Private Supplemental Insurance and Mental Health Care Utilization in Canada - An Investigation Using Nonparametric Estimation Methods

Transcription

1 Private Supplemental Insurance and Mental Health Care Utilization in Canada - An Investigation Using Nonparametric Estimation Methods Luc Clair Presented to Memorial University of Newfoundland September 8, / 76

2 Purpose The purpose of this paper is to estimate the impact of private supplemental insurance on the use of mental health care goods and services. 2 / 76

3 Motivation A person s ability to study, work, and make daily decisions depends on his or her mental health. Close to one-third of long-term disability claims are for mental health reasons (MHCC 2012). Every year depression and anxiety account for approximately $50 billion in lost GDP (Conference Board of Canada, 2016) People living with a mental illness utilized more physician visits, specialist visits, and hospital days, on average, compared to those without a mental illness (Prina et al. 2013, Lim et al. 2008). 3 / 76

4 Motivation Experiences with mental illness are not the same for everyone. Type of illness or illnesses Severity Heterogeneous nature of mental illness leads to specialized treatment plans for those who suffer from the disease. Medication Therapy/councelling Diet/exercise 4 / 76

5 Motivation Public health care system covers medically necessary physician and in-patient hospital services. Public coverage for non-physician services and prescription drugs Cost-sharing Patient Characteristics (Seniors or those on income assistance) Non-physician services and prescription drugs can be expensive. E.g. Psychologist services in Ontario cost $225/hr Antidepressants: $35-$280 for 90 day prescription Antipsychotics: $25-$165 for 90 day prescrition Benzodiazepines: $15-$25 for 90 tablets (Alberta College of Physicians, 2016) 5 / 76

6 Motivation Pie Chart of Supplementary Insurance Holders (Source: Statistics Canada, 2014) 6 / 76

7 Research Questions My research is guided by the following questions: 1. How does supplemental insurance affect the use of mental health pharmaceuticals? 2. How does supplemental insurance affect the use of health care professionals for mental health issues? 7 / 76

8 Previous Works Mulvale and Hurley (2009): 2002 Canadian Community Health Survey Medication usage measured as binary use/no use. Logit specification. Insurance positively affects the use of anti-depressants and anti-psychotics. Income is insignificantly related to mental health pharmaceutical utilization. Devlin, Sarma, and Zhang (2011): Used supplementary insurance for prescription drugs as proxy for other services. Physician health care services respond to the presence of insurance. Reduces hospital admissions for ambulatory care sensitive conditions. 8 / 76

9 Current Paper 2012 Canadian Comunity Health Survey - Mental Health Component Medication use measured as use/no use and as number of medications taken. Nonparametric estimation methods. Use of mental health care professionals measure in number of hours. Individuals with lower family-adjusted income have a higher probability of taking more medications for mental illness. 9 / 76

10 Data 2012 Canadian Community Health Survey - Mental Health Component (CCHS-MH) Canadians aged 15 years or older Excludes individuals in the military, living on native reserves, and individuals in institutions. Total of 25,113 observations. Purpose: Study a myriad of issues related to the mental health of Canadians. Assess mental health Access to health care services Assess the effect of mental health on individuals lives 10 / 76

11 Data 1. Did you take a [drug type] for mental illness in past two days? 2. How many [drug type] did you take in the past two days? 3. Did you visit a [health care provider] for a mental illness in the past 12 months? 4. How many hours did you spend with the [health care provider] dealing with your mental illness over the past 12 months? How many visits did you have with the [health care provider]? What was the average length of time spent with the [health care provider] per visit? 11 / 76

12 Data Do you have insurance that covers all or part of the price for prescription medications? CCHS-MH also contains information on health, socioeconomic status, social supports, and demographic information. 12 / 76

13 Methods I consider four models for estimating the effect of private supplemental insurance on mental health care utilization: 1. The effect of insurance on a binary use/no use measure of prescription drug utilization. 2. Conditional on having taken a medication, I examine how insurance influences the number of medications taken. 3. The impact of insurance on a binary use/no use measure of mental health care providers. 4. Conditional on having visited a health care provider, I examine how insurance influences the number of hours spent with a given health care provider. 13 / 76

