We first describe the Malawi Service Provision Assessment (SPA) (6) dataset, followed by the methods for the development and evaluation of BN models and the comparison of other statistical models. Finally, we describe the details of the decision tree that we developed for decision analysis.
2.1 The SPA Dataset
The SPA survey was conducted between July 2013 and February 2014 by the Ministry of Health of Malawi, with support from the Demographic and Health Surveys (DHS) Program, to assess the status of health facilities and quality of healthcare in Malawi. Data were collected from 1,060 facilities comprised of 97 hospitals, 489 health centers, 55 dispensaries, 369 clinics, and 28 health posts across three major regions in the country, and are representative at the national level by facility type and managing authority (6). These data have been used previously in studies to assess the quality of care and treatment for pneumonia in Malawi (24) and are freely available from the DHS program (25).
The survey dataset contains observations on 3,441 encounters with children aged 2 to 59 months presenting to an outpatient healthcare facility. For each encounter, the data contains demographic details (age, date of birth, and sex), clinical features (duration of illness, fever, diarrhea, anemia, etc.), mRDT result (if available), and the provider’s diagnosis.
2.2 Data Preprocessing
We assumed the result of the mRDT that is recorded in the dataset to be the gold standard malaria diagnosis. The mRDT has high sensitivity and specificity (0.997 and 0.995 respectively) for the diagnosis of malaria (26) and is recommended for confirmation of disease by both the WHO and Malawi’s malaria management guidelines (27). Thus, if the mRDT result was positive, we considered malaria to be present, and if the test result was negative, we considered malaria to be absent. This variable is referred to as ‘malaria’ or ‘malaria diagnosis’ in the following sections.
While it would have been ideal to have the mRDT result for each encounter in the dataset, this is not the case. Of the 3,441 encounters, an mRDT result was recorded for only 1,139 encounters, and we restricted our analyses to only these encounters. Table 1 shows the variables that we identified to include for modeling. These variables were chosen based on their inclusion in childhood illness management guidelines (2) as well as on expert domain knowledge. Two of the variables are continuous (age and duration of illness) and the remaining variables are categorical. We discretized the continuous variables since the BN algorithms we used are designed for discrete variables. We discretized age by months (< 2, 2-12, 13-24, 25-60, > 60) based on the varying epidemiology of the disease in children of different ages. We discretized the duration of illness by number of days as shown in Table 1. Every predictor variable had one or more missing values, and we denoted them with a special value called ‘Unknown’. The target variable, malaria, is binary, taking the values ‘Positive’ or ‘Negative’ that represent the mRDT result.
Table 1: Variables and values that were included in the models.
Variable
|
Values
|
Target variable
|
|
Malaria
|
Present
Absent
|
Predictor variable
|
|
Age
|
Less than 2 months
2-12 months
13-24 months
25-60 months
Over 60 months
Unknown
|
Duration of Illness
|
Less than or equal to 2 days
3-15 days
16-30 days
Over 30 days
Unknown
|
Conscious
|
Yes
No
Unknown
|
Anemia
|
Present
Absent
Unknown
|
Convulsions
|
Cough or Difficulty Breathing (CDB)
|
Diarrhea
|
History of Fever
|
Fever (temperature>37.5 C)
|
Lethargy
|
Malnutrition
|
Unable to Feed
|
Vomiting
|
2.3 Bayesian Network Models
A BN model is a probabilistic graphical model that is specified by a graphical structure and a set of numerical parameters (14). The graphical structure consists of nodes representing variables and arcs denoting associations between pairs of variables. In this paper, we use nodes and variables interchangeably. Each node in the network has an accompanying conditional probability table that constitutes the parameters of the node. A BN model can be used as a classifier where the model provides the posterior probability distribution of a target node (such as a disease diagnosis) given the values of all other nodes (such as clinical features) in the network (23). Several approaches are available to construct a BN model. In the first approach, both the structure and the parameters are specified manually using expert knowledge. In the second approach, the structure is specified manually and the parameters are estimated from data. In a third approach, both the structure and parameters are automatically estimated from data; a variety of algorithms have been developed to automatically derive BN models in this way. In this study, we used the second and third approaches to develop two BN models for the prediction of malaria using the GeNIe Modeler tool (28) using the variables listed in Table 1. For the first model (manual model) we manually specified the structure based on domain knowledge and computed the parameters of each node using the GeNIe Modeler. For the second model, we used the GeNIe Modeler to automatically derive the structure of a Tree Augmented Naïve Bayes model (described later) and the parameters of each node in the model. For both models, we used the GeNIe Modeler to compute the parameters of each node from the dataset by estimating the conditional probability distribution of the node given the values of its parent nodes (23).
Manual Model. Based on domain knowledge of malaria from experts and the literature, for the manual model, we modeled clinical features as conditionally independent of each other given malaria. Specifically, a clinical feature that was a symptom or a sign was represented as a child of the malaria node to create a Naïve Bayes structure. A feature that was not a sign or a symptom was represented as a parent of the malaria node. For example, a sign such as convulsions was represented as a child of malaria with the arc directed from malaria to convulsions. This encodes clinical knowledge that malaria can cause convulsions. As another example, age was represented as a parent of malaria with the arc directed from age to malaria. This denotes knowledge that younger children may be more vulnerable to contracting malaria than older children. In a Naïve Bayes disease model, each sign or symptom node has a single incoming arc from the disease node with no arcs among them.
