The validity of causal claims with repeated measures designs: A within-study comparison evaluation of differences-in-differences and the comparative interrupted time series

Modern policies are commonly evaluated not with randomized experiments but with repeated measures designs like difference-in-differences (DID) and the comparative interrupted time series (CITS). The key benefit of these designs is that they control for unobserved confounders that are fixed over time. However, DID and CITS designs only result in unbiased impact estimates when the model assumptions are consistent with the data at hand. In this paper, we empirically test whether the assumptions of repeated measures designs are met in field settings. Using a within-study comparison design, we compare experimental estimates of the impact of patient-directed care on medical expenditures to non-experimental DID and CITS estimates for the same target population and outcome. Our data come from a multi-site experiment that includes participants receiving Medicaid in Arkansas, Florida, and New Jersey. We present summary measures of repeated measures bias across three states, four comparison groups, two model specifications, and two outcomes. We find that, on average, bias resulting from repeated measures designs are very close to zero (less than 0.01 standard deviations; SDs). Further, we find that comparison groups which have pre-treatment trends that are visibly parallel to the treatment group result in less bias than those with visibly divergent trends. However, CITS models that control for baseline trends produced slightly more bias and were less precise than DID models that only control for baseline means. Overall, we offer optimistic evidence in favor of repeated measures designs when randomization is not feasible.

High-dose influenza vaccines for the prevention of hospitalization due to cardiovascular events in older adults in the nursing home: Post-hoc analysis of a cluster-randomized trial

Older adults are at high risk of major acute cardiovascular events (MACE) linked to influenza illness andpreventable by influenza vaccination. It is unknown whether high-dose vaccine might incrementally reduce the risk of MACE.We conducted a post-hoc analysis of data collected from a pragmatic cluster randomized study of 823 nursing homes (NH) randomized to standard-dose (SD) or high-dose (HD) influenza vaccine in the 2013-14 season. Adults age 65 year or older who are Medicare-enrolled long-stay residents were included in the analysis.There were no statistically significant differences in hospitalization for MACE, acute coronary syndromes (ACS), stroke or heart failure between the HD and SD arms. However, in the fee-for-service group, participants in the HD arm had significantly decreased risk of hospitalization for respiratory problems, which was not observed in the Medicare Advantage group.High-dose influenza vaccine was not shown to be incrementally protective against MACE relative to standard-dose vaccine.

Can we use temperature measurements to identify pre-symptomatic SARS-CoV-2 infection in nursing home residents?

BACKGROUND

COVID-19 has had a severe impact on morbidity and mortality among nursing home (NH) residents. Earlier detection of SARS-CoV-2 may position us to better mitigate the risk of spread. Both asymptomatic and pre-symptomatic transmission are common in outbreaks, and threshold temperatures, such as 38C, for screening for infection could miss timely detection in the majority of residents. We hypothesized that in long-term care residents, temperature trends with SARS-CoV-2 infection could identify infection in pre-symptomatic individuals earlier than standard screening.

METHODS

We conducted a retrospective cohort study using electronic health records in 6176 residents of the VA NHs who underwent SARS-CoV-2 testing triggered by symptoms. We collected information about age and other demographics, baseline temperature, and specific comorbidities. We created standardized definitions, and a hypothetical model to test measures of temperature variation and compare outcomes to the VA standard of care.

RESULTS

We showed that a change from baseline of 0.4C identified 47% of NH residents who became SARS-CoV-2 positive, earlier than standard testing by an average of 42.2 h. Temperature variability of 0.5C over 3 days when paired with a 37.2C temperature cutoff identified 55% of NH residents who became SARS-CoV-2 positive earlier than the standard of care testing by an average of 44.4 h. A change from baseline temperature of 0.4C when combined with temperature variability of 0.7C over 3 days identified 52% of NH residents who became SARS-CoV-2 positive, earlier than standard testing by an average of 40 h, and by more than 3 days in 22% of the residents. This earlier detection comes at the expense of triggering 57,793 tests, as compared to the number of trigger tests ordered in the VA system of 40,691.

CONCLUSIONS

Our model suggests that early temperature trends with SARS-CoV-2 infection may identify infection in pre-symptomatic long-term care residents.

