Improving risk prediction for depression via Elastic Net regression - Results from Korea National Health Insurance Services Data

Abstract

Depression, despite its high prevalence, remains severely under-diagnosed across the healthcare system. This demands the development of data-driven approaches that can help screen patients who are at a high risk of depression. In this work, we develop depression risk prediction models that incorporate disease co-morbidities using logistic regression with Elastic Net. Using data from the one million twelve-year longitudinal cohort from Korean National Health Insurance Services (KNHIS), our model achieved an Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) of 0.7818, compared to a traditional logistic regression model without co-morbidity analysis (AUC of 0.6992). We also showed co-morbidity adjusted Odds Ratios (ORs), which may be more accurate independent estimate of each predictor variable. In conclusion, inclusion of co-morbidity analysis improved the performance of depression risk prediction models.

Introduction

Depression is a highly prevalent disease with a large societal burden. Major depressive disorder has the one- year prevalence of 6%, and the lifetime prevalence of 17%¹, while persistent depressive disorder (dysthymia) has the one-year prevalence of 2%, and the lifetime prevalence of 3%². The estimated societal burden of unipolar depression was 83 billion dollars per year in the US alone in 2007³.

However, despite this burden, depression is under-diagnosed at large across the health care system in all care settings. A meta-analysis in 2009 concluded that the weighted sensitivity of primary care physicians’ diagnosis on depression was only about half (41.3-59.0%) without the assistance of screening tools⁴. This lead to the underdiagnosis or delayed diagnosis of depression, because many of depressed patients initially present with somatic symptoms to the primary care clinics. In general, 69-73% of depression patients presented to their primary care physicians with somatic symptoms, such as pain, fatigue, and sleep problems⁵.

Data-driven risk prediction models can be beneficial by rapidly classifying high-risk patients who need further evaluation. Risk prediction models can be implemented on Electronic Health Record system (EHRs) in order to provide high-risk alert, as well as clinical decision support. Risk prediction models can also be implemented in health insurance claims data in order to classify high-risk patients, and can be used for accountable care strategy⁶.

Previous work on the prediction modelling of depression include a regression-based depression risk prediction model based on Electronic Health Record data, developed at Stanford University, which reported an area under the receiver operating characteristic (AUROC) of 0.80 for current classification, 0.712 for 6-month prediction, and 0.701 for 12-month prediction⁷. Another work of the depression risk prediction model based on clinical trial data, developed at University of Southern California, reported to have a current classification with an AUROC of 0.81, as well as a sensitivity of 0.65 and a specificity of 0.81 at the institution’s optimized threshold⁸.

However, both these approaches did not explicitly apply co-morbid medical conditions as independent predictors in the depression prediction model. Many medical conditions can affect depression⁹, and depression can also affect certain medical conditions¹⁰. Therefore, application of co-morbidity analysis can improve the performance of the risk prediction models.

Hence, our main hypothesis and the research question to be addressed in this study was whether the comorbidity analysis can improve the performance of prediction models for depression risk. Our preliminary results indicate that the inclusion of co-morbidity analysis improved the performance of depression risk prediction model.

Study Setting and Data

In this study, co-morbidity analysis and risk prediction modeling was made from one million twelve-year longitudinal data from Korea National Health Insurance Services (KNHIS)¹¹. The sample cohort (N= 1,025,340) was established in 2002 from 2.2% of 46,605,433 individuals from the National Health Information Database, in order to provide public health researchers and policy makers with representative information regarding the utilization of health insurance and health examinations¹². The data include demographic profile, health insurance claims data (including in-patient, out-patient, and pharmacy claims), death registry, disability registry, and national health check-up data. With the combination of 18 age groups, 2 genders, and 41 income groups, total 1476 strata were undergone systematic stratified random sampling with proportional allocation¹³ within each stratum, using the individual’s total annual medical expenses as a target variable. During the follow-up years, annual drop-out by death was 0.5% (ranging from 4,929 to 5,229). Each year, a representative sample of newborns (ranging from 7,872 to 9,581), sampled across 82 strata (2 for gender, 41 for parents’ income group), was added to ensure the representativeness of the data.

