An alternative model of maternity care for low-risk birth: Maternal and neonatal outcomes utilizing the midwifery-based birth center model.

OBJECTIVE: To assess key birth outcomes in an alternative maternity care model, midwifery-based birth center care. DATA SOURCES: The American Association of Birth Centers Perinatal Data Registry and birth certificate files, using national data collected from 2009 to 2019. STUDY DESIGN: This observational cohort study compared key clinical birth outcomes of women at low risk for perinatal complications, comparing those who received care in the midwifery-based birth center model versus hospital-based usual care. Linear regression analysis was used to assess key clinical outcomes in the midwifery-based group as compared with hospital-based usual care. The hospital-based group was selected using nearest neighbor matching, and the primary linear regressions were weighted using propensity score weights (PSWs). The key clinical outcomes considered were cesarean delivery, low birth weight, neonatal intensive care unit admission, breastfeeding, and neonatal death. We performed sensitivity analyses using inverse probability weights and entropy balancing weights. We also assessed the remaining role of omitted variable bias using a bounding methodology. DATA COLLECTION: Women aged 16-45 with low-risk pregnancies, defined as a singleton fetus and no record of hypertension or cesarean section, were included. The sample was selected for records that overlapped in each year and state. Counties were included if there were at least 50 midwifery-based birth center births and 300 total births. After matching, the sample size of the birth center cohort was 85,842 and the hospital-based cohort was 261,439. PRINCIPAL FINDINGS: Women receiving midwifery-based birth center care experienced lower rates of cesarean section (-12.2 percentage points, p < 0.001), low birth weight (-3.2 percentage points, p < 0.001), NICU admission (-5.5 percentage points, p < 0.001), neonatal death (-0.1 percentage points, p < 0.001), and higher rates of breastfeeding (9.3 percentage points, p < 0.001). CONCLUSIONS: This analysis supports midwifery-based birth center care as a high-quality model that delivers optimal outcomes for low-risk maternal/newborn dyads.

Analysis of Affordable Health Care.

BACKGROUND: Lack of affordable health care affects the uninsured, commercially insured, and Medicare beneficiaries. Yet, the wide variation in providers' prices and practice styles suggests that more affordable care already may be available and data on low value and wasteful care suggest that lower cost care need not come at the expense of better quality. Although price variation has received the most attention in the literature and legislation, total cost of care is a function of both unit prices (fees) and the quantity of services. OBJECTIVE: To partition provider-specific variation in total annual risk-adjusted per capita expenditures on health care services into variation in unit prices (fees) versus quantities of services, and to explore the relationship between low value, avoidable, discretionary, and recommended care to total health expenditures. The analysis is important because both prices and quantities of services can affect affordability and reductions in prices versus quantities have very different effects on providers' profits. SETTING: 2018 data from the Minnesota State Employees Group Insurance Program (SEGIP) that offers a tiered cost-sharing health insurance benefit design to 130,000 State employees and their dependents (SEGIP "members"). EXPOSURE: Each year during open enrollment, SEGIP members choose a primary care clinic (PCC). The PCC can make decisions regarding both unit prices and prescribed services. PCCs are placed in one of four cost-sharing tiers based on the total annual risk-adjusted per capita health expenditures for the SEGIP members who choose their clinic. Members choosing higher cost PCCs face higher deductibles, copayments, and maximum out-of-pocket spending limits. MEASURES: Overall prices and use of inpatient, outpatient hospital, professional, and pharmaceutical services, total and avoidable use of emergency department visits and inpatient admissions, low value care, testing for patients with pneumonia, and recommended preventive care. RESULTS: Differences in total risk-adjusted annual per capita health expenditures across the care systems were substantial. Higher cost providers had both higher unit prices and higher use of services. Variation in the quantity of health care services explained more of the variance in total spending than variation in prices. Prices for professional services and use of inpatient, outpatient hospital, and pharmaceutical services, and ambulatory care sensitive admissions, contributed significantly to high total expenditures. Lower cost PCCs in the lowest cost-sharing tier had higher rates of low value care and lower emergency department visits per capita. Neither the number of investigations for patients with pneumonia nor the receipt of recommended mammography screening varied systematically by tier. CONCLUSIONS: Efforts to identify and expand sources of affordable care, including improved information and incentives for consumers, need to account for variation in both prices and quantities of services. Efforts to encourage more efficient use of health care services by providers need to consider the effect of those efforts on the provider's internal costs and thus their profits.

