Female Rockville 0411 499

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Try out PMC Labs and tell us what you think. Learn More. To test for the presence and nature of any gender differences in quality of care across MA Plans, overall and by domain; to identify those most at risk of poor care. For each measure, we assess variation in gender gaps and their correlation with plan performance. Men experienced better quality on nine measures, including four related to cardiovascular disease and three to potentially harmful drug-disease interactions. Women generally experienced better quality of care than men. However, women experienced poorer care for cardiovascular disease-related intermediate outcomes and potentially harmful drug-disease interactions.

Quality improvement may be especially important for men in low-performing plans and for cardiovascular-related care and drug-disease interactions for women.

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Gender-stratified reporting could reveal gender gaps, identify plans for which care varies by gender, and motivate efforts to address faults and close the gaps in the delivery system. A small body of research suggests unexplained differences in the quality of health care received by women and men.

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Less clear is whether this advantage extends to other aspects of care or intermediate outcomes. Gender gaps in care for seniors could have substantial health and cost consequences due to high prevalence of comorbidities which may confound prevention and treatmenthigher socioeconomic vulnerability than the general population, and higher mortality risk. We focus on three questions. First, how did performance scores differ by gender among MA beneficiaries? Consistent gaps across measures would indicate a need to improve overall care for one gender.

Alternatively, gaps that differ by measure would suggest focus on the care of one gender in those areas. These patterns would warrant different quality improvement strategies to close observed gaps. For example, if women are generally more internally motivated to seek care, men might especially benefit from nudges that help those who are less activated.

Second, did the MA plans that provided the highest quality care for women also tend to do so for men? Here, we ask whether there is a plan-level gender gap that varies among plans. If the gap varies little, both women and men can use plan performance scores as an accurate quality indicator for their gender.

If gender gaps vary substantially, gender-specific performance reporting might better inform MA beneficiaries about the plans that would offer them the best care. Third, if gender gaps in care differ by plan, are gaps more favorable to one gender where quality of care is high? Determining whether and how care falls short for one or both genders could inform interventions to improve overall quality and to address gender gaps. HEDIS data is collected by the Centers for Medicare and Medicaid Services CMS and consists of health care process measures and intermediate outcome measures based on individual-level administrative data, supplemented in some cases by information from medical records.

An MA contract, hereafter called a planis a set of offerings or benefit packages from a single sponsor, usually in a specific geographic area. The analytic sample includes We reverse-coded 3 measures of drug-disease interaction prevalence so that a higher score corresponds to better care for all measures. Thus, a higher value reflects better quality of care for all measures examined. To address our first research question, we estimate female-male differences both within plan and overall.

We fit two-level binomial mixed-effect models 21 using individual-level HEDIS scores as outcomes, fixed effects for gender, random plan intercepts, and random plan slopes for female-male; these models for clustering of patients within contracts. This approach reduces the likelihood that any apparent convergence of quality of care by gender at high or low levels of quality of care reflects a mere ceiling or floor effect. Because the official scoring specifications for HEDIS measures do not involve case-mix adjustment, no other covariates were included.

We employ the same models to address the Female Rockville 0411 499 and third research questions by calculating the informativeness of gender-specific plan scores. Conceptually, a measure is informative if the gender gap here the female minus male difference within a plan varies from plan to plan, and the best plan for men is not necessarily the best plan for women. More formally, informativeness is the proportion of variance in plan scores for one gender that cannot be predicted from the overall plan scores.

are from a series of binary mixed-effect regression models predicting HEDIS outcomes from a female fixed effect, random plan intercepts, and random plan slopes for female-male differences. It is calculated as 1- R 2 where R is the disattenuated correlation of plan scores by gender, calculated as the plan intercept variance component divided by the sum of the plan intercept variance component and the plan-level random slope by gender variance component. These variance components are derived from the mixed effects regression models described above. To illustrate the correlations between plan performance and plan gender gap graphically for each HEDIS measure, we classified plan into quintiles based on their performance score on that measure.

Of the 9 measures favoring men, 4 relate to cardiovascular disease and 3 to potentially harmful drug-disease interactions. When calculated within plans, 2 ORs were no longer statistically ificant 1 favoring men and the OR Female Rockville 0411 499 1 measure reversed from favoring women to favoring men.

