Use of menopausal hormone therapy beyond age 65 years and its effects on women's health outcomes by types, routes, and doses.

OBJECTIVES: The study aims to assess the use of menopausal hormone therapy beyond age 65 years and its health implications by types of estrogen/progestogen, routes of administration, and dose strengths. METHODS: Using prescription drug and encounter records of 10 million senior Medicare women from 2007-2020 and Cox regression analyses adjusted for time-varying characteristics of the women, we examined the effects of different preparations of menopausal hormone therapy on all-cause mortality, five cancers, six cardiovascular diseases, and dementia. RESULTS: Compared with never use or discontinuation of menopausal hormone therapy after age 65 years, the use of estrogen monotherapy beyond age 65 years was associated with significant risk reductions in mortality (19% or adjusted hazards ratio, 0.81; 95% CI, 0.79-0.82), breast cancer (16%), lung cancer (13%), colorectal cancer (12%), congestive heart failure (CHF) (5%), venous thromboembolism (3%), atrial fibrillation (4%), acute myocardial infarction (11%), and dementia (2%). For the use of estrogen and progestogen combo-therapy, both E+ progestin and E+ progesterone were associated with increased risk of breast cancer by 10%-19%, but such risk can be mitigated using low dose of transdermal or vaginal E+ progestin. Moreover, E+ progestin exhibited significant risk reductions in endometrial cancer (45% or adjusted hazards ratio, 0.55; 95% CI, 0.50-0.60), ovarian cancer (21%), ischemic heart disease (5%), CHF (5%), and venous thromboembolism (5%), whereas E+ progesterone exhibited risk reduction only in CHF (4%). CONCLUSIONS: Among senior Medicare women, the implications of menopausal hormone therapy use beyond age 65 years vary by types, routes, and strengths. In general, risk reductions appear to be greater with low rather than medium or high doses, vaginal or transdermal rather than oral preparations, and with E2 rather than conjugated estrogen.

Trends in Racial Disparities in Healthcare Expenditures Among Senior Medicare Fee-for-service Enrollees in 2007-2020.

Despite the universal healthcare coverages, racial disparities in healthcare expenditures among senior Medicare beneficiaries exist. A few studies explored how racial disparities in healthcare expenditures changed over past decades and how it affected differently across 4 minoritized races, by type of Medicare and poverty levels. We conducted a longitudinal study of 21 healthcare expenditures from senior Medicare fee-for-service enrollees to determine overall and secular trends in racial disparities in healthcare expenditures between 2007 and 2020, during which the Affordable Care Act (ACA) came into full force and the COVID-19 pandemic had begun. We found important disparities in healthcare expenditures across 4 minoritized races compared to Whites, even after adjusting for possible confounders for such disparities. Disparities between Hispanics/Asians and Whites were much greater than disparities between Blacks and Whites, in all Parts A, B, and D expenditures. This reality has not been sufficiently emphasized in the literature. Importantly, Black-White disparities in total Part B expenditure gradually worsened between 2007 and 2020, and Hispanic-White and Asian-White disparities worsened greatly during that time window. Health planners need to focus on these large disparities and develop methods to shrink them.

Nirmatrelvir and Molnupiravir and Post-COVID-19 Condition in Older Patients.

This observational cohort study assesses the occurrence of post­COVID-19 condition symptoms in Medicare enrollees prescribed nirmatrelvir and molnupiravir.

Prevalence and characteristics of long COVID in elderly patients: An observational cohort study of over 2 million adults in the US.

BACKGROUND: Incidence of long COVID in the elderly is difficult to estimate and can be underreported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call "long Flu." In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients. METHODS AND FINDINGS: This is a cohort study of Medicare (the US federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza, and residual symptoms. Long COVID was identified by (a) the designated long COVID code B94.8 (code-based definition), or (b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from 1 to 3 months post-infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in 4 outcome measures: (a) hospitalization (any cause); (b) hospitalization (for long COVID symptom); (c) emergency department (ED) visit (for long COVID symptom); and (d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients, respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell, and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) versus 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05 to 1.08, p < 0.001)), and more outpatient visits than long Flu outpatients (mean 2.9(SD 3.4) versus 2.5(SD 2.7) visits, incidence rate ratio 1.09 (95% CI 1.08 to 1.10, p < 0.001)). There were less ED visits in long COVID patients, probably because of reduction in ED usage during the pandemic. The main limitation of our study is that the diagnosis of long COVID in is not independently verified. CONCLUSIONS: Relying on specific long COVID diagnostic codes results in significant underreporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu.