The diagnosis codes in KNHIS are based on the Korean Classification of Diseases, Sixth Revision (KCD-6), which is compatible with International Classification of Diseases, Tenth Revision (ICD-10). These diagnoses were classified with Chronic Conditions Data Warehouse (CCW) Condition Algorithms (rev. 01/2016) by Centers for Medicare & Medicaid Services (CMS)¹⁴. The CCW condition category algorithms are claims-based algorithms to indicate whether treatment for the condition appears to have taken place, which include 27 chronic condition categories and 33 other chronic or potentially disabling conditions categories. Table 1 shows the ICD-10 codes of the depressive disorder, bipolar disorder, schizophrenia in the CMS-CCW algorithm. The ICD-10 codes for depressive disorder, bipolar disorder, schizophrenia were used in the operational definition of the depression case group in this study. The study subjects had two or more encounters with depression diagnosis codes, but less than two encounters with either bipolar or schizophrenia diagnosis codes. The inclusion and exclusion criteria is based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5)¹⁵.

Table 1.

ICD-10 codes of the depressive disorder, bipolar disorder, schizophrenia in the Chronic Conditions Data Warehouse (CCW) Condition Algorithms (rev. 01/2016) by Centers for Medicare & Medicaid Services (CMS).

CMS-CCW Conditions	Valid ICD-10 Codes
Depressive Disorders	F32.0, F32.1, F322, F32.3, F32.4, F32.5, F32.9, F33.0, F33.1, F33-2, F33.3, F33.40, F33.41, F33.42, F33.9, F34.1, Z13.89
Bipolar Disorder	F30.10,F30.ll, F30.12, F30.13,F30.2,F30.3,F30.4, F30.8, F30.9, F31.0, F31.10, F31.ll, F31.12, F31.13, F31.2, F31.30, F31.31, F31.32, F31.4, F31.5, F31.60, F31.61. F31.62, F31.63, F31.64, F31.70, F31.71, F31.72, F31.73, F31.74, F31.75, F31.76, F31.77, F31.78.F31.81, F31.89, F31.9, F32.8, F33.8.F34.8, F34.9.F39
Schizophrenia	F20.0, F20.1, F202, F20.3, F20.5, F20.81, F20.89, F20.9, F25.0, F25.1, F25.8, F25. 9

The univariate and bivariate statistics of selected demographic and co-morbidity variables between the depression case group (N=28,256) and complement comparison group (N=1,085,400), based on the operational definition of the case group described in the method section, are shown in Table 2. The case group showed significantly higher percentage of females (68.1%), age (mean 48, standard deviation 19), income decile (mean 5.8, standard deviation 2.5), limb disability (3.3%), neurologic disability (0.9%), visual disability (0.7%), hearing disability (0.6%), but showed significantly lower percentage of social security beneficiaries (1.0%). The case group showed no significant difference in the percentage of residents in Seoul metropolitan area, and cognitive disability. Most of the co-morbidity variables showed statistically significant difference between the case group and comparison group, except cerebral palsy (p = 0.121). The most noticeable difference in the co-morbidity by the ratio of percentage (twelve-year prevalence) was personality disorder (0.9% vs 0.1%), followed by anxiety disorder (31.8% vs 4.9%), dementia & Alzheimer’s disease (2.0% vs 0.4%), and osteoporosis (5.2% vs 1.3%).

Table 2.

Univariate and bivariate statistics of selected demographic, socio-economic, disability registry and comorbidity variables between the depression case group (N=28,256) and complement comparison group (N=1,085,400). For categorical variables, the observed frequencies of the categories and percentages (twelve-year prevalence) were reported, and for numerical variables, means (and standard deviations) were reported. P-values were of clii-square tests for categorical variables, and t-tests for numerical variables.