Adverse events following third dose of mRNA COVID-19 vaccination among nursing home residents who received the primary series

BACKGROUND

We sought to compare rates of adverse events among nursing home residents who received an mRNA COVID-19 vaccine booster dose with those who had not yet received their booster.

METHODS

We assessed a prospective cohort of 11,200 nursing home residents who received a primary COVID-19 mRNA vaccine series at least 6 months prior to September 22, 2021 and received a third "booster dose" between September 22, 2021 and February 2, 2022. Residents lived in 239 nursing homes operated by Genesis HealthCare, spanning 21 U.S. states. We screened electronic health records for 20 serious vaccine-related adverse events that are monitored following receipt of COVID-19 vaccination by the CDC's Vaccine Safety Datalink. We matched boosted and yet-to-be boosted residents during the same time period, comparing rates of events occurring 14 days after booster administration with those occurring 14 days prior to booster administration. To supplement previously reported background rates of adverse events, we report background rates of medical conditions among nursing home residents during 2020, before COVID-19 vaccines were administered in nursing homes. Events occurring in 2021-2022 were confirmed by physician chart review. We report unadjusted rates of adverse events and used a false discovery rate procedure to adjust for multiplicity of events tested.

RESULTS

No adverse events were reported during the 14 days post-booster. A few adverse events occurred prior to booster (ischemic stroke: 49.4 per 100,000 residents, 95% CI: 21.2, 115.7; venous thromboembolism: 9.9 per 100,000 residents, 95% CI: 1.7, 56.0), though differences in event rates pre- versus post-booster were not statistically significant (p < 0.05) after adjusting for multiple comparisons. No significant differences were detected between post-booster vaccination rates and prior year 14-day background rates of medical conditions.

CONCLUSIONS

No safety signals were detected following a COVID-19 mRNA vaccine booster dose in this large multi-state sample of nursing home residents.

The effect of age on fever response among nursing home residents with SARS-COV-2 infection

Over 15,000 veterans in 135 VA nursing homes were systematically tested for SARS-CoV-2 and had daily temperatures assessed from March to August, 2020. Lower baseline temperatures, and in SARS-CoV-2+ , lower maximum temperatures were observed with advancing age. Clinicians should be aware of the potential diminished fever response in the elderly with SARS-CoV-2.

Evaluating the Findings of the IMPACT-C Randomized Clinical Trial to Improve COVID-19 Vaccine Coverage in Skilled Nursing Facilities

IMPORTANCE

Identifying successful strategies to increase COVID-19 vaccination among skilled nursing facility (SNF) residents and staff is integral to preventing future outbreaks in a continually overwhelmed system.

OBJECTIVE

To determine whether a multicomponent vaccine campaign would increase vaccine rates among SNF residents and staff.

DESIGN, SETTING, AND PARTICIPANTS

This was a cluster randomized trial with a rapid timeline (December 2020-March 2021) coinciding with the Pharmacy Partnership Program (PPP). It included 133 SNFs in 4 health care systems across 16 states: 63 and 70 facilities in the intervention and control arms, respectively, and participants included 7496 long-stay residents (>100 days) and 17 963 staff.

INTERVENTIONS

Multicomponent interventions were introduced at the facility level that included: (1) educational material and electronic messaging for staff; (2) town hall meetings with frontline staff (nurses, nurse aides, dietary, housekeeping); (3) messaging from community leaders; (4) gifts (eg, T-shirts) with socially concerned messaging; (5) use of a specialist to facilitate consent with residents' proxies; and (6) funds for additional COVID-19 testing of staff/residents.

MAIN OUTCOMES AND MEASURES

The primary outcomes of this study were the proportion of residents (from electronic medical records) and staff (from facility logs) who received a COVID-19 vaccine (any), examined as 2 separate outcomes. Mixed-effects generalized linear models with a binomial distribution were used to compare outcomes between arms, using intent-to-treat approach. Race was examined as an effect modifier in the resident outcome model.

