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INTRODUCTION
Multiple drug–resistant tuberculosis (MDR-TB) is caused by strains of Mycobacterium tuberculosis (MTB) that are resistant to isoniazid (INH) and rifampicin (RFP) at least. It is linked to the high morbidity and mortality1 of TB and threatens the achievements of TB control at present. In 2015, MDR-TB cases in India, China, and the Russian Federation accounted for 45% of the total 580,000 cases.2 Although the detection rate of MDR-TB among smear positive TB patients was high,3 the diversity, and complexity of drug resistant spectrum poses threat to TB control in China. Abundant literature demonstrated many risk factors,4,5 (demographics, environment, behavior, and genetic susceptibility6,7) impact on the incidence of MDR-TB complicatedly. However, the correlation between the mechanism of these risk factors and the incidence of MDR-TB still remains unclear. Some researchers have attempted to screen and identify a set of optimal predictors potentially for MDR-TB in terms of pathogenesis through a logistic regression model.8,9 While the techniques of the logistic regression model are relatively mature, there are still several weaknesses (co-linearity of variables, interactions between variables, and judgment of high-risk groups10). By contrast, as outstanding data mining technology, the classification tree method becomes an alternative strategy to make up for shortcomings of traditional parametric tests, and identify some main factors affecting the occurrence of disease effectively. It has been used to predict a variety of diseases.11–13 Chi-square Automatic Interaction Detector (CHAID) decision tree has been used to formulate pathways for the early detection of metabolic syndrome in young adults.14 It also contributes to identify clients for methadone treatment who experienced poorer treatment outcomes. Besides, it worked for investigators to build a prediction model for the incidence risk of ischemic stroke.13
Therefore, we hypothesized that classification tree model based on simple and reasonable decision rules can be established to predict the incidence risk of MDR-TB. Thus, we conducted a case–control study to assess the risk factors of MDR-TB and how these factors interact, and to evaluate the model, thus providing evidence for the early prevention of MDR-TB in China.
MATERIALS AND METHODS
Study subjects.
From January to June in 2013, a nonmatching case-control study was conducted to identify risk factors of MDR-TB. Seventy four newly diagnosed MDR-TB patients served as the case group. To ensure the representation and comparability of the control, 95 patients without TB from the same medical institution were randomly selected as the control group. Sputum culture and drug susceptibility testing with the proportion method for INH, RFP, ethambutol, and streptomycin were used to identify MDR-TB according to the guidelines for drug-resistant surveillance in TB (4th edition) published by the World Health Organization.15
Data collection.
We used a structured questionnaire, including sociodemographic characteristics, living environment, dietary patterns, daily life, mental status, past medical history, and other potential risk factors, to explore risk factors of MDR-TB. All data were collected by self-reporting. “Town/city” and “Rural area” were two options of “Residence.” Exposure to patients with TB and with/without a mask when talking was also recorded. If a participant was in line with the description, record “Yes”; otherwise, record “No.” History of other chronic respiratory diseases was identified through asking participants “Has a doctor ever told you that you have chronic obstructive pulmonary disease, bronchial asthma, lung cancer, or etc.?” “Cigarette smoking” was defined as people who have smoked more than 100 cigarettes in their lifetime. Family economic condition was assessed according to the selection of “below median” and “equal to or over median,” with median income of 5,000 RMB/month. Physical exercise was investigated by two alternative terms: “less than three times a week” and “equal to or over three times a week.” The questionnaire was pretested. All interviews were conducted by unified-training interviewers under protection of the privacy of subjects.
Ethical approval.
The study protocol was approved by the Ethics Review Committee of School of Public Health, Tongji Medical College, Huazhong University of Science and Technology. Before interviews, written informed consents were obtained.
Statistical analysis.
First, we conducted a univariate analysis to identify significant variables primarily. Secondly, the multivariate unconditional logistic model was established to identify potential risk factors. Finally, the classification tree model with CHAID technique was applied to analyze the relationship between MDR-TB and risk factors discovered. We used stepwise analysis with the most significant predictor (the largest χ2 value) to divide the entire cases into two or more mutually exclusive subgroups. Same as the first step, the cases in subgroups are further separated by the second significant predictor of the original outcome. The analysis continued until there were no more significant predictors. A two-sided P value ≤ 0.05 was considered with statistical significance.