Tree Augmented Naïve Bayes Model. While the manual model is simple and interpretable, the conditional independence assumption may be overly simplistic. Hence, we developed a second model by automatically deriving a Tree Augmented Naïve Bayes (TAN) model using the GeNIe Modeler. The TAN model extends the Naïve Bayes model by allowing arcs among child nodes (21). For example, in a Naïve Bayes model, diarrhea and convulsions are linked only by incoming arcs from the malaria node while in the TAN model an additional arc may be included from diarrhea to vomiting that implies vomiting is associated with both malaria and diarrhea. The TAN algorithm in GeNIe Modeler enables efficient learning of both the structure and the parameters of a TAN model.
2.4 Comparison Models
For comparison with the diagnostic predictions of BN models, we derived several commonly used statistical models including logistic regression and random forest to predict malaria. Instead of discretizing the continuous variables, age and duration of illness, we scaled them so that the values had unit variance; when a variable had missing values, we imputed its value as the mean of its non-missing values. We treated the categorical variables in the same way as for the BN models. We derived and evaluated the models using the scikit-learn library (29) in Python. The logistic regression model was derived using the L2 penalty and the for the value of regularization hyperparameter a search was conducted over seven possible values (0.001, 0.01, 0.1, 1, 10, 100, 1000). The hyperparameters (and the values over which the search was performed) for the random forest model included number of trees in the forest (100, 200, 500), criterion for the split (“gini”, “entropy”), maximum depth of tree (4, 5, 6, 7, 8), and the number of features (square root of total features, log of total features, total features).
2.5 Derivation and Evaluation
We derived and evaluated the manual BN, TAN, logistic regression, and random forest models using 10-fold cross-validation. The dataset was divided into 10 folds, stratified on malaria diagnosis. Over 10 iterations, each fold was used as a test set in turn and the remaining folds were combined to form the training set. For the manual BN model, we estimated the parameters of the model using 10-fold cross-validation while the structure was fixed across all iterations. For the TAN model, we estimated both the structure and parameters using 10-fold cross-validation. For the logistic regression and random forest models, during each iteration of cross-validation, the hyperparameters were chosen using the training set.
During each iteration of cross-validation, we applied the models to predict the probability of malaria in the test set. Using these predictions, we computed the area under the Receiver Operating Characteristic curve (AUC). The AUC value indicates the diagnostic discrimination performance of the model, where perfect performance has an AUC of 1. Then, we converted the probability into a binary prediction of malaria present or absent by using two probability thresholds, including the default threshold of 0.5 and an optimal threshold obtained by maximizing the Youden Index. The threshold that maximizes the Youden Index is the threshold that optimizes the model’s ability when equal weight is given to sensitivity and specificity (30). With the binary predictions, we computed balanced accuracy (BAC), sensitivity, specificity, and the net reclassification improvement (NRI) at both thresholds. BAC is the average of sensitivity and specificity and is more useful than accuracy when the proportion of the target values are imbalanced. NRI quantifies how well a new model correctly reclassifies children with and without malaria compared to a baseline model (31). NRI is computed as:
(sensitivity of new model – sensitivity of baseline model) + (specificity of new model – specificity of baseline model).
For statistical comparisons, we used the DeLong’s test to compare AUCs of two models (32), the paired two-sample Wilcoxon test to compare BACs of a pair of models (33), and McNemar’s Chi-Square test to compare sensitivities and specificities of two models (33).
2.6 Decision Tree Development
To conserve the use of mRDT in a resource-constrained setting like a rural health post in Malawi, we developed a decision tree to compare the consequences of using and not using the mRDT. The decision tree integrates the probability of having malaria (that is obtained from a predictive model) with the costs of testing and treatment and identifies the optimal decision (relative to a set of probabilities and utilities) – to use mRDT or not – in a specific patient.
The decision tree that we developed is shown in Figure 1 and uses a common approach to model sequential decisions (34). The decision is driven by the expected costs of testing and treatment that are denoted by ‘mRDT?’ and ‘Treat?’ nodes. We calculated the expected cost of the [mRDT?=no] branch using the probability of malaria from a predictive model and costs associated with each decision as
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)
In Figure 1, P(malaria+|F) is the probability that malaria is present given the clinical features of the patient. P(malaria-|F) is the probability that malaria is absent given the features. The costs (shown in the hexagons) in the decision tree are from the perspective of a payer of healthcare costs, such as the government of Malawi, and depend on the resources used, including mRDT and ACT drugs. We used the following costs based on the literature: a mRDT costs US $0.60 (8) and a course of ACT for uncomplicated malaria costs US $1.00 (35). We estimated the cost of mistakenly not treating a child with malaria at US $16.60 based on the assumption that the cost may go up to 10 times the cost of mRDT and ACT drugs for uncomplicated malaria if the untreated disease becomes severe, resulting in hospital admission.
We computed the expected cost of the [mRDT?=yes] branch as
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)
In the above equations, P(malaria+|mRDT+, F) is the probability of malaria being present given that the mRDT result is positive and the clinical features of the patient, and P(malaria-|mRDT-, F) is the probability of malaria being absent given that the mRDT result is negative and the clinical features of the patient. P(mRDT+|F) and P(mRDT-|F) represent the probabilities of mRDT being positive or negative, respectively, given the clinical features of the patient. These probabilities are obtained from a model such as the manual BN model and are assumed to be equal to P(malaria+|F) and P(malaria-|F) respectively. We have assumed that a positive result on the mRDT is equivalent to the child having malaria.
We also performed a sensitivity analysis to determine how dependent the strategy selection is on the probability of malaria. We varied the probability of malaria, P(malaria+|F) or P(mRDT+|F), from 0 to 1 and calculated the expected costs of using and not using the mRDT to determine the probability ranges in which a child may be treated based on clinical features alone without performing mRDT.