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Of these measures, 15 have informativeness above 0. For these 15 measures, the best plans for men and women may differ meaningfully. Specifically, relative to women in low-performing plans, men in low-performing plans had even lower scores for these 15 measures than would otherwise be expected. On these 8 measures, women had higher performance scores than men. The correlation between gender differences and overall plan performance was positive and ificant for 2 measures, 1 of which favored men and the other of which had no average gender gap within plans.

Thus, for these 2 measures, the gap is more favorable to men in plans with higher scores. Diabetic women were also more likely than diabetic men to receive an eye exam ificant OR of 1. However, we did not find evidence that this gender gap was correlated with overall plan performance; it was not likely to be larger or smaller in low-performing plans. Thus, for all 4 of these measures, while quality of care increases for both genders across quintiles of care quality, the relative gains are greater for men than women, which in a smaller gender disparity in the highest-performing quintile.

Although the plan-level correspondence between plan scores for women and men was generally high, the best-performing plan for women was not high-performing for men in some cases. In the two areas with gaps favoring men, intermediate outcome measures related to control of cardiovascular risk factors and treatment measures regarding potentially harmful drug-disease interactions, point to aspects of health care which might place women at increased risk of poorer quality of care.

Mosca and colleagues found that providers were more likely to as women with intermediate cardiovascular risk as assessed by the Framingham Risk Score to a lower risk category than men with identical risk factors.

We also found that the gender gap is often larger in plans Female Rockville 0411 499 perform poorly overall. On some measures for which women score higher than men, the difference tends to be smaller for high-performing plans. On measures for which men score higher than women, the gender gap typically varies little across plans.

The findings suggest that stratifying quality assessments by gender could identify plans where either women or men are receiving worse care than expected based on what is known about plan performance. Gender-based quality reporting may also motivate quality improvement efforts for the lagging group.

Provider groups and plans may need to coordinate to improve services. Even small unexplained disparities in performance by gender al faults in the delivery system that should be addressed. By analogy, very few planes crash and cause passenger deaths, but when they do, they present an opportunity to examine factors that contribute and create remedies that reduce future crashes and prevent other problems.

Although gender differences in quality-of-care measures were often less than five percentage points, they reflect the care of millions of seniors. Therefore, a substantial of Female Rockville 0411 499 might benefit if gender gaps were closed through improvement for lagging groups.

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For example, Gender differences, which are nearly constant across plans for some measures, suggest an opportunity to make gender gaps visible to plans, providers, and older adults and their families so that they will take actions to improve care and reduce gender disparities in quality. They can also use payment incentives and contracting requirements to motivate improvement. Addressing gender gaps among MA beneficiaries might also drive efforts to address gaps in employer-sponsored care which in turn could lead to improved health trajectories of older women and men as well as Medicare cost savings.

This study has several limitations. We can only speculate about the underlying causes of these gender differences. Although we lacked information on patient health and comorbidities, these quality measures refer specifically to care for which there is clinical consensus that it is indicated for the entire population for which it is assessed; the denominator specification for each measure includes only beneficiaries who meet the eligibility criteria for the service specified by the measure numerator.

These limitations notwithstanding, these have important clinical implications. HEDIS measures offer evidence-based standards of care for which there is general agreement. The observed gender disparities could result in adverse outcomes for men across the wide range of measures for which they experienced worse care and adverse outcomes for women in the areas of cardiovascular care and potentially harmful drug-disease interactions.

Further research is needed to assess Female Rockville 0411 499 underlying causes of gender-specific gaps in system performance and associated opportunities to improve care and outcomes. We would like to thank Fergal McCarthy, M. Chloe E. Bird does not have a conflict of interest. Marc N. Elliott does not have a conflict of interest.

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John L. Adams does not have a conflict of interest. Eric C. Schneider does not have a conflict of interest. David J. Klein does not have a conflict of interest.

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Jacob W. Dembosky does not have a conflict of interest. Sarah Gaillot does not have a conflict of interest, although please note Sarah is an employee of the sponsoring agency, Centers for Medicare and Medicaid Services. Allen M. Fremont does not have a conflict of interest.

Amelia M. Haviland does not have a conflict of interest. National Center for Biotechnology InformationU.

Female Rockville 0411 499

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