RESULTS

Most facilities were for-profit (95; 71.4%), and 1973 (26.3%) of residents were Black. Among residents, 82.5% (95% CI, 81.2%-83.7%) were vaccinated in the intervention arm, compared with 79.8% (95% CI, 78.5%-81.0%) in the usual care arm (marginal difference 0.8%; 95% CI, -1.9% to 3.7%). Among staff, 49.5% (95% CI, 48.4%-50.6%) were vaccinated in the intervention arm, compared with 47.9% (95% CI, 46.9%-48.9%) in usual care arm (marginal difference: -0.4%; 95% CI, -4.2% to 3.1%). There was no association of race with the outcome among residents.

CONCLUSIONS AND RELEVANCE

A multicomponent vaccine campaign did not have a significant effect on vaccination rates among SNF residents or staff. Among residents, vaccination rates were high. However, half the staff remained unvaccinated despite these efforts. Vaccination campaigns to target SNF staff will likely need to use additional approaches.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04732819.

Adverse Events Following One Dose of mRNA COVID-19 Vaccination Among US Nursing Home Residents With and Without a Previous SARS-CoV-2 Infection

OBJECTIVES

To compare rates of adverse events following Coronavirus Disease 2019 (COVID-19) vaccination among nursing home residents with and without previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

DESIGN

Prospective cohort.

SETTING AND PARTICIPANTS

A total of 20,918 nursing home residents who received the first dose of messenger RNA COVID-19 vaccine from December 18, 2020, through February 14, 2021, in 284 facilities within Genesis Healthcare, a large nursing home provider spanning 24 US states.

METHODS

We screened the electronic health record for adverse events, classified by the Brighton Collaboration, occurring within 15 days of a resident's first COVID-19 vaccine dose. All events were confirmed by physician chart review. To obtain risk ratios, multilevel logistic regression model that accounted for clustering (variability) across nursing homes was implemented. To balance the probability of prior SARS-CoV-2 infection (previous positive test or diagnosis by the International Classification of Diseases, 10^th Revision, Clinical Modification) more than 20 days before vaccination, we used inverse probability weighting. To adjust for multiplicity of adverse events tested, we used a false discovery rate procedure.

RESULTS

Statistically significant differences existed between those without (n = 13,163) and with previous SARS-CoV-2 infection [symptomatic (n = 5617) and asymptomatic (n = 2138)] for all baseline characteristics assessed. Only 1 adverse event was reported among those with previous SARS-CoV-2 infection (asymptomatic), venous thromboembolism [46.8 per 100,000 residents 95% confidence interval (CI) 8.3-264.5], which was not significantly different from the rate reported for those without previous infection (30.4 per 100,000 95% CI 11.8-78.1). Several other adverse events were observed for those with no previous infection, but were not statistically significantly higher than those reported with previous infection after adjustments for multiple comparisons.

CONCLUSIONS AND IMPLICATIONS

Although reactogenicity increases with preexisting immunity, we did not find that vaccination among those with previous SARS-CoV-2 infection resulted in higher rates of adverse events than those without previous infection. This study stresses the importance of monitoring novel vaccines for adverse events in this vulnerable population.

Can we clinically identify pre-symptomatic and asymptomatic COVID-19?

OBJECTIVES

COVID-19 has had a severe impact on morbidity and mortality among nursing home (NH) residents. Earlier detection of SARS-CoV-2 may position us to better mitigate risk of spread. Both asymptomatic or pre-symptomatic transmission are common in outbreaks, and threshold temperatures, such as 38C, for screening for infection could miss timely detection in the majority.

DESIGN

Retrospective cohort study using electronic health records.

METHODS

We hypothesized that in long-term care residents, temperature trends with SARS-CoV-2 infection could identify infection in pre-symptomatic and asymptomatic individuals earlier. We collected information about age and other demographics, baseline temperature, and specific comorbidities. We created standardized definitions, and an alternative hypothetical model to test measures of temperature variation and compare outcomes to the VA reality.

SETTINGS AND PARTICIPANTS

Our subjects were 6,176 residents of the VA NHs who underwent SARS-CoV-2 trigger testing.