14 Methods: Discrete outcomes Models 1 to 3 have discrete outcome variables. Suppose we have a binary outcome variable Y {0, 1} and a predictor variable X. We are often interested in estimating the conditional mean (or average) of Y given X or E(Y X). When Y is binary, E(Y X) = Pr(Y = 1 X) The probability that Y = 1, given X. 14 / 76

15 Methods: Discrete outcomes Typically this problem is estimated using a Logit or a Probit, setting Pr(Y = 1 X) to follow a cummulative distribution function (CDF). Gaussian CDF for Probit Logistic CDF for Logit If the CDF is misspecified, the results will be untrustworthy. Instead, one can use a nonparametric conditional probability distribution function estimator Makes no functional form assumptions Free from misspecification. 15 / 76

16 Nonparametric Conditional Density Estimation Letting f (Y X), f (Y, X), and f (X) denote the conditional PDF of Y given X, the joint probability of X and Y, and the marginal density of X, respectively, then: f (Y X) = f (Y, X) f (X). 16 / 76

17 Nonparametric Conditional Density Estimation The methods I use are kernel estimation methods. For some variable z the kernel density estimator ˆf (z) looks like: 17 / 76

18 Nonparametric Conditional Density Estimation Replace f (Y, X), and f (X) by their respective kernel density estimators, ˆf (Y, X) and ˆf (X), repectively. The conditional density estimator is then given by: Can estimate f (Y = 1 X) ˆf (Y X) = ˆf (Y, X) ˆf (X) For Y {0, 1, 2, 3,...}, one can compute f (Y = 1 X), f (Y = 2 X), f (Y = 3 X), etc. 18 / 76

19 Nonparametric Regression Model 4 has a continuous outcome variable. Let Y C denote a continuous outcome variable. Mathematical definition of conditional mean function is: E(Y c X) = Yf (Y c X)dy = Y f (Y c, X) dy f (X) Substituting ˆf (Y C X) for f (Y C X) gives: Ê(Y c X) = n i=1 Y c i W i where W i is a weight variable made up of kernel functions. 19 / 76

20 Outcome Variables I consider four categories of medication: 1. Antidepressants (ADEP) 2. Antipsychotics (APSY) 3. Benzodiazepines (anti-anxiety) (BENZ) 4. Any medication (3 above plus other drugs, e.g. drugs for alcohol dependence) I consider three types of providers: 1. Psychiatrist 2. General practitioner (GP) 3. Psychologists 20 / 76

21 Predictor Variables Private insurance Mental health status Depression Anxiety Self-assessed mental health (SAMH) Health Status Self-assessed health (SAH) Comorbid condition Overweight Smoker Type Demographic variables Age Sex Marital Status Immigrant status Urban/rural 21 / 76

22 Predictor Variables Socioeconomic variables Family-adjusted income Education level Social Provision Score (SPS) 22 / 76

23 Models Let X represent variables on insurance, health, socioeconomic status, social supports, and demographic information. Model Object of interest Sample 1 f (Used [drug type] = Yes X) Full 2 For ADEP, APSY, and BENZ f (No. of [drug type] 2 X, Used [drug type] = Yes) Subset For Any Medication f (No. of [drug type] = 1 X, Used [drug type] = Yes) f (No. of [drug type] = 2 X, Used [drug type] = Yes) f (No. of [drug type] = 3 X, Used [drug type] = Yes) f (No. of [drug type] 4 X, Used [drug type] = Yes) 3 f (Visited [Provider] = Yes X) Full 4 E(No. of Hrs with [Provider] X, Visited [Provider] = Yes) Subset 23 / 76

24 Bandwidth Selection Bandwidths are the widths or the bars of the smooth histogram 24 / 76

25 Bandwidth Selection ˆf (Visited Psychologist = YES X) Versus SPS Fitted Values SPS 25 / 76

26 Results: Descriptive Statistics Used a Medication in the Past Two Days Variable Description Percent Antidepressant Yes 5.50 No Antipsychotic Yes 0.90 No Benzodiazepines Yes 1.30 No Any drug Yes 6.70 No / 76

27 Results: Descriptive Statistics Number of Medications Used in the Past Two Days Variable Description Sample Size Percent No. of Antidepressants or more No. of Antipsychotics or more No. of Benzodiazepines or more No. of Medications or more / 76