Return to work after deceased donor kidney transplant under the kidney allocation system.

BACKGROUND: The Kidney Allocation System (KAS) includes a scoring system to match transplant candidate life expectancy with expected longevity of the donor kidney, and a backdating policy that gives waitlist time credit to patients waitlisted after starting dialysis treatment (post-dialysis). We estimated the effect of the KAS on employment among patient subgroups targeted by the policy. METHODS: We used a sample selection model to compare employment after transplant before and after KAS implementation among patients on the kidney-only transplant waitlist between December 4, 2011 and December 31, 2017. RESULTS: Post-dialysis transplant recipients aged 18-49 were significantly more likely to be employed 1-year post transplant in the post-KAS era compared to the pre-KAS era. Transplant recipients aged 35-64 with no dialysis treatment were significantly less likely to be employed 1 year after transplant in the post-KAS era compared to the pre-KAS era. CONCLUSIONS: This study provides the first assessment of employment after DDKT under the KAS and provides important information about both the methods used to measure employment after transplant and the outcome under the KAS. Changes in employment after DDKT among various patient subgroups have important implications for assessing long-term patient and societal effects of the KAS and organ allocation policy.

Fee-for-service payment is not the (main) problem.

OBJECTIVE: To understand the effect of physician payment incentives on the allocation of health care resources. DATA SOURCES/STUDY SETTING: Review and analysis of the literature on physician payment incentives. STUDY DESIGN: Analysis of current physician payment incentives and several ways to modify those incentives to encourage increased efficiency. PRINCIPAL FINDINGS: Fee-for-service payments can be incorporated into systems that encourage efficient pricing - prices that are close to the provider's marginal cost - by giving consumers information on provider-specific prices and a strong incentive to choose lower cost providers. However, efficient pricing of services ultimately will need to be supplemented by incentives for efficient production of health and functional status. CONCLUSIONS: The problem with current FFS payment is not paying a fee for each service, per se, but the way in which the fees are determined.

Cost-sharing payments for out-of-network care in commercially insured adults.

OBJECTIVES: Providers who do not contract with insurance plans are considered out-of-network (OON) providers. There were 2 objectives in this study: (1) to examine the variations of OON cost sharing, both at the state level and by care settings, and (2) to investigate the pattern of OON care use and cost sharing associated with OON care over time. STUDY DESIGN: Secondary data analysis using claims data of employer-sponsored insurance enrollees. METHODS: The study sample included adults aged 18 to 64 years who were continuously enrolled for at least a full calendar year with medical and prescription drug coverage and for whom OON care payment data were available. We examined levels and distributions of cost sharing for OON care from 2012 to 2017, in both emergency department (ED) and non-ED care settings. Outcome measures included annual use of health plan-covered OON care and total out-of-pocket (OOP) cost sharing for OON care. We also measured the use of and cost-sharing spending for OON care based on urgency and site of service. Logistic regression models were constructed to estimate the probability of OON care. Among those with each type of OON care, a generalized linear regression model was used to estimate the OOP spending on OON care. RESULTS: Slowly decreasing rates of OON care over time occurred in different care settings and at different urgency levels. The cost-sharing amounts for OON care rose rapidly from 2012 through 2016, before slowing slightly in 2017. The growth of cost sharing for OON care during nonemergent hospitalizations especially increased from $671 to $1286 during the study period. The amount enrollees spent on OON care grew in most states, but there were substantial variations. CONCLUSIONS: Cost-sharing payments for OON care represent a growing financial burden for some enrollees. Consumers should be held harmless from higher cost sharing for OON care when it occurs without their knowledge or consent. Further, health plan network adequacy may also merit closer scrutiny. Leveraging provider participation in narrow networks must be balanced with broader consumer protections.

Health Care Service Use Among Elderly Seasonal Migrators.