Univariate and Bivariate Statistics of Selected Variables	Complement Control Group (N=1085400)	Depression Case Group (N=28256)	Total (N=1113656)	P-value
< Demographic and Socio-Economic Variables >
Female	527644 (49.4%)	19238 (68.1%)	546882 (49.9%)	<0.001
Age	36 ±21	48 ± 19	36 ±21	<0.001
Income (Decile)	5.7 ±2.5	5.8 ±2.5	5.7 ±2.5	<0.001
Insurance Status - Social Security Beneficiaries	17288 (1.6%)	288 (1.0%)	17576 (1.6%)	<0.001
Residents In Seoul Metropolitan Area	158874(14.9%)	4138 (14.6%)	163012 (14.9%)	0.278
< Disability Registry Variables >
Limb Disability	20828 (2.0%)	936 (3.3%)	21764 (2.0%)	<0.001
Neurologic Disability	5528(0.5%)	258 (0.9%)	5786 (0.5%)	<0.001
Visual Disability	4321 (0.4%)	197 (0.7%)	4518(0.4%)	<0.001
Hearing Disability	3467 (0.3%)	172(0.6%)	3639(0.3%)	<0.001
Cognitive Disability	2791 (0.3%)	63 (0.2%)	2854 (0.3%)	0.233
< Co-morbidity Variables (Alphabetical Order) >
Acquired Hypothyroidism	24496 (2.3%)	1805 (6.4%)	26301 (2.4%)	<0.001
Acute Myocardial Infarction	2518 (0.2%)	115 (0.4%)	2633 (0.2%)	<0.001
Alzheimer’s Disease	1707 (0.2%)	242 (0.9%)	1949 (0.2%)	<0.001
Anemia	60602 (5.7%)	2875 (10.2%)	63477 (5.8%)	<0.001
Anxiety Disorder	51935 (4.9%)	8980 (31.8%)	60915 (5.6%)	<0.001
Arthritis	213785 (20.0%)	12578 (44.5%)	226363 (20.7%)	<0.001
Asthma	191650 (17.9%)	6468 (22.9%)	198118(18.1%)	<0.001
Atrial Fibrillation	3730 (0.3%)	245 (0.9%)	3975 (0.4%)	<0.001
Attention Deficit Hyperactivity & Conduct Disorder	5932 (0.6%)	444 (1.6%)	6376 (0.6%)	<0.001
Benign Prostatic Hyperplasia	31351 (2.9%)	1673 (5.9%)	33024 (3.0%)	<0.001
Brain Injury	15399 (1.4%)	728 (2.6%)	16127 (1.5%)	<0.001
Breast Cancer	2976 (0.3%)	199 (0.7%)	3175 (0.3%)	<0.001
Cataract	66979 (6.3%)	4931 (17.5%)	71910 (6.6%)	<0.001
Cerebral Palsy	1004 (0.1%)	18 (0.1%)	1022 (0.1%)	0.121
Chrome Kidney Disorder	39841 (3.7%)	2324 (8.2%)	42165 (3.8%)	<0.001
Chronic Obstructive Pulmonary Disease (COPD)	74832 (7.0%)	3715 (13.1%)	78547 (7.2%)	<0.001
Chronic Ulcers	2924 (0.3%)	210 (0.7%)	3134 (0.3%)	<0.001
Colorectal Cancer	4982 (0.5%)	260 (0.9%)	5242 (0.5%)	<0.001
Dementia	2589 (0.2%)	320(1.1%)	2909 (0.3%)	<0.001
Diabetes	80495 (7.5%)	4708 (16.7%)	85203 (7.8%)	<0.001
Endometrial Cancer	513 (0.0%)	41 (0 1%)	554 (0.1%)	<0.001
Epilepsy	8698 (0.8%)	665 (2.4%)	9363 (0.9%)	<0.001
Fibromyalgia and Pam Syndrome	69260 (6.5%)	3995 (14.1%)	73255 (6.7%)	<0.001
Glaucoma	37216 (3.5%)	2123 (7.5%)	39339 (3.6%)	<0.001
Hearing Impairment	40214 (3.8%)	2484 (8.8%)	42698 (3.9%)	<0.001
Heart Failure	19199 (1.8%)	1408 (5.0%)	20607 (1.9%)	<0.001
Hyperlipidemia	89161 (8.4%)	5700 (20.2%)	94861 (8.7%)	<0.001
Hypertension	57022 (5.3%)	3531 (12.5%)	60553 (5.5%)	<0.001
Ischemic Heart Disease	47457 (4.4%)	3692 (13.1%)	51149(4.7%)	<0.001
Leukemia And Lymphoma	1461 (0.1%)	64 (0.2%)	1525 (0.1%)	<0.001
Liver Disease (Except Viral Hepatitis)	127475 (11.9%)	6552 (23.2%)	134027 (12.2%)	<0.001
Lung Cancer	4217 (0.4%)	212 (0.8%)	4429 (0.4%)	<0.001
Migraine And Chronic Headache	119753 (11.2%)	8192 (29.0%)	127945 (11.7%)	<0.001
Mobility’ Impairments	11480 (1.1%)	866 (3.1%)	12346 (1.1%)	<0.001
Osteoporosis	13986 (1.3%)	1463 (5.2%)	15449 (1.4%)	<0.001
Pelvic Fractures	3902 (0.4%)	299 (1.1%)	4201 (0.4%)	<0.001
Peripheral Vascular Disease	22793 (2.1%)	1852 (6.6%)	24645 (2.2%)	<0.001
Personality Disorders	980(0.1%)	244 (0.9%)	1224 (0.1%)	<0.001
Spinal Cord Injury	8191 (0.8%)	590 (2.1%)	8781 (0.8%)	<0.001
Stroke And Transient Ischemic Attack (TLA)	49791 (4.7%)	3946 (14.0%)	53737 (4.9%)	<0.001
Viral Hepatitis	43294(4.1%)	1877 (6.6%)	45171 (4.1%)	<0.001