RESULTS

We showed that a change from baseline of >0.4C identifies 47% of the SARS-CoV-2 positive NH residents early, and achieves earlier detection by 42.2 hours. Range improves early detection to 55% when paired with a 37.2C cutoff, and achieves earlier detection by 44.4 hours. Temperature elevation >0.4C from baseline, when combined with a 0.7C range, would detect 52% early, leading to earlier detection by more than 3 days in 22% of the residents. This earlier detection comes at the expense of triggering 57,793 tests, as compared to the number of trigger tests ordered in the VA system of 40,691.

CONCLUSION AND IMPLICATIONS

Our model suggests that current clinical screening for SARS-CoV-2 in NHs can be substantially improved upon by triggering testing using a patient-derived baseline temperature with a 0.4C degree relative elevation or temperature variability of 0.7C trigger threshold for SARS-CoV2 testing. Such triggers could be automated in facilities that track temperatures in their electronic records.

Derivation and Validation of a Predictive Algorithm for Long-Term Care Admission or Death

OBJECTIVES

Older veterans prefer to remain in their homes and communities as long as possible. Although targeted delivery of home- and community-based services for veterans might delay long-term care placement, often, access to these services is inconsistently organized or delayed. To aid in early recognition of veterans at high risk for long-term care placement or death, we developed and validated a predictive algorithm, "Choose Home."

DESIGN

A retrospective observational cohort analysis was used.

SETTING AND PARTICIPANTS

Two cohorts of Veterans Health Administration (VHA; a large integrated health care system) users were assembled: Derivation (4.6 million) and Confirmation (4.7 million). The Derivation Cohort included Veterans Administration users from fiscal year 2013; the Confirmation Cohort included Veterans Administration users from fiscal year 2014.

METHODS

A total of 148 predictor variables, including demographics, comorbidities, and utilizations were selected using logistic regression to predict placement in a long-term care facility for >90 days or death within 2 years.

RESULTS

Veterans were predominantly male [92.8% (Derivation), 92.5% (Confirmation)] and older [61.7±15.5 (Derivation), 61.5±15.6 years (Confirmation)], with a high prevalence of comorbid conditions. Between the Derivation and Confirmation Cohorts, the areas under the receiver operating characteristic curves were found to be 0.80 [95% confidence interval (CI) 0.799, 0.802] and 0.80 (95% CI 0.800, 0.802), respectively, indicating good discrimination for determining at-risk veterans.

CONCLUSIONS AND IMPLICATIONS

We created a predictive algorithm that identifies veterans at highest risk for long-term institutionalization or death. This algorithm provides clinicians with information that can proactively inform clinical decision making and care coordination. This study provides the groundwork for future investigations on how home- and community-based services can target older adults at highest risk to extend time in their communities.

Proportion of SARS-CoV-2 positive tests and vaccination in Veterans Affairs Community Living Centers

BACKGROUND/OBJECTIVES

COVID-19 has caused significant morbidity and mortality in nursing homes. Vaccination against SARS-COV-2 holds promise for reduction in COVID-19. This operational analysis describes the proportion of SARS-COV-2 positive tests before, during, and after vaccination.

DESIGN

Retrospective longitudinal cohort analysis from October 1, 2020 until February 14, 2021.

SETTING

A total of 130 Department of Veterans Affairs (VA) Community Living Centers (CLC), analogous to nursing homes.

INTERVENTION

Vaccination for SARS-CoV-2.

MEASUREMENTS

The primary measure is the proportion of SARS-CoV-2 positive tests among CLC residents. In a pooled analysis of weekly testing and vaccine data, the proportion of positive tests was compared for the unvaccinated, first dose, and second dose. For each CLC, we identified the week in which 50% of CLC residents were vaccinated (index week). The analysis aligned the index week for CLCs and examined the proportion of SARS-CoV-2 positive tests at the CLC level before and after. As a reference, we plotted the proportion of positive tests in nursing homes in the same county as the CLC using publicly reported data.

RESULTS

Within the pooled VA CLCs, the first SARS-CoV-2 vaccine dose was delivered to 50% of CLC residents within 1 week of availability and second dose within 5 weeks. Relative to the index week, the risk ratio of SARS-CoV-2 positive tests in the vaccinated relative to unvaccinated was significantly lower in Week 4 (relative risk 0.37, 95% confidence interval 0.20-0.68). Throughout the study period, the proportion of SARS-CoV-2 positive tests in community nursing homes was higher compared to VA CLC and also declined after vaccine availability.