Mis-mappings between a producer's quantitative test codes and LOINC codes and an algorithm for correcting them.

OBJECTIVES: To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems. MATERIALS AND METHODS: We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term. RESULTS: Of included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name. DISCUSSIONS: Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further. CONCLUSIONS: Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients.

BACKGROUND: Maintenance drugs are used to treat chronic conditions. Several classes of maintenance drugs have attracted attention because of their potential to affect susceptibility to and severity of COVID-19. METHODS: Using claims data on 20% random sample of Part D Medicare enrollees from April to December 2020, we identified patients diagnosed with COVID-19. Using a nested case-control design, non-COVID-19 controls were identified by 1:5 matching on age, race, sex, dual-eligibility status, and geographical region. We identified usage of angiotensin-converting enzyme inhibitors (ACEI), angiotensin-receptor blockers (ARB), statins, warfarin, direct factor Xa inhibitors, P2Y12 inhibitors, famotidine and hydroxychloroquine based on Medicare prescription claims data. Using extended Cox regression models with time-varying propensity score adjustment we examined the independent effect of each study drug on contracting COVID-19. For severity of COVID-19, we performed extended Cox regressions on all COVID-19 patients, using COVID-19-related hospitalization and all-cause mortality as outcomes. Covariates included gender, age, race, geographic region, low-income indicator, and co-morbidities. To compensate for indication bias related to the use of hydroxychloroquine for the prophylaxis or treatment of COVID-19, we censored patients who only started on hydroxychloroquine in 2020. RESULTS: Up to December 2020, our sample contained 374,229 Medicare patients over 65 who were diagnosed with COVID-19. Among the COVID-19 patients, 278,912 (74.6%) were on at least one study drug. The three most common study drugs among COVID-19 patients were statins 187,374 (50.1%), ACEI 97,843 (26.2%) and ARB 83,290 (22.3%). For all three outcomes (diagnosis, hospitalization and death), current users of ACEI, ARB, statins, warfarin, direct factor Xa inhibitors and P2Y12 inhibitors were associated with reduced risks, compared to never users. Famotidine did not show consistent significant effects. Hydroxychloroquine did not show significant effects after censoring of recent starters. CONCLUSION: Maintenance use of ACEI, ARB, warfarin, statins, direct factor Xa inhibitors and P2Y12 inhibitors was associated with reduction in risk of acquiring COVID-19 and dying from it.

The Mortality Risk of Proton Pump Inhibitors in 1.9 Million US Seniors: An Extended Cox Survival Analysis.

BACKGROUND & AIMS: Observational studies have linked proton pump inhibitors (PPIs) with increased risk of mortality and other safety outcomes, in contradiction with a recent PPI randomized controlled trial (RCT). Observational studies may be prone to reverse causality, where deaths are attributed to the treatment rather than the conditions that are treated (protopathic bias). METHODS: We analyzed an incident drug user cohort of 1,930,728 elderly Medicare fee-for-service beneficiaries to evaluate the PPI-associated risk of death with a Cox regression analysis with time-varying covariates and propensity score adjustments. To correct for protopathic bias which occurs when a given drug is associated with prodromal signs of death, we implemented a lag-time approach by which any study drug taken during a 90-day look-back window before each death was disregarded. RESULTS: Among 1,930,728 study individuals, 80,972 (4.2%) died during a median 3.8 years of follow-up, yielding an overall unadjusted death rate/1000 person-years of 9.85; 14.31 for PPI users and 7.93 for non- users. With no lag-time, PPI use (vs no use) was associated with 10% increased mortality risk (adjusted HR=1.10; 95% CI 1.08-1.12). However, with a lag-time of 90 days, mortality risk associated with PPI use was near zero (adjusted HR=1.01; 95% CI 0.99-1.02). CONCLUSION: Given the usage patterns of PPIs in patients with conditions that may presage death, protopathic bias may explain the association of PPIs with increased risk of death reported in observational studies.

Independent effects of 15 commonly prescribed drugs on all-cause mortality among US elderly patients with type 2 diabetes mellitus.