28 Results: Descriptive Statistics Visited a Health Care Professional in the Past 12 Months Variable Description Percent Visited a psychiatrist Yes 2.00 No Visited a GP Yes 6.80 No Visted a psychologist Yes 2.21 No / 76

29 Results: Descriptive Statistics Number of Hours Spent with Health Care Professional Variable Description Sample Size Mean Std. Dev. Hrs with a psychiatrist Hrs with a GP Hrs with a psychologist / 76

30 Results: Descriptive Statistics Variable Description Percent Insurance Yes 77.9 No 22.1 Depression Yes 5.16 No Anxiety Yes 2.26 No Comorbid condition Yes No Variable Description Percent SAMH Poor 1.30 Fair 5.49 Good Very Good Excellent SAH Poor 1.53 Fair 7.03 Good Very Good Excellent / 76

31 Results: Descriptive Statistics Variable Description Percent Smoker Type Daily Occassional 5.50 Former Daily Former Occassional Never Overweight Yes No Sex Male 49.3 Female 50.7 Variable Description Percent Marital Status Married Common Law Widdower 1.33 Separated/divorced 4.83 Single Immigrant Yes No Urban Yes No / 76

32 Results: Descriptive Statistics Variable Description Percent Education < high school High School Some post sec Post Sec. Grad Variable Description Mean Std. Dev. Age (Years) Adjusted-family income ($) 47, , Social provision score / 76

33 Results: Binary Use/No Use of Medications Percent Change in ˆf (Used Med. = YES X) Versus Discrete Predictors Variable Anti- Anti- Benzodiazepines Any Depressants Psychotics Insurance Yes Depression Yes Anxiety Yes SAH Poor Fair Good Very Good SAMH Poor Fair Good Very Good / 76

34 Results: Binary Use/No Use of Medications Percent Change in ˆf (Used Med. = YES X) Versus Discrete Predictors (Cont d) Variable Anti- Anti- Benzodiazepines Any Depressants Psychotics Comorbid Condition Yes Smoker Type Daily Occasional Former Daily Former Occasional Overweight Yes Sex Male Marital Status Common-Law Widowed Divorced Single / 76

35 Results: Binary Use/No Use of Medications Percent Change in ˆf (Used Med. = YES X) Versus Discrete Predictors (Cont d) Variable Anti- Anti- Benzodiazepines Any Depressants Psychotics Immigrant Yes Education Secondary Some Post Sec Post Sec. Grad Urban Yes / 76

36 Results: Binary Use/No Use of Medications Versus Age ˆf (Used Antidepressant = YES X) Versus Age Fitted Values Age 36 / 76

37 Results: Binary Use/No Use of Medications Versus Age ˆf (Used Antipsychotics = YES X) Versus Age Fitted Values Age 37 / 76

38 Results: Binary Use/No Use of Medications Versus Age ˆf (Used Benzodiazepine = YES X) Versus Age Fitted Values Age 38 / 76

39 Results: Binary Use/No Use of Medications Versus Age ˆf (Used Any Medication = YES X) Versus Age Fitted Values Age 39 / 76

40 Results: Binary Use/No Use of Medications Versus SPS ˆf (Used Antidepressant = YES X) Versus SPS Fitted Values SPS 40 / 76

41 Results: Binary Use/No Use of Medications Versus SPS ˆf (Used Antipsychotic = YES X) Versus SPS Fitted Values SPS 41 / 76

42 Results: Binary Use/No Use of Medications Versus SPS ˆf (Used Benzodiazepine = YES X) Versus SPS Fitted Values SPS 42 / 76

43 Results: Binary Use/No Use of Medications Versus SPS ˆf (Used Any Medication = YES X) Versus SPS Fitted Values SPS 43 / 76

44 Results: Binary Use/No Use of Medications Family-adjusted income was not relevant in predicting ˆf (Used Meds = Yes X) for all categories of pharmaceuticals. 44 / 76

45 Results: Number of Medications % Change in ˆf (No. of Meds 2 X, Used Med.=YES), For ADEP, APSY, and BENZ Variable Antidepressants Antipsychotics Benzodiazepines (n = 1562) (n = 313) (n = 483) Insurance Yes Depression Yes Anxiety Yes SAH Poor Fair Good Very Good SAMH Poor Fair Good Very Good / 76