All statistical analyses were performed using the SPSS statistical package, version 21.0.
RESULTS
Characteristics of subjects.
All participants were Han, and the male-to-female sex ratio was 88:81(52.1% were men). The average age of the case group (38.37 ± 14.55 years) was higher than that of the control group (36.86 ± 13.03 years), but there was no significant difference between two groups on age distribution (Table 1).
P value and estimated OR-value of risk factors from unconditional logistic regression model
| Variables | Wald χ2 value | P value | OR (95% CI) |
|---|---|---|---|
| Residence | |||
| Town/city | – | – | – |
| Rural area | 5.488 | 0.019 | 3.664(1.236–10.860) |
| Family financial condition | |||
| Equal to or over median | – | – | – |
| Below median | 7.166 | 0.007 | 3.881(1.538–10.476) |
| With other respiratory diseases | |||
| No | – | – | – |
| Yes | 12.324 | 0.000 | 83.522(7.061–987.995) |
| Exposure to TB patients | |||
| No | – | – | – |
| Yes | 41.407 | 0.000 | 30.593(10.792–86.719) |
| History of smoking | |||
| No | – | – | – |
| Yes | 6.146 | 0.013 | 4.069(1.342–12.339) |
| Physical exercise | |||
| Less than three times a week | – | – | – |
| ≥ three times a week | 4.028 | 0.045 | 0.342(0.120–0.975) |
| Mask his nose when talking | |||
| No | – | – | – |
| Yes | 6.502 | 0.011 | 0.262(0.094–0.734) |
CI - confidence interval; OR = odds ratio.
The univariate analysis demonstrated that the differences of the characteristics and risk factors between two groups were statistically significant: occupation (χ2 = 4.334, P = 0.037), residence (χ2 = 4.164, P = 0.041), whether belongs to the floating population (χ2 = 6.917, P = 0.009), family economic condition (χ2 = 16.761, P < 0.001), vaccination of Bacillus Chalmette Guerin (χ2 = 12.161, P = 0.002), history of other chronic respiratory diseases (χ2 = 4.594, P = 0.032), history of exposure to TB patients (χ2 = 56.273, P < 0.001), history of smoking (χ2 = 12.325, P < 0.001), exercise (χ2 = 9.309, P = 0.002), whether mask nose when talking with others (χ2 = 4.509, P = 0.034), and whether avoid others when cough (χ2 = 8.216, P = 0.004).
The unconditional logistic regression model was built to explore risk factors of MDR-TB. The results are presented in Table 1, which shows that some risk factors are found to be significantly associated with MDR-TB. These factors are residence, family with financial difficulties, suffering from other chronic respiratory diseases, exposure to TB patients, smoking history, physical exercise, and wore a mask when talking.
Results from classification tree method and rules for predicting the incidence risk of MDR-TB.
Figure 1 revealed that the classification tree included four major predictor variables and nine nodes (including five terminal nodes) with a growing depth of three. Exposure to TB patients was the most important predictor as it split the first level of the tree into two branches. Subjects with exposure to TB patients had a significantly higher risk of MDR-TB than those without (81.4% to 17.2%, P < 0.05). For the group of subjects with exposure to TB patients, subjects whose families had financial difficulty would have a significantly higher MDR-TB risk (91.1% to 64.0%, P < 0.05). For the group of subjects who were not exposed to TB patients, the risk of MDR-TB between the subjects with or without other chronic respiratory diseases was significantly different (80.0% to 13.8%, P < 0.05). History of smoking was identified as the third prominent variable. Subjects with history of smoking had a higher risk of MDR-TB.
The classification tree diagram by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The classification tree diagram by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The classification tree diagram by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
Table 2 demonstrates the decision rules of the model. The terminal nodes were ranked by the probability of MDR-TB (from 9.3% in node 7 to 91.1% in node 4). The decision rules included four significant predictor variables of MDR-TB: exposure to TB patients, family with financial difficulties, with other chronic respiratory diseases, and history of smoking. Subjects who were exposed directly to drug-resistant TB (cases of the second generation of TB patients) were those who had exposure to TB patients. Subjects who could not afford liver protection drugs were perceived as family with financial difficulties. Calculation of the gain and index are presented in Table 3. The gain percent equaled to the gain number divided by the total cases. The method for calculating the response percentage was similar to that of the probability mentioned previously. The nodes 4, 5, and 3 had more cases than other cases (with index values greater than 100%), but the nodes 8 and 7 (with index values less than 100%) showed opposite results.