CONCLUSION

The proportion of SARS-CoV-2 positive tests significantly declined in VA CLCs 4 weeks after vaccine delivery and continued to decline in vaccinated and unvaccinated residents. The results describe the importance of SARS-CoV-2 surveillance and vaccination in VA nursing home residents.

COVID-19 Mortality Rates Among Nursing Home Residents Declined From March To November 2020

Improved therapeutics and supportive care in hospitals have helped reduce mortality from COVID-19. However, there is limited evidence as to whether nursing home residents, who account for a disproportionate share of COVID-19 deaths and are often managed conservatively in the nursing home instead of being admitted to the hospital, have experienced similar mortality reductions. In this study we examined changes in thirty-day mortality rates between March and November 2020 among 12,271 nursing home residents with COVID-19. We found that adjusted mortality rates significantly declined from a high of 20.9 percent in early April to 11.2 percent in early November. Mortality risk declined for residents with both symptomatic and asymptomatic infections and for residents with both high and low clinical complexity. The mechanisms driving these trends are not entirely understood, but they may include improved clinical management within nursing homes, improved personal protective equipment supply and use, and genetic changes in the virus.

LB-19. Association between contract staffing and reported outbreaks of SARS-CoV-2 in a cluster-randomized trial of 965 U.S. nursing homes

Background

Nursing home residents account for 45% SARS-CoV-2 related deaths in the U.S. but only 0.6% of the population. Our research group conducted a large pragmatic cluster randomized influenza vaccine trial in 965 nursing homes (NCT03965195). Due to the pandemic and its impact after the influenza season, we prospectively collected reports of SARS-CoV-2 outbreaks and performed a prospective study on the association between contract staffing and reported outbreaks of SARS-CoV-2. We hypothesized those using more contract nursing care would have higher risk of an outbreak.

Methods

From February through April, we collected monthly facility-level, self-reported data on SARS-CoV-2 outbreaks. Facility characteristics were taken from public data from Centers for Medicaid and Medicare services. Predictors of SARS-CoV-2 outbreaks were identified using a LASSO variable selection procedure, with a generalized linear, Poisson family model. Facility characteristics evaluated include demographics (e.g. number of residents), influenza vaccination rates, quality measures (e.g. % with UTI), and functional status (e.g. % with tube feedings). Facilities with contract staffing hours in the upper 25% quantile of direct care (RN, LPN, CNA) were considered ‘heavy use’.

Results

Of 965 randomized NHs, 663/965 (69%) reported data on SARS-CoV-2 outbreaks. On average, 13% of facilities had at least one outbreak, with 5/842 (0.5%) outbreaks in February, 91/835 (10.8%) in March and 217/686 (30%) in April. SARS-CoV-2 (+) facilities were larger (average total beds, 151 vs. 117), but were mostly similar by functional and cognitive status. Occupancy rate, total residents, Influenza vaccination rate, % with UTI, receiving respiratory treatments, tube feedings, and Medicaid payers were adjusted for in the analysis. The ‘heavy use’ of contract staffing included those with >223 hours per quarter. A multivariable regression found the relative risk SARS-CoV-2 outbreak was 1.56 (95% Confidence Interval: 1.22, 1.99) with heavy use of contract staffing.

Conclusion

The participating nursing homes in our vaccine trial with SARS-CoV-2 outbreaks were larger. Our study highlights that heavy use of contract staffing was associated with 56% increased risk of an outbreak.

Disclosures

Kevin McConeghy, Pharm.D., Pfizer (Grant/Research Support)Sanofi-Pasteur (Grant/Research Support)Seqirus Pharmaceuticals (Grant/Research Support) H. Edward Davidson, PharmD, MPH, Sanofi pasteur (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support)Seqirus (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support) Lisa Han, MPH, Sanofi Pasteur (Grant/Research Support)Seqirus (Grant/Research Support) David Canaday, M.D., Pfizer (Research Grant or Support)Sanofi Pasteur (Research Grant or Support)Seqirus (Advisor or Review Panel member, Research Grant or Support) Vincent Mor, Ph.D., naviHealth (Consultant)

The SHIELD Orange County Project: Multidrug-resistant Organism Prevalence in 21 Nursing Homes and Long-term Acute Care Facilities in Southern California

BACKGROUND

Multidrug-resistant organisms (MDROs) spread between hospitals, nursing homes (NHs), and long-term acute care facilities (LTACs) via patient transfers. The Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County is a regional public health collaborative involving decolonization at 38 healthcare facilities selected based on their high degree of patient sharing. We report baseline MDRO prevalence in 21 NHs/LTACs.