Elderly seasonal migrators share time between homes in different states, presenting challenges for care coordination and patient attribution methods. Medicare has prioritized alternative payment models, putting health care providers at risk for quality and value of services delivered to their attributed patients, regardless of the location of care. Little research is available to guide providers and payers on the service use of seasonal migrators. The authors use claims data on fee-for-service (FFS) Medicare beneficiaries' locations throughout the year to (1) identify seasonal migrators and (2) describe the care they receive in each seasonal home, focusing on primary care and emergency department (ED) visits and the relationships between the two. In all, 5.5% of the Medicare aged FFS population were identified as seasonal migrators, with 4.1% following the traditional snowbird pattern of migration, spending warm months in the north and cold months in the south. Migrators had higher rates of ED visits and primary care treatable (PCT) ED visits than the nonmigratory groups, controlling for location, age, race, sex, Medicaid status, season, and comorbidities. They also had more visits with specialist physicians, more days with outpatient services, and more days seeing a physician in any setting. Having local primary care strongly reduced rates of both PCT ED visits and total ED visits for all migration categories, with the greatest reduction seen in PCT ED visits by migrators (local primary care was associated with a 58% reduction in PCT ED visits by snowbirds and a 65% reduction in PCT ED visits by other migrators).

Log Odds and the Interpretation of Logit Models.

OBJECTIVE: We discuss how to interpret coefficients from logit models, focusing on the importance of the standard deviation (σ) of the error term to that interpretation. STUDY DESIGN: We show how odds ratios are computed, how they depend on the standard deviation (σ) of the error term, and their sensitivity to different model specifications. We also discuss alternatives to odds ratios. PRINCIPAL FINDINGS: There is no single odds ratio; instead, any estimated odds ratio is conditional on the data and the model specification. Odds ratios should not be compared across different studies using different samples from different populations. Nor should they be compared across models with different sets of explanatory variables. CONCLUSIONS: To communicate information regarding the effect of explanatory variables on binary {0,1} dependent variables, average marginal effects are generally preferable to odds ratios, unless the data are from a case-control study.

Decomposition of moral hazard.

This study seeks to simulate the portion of moral hazard that is due to the income transfer contained in the coinsurance price reduction. Healthcare spending of uninsured individuals from the MEPS with a priority health condition is compared with the predicted counterfactual spending of those same individuals if they were insured with either (1) a conventional policy that paid off with a coinsurance rate or (2) a contingent claims policy that paid off by a lump sum payment upon becoming ill. The lump sum payment is set to be equal to the insurer's predicted spending under the coinsurance policy. The proportion of moral hazard that is efficient is calculated as the proportion of total moral hazard that is generated by this lump sum payment. We find that the efficient proportion of moral hazard varies from disease to disease, but is the highest for those with diabetes and cancer.

Texas Medicaid Payment Reform: Fewer Early Elective Deliveries And Increased Gestational Age And Birthweight.

Infants born at full term have better health outcomes. However, one in ten babies in the United States are born via a medically unnecessary early elective delivery: induction of labor, a cesarean section, or both before thirty-nine weeks gestation. In 2011 the Texas Medicaid program sought to reduce the rate of early elective deliveries by denying payment to providers for the procedure. We examined the impact of this policy on clinical care practice and perinatal outcomes by comparing the changes in Texas relative to comparison states. We found that early elective delivery rates fell by as much as 14 percent in Texas after this payment policy change, which led to gains of almost five days in gestational age and six ounces in birthweight among births affected by the policy. The impact on early elective delivery was larger in magnitude for minority patients. Other states may look to this Medicaid payment reform as a model for reducing early elective deliveries and disparities in infant health.

Affordable Care Act's Mandate Eliminating Contraceptive Cost Sharing Influenced Choices Of Women With Employer Coverage.

Patient cost sharing for contraceptive prescriptions was eliminated for certain insurance plans as part of the Affordable Care Act. We examined the impact of this change on women's patterns of choosing prescription contraceptive methods. Using claims data for a sample of midwestern women ages 18-46 with employer-sponsored coverage, we examined the contraceptive choices made by women in employer groups whose coverage complied with the mandate, compared to the choices of women in groups whose coverage did not comply. We found that the reduction in cost sharing was associated with a 2.3-percentage-point increase in the choice of any prescription contraceptive, relative to the 30 percent rate of choosing prescription contraceptives before the change in cost sharing. A disproportionate share of this increase came from increased selection of long-term contraception methods. Thus, the removal of cost as a barrier seems to be an important factor in contraceptive choice, and our findings about long-term methods may have implications for rates of unintended pregnancy that require further study.

Validation of an algorithm to determine the primary care treatability of emergency department visits.