Analytic Approach

The operational definition of diagnosis of depression was analyzed in a logistic regression model with socioeconomic and co-morbid predictors. Among the available socio-economic variables and co-morbid conditions in KNHIS data, variables for the final logistic regression model was selected with Elastic Net¹⁶. The performance of the final logistic regression model with co-morbidity analysis was compared with that of the traditional logistic regression model without co-morbidity analysis.

When the number of predictors is large compared to the sample size, traditional variable selection methodologies may have poor prediction performance for external datasets by overfitting random error or noise, and it has been criticized that the goodness of fit¹⁷, significance¹⁸, and degrees of freedom¹⁹ do not reflect the reality. In order to overcome this problem, regularization and shrinkage methods for regression have been developed²⁰. Elastic Net is a regularization method for regression and classification models which compromises the Least Absolute Shrinkage And Selection Operator (LASSO) penalty (L₁) and the ridge penalty (L₂)¹⁶. The LASSO (L₁) penalty function performs variable selection and dimension reduction by shrinking coefficients, while the ridge (L₂) penalty function shrinks the coefficients of correlated variables toward their average. The overall Elastic Net is a function of parameters λ and α (0 ≤ α ≤ 1), where λ being a parameter for the level of penalty, while α being the weight of L₁ penalty and (1 - α) being that of L₂ penalty function. Hence, in this work, we performed variable selection and penalization of collinear predictors by Elastic Net for developing the final logistic regression model.

A robust way to determine the best combination of λ and a is via a k-fold cross-validation. For the validation of the predictive model, 10% of the data (N = 111,366) was set aside as a test data, and 90% of the data was used as a training data (N = 1,002,290). We used 10-fold cross-validation on training data, where total observations of the dataset are randomly divided into 10 folds, or partitions. One of the 10 folds is reserved as the internal validation data (N = 100,229), and the rest of the folds consist the internal training data (N = 902,061), where statistical models are fitted. After fitting the models, or calculating the coefficients, the models are validated against the reserved fold. This overall process is iterated (repeated) 10 times, so that every folds can be a validation set. This is a preferred method especially when the prediction models need to perform prediction for external datasets, that is, outside of the overall dataset used in the research.

