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Healthcare costs and uses are difficult to estimate because their distribution is often skewed by a large fraction near zero. In this article, we show how to estimate and analyze the effects of a natural experiment using two types of nonlinear statistical models: one for health care costs and one for the number of health care use.

We expand on previous studies to examine the impact of the ACA’s youth expansion on three distinct outcomes: total health care spending, office visits, and hospital visits. emergency department. Instead of a single-equation model, a two-part model or a barrier model demonstrates that the ACA policy promoted office visits but reduced emergency department visits and total costs.

**Health care** cost and use statistics often exhibit two distinct statistical characteristics. To begin, their distributions exhibit significant skewness, as seen by empirical densities with long, thin right tails. Second, their distributions have a significant zero point mass.

When modeling such outcomes, particularly in the context of natural experiments, one of which we will use as an example in this article, it is tempting to ignore the skewness and mass at zero and instead estimate linear regression models using ordinary least squares (OLS) or weighted least squares (WLS) (when the data include sampling weights).

However, during the past several decades, new statistical approaches have proliferated that are better suited to outcomes such as health care spending and use. Researchers can now estimate such sophisticated statistical models more quickly than ever before due to advancements in computer power.

Researchers may now interpret estimates from these models in ways that were previously impossible. As a result, we believe that best practice should involve a thorough examination of alternative models that are not constrained by conventional constraints of computation and interpretation.

The impact of a change in insurance policy on health care expenses is estimated and interpreted using OLS using a two-part model. The two-part model is based on a statistical decomposition of the outcome’s density into a zero-generating process and a positive-generating process.

Typically, a logit or probit model estimates the parameters that define the outcome’s threshold between zero and nonzero values. In general, various binary choice model specifications (first portion) provide virtually comparable results. However, the model used to describe the distribution of the result if it is positive (the second portion) is key.

Different models may provide wildly different outcomes. We estimate the factors that determine positive values using a generalized linear model. Generalized linear models naturally accept skewness, provide the researcher with extensive modeling flexibility, and are exceptionally well suited to health care costs.

To determine if the ACA improved health insurance coverage while simultaneously affecting health care spending and use, certain statistics are required. We want data on a large number of representative young American individuals aged 26 years and younger, as well as observations of them in the years before and after the rule change’s implementation in 2010.

We want precise estimates of health care spending and use, as well as thorough assessments of health status and other observable factors associated with spending and utilization. Such data are available in the Medical Expenditure Panel Study (MEPS) (https://meps.ahrq.gov/mepsweb/), a nationwide survey on the funding and use of medical care in the United States. MEPS data have been gathered annually since 1996 by the Agency for Healthcare Research and Quality (AHRQ), a federal government agency in the United States.

The data for these instances are mostly obtained from the Household Component, which comprises information on a random sample of families and people chosen from a nationally representative subsample of homes that participated in the preceding year’s National Health Interview Survey. AHRQ utilizes the MEPS to estimate yearly health care expenditures and use, health status, health insurance coverage, and sources of payment for health services in the United States.

The primary independent variables included in the difference-in-differences analysis are indicators for the treatment and control groups, as well as the pre- and post-treatment periods. We assume that individuals aged 23–25 are at risk of being impacted by the ACA policy and so belong to the treatment group. The control group consists of individuals aged 27–29.

We exclude individuals who are 26 years old due to insufficient coverage throughout the year. We are especially concerned in the ACA’s impact on people under the age of 26, or in the treatment effect on the treated. As a result, we may compare the treatment group (aged 25 and under) to the control group in the years before and after the policy change. The data are dispersed reasonably equally across ages and years.

Typically, modeling health care expenses presents various difficulties due to the dependent variable’s distribution. Health spending statistics for those who utilize health care are often highly skewed. In the United States, a tiny percentage of the population consumes a disproportionate share of overall expenditures.

Berk and Monheit find that 5% of the population is responsible for the bulk of health expenditures and that the significantly right-skewed concentration of health care spending has remained steady over decades. The dependent variable in this study has strongly skewed positive values and may be heteroskedastic.

Although one may theoretically use OLS to model positive values that are skewed, there are more effective options [see Deb et al. For severely skewed data, generalized linear models (GLM) provide a variety of different functional forms that correspond to the connection between the predicted value of the dependent variable and the covariates’ linear index.

GLMs are a more broad kind of linear regression model than conventional linear regression models . The GLM extends the conventional linear regression model by enabling the expectation of the outcome variable to be a function (referred to as the link function) of the linear index of covariates, rather than a simple linear function of the index. For example, expenditure data often fit well with a log link, in which the natural logarithm of the dependent variable’s predicted value is used as the linear index. We compare the log connection to numerous different functional forms.

The number of physician office visits and emergency department visits are expressed as nonnegative integers or count variables. Both have probability mass distributions that are strongly skewed, naturally heteroskedastic, and have variances that grow with the mean. Both have a limited number of discrete values, generally zero and a few tiny positive integers, however the right tail for office-based visits is somewhat lengthy.

If one is just interested in the conditional mean’s prediction or in its reaction to a covariate, it may be tempting to disregard the discreteness and skewness and simply estimate the desired responses using linear or modified linear regression techniques. However, models that disregard discreteness may be highly inefficient, resulting in significant statistical power losses . Equally significant is the fact that, in the case of discrete data, the estimate of event probabilities may be of substantial relevance.

Formal estimate of a count data process is critical in these instances. Cameron & Trivedi , Hardin & Hilbe and Winkelmann provide extensive descriptions of count data regression models . Deb et al discuss a number of count data models with a particular emphasis on health care use measurements.

Direct costs are the expenses associated with implementing the intervention. These expenditures include inpatient and outpatient services (which include professional fees, personnel costs, and equipment costs), medications, and other direct costs associated with the delivery of health care.

The cost of medical treatment is the single largest factor driving healthcare expenditures in the United States, accounting for 90% of spending. These costs reflect the growing expense of caring for people with chronic or long-term medical illnesses, an aging population, and the increased cost of new drugs, treatments, and technology.

The two-part model is based on a statistical decomposition of the outcome’s density into a zero-generating process and a positive-generating process. Typically, a logit or probit model estimates the parameters defining the boundary between zero and nonzero values of the result.

Kenya spent KES 234 billion (US$2,743 million) for health. This is the equivalent of 7% of the country’s GDP, or the whole value of our agricultural produce, including profits from tourism and other industries.

The article discusses strategies for developing models of health care spending and their use that go beyond linear regression. In a wide body of literature, it has been shown that GLMs, two-part models, Poisson regressions, negative binomial regressions, and hurdle models outperform linear regression approaches [see also Deb et al.]. We see them as optimal practices for achieving such results.

We found that the ACA’s young adult expansion reduced health care costs, boosted office-based visits, and decreased emergency department visits when these techniques are used. Modeling the huge number of zeros using two-part and hurdle count models significantly improves model fit and provides for a more intuitive comprehension of the findings.

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