Decision rules for the classification tree model of MDR-TB
| Node number | Exposure to TB patients | Family with financial difficulties | With other chronic respiratory diseases | History of smoking | Probability of MDR-TB (%) |
|---|---|---|---|---|---|
| No.4 | Yes | Yes | – | – | 91.1 |
| No.5 | No | – | Yes | – | 80.0 |
| No.3 | Yes | No | – | – | 64.0 |
| No.8 | No | – | No | Yes | 31.6 |
| No.7 | No | – | No | No | 9.3 |
MDR-TB = multidrug-resistant tuberculosis.
Calculation of gain and index for nodes (node by node)
| Node number | Node | Gain | Response, % | Index, % | ||
|---|---|---|---|---|---|---|
| N* | Percent†, % | N | Percent, % | |||
| No.4 | 45 | 26.6 | 41 | 55.4 | 91.1 | 208.1 |
| No.5 | 5 | 3.0 | 4 | 5.4 | 80.0 | 182.7 |
| No.3 | 25 | 14.8 | 16 | 21.6 | 64.0 | 146.2 |
| No.8 | 19 | 11.2 | 6 | 8.1 | 31.6 | 72.1 |
| No.7 | 75 | 44.4 | 7 | 9.5 | 9.3 | 21.3 |
Growing method: chi-square automatic interaction detector; dependent variable; multidrug-resistant tuberculosis.
Represents the number of cases in each node.
Represents the percentage of cases in the total number of subjects for each node.
The CHAID classification tree analysis showed that all participants were initially split based on the exposure to TB patients; hence, exposure to TB patients was the most important factor in regards to MDR-TB (Figure 2). Subjects with exposure to TB patients were at a higher risk of MDR-TB. Family with financial difficulties was another important factor. When Subjects had low income (node 4), 91.1% of the subjects developed MDR-TB. History of other chronic respiratory diseases was also an important variable for the development of MDR-TB. Without other chronic respiratory diseases (node 6), few subjects (13.8%) became MDR-TB, but with other chronic respiratory diseases (node 5), 80.0% became MDR-TB. The CHAID classification tree also showed that subjects reported of smoking were more likely to suffer from MDR-TB. As nodes 7 and 8 showed, if the subjects suffered to other chronic respiratory diseases, the rate of MDR-TB in those smoked was more than three times as high as in subjects without history of smoking (31.6% versus 9.3%, respectively).
The gains and index charts of the classification tree by chi-square automatic interaction detector (CHAID) algorithm for predicting the incidence risk of multidrug-resistant tuberculosis (MDR-TB) (Growing method: CHAID; dependent variable; MDR-TB; Target category: cases of MDR-TB).
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The gains and index charts of the classification tree by chi-square automatic interaction detector (CHAID) algorithm for predicting the incidence risk of multidrug-resistant tuberculosis (MDR-TB) (Growing method: CHAID; dependent variable; MDR-TB; Target category: cases of MDR-TB).
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The gains and index charts of the classification tree by chi-square automatic interaction detector (CHAID) algorithm for predicting the incidence risk of multidrug-resistant tuberculosis (MDR-TB) (Growing method: CHAID; dependent variable; MDR-TB; Target category: cases of MDR-TB).
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The evaluation of exhaustive CHAID prediction model.
The prediction model was assessed by the misclassification risk estimate. Quantitatively, 84.0% of the subjects were correctly classified through the decision rules of this model (with 0.160 risk estimate and 0.028 standard errors). Graphically, the index value and the gains chart were both compliant with the standards: the index value started above 100%, remained on a high plateau as it moved along and then declined rapidly toward 100%; the gains chart rose steadily toward 100% (Figure 2). Furthermore, the results of the receiver operating characteristic curve (ROC) were presented in Figure 3. Specificity, sensitivity and the area under ROC curve were 82.4%, 85.3%, and 0.838, respectively. The area under ROC curve was statistically significant.