46 Results: Number of Medications % Change in ˆf (No. of Meds 2 X, Used Med.=YES), For ADEP, APSY, and BENZ (Cont d) Variable Antidepressants Antipsychotics Benzodiazepines (n = 1562) (n = 313) (n = 483) Comorbid Condition Yes Smoker Type Daily Occasional Former Daily Former Occasional Overweight Yes Sex Male Marital status Common-Law Widowed Divorced Single / 76

47 Results: Number of Medications % Change ˆf (No. of Meds 2 X, Used Med.=YES), For ADEP, APSY, and BENZ (Cont d) Variable Antidepressants Antipsychotics Benzodiazepines (n = 1562) (n = 313) (n = 483) Immigrant Yes Education Secondary Some Post Sec Post Sec. Grad Urban Yes / 76

48 Results: Number of Medications Versus Age ˆf (No. of Antidepressants 2 X, P(Y=2 Usedor Antidepressant=YES) More) Versus Age Fitted Values Age 48 / 76

49 Results: Number of Medications Versus Age ˆf (No. of Benzodiazepine 2 X, P(Y=2 Usedor Benzodiazepine=YES) more) Versus Age Fitted Values Age 49 / 76

50 Results: Number of Medications Versus Adjusted Income ˆf (No. of Antidepressants 2 X, Used Antidepressant=YES) Versus Adjusted Income P(Y=2 or More) Fitted Values e+00 1e+05 2e+05 3e+05 4e+05 5e+05 inc 50 / 76

51 Results: Number of Medications Versus Adjusted Income ˆf (No. of Benzodiazepine 2 X, Used P(Y=2 Benzodiazepine=YES) or more) Versus Income Fitted Values e+00 1e+05 2e+05 3e+05 4e+05 5e+05 inc 51 / 76

52 Results: Number of Medications Versus SPS ˆf (No. of Antipsychotics 2 X, Used P(Y=2) Antipsychotic=YES) Versus SPS Fitted Values sps 52 / 76

53 Results: Number of Medications Age and family-adjusted income were irrelevant in predicting the use of two or more antipsychotics. Social provision score was irrelevant in predicting the use of two or more antidepressants or benzodiazepines. 53 / 76

54 Results: Number of Medications Percent Change in ˆf (No. of Meds X, Used Med.=YES), For Any Medication (n = 1993) Variable Y = 1 Y = 2 Y = 3 Y 4 Insurance Yes Depression Yes Anxiety Yes SAH Poor Fair Good Very Good SAMH Poor Fair Good Very Good / 76

55 Results: Number of Medications Percent Change ˆf (No. of Meds X, Used Med.=YES), For Any Medication (Cont d) (n = 1993) Variable Y = 1 Y = 2 Y = 3 Y 4 Comorbid Condition Yes Smoker Type Daily Occasional Former Daily Former Occasional Overweight Yes Sex Male Marital status Common-Law Widowed Divorced Single / 76

56 Results: Number of Medications Percent Change in ˆf (No. of Meds X, Used Med.=YES), For Any Medication (Cont d) (n = 1993) Variable Y = 1 Y = 2 Y = 3 Y 4 Immigrant Yes Education Secondary Some Post Sec Post Sec. Grad Urban Yes / 76

57 Results: Number of Medications Versus Age ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus Age P(Y=1) P(Y=2) Fitted Values Fitted Values Age Age 57 / 76

58 Results: Number of Medications Versus Age ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus Age P(Y=3) P(y=4 or More) Fitted Values Fitted Values Age Age 58 / 76

59 Results: Number of Medications Versus Adjusted Income ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus Adjusted Income P(Y=1) P(Y=2) Fitted Values Fitted Values e+00 2e+05 4e+05 6e+05 8e+05 inc 0e+00 2e+05 4e+05 6e+05 8e+05 inc 59 / 76

60 Results: Number of Medications Versus Adjusted Income ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus Adjusted Income P(Y=3) P(y=4 or More) Fitted Values Fitted Values e+00 2e+05 4e+05 6e+05 8e+05 inc 0e+00 2e+05 4e+05 6e+05 8e+05 inc 60 / 76

