CC BY
5
https://doi.org/10.29013/EJEMS -21-2-63-67
Elizabeth Ni, Academy for Allied Health Science E-mail: [email protected]; [email protected]
FINANCIAL WORRIES OVER MEDICAL COST AMONG ADULTS
Abstract
Aim: This study aims to 1) examine the predictors of adults' financial worries over medical cost in 2017 2) build a predictive model for adults' financial worries over medical cost using artificial neural network and compare its performance to logistic regression model.
Method: The National Health Interview Survey (NHIS) in 2017 was used. All the participants who were eligible were randomly assigned into 2 groups: training sample and testing sample. Two models were built using training sample: artificial neural network and logistic regression. Receiver operating characteristic (ROC) were calculated and compared for these two models for their discrimination capability.
Results: About 26.4% of 26031 Adults had Financial worries over medical cost, about 28.2% among the female and 24.3% among the male. According to the logistic regression, the male was 19.6% less likely than the female to have financial worries over medical cost. The non-Hispanic adults were 55.2% less likely to have financial worries over medical cost than Hispanic adults. Married people were 12.3% less worried. White were 10.7% less worried and Black population were 31.8% more worried. Compared to residents in Northeast, people in Midwest (12.0%), South (28.0%) and West (11.0%) were more worried about the medical cost. Compared to people who were not employed but looking, people who were employed (29.4%) or not employed and not looking (46.5%) were less worried. According to this neural network, the most important predictors was age, sex, working status and race. For training sample, the ROC was 0.61 for the Logistic regression and 0.67. In testing sample, the ROC was 0.60 for the Logistic regression.
Conclusions: In this study, we identified several important predictors for parents' financial worries over medical cost in 2017 e.g., age, gender, race and working status. The findings can help identify people at higher risk of having the financial worries over medical cost.
Keywords: Financial worries, medical cost, logistic regression, model.
1. Introduction: pected to surpass $10,000 in 2016 and then march
According to the most recent data available steadily higher to $14,944 in 2023." from the Centers for Medicare and Medicaid Ser- American adults' biggest financial worry is the vices (CMS), "the average American spent $9,596 inability to pay the medical costs in the event of a on healthcare" in 2012, which was "up significantly serious illness or accident, reports Gallup in new from $7,700 in 2007." It was also more than twice survey data. A majority (54%) of the more than the per capita average of other developed nations, 1,000 US adults surveyed said they're either very but still, in 2015, experts predicted continued sharp (30%) or moderately (24%) worried about this. increases: "Health care spending per person is ex-
This study aims to 1) examine the predictors of adults' financial worries over medical cost in 2017; 2) build a predictive model for adults' financial worries over medical cost using artificial neural network and compare its performance to logistic regression model.
2. Data and Methods:
2.1 Data:
The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Interview Survey (NHIS) Data 2017 was used in this study.
2.2 Models:
We used logistic regression models to calculate the predicted risk. Logistic regression is a part ofa category of statistical models called generalized linear models, and it allows one to predict a discrete outcome from a set of variables that may be continuous, discrete, di-chotomous, or a combination of these. Typically, the dependent variable is dichotomous and the independent variables are either categorical or continuous.
AGC P
Figure 1. Matrix of correlations between variables
The logistic regression model can be expressed with the formula: ln(P/P-l) = £0 + £1*X1 + (32*X2 + ... .+ (3n*Xn A package called "neuralnet" in R was used to conduct neural network analysis. The package neuralnet focuses on multi-layer perceptrons (MLP, Bishop, 1995), which are well applicable when modeling functional relationships.
The outcome variable is percentage of How worried are you right now about not having enough money for Medical Cost? (ASIRETR) 3. Results
About 26.4% of26031 Adults had Financial worries over Medical Cost, about 28.2% among the female and 24.3% among the male.
Basically, a corrgram is a graphical representation of the cells of a matrix of correlations. The idea is to display the pattern of correlations in terms of their signs and magnitudes using visual thinning and correlation-based variable ordering. Moreover, the cells of the matrix can be shaded or colored to show the correlation value. The positive correlations are shown in blue, while the negative correlations are shown in red; the darker the hue, the greater the magnitude of the correlation.
Table 2. - Logistic Regression for Having Financial worries over Medical Cost
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.156 0.097 1.600 0.110
AGE_P 0.000 0.001 0.107 0.915
Male -0.219 0.029 -7.519 0.000 ***
HISPAN_NO -0.802 0.041 -19.613 0.000 ***
MARRIED -0.131 0.029 -4.456 0.000 ***
White -0.113 0.056 -2.017 0.044 *
Black 0.276 0.068 4.075 0.000 ***
Midwest 0.113 0.048 2.377 0.017 *
South 0.247 0.044 5.635 0.000 ***
West 0.104 0.048 2.167 0.030 *
Working -0.348 0.058 -6.025 0.000 ***
NWorNLor -0.625 0.062 -10.128 0.000 ***
According to the logistic regression, the male was 19.6% less likely than the female to have financial worries over medical cost. The non-Hispanic adults were 55.2% less likely to have financial worries over medical cost than Hispanic adults. Married people were 12.3% less worried. White were 10.7%
worried. Compared to residents in Northeast, people in Midwest (12.0%), South (28.0%) and West (11.0%) were more worried about the medical cost. Compared to people who were not employed but looking, people who were employed (29.4%) or not employed and not looking (46.5%) were less worried.
less worried and Black population were 31.8% more
Table 2 a. - Odds Ratio and Risk Increase Based on the Logistic Regression
Estimate Odds Ratio Risk Increase
(Intercept) 0.156 116.9% 16.9%
AGE_P 0.000 100.0% 0.0%
Male -0.219 80.4% -19.6%
HISPAN_NO -0.802 44.8% -55.2%
MARRIED -0.131 87.7% -12.3%
White -0.113 89.3% -10.7%
Black 0.276 131.8% 31.8%
Midwest 0.113 112.0% 12.0%
South 0.247 128.0% 28.0%
West 0.104 111.0% 11.0%
Working -0.348 70.6% -29.4%
NWorNLor -0.625 53.5% -46.5%
False positive rate
Figure 4: ROC in training sample for Logistic Regression
4. Discussion
This study aimed to 1) examine the predictors of adults' financial worries over Medical Cost in 2017; 2) build a predictive model for adults' financial worries over Medical Cost using artificial neural network and compare its performance to logistic regression model.
About 26.4% of26031 Adults had Financial worries over Medical Cost, about 28.2% among the female and 24.3% among the male.
According to the logistic regression, the male was 19.6% less likely than the female to have financial worries over medical cost. The non-Hispanic adults were 55.2% less likely to have financial worries over medical
cost than Hispanic adults. Married people were 12.3% less worried. White were 10.7% less worried and Black population were 31.8% more worried. Compared to residents in Northeast, people in Midwest (12.0%), South (28.0%) and West (11.0%) were more worried about the medical cost. Compared to people who were not employed but looking, people who were employed (29.4%) or not employed and not looking (46.5%) were less worried. According to this neural network, the most important predictors was age, sex, working status and race.
For training sample, the ROC was 0.61 for the Logistic regression and in testing sample, the ROC was 0.60 for the Logistic regression.
References:
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There are limitations in this study. For example we did not include the health conditions in this study when examing the factors of the financial worries of the medical cost. Health status could be a important factor for this topic and should be included in the future analysis when feasible.
In this study, we identified several important predictors for parents' financial worries over medical Cost in 2017 e.g., age, gender, race and working status. We built a predictive model using artificial neural network as well as logistic regression to provide a tool for early detection.
