. Escarce, MD, PhD
Improvements in Access and Care Through the Affordable Care Act
Julie A. Schmittdiel, PhD; Jennifer C. Barrow, MSPH; Deanne Wiley, BA; Lin Ma, MS; Danny Sam, MD; Christopher V. Chau, MPH; Susan M. Shetterly, MS
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Does Paid Versus Unpaid Supplementary Caregiving Matter in Preventable Readmissions?
Hsueh-Fen Chen, PhD; Taiye Oluyomi Popoola, MBBS, MPH; and Sumihiro Suzuki, PhD

Does Paid Versus Unpaid Supplementary Caregiving Matter in Preventable Readmissions?

Hsueh-Fen Chen, PhD; Taiye Oluyomi Popoola, MBBS, MPH; and Sumihiro Suzuki, PhD
Home health beneficiaries with diabetes using paid supplementary caregivers had 68% higher hazards of readmission due to urinary tract infection than those with unpaid supplementary caregivers.
Dependent variables. The dependent variable was 30-day preventable readmissions. We applied the Agency for Health Research and Quality (AHRQ) Prevention Quality Indicator (PQI) software version 4.5 on the MedPAR to identify readmissions due to preventable conditions.26 These conditions include diabetes with short-term complications (PQI 01), perforated appendix (PQI 02), diabetes with long-term complications (PQI 03), chronic obstructive pulmonary disease (COPD) or asthma in older adults (PQI 05), hypertension (PQI 07), heart failure (PQI 08), dehydration (PQI 10), bacterial pneumonia (PQI 11), urinary tract infection (UTI) (PQI 12), angina without procedure (PQI 13), uncontrolled diabetes (PQI 14), and lower-extremity amputation among patients with diabetes (PQI 16). Hospitalizations due to these conditions are preventable if patients are able to receive proper and timely care in the community.26-28 Home health beneficiaries receive professional home healthcare at their residences; with proper care and interventions after hospital discharge, readmissions due to preventable conditions identified by AHRQ are avoidable for our study population.

We excluded perforated appendix because none of the matched cohorts had readmission due to this condition. Because the frequency for some preventable conditions was small (eg, hypertension), we regrouped the preventable conditions based on the human organ system. The groups for preventable readmissions are: 1) diabetes-related conditions (PQI 01, PQI 03, PQI 14, and PQI 16), 2) heart disease-related conditions (PQI 07, PQI 08, PQI 13), 3) COPD, 4) UTI, 5) pneumonia, and 6) dehydration.  These preventable readmissions were finally defined as a time-to-first event in our analytical model.

Covariates for estimated model. Given the conceptual framework, the covariates in the analytical model are the intensity of home health visits and the characteristics of hospitals, home health agencies, and communities. Hospital characteristics—extracted from the AHA Annual Survey—included hospitals’ ownership, teaching status, and system affiliation. Ownership of home health agencies was extracted from the POS files. Community characteristics, which were extracted from the AHRF, included a rural/nonrural county characterization and the number of primary care physicians and acute hospital beds per 1000 population at the county level. A log-transformed median household income at the zip code level was extracted from the PCSA. (Information regarding how to merge all datasets is available in the eAppendix.)

Statistical Methods

Matching model to find matched cohorts. Approximately 4% of beneficiaries with diabetes had more than 1 home health episode for post acute care in 2009. To account for the cluster from the same beneficiary using multiple home health episodes, we applied a logistic regression with the Huber Sandwich Estimator on the selected variables from patients’ predisposing, enabling, and health need factors to estimate the propensity score. We then applied a caliper without replacement on propensity score to find the matched cohorts.23,24 We compared the 3 factors of beneficiaries solely assisted by paid supplementary caregivers versus those solely assisted by unpaid supplementary caregivers, before and after matching, using χ2 testing.

Estimated model on matched cohorts. We applied a Cox proportional hazards regression model on the matched cohorts to estimate the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for paid supplementary caregivers, after adjusting for the covariates discussed previously. The Cox regression model was applied to 6 preventable conditions individually. Approximately 1% of the matched cohorts were from the same beneficiary; thus, we fit the model with standard errors adjusted for clustering on each individual beneficiary.

RESULTS

We identified 120,208 diabetes-related postacute home health episodes. Among them, 85.79% had primary caregivers (based on code M0360 in the OASIS) and 98% received assistance from paid or unpaid caregivers (based on codes M0350A-M0350E). After excluding unqualified episodes (previously discussed in the study sample), we identified 81,481 episodes in the unmatched study sample, with 70,350 (86.34%) and 5982 (7.34%) observations having solely unpaid and solely paid supplementary caregivers, respectively (5149 observations had both paid and unpaid supplementary caregivers). Before matching, patients’ predisposing, enabling, and health need factors statistically differed between beneficiaries with paid supplementary caregivers and those with unpaid supplementary caregivers (P <.05). In our matched sample (5860 observations for each group), the difference in predisposing, enabling, and health need factors between these 2 groups became statistically insignificant. Table 1 presents the descriptive analysis for these 3 matching factors between unpaid and paid supplementary caregivers before and after matching.