61 Results: Number of Medications Versus SPS ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus SPS P(Y=1) P(Y=2) Fitted Values Fitted Values Social Provision Score Social Provision Score 61 / 76

62 Results: Number of Medications Versus SPS ˆf (No. of Any Drugs X, Used Any Drug=YES) Versus SPS P(Y=3) P(y=4 or More) Fitted Values Fitted Values Social Provision Score Social Provision Score 62 / 76

63 Results: Binary Use/No Use of Health Care Provider Percent Change in ˆf (Visited Provider = YES X) Versus Discrete Predictors Variable Psychiatrist GP Psychologist Insurance Yes Depression Yes Anxiety Yes SAH Poor Fair Good Very Good SAMH Poor Fair Good Very Good / 76

64 Results: Binary Use/No Use of Health Care Provider Percent Change in ˆf (Visited Provider = YES X) Versus Discrete Predictors Variable Psychiatrist GP Psychologist Comorbid Condition Yes Smoker Type Daily Occasional Former Daily Former Occasional Overweight Yes Sex Male Marital Status Common-Law Widowed Divorced Single / 76

65 Results: Binary Use/No Use of Health Care Provider Percent Change in ˆf (Visited Provider = YES X) Versus Discrete Predictors Variable Psychiatrist GP Psychologist Immigrant Yes Education Secondary Some Post Sec Post Sec. Grad Urban Yes / 76

66 Results: Binary Use/No Use of Provider Versus Age ˆf (Visited Psychiatrist = YES X) Versus Age Fitted Values Age 66 / 76

67 Results: Binary Use/No Use of Provider Versus Age ˆf (Visited GP = YES X) Versus Age Fitted Values Age 67 / 76

68 Results: Binary Use/No Use of Provider Versus Age ˆf (Visited Psychologist = YES X) Versus Age Fitted Values Age 68 / 76

69 Results: Binary Use/No Use of Provider Versus SPS ˆf (Visited Psychiatrist = YES X) Versus SPS Fitted Values SPS 69 / 76

70 Results: Binary Use/No Use of Provider Versus SPS ˆf (Visited GP = YES X) Versus SPS Fitted Values SPS 70 / 76

71 Results: Binary Use/No Use of Provider Family-adjusted income was irrelevant in the prediction of visitng all categories of health care providers Social provision score was irrelevant in the prediction of visiting a psychologist. 71 / 76

72 Results: Hours Spent With Providers Percent Change in Hours Spent With Health Care Providers (All other variables held constant) Variable Psychiatrist GP Psychologist Insurance / 76

73 Discussion Supplemental Insurance increased the likelihood of using antidepressants, antipsychotics, any medication for mental illness, and visiting a psychologist. Supplemental insurance also increased the likelihood of more medication and spending more time with a psychologist. Not certain whether this effect is due to moral hazard or due to need-based utilization. 73 / 76

74 Discussion Medium to strong social supports help reduce the likelihood of using mental health care goods and services. Similar to Mulvale and Hurley (2008) income was not relevant in predicting the likelihood of using medication. However, those with lower incomes are more likely to take a higher number of medications for mental illness. 74 / 76

75 Acknowledgements This work would not have been possible without help from the Department of Economics at Memorial University of Newfoundland and the CARE initiative. I would like to thank Wade Locke, Doug May, Scott Lynch, and Lynn Gambin for this opportunity. 75 / 76

76 References Conference Board of Canada Healthy Brains at Work: Estimating the Impact of Workplace Mental Health Benefits and Programs. URL: Devlin, Rose Anne; Sarma, Sisira; and Zhang, Qi The role of supplemental coverage in a universal health insurance system: Some Canadian evidence. Health Policy. 100: Mental Health Comission of Canada Why Investing in Mental Health Will Contribute to Canada s Economic Prosperity and to the Sustainability of our Health Care System:Backgrounder- Key Facts. en.pdf Accessed on February 1, Mulvale, Gillian and Hurley, Jeremiah Insurance Coverage and the Treatment of Mental Illness: Effect on Medication and Provider Use. Journal of Mental Health Policy and Economics. 11: Prina, A. M., M. Huisman, B. B. Yeap, G. J. Hankey, L. Flicker, C. Brayne, and O. P. Almeida Association between depression and hospital outcomes among older men. Canadian Medical Association Journal. 185 (2): / 76