METHODS

A random sample of 50 adults for 21 NHs/LTACs (18 NHs, 3 LTACs) were screened for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), extended-spectrum β-lactamase-producing organisms (ESBL), and carbapenem-resistant Enterobacteriaceae (CRE) using nares, skin (axilla/groin), and peri-rectal swabs. Facility and resident characteristics associated with MDRO carriage were assessed using multivariable models clustering by person and facility.

RESULTS

Prevalence of MDROs was 65% in NHs and 80% in LTACs. The most common MDROs in NHs were MRSA (42%) and ESBL (34%); in LTACs they were VRE (55%) and ESBL (38%). CRE prevalence was higher in facilities that manage ventilated LTAC patients and NH residents (8% vs <1%, P < .001). MDRO status was known for 18% of NH residents and 49% of LTAC patients. MDRO-colonized adults commonly harbored additional MDROs (54% MDRO+ NH residents and 62% MDRO+ LTACs patients). History of MRSA (odds ratio [OR] = 1.7; confidence interval [CI]: 1.2, 2.4; P = .004), VRE (OR = 2.1; CI: 1.2, 3.8; P = .01), ESBL (OR = 1.6; CI: 1.1, 2.3; P = .03), and diabetes (OR = 1.3; CI: 1.0, 1.7; P = .03) were associated with any MDRO carriage.

CONCLUSIONS

The majority of NH residents and LTAC patients harbor MDROs. MDRO status is frequently unknown to the facility. The high MDRO prevalence highlights the need for prevention efforts in NHs/LTACs as part of regional efforts to control MDRO spread.

High Prevalence of Multidrug-Resistant Organism Colonization in 28 Nursing Homes: An "Iceberg Effect"

OBJECTIVE

Determine the prevalence of methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus spp. (VRE), extended-spectrum beta-lactamase producing organisms (ESBLs), and carbapenem-resistant Enterobacteriaceae (CRE) among residents and in the environment of nursing homes (NHs).

DESIGN

Point prevalence sampling of residents and environmental sampling of high-touch objects in resident rooms and common areas.

SETTING

Twenty-eight NHs in Southern California from 2016 to 2017.

PARTICIPANTS

NH participants in Project PROTECT, a cluster-randomized trial of enhanced bathing and decolonization vs routine care.

METHODS

Fifty residents were randomly sampled per NH. Twenty objects were sampled, including 5 common room objects plus 5 objects in each of 3 rooms (ambulatory, total care, and dementia care residents).

RESULTS

A total of 2797 swabs were obtained from 1400 residents in 28 NHs. Median prevalence of multidrug-resistant organism (MDRO) carriage per NH was 50% (range: 24%-70%). Median prevalence of specific MDROs were as follows: MRSA, 36% (range: 20%-54%); ESBL, 16% (range: 2%-34%); VRE, 5% (range: 0%-30%); and CRE, 0% (range: 0%-8%). A median of 45% of residents (range: 24%-67%) harbored an MDRO without a known MDRO history. Environmental MDRO contamination was found in 74% of resident rooms and 93% of common areas.

CONCLUSIONS AND IMPLICATIONS

In more than half of the NHs, more than 50% of residents were colonized with MDROs of clinical and public health significance, most commonly MRSA and ESBL. Additionally, the vast majority of resident rooms and common areas were MDRO contaminated. The unknown submerged portion of the iceberg of MDRO carriers in NHs may warrant changes to infection prevention and control practices, particularly high-fidelity adoption of universal strategies such as hand hygiene, environmental cleaning, and decolonization.