Table 2 presents the descriptive analysis for the dependent variables and covariates for the matched cohorts. Among readmission conditions, beneficiaries solely assisted by paid supplementary caregivers had about 0.46% higher readmissions due to UTIs than those solely assisted by unpaid supplementary caregivers (P <.01). Table 3 presents the HR and 95% CI from the Cox regression models for the matched cohorts. For UTIs, the HR is 1.68 (95% CI, 1.13-2.50); namely, beneficiaries with solely paid supplementary caregivers had 68% higher hazards of readmission due to UTIs than whose with solely unpaid supplementary caregivers. We did not find statistical differences for COPD, heart-related diseases, dehydration, diabetes-related conditions, and pneumonia. 

DISCUSSION 

Although existing literature primarily focuses on volunteer family caregivers, there is a growing interest in studying paid informal caregivers due to the reduction in family size and rising number of women in the workforce. Lindquist et al (2011, 2012) found that paid informal caregivers had a good heart to help elderly patients, but were likely to have low health literacy and receive little training before they were assigned to patients’ homes.29,30 Among previous studies, one focused on Medicare home health beneficiaries found that beneficiaries with paid informal caregivers had poorer functional improvement than those with unpaid informal caregivers.2 Another study found that Medicare heart failure beneficiaries with paid informal caregivers had a higher risk of 1-year readmission than those with unpaid informal caregivers.13

Among all preventable readmissions, we found that beneficiaries solely assisted by paid supplementary caregivers had a significantly higher hazard of readmissions due to UTIs than those solely assisted by unpaid supplementary caregivers. Given that prevention of a UTI requires good genital care and regular bladder emptying, this type of personal care necessitates a good relationship between patients and supplementary caregivers. Paid supplementary caregivers in our study were not beneficiaries’ family members; one study showed that patients with family caregivers felt safer and had higher satisfaction in their interpersonal relationship than those with paid nonfamily caregivers,31 which explains our significant findings for readmissions due to UTI. 

A previous study based on the information from code M0360 in the OASIS found that 83.4% of Medicare home health beneficiaries had primary informal caregivers.2 Based on code M0360, our study found that 85.79% of Medicare home health beneficiaries with diabetes had primary informal caregivers. Our study moved further and found that for the beneficiaries who received assistance several times during the day and night or several times during only the day, 7.34% were solely assisted by paid supplementary caregivers, 86.34% were solely assisted by unpaid supplementary caregivers, and 6.32% received assistance from both paid and unpaid supplementary caregivers. Our findings also showed that paid and unpaid supplementary caregivers heavily involved in caregiving had a significant impact on health outcomes for these frail, community-dwelling homebound beneficiaries. In order to obtain the full picture for the difference in quality of care provided by paid and unpaid supplementary caregivers, future studies focusing on other quality indicators, such as emergency visits and mortality, are recommended. 

Limitations

Our datasets did not provide information on competence, stress, and support systems for paid and unpaid supplementary caregivers, which are likely to affect patients’ outcomes.32 Additionally, we focused on Medicare FFS home health beneficiaries with diabetes without identifying type 1 and 2 diabetes or insulin users. We also excluded beneficiaries with dual eligibility, those receiving assistance once daily or less, and those receiving care from both paid and unpaid supplementary caregivers. Furthermore, we used OASIS, which contains only data in the United States, to provide clinical and functional conditions for Medicare home health beneficiaries and allows researchers to merge with other Medicare claim data, such as MedPAR. Readers must also be aware of the debate concerning the reliability and validity of OASIS.33,34 

Moreover, we used preventable conditions defined by AHRQ. The conditions for preventable readmissions defined in the 3M Potentially Preventable Readmissions Grouping Software are slightly different from the preventable conditions defined by AHRQ.  Future studies based on the 3M Potentially Preventable Readmissions Grouping Software are encouraged. Also, the propensity score matching was based on the variables available in our data. There were missing variables in our study that should be taken into consideration when interpreting our findings. Our datasets do not have beneficiaries’ social/contextual factors, such as their education and whether they received social support from other organizations that were associated with the likelihood of hiring a supplementary caregiver and preventable readmissions. 

Finally, we used 2009 datasets. Several programs, such as the Hospital Readmissions Reduction Program introduced in 2013 under the ACA,35 are likely to have a spillover effect on preventable readmissions for Medicare home health beneficiaries. Future studies using recent data are recommended. 

CONCLUSIONS

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
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