OBJECTIVES: We propose a new claims-computable measure of the primary care treatability of emergency department (ED) visits and validate it using a nationally representative sample of Medicare data. STUDY DESIGN AND SETTING: This is a validation study using 2011-2012 Medicare claims data for a nationally representative 5% sample of fee-for-service beneficiaries to compare the new measure's performance to the Ballard variant of the Billings algorithm in predicting hospitalisation and death following an ED visit. OUTCOMES: Hospitalisation within 1 day or 1 week of an ED visit; death within 1 week or 1 month of an ED visit. RESULTS: The Minnesota algorithm is a strong predictor of hospitalisations and deaths, with performance similar to or better than the most commonly used existing algorithm to assess the severity of ED visits. The Billings/Ballard algorithm is a better predictor of death within 1 week of an ED visit; this finding is entirely driven by a small number of ED visits where patients appear to have been dead on arrival. CONCLUSIONS: The procedure-based approach of the Minnesota algorithm allows researchers to use the clinical judgement of the ED physician, who saw the patient to determine the likely severity of each visit. The Minnesota algorithm may thus provide a useful tool for investigating ED use in Medicare beneficiaries.

PQRS Participation, Inappropriate Utilization of Health Care Services, and Medicare Expenditures.

Medicare's Physician Quality Reporting System (PQRS) is the largest quality-reporting system in the U.S. health care system and a basis for the new value-based modifier system for physician payment. The PQRS allows health care providers to report measures of quality of care that include both the process of care and physiological outcomes. Using a multivariate difference-in-differences model, we examine the relationship of PQRS participation to three claims-computable measures of inappropriate utilization of health care services and risk-adjusted per capita Medicare expenditures. The data are a national random sample of PQRS-participating providers matched to nonparticipating providers by zip code and caseload. We found few significant relationships in the overall analysis. However, the magnitude and statistical significance of the desirable associations increased in subgroups of providers and beneficiaries more prone to overutilization (e.g., males, older beneficiaries, beneficiaries treated in larger medical practices or by nonphysicians, and practices in rural areas), and among beneficiaries with heart conditions, diabetes, and eye problems.

Estimating True Resource Costs of Outpatient Care for Medicare Beneficiaries: Standardized Costs versus Medicare Payments and Charges.

OBJECTIVE: To compare standardized estimates of the true resource costs of outpatient health care to the allowable and billed charges for that care among Medicare Fee for Service (FFS) beneficiaries. DATA SOURCES/STUDY SETTING: Medicare Carrier and Outpatient Standard Analytic (SAF) files linked to participant data in the Study of Osteoporotic Fractures from 2004 through 2010. Participants were 3,435 female Medicare Fee for Service enrollees age 80 and older recruited in one rural and three metropolitan areas of the United States. STUDY DESIGN: We estimated standardized costs for Carrier and OP-SAF claims using Medicare payment weights, and compared them to allowable and billed charges for those claims. We used semilog linear regression to estimate the associations of age, race, bone mineral density, prior fracture, and geriatric depression scale score with allowable charges, billed charges, and standardized costs. RESULTS: Estimated associations of patient characteristics with standardized costs were not statistically different than the associations with allowable charges (chi-squared [χ(2)]: 8.6, p = .13) but were different from associations with billed charges (χ(2): 25.5, p < .001). CONCLUSION: Allowable charges for outpatient utilization in the Carrier file and OP-SAF may be good surrogates for standardized costs that reflect patient medical and surgical acuity.

Computation of standard errors.

OBJECTIVES: We discuss the problem of computing the standard errors of functions involving estimated parameters and provide the relevant computer code for three different computational approaches using two popular computer packages. STUDY DESIGN: We show how to compute the standard errors of several functions of interest: the predicted value of the dependent variable for a particular subject, and the effect of a change in an explanatory variable on the predicted value of the dependent variable for an individual subject and average effect for a sample of subjects. EMPIRICAL APPLICATION: Using a publicly available dataset, we explain three different methods of computing standard errors: the delta method, Krinsky­Robb, and bootstrapping. We provide computer code for Stata 12 and LIMDEP 10/NLOGIT 5. CONCLUSIONS: In most applications, choice of the computational method for standard errors of functions of estimated parameters is a matter of convenience. However, when computing standard errors of the sample average of functions that involve both estimated parameters and nonstochastic explanatory variables, it is important to consider the sources of variation in the function's values.