LONG-TERM CARE FACILITY VARIATION IN THE INCIDENCE OF PNEUMONIA AND INFLUENZA HOSPITALIZATIONS

Pneumonia and influenza (P&I) increase morbidity and mortality among older adults, especially those residing in long-term care facilities (LTCFs). Facility-level characteristics may affect P&I risk beyond resident-level determinants. However, the relationship between facility characteristics and P&I is poorly understood. We therefore identified potentially modifiable facility-level characteristics that might influence the incidence of P&I across LTCFs. We conducted a retrospective cohort study using 100% of 2013-2015 Medicare claims linked to Minimum Data Set 3.0 and LTCF-level data. Short-stay (<100 days) and long-stay (≥100 days) LTCF residents aged ≥65 were followed for the first occurrence of hospitalization, LTCF discharge, Medicare disenrollment, or death. We calculated LTCF risk-standardized incidence rates (RSIRs) per 100 person-years for P&I hospitalizations by adjusting for over 30 resident-level demographic and clinical covariates using hierarchical logistic regression. The final study cohorts included 1,767,241 short-stay (13,683 LTCFs) and 922,863 long-stay residents (14,495 LTCFs). LTCFs with lower RSIRs had more Physician Extenders (Nurse Practitioners or Physician’s Assistants) among short-stay (44.9% vs. 41.6%, p<0.001) and long-stay residents (47.4% vs. 37.9%, p<0.001), higher Registered Nurse hours/resident/day among short-stay and long-stay residents (Mean (SD): 0.5 (0.7) vs. 0.4 (0.4), p<0.001), and fewer residents prescribed antipsychotics among short-stay (21.4% (11.6) vs. 23.6% (13.2), p<0.001) and long-stay residents (22.2% (14.3) vs. 25.5% (15.0), p<0.001). LTCF characteristics may play an important role in preventing P&I hospitalizations. Hiring more Registered Nurses and Physician Extenders, increasing staffing hours, and reducing antipsychotic use may be modifiable means of reducing P&I in LTCFs. Funding provided by Sanofi Pasteur.

EVALUATING THE IMPACT OF STAR-VA ON VETERANS’ PSYCHOTROPIC MEDICATION USE IN CLCS

The impact of STAR-VA on psychotropic drug use among residents with behavioral symptoms of dementia was evaluated through a difference-in-differences framework. STAR-VA residents enrolled 2013-2017 were evaluated longitudinally pre-post intervention. The primary outcome was the number of as needed administrations with an indication of ‘anxiety’ or ‘agitation’. The analytical cohort included 214 training cases and 1,870 controls from untrained sites meeting eligibility criteria. STAR-VA cases were less white (48% vs. 54%), less black (11% vs. 14%), and had significantly longer median length of stay (830 vs. 261 days), respectively. STAR-VA cases had on average 3.5 as needed doses/month of psychotropic medication before the intervention and 1.7 after, controls averaged 1.8 doses/month. After adjustment for person-time-fixed effects, enrollment was associated with 55% (95% CI:30, 68) reduction or an average 0.8 as needed psychotropic doses/month. Findings demonstrate effectiveness in decreasing as-needed psychotropic drug use among CLC residents, supporting continued implementation of STAR-VA.

2730. Estimating Deaths Attributable to Influenza Mortality Using Traditional and Novel Forecasting Methods

Background

Seasonally-adjusted linear models (‘Serfling’ models) serve as an important surveillance measure to estimate influenza (flu) attributable deaths for resource allocation to public health programs (e.g., vaccination campaigns). We compared performance of traditional time-series and viral activity-based models to a novel open-source R-package ‘Prophet’ for estimating the number of deaths attributable to influenza per season.

Methods

We evaluated deaths from the 122-Cities Mortality Reporting System which reports the total number of death certificates where pneumonia or flu was listed as a contributing cause of death. Models were fitted to 2010–2015 influenza seasons. The first Serfling model (M1) used baseline periods of low-endemicity (summer months) to estimate attributable deaths during a flu season (OCT-MAY), the second Serfling model (M2) incorporated both baseline and virology data (count laboratory proven flu cases in a given period). The Prophet model (M3) uses generalized additive models incorporating annual, seasonal terms and viral activity data. The difference between observed deaths, and those predicted by each model in the absence of flu were ‘attributable death.” Epidemic weeks exceeded the 95% upper prediction interval. Model performance was assessed by Root Mean Square Error (RMSE).