OBJECTIVE: Most patients with type 2 diabetes mellitus (T2DM) also have hypertension and hyperlipidemia. Consequently, they are taking medications for all three conditions concurrently and the effect of one drug could be confounded with that of another. This study aimed to determine the independent effects of 15 commonly prescribed medications for three conditions on the risk of all-cause mortality among elderly patients with T2DM. RESEARCH DESIGN AND METHODS: A cohort of 360 437 elderly patients with T2DM from 2007 to 2016 US Medicare data was traced along with cumulative uses of 8 diabetes, 6 hypertension and 1 hyperlipidemia drugs. The relative risk of all-cause mortality for each study drug was estimated using an extended Cox regression analysis adjusting for the concurrent use of other study drugs. RESULTS: Compared with the no use of each study medication, mortality risk declined with use of 3 diabetes drugs, sodium-glucose cotransporter-2 inhibitors (HR=0.73; 95% CI 0.64 to 0.84), glucagon-like peptide-1 receptor agonists (HR=0.75; 95% CI 0.70 to 0.80) and dipeptidyl peptidase-4 inhibitors (HR=0.94; 95% CI 0.91 to 0.98), the use of 3 blood pressure medications, diuretics (HR=0.89; 95% CI 0.87 to 0.92), angiotensin receptor blockers (HR=0.86; 95% CI 0.84 to 0.89), ACE inhibitors (HR=0.98; 95% CI 0.95 to 1.01) as well as statins (HR=0.83; 95% CI 0.80 to 0.85). It increased moderately with insulin (HR=1.55; 95% CI 1.51 to 1.59), sulfonylureas (HR=1.16; 95% CI 1.13 to 1.20), a small inconsistent amount with metformin (HR=1.05), beta-blockers (HR=1.07), dihydropyridine calcium-channel blockers (HR=0.99) and non-dihydropyridine calcium-channel blockers (HR=1.05). The use of thiazolidinedione had no effect. CONCLUSION: Among older patients with diabetes, mortality risk decreased importantly with three new diabetes drugs, 3 blood pressure drugs and statins. It increased moderately with sulfonylurea and insulin. Studies of aggressive use of new T2DM drugs instead of sulfonylureas and insulin are needed. Our statin results empirically validate two national guidelines for using statins in older patients with diabetes. However, 23% of study patients never took a statin, suggesting missed opportunities for prevention.

A method for prioritizing risk groups for early SARS-CoV-2 Vaccination, By the Numbers.

Background: Given the limited supply of two COVID-19 vaccines, it will be important to choose which risk groups to prioritize for vaccination in order to get the most health benefits from that supply. Method: In order to help decide how to get the maximum health yield from this limited supply, we implemented a logistic regression model to predict COVID-19 death risk by age, race, and sex and did the same to predict COVID-19 case risk. Results: Our predictive model ranked all demographic groups by COVID-19 death risk. It was highly concentrated in some demographic groups, e.g. 85+ year old Black, Non-Hispanic patients suffered 1,953 deaths per 100,000. If we vaccinated the 17 demographic groups at highest COVID-19 death ranked by our logistic model, it would require only 3.7% of the vaccine supply needed to vaccinate all the United States, and yet prevent 47% of COVID-19 deaths. Nursing home residents had a higher COVID-19 death risk at 5,200 deaths/100,000, more than our highest demographic risk group. Risk of prison residents and health care workers (HCW) were lower than that of our demographic groups with the highest risks.We saw much less concentration of COVID-19 case risk in any demographic groups compared to the high concentration of COVID-19 death in some such groups. We should prioritize vaccinations with the goal of reducing deaths, not cases, while the vaccine supply is low. Conclusion: SARS-CoV-2 vaccines protect against severe COVID-19 infection and thus against COVID-19 death per vaccine studies. Allocating at least some of the early vaccine supplies to high risk demographic groups could maximize lives saved. Our model, and the risk estimate it produced, could help states define their vaccine allocation rules.

Analysis of Healthcare Cost and Utilization in the First Two Years of the Medicare Shared Savings Program Using Big Data from the CMS Enclave.

The Medicare Shared Savings Program (MSSP) is the larger of the first two Accountable Care Organization (ACO) programs by the Centers for Medicare and Medicaid Services (CMS). In this study we assessed healthcare cost and utilization of 1.71 million Medicare beneficiaries assigned to the 333 MSSP ACOs in the calendar years of 2013 and 2014, in comparison to years 2010 and 2011, using the official CMS data. We employed doubly robust estimation (propensity score weighting followed by generalized linear regression) to adjust the analyses to beneficiary personal traits, history of chronic conditions, previous healthcare utilization, ACO administrative region, and ZIP code socioeconomic factors. In comparison to the care delivered to the control cohort of 17.7 million non-ACO beneficiaries, we found that the care patterns for ACO beneficiaries shifted away from some costly types of care, but at the expense of increased utilization of other types, increased imaging and testing expenditures, and increased medication use, with overall net greater increase in cost instead of smaller increase.