The receiver operating characteristic charts of the classification tree by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The receiver operating characteristic charts of the classification tree by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
The receiver operating characteristic charts of the classification tree by chi-square automatic interaction detector algorithm for predicting the incidence risk of multidrug-resistant tuberculosis. This figure appears in color at www.ajtmh.org.
Citation: The American Journal of Tropical Medicine and Hygiene 97, 6; 10.4269/ajtmh.17-0029
DISCUSSION
Classification tree is an effective data mining method in selecting risk factors and predicting the risk of multifactorial association diseases. CHAID of classification tree method has been widely used in various fields of research.13,14,16–18 Compared with traditional model methods, such as multiple linear regressions, Cox proportional hazards model, and logistic regression models, CHAID has distinctive superiority in dealing with problems such as risk factors screening and risk prediction, and can particularly demonstrates the complicated multifactorial interactions.10 Classification tree method with CHAID technique would be completely unaffected by co-linearity, outliers, or distribution errors. In addition, it can discover and expose the interactions between the selected variables.13
On the basis of extensive literature review, we found that the classification tree model suited to studies of chronic diseases (hypertension, diabetes). Because TB is one of the major public health problems worldwide, we attempt to use this method to analyze its incidence risk for individuals. In our research, we identified four variables (exposure to TB patients, family with financial difficulties, history of other chronic respiratory diseases, and history of smoking) for the prediction of MDR-TB incidence risk using the classification tree model with simple and reasonable decision rules. Individuals who are exposed directly to cases of the second generation of TB patients may become primary MDR-TB, not acquired MDR-TB during or after treatment.19–23 It is the most important factor that influences the risk of developing MDR-TB.24 Individuals who cannot afford liver protection drugs may result in bad therapy outcome, thereby becoming more financial difficult family.23,25–36 Individuals with other chronic respiratory diseases have low resistance to diseases and are more likely to get MDR-TB.28,29 And individuals who smoke or smoked before are regarded as population easier to be infected with diseases, including MDR-TB.30,31 Compared with logistic regression models, this classification tree model generated by CHAID algorithm reveals the multifactorial interactions among risk factors and determines the individuals who are at high risk of MDR-TB.
Contact with TB, family income, chronic respiratory diseases, and smoking, which were in turn selected by CHAID algorithm of classification tree method, may play more cardinal roles in the incidence risk of MDR-TB than other factors. These selected variables could be considered as the most fundamental factors of MDR-TB and be targeted as the major aspects in the primary prevention strategies of MDR-TB. The assessment of the prediction model demonstrated that it could determine the major risk factors of MDR-TB and reveal their hidden interactions reliably. Therefore, the results may help us screen individuals who are at high risk of MDR-TB and predict the incidence risk for particular group of people on the basis of the decision rules of classification tree. However, previous treatment of TB, the most significant risk factor associated with MDR-TB found in many studies6,8,23,25,27,30–36 was not included in this analysis. The reason was that we selected 95 healthy people without history of TB as controls in this study.
Some limitations in the process of this research should be acknowledged. First, the sample size was comparatively small, which might have a great impact on the results when the parameters of classification tree model were changed. To some extent, this would affect the accuracy of prediction and the causal association between risk factors and MDR-TB. Second, although this study was conducted under a case–control design that 95 healthy people without history of TB from the same medical institution as the control group. History of TB treatment, which was found as the most important risk factor of MDR-TB in many studies, was not included in the analysis. And the information biases might be inevitable when data were collected. Finally, we designed the risk model aimed at validating the risk factors identified from the former meta-analysis and found that the risk factors have the high degree to match the identified risk factors. Although this study is a validated study, overfitting cannot be ruled out either. At that time, individuals who are at high risk of MDR-TB will be more easily identified and health promotion or preventive treatment can be taken immediately to prevent the occurrence of MDR-TB.
CONCLUSION
In summary, we identified four significant predictor variables (exposure to TB patients, family with financial difficulties, history of other chronic respiratory diseases, and history of smoking) of MDR-TB through a case-control study and established a prediction model by means of CHAID algorithm of classification tree method. It explained the main risk factors and their latent interactions to predict the risk of MDR-TB. Parameters of ROC curve suggested that the classification tree model worked well for predicting MDR-TB.
Acknowledgment:
We thank Hong Xie and Qionghong Duan for their guidance and assistance in the process of field data collection during our study.
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