The variables for the traditional logistic regression model without co-morbidity analysis was driven by performing the stepwise backward selection using Akaike’s Information Criterion (AIC)²¹. The selected variables for the traditional logistic regression model include sex, age, income decile, and disability registration. The variable selection for the final logistic regression model was applied with Elastic Net from the training data, as described above. The selected value of α was 0.75, and the optimized values of λ was 0.001390648 (log(λ) -6.577986), although other α values, including 0.25, 0.5, and 1, did not change the results much. The plot obtained from cross-validation of Elastic Net, showing the change of the Area Under the Curve (AUC) of ROC with different λ (in log scale) for a model assuming an α of 0.75, is shown in Figure 1. This gives a minimum of 28 variables needed for building an optimized model. Two more variables, acute myocardial infarction and dementia, were added to the final model, because even though those conditions were separated by the CMS-CCW algorithm, the conditions were in spectrum with ischemic heart disease and Alzheimer’s disease, respectively. Therefore, 30 variables were selected for the final logistic regression model. These variables include: sex, age, income decile, acquired hypothyroidism, acute myocardial infarction, Attention Deficit Hyperactivity Disorder (ADHD) and conduct disorder, Alzheimer’s disease, anemia, anxiety disorder, arthritis, atrial fibrillation, brain injury, chronic kidney disorder, colorectal cancer, chronic obstructive pulmonary disease (COPD), dementia, diabetes, epilepsy, glaucoma, hearing impairment, hyperlipidemia, ischemic heart disease, liver disease (except viral hepatitis), migraine and chronic headache, mobility impairments, osteoporosis, peripheral vascular disease, personality disorders, stroke and transient ischemic attack (TIA), and viral hepatitis.

Figure 1.

The plot obtained from cross-validation of Elastic Net, showing the change of the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC) with different λ (in log scale) with α of 0.75. The numeric values above the plot indicates the number of variables selected in the between 28 and 61. In other words, 28 (when log(λ) is -6.577986) is the minimum number of variables that guarantees the maximum AUC.

In order to get more robust Receiver Operation Characteristics (ROC) that reflect the prediction performance also for external datasets, another layer of validation on the test data (N = 111,366), which was set aside and unseen during the training phase, was applied to derive ROC. With the variable selected via Elastic Net, we developed a final logistic regression model with co-morbidity analysis, and obtained the ROC of the final logistic regression model with co-morbidity analysis on the test data. We then compared the ROC with that of the traditional logistic regression model without co-morbidity analysis. R version 3.1.3²² with R software packages, glmnet²³, and pROC²⁴ were used for this study.

Results

The Odds Ratio (OR) plot for the traditional logistic regression model without co-morbidity analysis is presented in Figure 2(a) and the same for the final logistic regression model with co-morbidity analysis in Figure 2(b). It is noticeable that adjusted ORs for the same variables differs between the two models. For example, the adjusted OR of being female is 2.07 from the traditional logistic regression model without co-morbidity analysis, but is 1.63 from the final logistic regression model with co-morbidity analysis. Likewise, the adjusted OR of age is 1.03 from the traditional logistic regression model without co-morbidity analysis, but is 1.01 from the final logistic regression model with co-morbidity analysis. Finally, the adjusted OR of income decile is 1.04 from the traditional logistic regression model without co-morbidity analysis, but is 1.02 from the final logistic regression model with comorbidity analysis. The ORs for the disability registration variables in the traditional logistic regression model without co-morbidity analysis ranged from 1.03 (cognitive disability) to 1.42 (hearing disability). The ORs for the comorbidity variables in the final logistic regression model with co-morbidity analysis ranged from 0.78 (acute myocardial infarction) to 5.81 (ADHD and conduct disorder).

Figure 2.