Results

From 2010 to 2015, the average deaths due to pneumonia and influenza per season numbered 824 per week (total 198,692). Compared with the traditional Serfling model (M1), the Prophet model estimated 52% more influenza-attributable deaths (13,443 vs. 8,800) and more epidemic weeks (25 vs. 10) with lower RMSE (75.9 vs. 95.3 [lower is better]). Compared with the viral activity-based model (M2), the Prophet model estimated 6% fewer attributable deaths (13,443 vs. 14,326), with more epidemic weeks (25 vs. 19) and lower RMSE (RMSE 75.9 vs. 92.6).

Conclusion

Generalized additive models, implemented through the R-package Prophet, are superior in terms of reducing model prediction error for influenza mortality vs. traditional models. Based on superior model performance, the attributable mortality estimated by these novel models may be preferred over traditional regression models. This study was funded by Sanofi Pasteur.

Disclosures

All authors: No reported disclosures.

Defining hip fracture with claims data: outpatient and provider claims matter

Medicare claims are commonly used to identify hip fractures, but there is no universally accepted definition. We found that a definition using inpatient claims identified fewer fractures than a definition including outpatient and provider claims. Few additional fractures were identified by including inconsistent diagnostic and procedural codes at contiguous sites.

INTRODUCTION

Medicare claims data is commonly used in research studies to identify hip fractures, but there is no universally accepted definition of fracture. Our purpose was to describe potential misclassification when hip fractures are defined using Medicare Part A (inpatient) claims without considering Part B (outpatient and provider) claims and when inconsistent diagnostic and procedural codes occur at contiguous fracture sites (e.g., femoral shaft or pelvic).

METHODS

Participants included all long-stay nursing home residents enrolled in Medicare Parts A and B fee-for-service between 1/1/2008 and 12/31/2009 with follow-up through 12/31/2011. We compared the number of hip fractures identified using only Part A claims to (1) Part A plus Part B claims and (2) Part A and Part B claims plus discordant codes at contiguous fracture sites.

RESULTS

Among 1,257,279 long-stay residents, 40,932 (3.2%) met the definition of hip fracture using Part A claims, and 41,687 residents (3.3%) met the definition using Part B claims. 4566 hip fractures identified using Part B claims would not have been captured using Part A claims. An additional 227 hip fractures were identified after considering contiguous fracture sites.

CONCLUSIONS

When ascertaining hip fractures, a definition using outpatient and provider claims identified 11% more fractures than a definition with only inpatient claims. Future studies should publish their definition of fracture and specify if diagnostic codes from contiguous fracture sites were used.

Geographic Variation in Hip Fracture Among United States Long-Stay Nursing Home Residents

INTRODUCTION

Despite high rates of hip fracture among United States (US) nursing home (NH) residents, little is known about geographic variation in hip fracture incidence. We used nationally representative data to identify geographic variation in hip fracture among US NH residents.

DESIGN AND SETTING

Retrospective cohort study using Part A claims for a 100% of Medicare enrollees in 15,289 NHs linked to NH minimum data set and Online Survey, Certification, and Reporting databases.

PARTICIPANTS

A total of 891,085 long-stay (continuous residence of ≥100 days) NH residents ≥65 years old.

MEASUREMENTS

Medicare Part A claims documenting a hip fracture. Mean incidence rates of hip fracture for long-stay NH residents were calculated for each state and US Census Division from 2007 to 2010.

RESULTS

The age-, sex-, and race-adjusted incidence rate of hip fracture ranged from 1.49 hip fractures/100 person-years (Hawaii) to 3.60 hip fractures/100 person-years (New Mexico), with a mean of 2.38 (standard deviation 0.43) hip fractures/100 person-years. The mean incidence of hip fracture was 1.7-fold greater in the highest quintile than the lowest.

CONCLUSIONS

We observed modest US state and regional variation in hip fracture incidence among long-stay NH residents. Future studies should assess whether state policies or NH characteristics explain the variation.