Odds Ratio (OR) plot of the traditional logistic regression model without co-morbidity analysis (b). Odds Ratio (OR) plot of the final logistic regression model with co-morbidity analysis. The point values indicate the adjusted Odds Ratios, horizontal lines indicate the 95% confidence intervals, and the asterisks indicate the level of statistical significance (***: p < 0.001, **: p < 0.01, *: p < 0.05). The variables included in both the traditional model without co-morbidity analysis (a) and the final model with co-morbidity analysis (b) are highlighted in yellow. The adjusted Odds Ratios can differ if variable selection is different. For example, the adjusted OR of being female is 2.07 from the traditional logistic regression model without co-morbidity analysis, but is 1.63 from the final logistic regression model with co-morbidity analysis.

Receiver Operating Characteristic (ROC) curve of the traditional logistic regression model without comorbidity analysis and the final logistic regression model with co-morbidity analysis on the test data, which were unseen during the training phase, are shown in Figure 3. The Area Under the Curve (AUC) of the ROC increased from 0.6992 (the traditional logistic regression model without co-morbidity analysis) to 0.7818 (the final logistic regression model with co-morbidity analysis). Selected performance measures for the 30-predictor co-morbidity model, including sensitivities, specificities, Positive Prediction Values, Negative Prediction Values, Accuracies, and F measures for nine distinct threshold points on the ROC are shown in Table 3.

Figure 3.

Receiver Operating Characteristic (ROC) curve of the traditional logistic regression model without comorbidity analysis (red) and the final logistic regression model with co-morbidity analysis (blue) on the test data, which was unseen during the training phase. The Area Under the Curve (AUC) of the ROC increased from 0.6992 (red) to 0.7818 (blue).

Table 3.

Selected performance measures of the 30- predictor co-morbidity model, including sensitivities, specificities, Positive Prediction Values (PPV), Negative Prediction Values (NPV), Accuracies, and F measures for nine distinct threshold points on the blue curve of the Receiver Operating Characteristic (ROC) shown in Figure 3. The performance measures were evaluated with the test data, which was unseen during the training phase.

Sensitivity	Specificity	PPV	NPV	Accuracy	F.measure
0.100	0.992	0.246	0.977	0.969	0.142
0.200	0.978	0.189	0.979	0.958	0.195
0.300	0.960	0.165	0.981	0.944	0.213
0.400	0.928	0.127	0.983	0.915	0.193
0.500	0.883	0.100	0.985	0.873	0.167
0.600	0.808	0.075	0.987	0.803	0.134
0.700	0.718	0.061	0.989	0.718	0.112
0.800	0.591	0.049	0.991	0.596	0.092
0.900	0.408	0.038	0.994	0.421	0.073

Discussion

Given that depression remains significantly under-diagnosed in all settings of the healthcare system⁴,⁵, data- driven prediction models can play an important role in the screening of depression patients. Although previous work has shown promising results on the ability to predict future diagnoses of depression, such models have not explicitly applied co-morbid medical conditions as independent predictors. Depression is a characteristic disease which can be affected by many medical condition⁹, and can also affect certain medial conditions¹⁰. In 2013, psychological factors affecting medical conditions (PFAOMC) was included as a new diagnosis in DSM-V¹⁵. PFAOMC are the factors which may precipitate or exacerbate the medical condition, interfere with treatment, or contribute to morbidity and mortality. The mechanism of PFAOMC include promotion of known risk factors (i.e. smoking), influence on the underlying pathophysiology (i.e. bronchospasm in asthma), and the interference on the treatment (i.e. poor compliance). Therefore, addressing the co-morbidities related to depression will be a rationally important step in understanding the course of depression, and the analysis of these co-morbidities will likely improve the performance of depression risk prediction models.

In this study, we showed that the AUC of the ROC increased from 0.6992 (the traditional logistic regression model without co-morbidity analysis) to 0.7818 (the final logistic regression model with co-morbidity analysis), after applying the optimized variable selection from Elastic Net (Figure 3). Because neither questionnaire-based screening results (i.e. Patient Health Questionnaire²⁵) nor physician clinical notes are available in claims data, there is no direct information about patients’ moods or symptoms. Given this limitation, this improvement could be interpreted very significant improvement, and the inclusion of co-morbidity analysis could be a key component in improving the performance of depression risk prediction models.

Furthermore, since odds ratio estimates change for some variables after adjusting for co-morbid conditions, the adjusted OR in the final logistic regression model with co-morbidity analysis could reflect estimates closer to the truth. For example, the adjusted OR of being female is 2.07 from the traditional logistic regression model without co-morbidity analysis, but is 1.63 from the final logistic regression model with co-morbidity analysis (Figure 2). Given that, females have a higher co-morbidity burden in general, the traditional logistic regression model will give higher OR for females, by not adjusting for co-morbidities.

The one million twelve-year Korea National Health Insurance Service (KNHIS) longitudinal data used in this study has many advantages for analyzing large scale statistical models. As KNHIS is the only health insurance system which covers all Korean citizens, the random sample cohort from KNHIS can be considered as a nationally representative health data²⁶. Factors arising from multiple health insurance systems effecting diagnosis of depression (i.e. some health insurance plans might have lower coverage for mental health) can be avoided in the single health insurance system, and therefore higher statistical power can be achieved. Therefore, adjusted ORs from the logistic regression model with co-morbidity analysis may represent the risks of each variable in the population.

Cautions are needed when interpreting the epidemiologic results from this study, however. The large sample size in this study is over-powered to detect small effects, so more emphasis should be placed on the magnitude of estimates rather than the statistical significance. Furthermore, the operational definition of depression case group is based on the diagnosis codes in the claims data. Therefore, the depression risk prediction model in this study is predicting the probabilities of each person’s visiting physicians and diagnosed as depressed by physicians, and this will limit the ability of detecting the underdiagnosed depressed population. However, it is noticeable that the findings are consistent with previous studies revealed the relationship between co-morbidities and depression in Korean population with cross-sectional survey study²⁷, as well as Korean Longitudinal Study of Aging²⁸.

In order to develop a better depression risk prediction model which can also address the currently underdiagnosed depressed population, reaching out to the underdiagnosed depressed population with gold standard screening tools will be necessary. Further work is also needed to investigate possible difference in the co-morbidity patterns in different gender, age-group, and socio-economic status. Higher prevalence of depression among female has been discussed to be related to both biological and environmental factors²⁹. Features of depressions can also be vary among different age-groups³⁰, and certain age-groups may have additional risks³¹. Socio-economic factors³² of depression and disparity³³ in depression treatment are also very important topic in public health.

Additional research is needed for optimizing the chronic conditions clusters, or categories. Although CMS- CCW algorithm is a well validated algorithm using ICD codes, optimized clusters developed using insurance claims data might be different when compared to actual clinical manifestation of depression. Even within the clinical practice, the disease classification or categorization can differ among various clinical specialties and subspecialties. Therefore, optimization for co-morbid conditions clusters will be needed for better prediction models³⁴.

Furthermore, integration of medication prescription data will allow better operational definitions with lesser false positives. Further research is also needed for variable interactions (i.e. epilepsy of young female may have different effect from epilepsy of elderly male), as well as time-to-event analysis (i.e. Cox Proportional Hazard regression³⁵), dealing with time-dependent covariates.

Although the focus of this study was on the prediction of the existence of depression based on health insurance claims data, further studies will be needed to confirm if co-morbidity analysis can also improve the performance of the prediction model for treatment response^{36, 37}, or prediction model based on lexical data³⁸, as well as on electronic health records^{39, 40}.

Conclusion

In conclusion, the inclusion of co-morbidity analysis could improve the performance of risk prediction for depression, and the co-morbidity adjusted ORs may indicate the true independent OR of each predictor variable. Further studies will be needed to cover the currently underdiagnosed depressed population, as well as optimizing the chronic conditions clusters.