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Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60http://www.biomedcentral.com/1472-6947/13/60 Using latent class analysis to model prescriptionmedications in the measurement of fallingamong a community elderly population Patrick C Hardigan1*†, David C Schwartz2† and William D Hardigan3† Background: Falls among the elderly are a major public health concern. Therefore, the possibility of a modelingtechnique which could better estimate fall probability is both timely and needed. Using biomedical,pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool forthe prediction of falls among community dwelling elderly.
Methods: Using a retrospective data-set a two-step LCA modeling approach was employed. First, we looked forthe optimal number of latent classes for the seven medical indicators, along with the patients' prescriptionmedication and three covariates (age, gender, and number of medications). Second, the appropriate latent classstructure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator wasmaximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for modelcomparisons.
Results: A review of the model fit indices with covariates shows that a six-class solution was preferred. Thepredictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, wasgood at 90%. Specific prescription medications were found to strongly influence group membership.
Conclusions: In conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medicaldata.
model assumptions In this paper, we demonstrate Latent Class Analysis (LCA) is a statistical method for the utility of LCA for the prediction of falls among finding subtypes of related cases (latent classes) from community dwelling elderly.
multivariate categorical data The most common use Falls among the elderly are a major public health con- of LCA is to discover case subtypes (or confirm hypoth- cern. Research on falls and fall-related behavior among esized subtypes) based on multivariate categorical data the elderly has found that falls are the leading cause of LCA is well suited to many health applications injury deaths among individuals who are over 65 years where one wishes to identify disease subtypes or diag- of age Research has shown that sixty percent of nostic subcategories LCA models do not rely on fall-related deaths occur among individuals who are traditional modeling assumptions (normal distribution, 75 years of age or older Demography research linear relationship, homogeneity) and are therefore, less estimates that by 2030, the population of individuals subject to biases associated with data not conforming to who are 65 years of age or older will double and by 2050the population of individuals who are 85 years of age orolder will quadruple * Correspondence: †Equal contributors Predicting elderly falling can be complex and often 1Department of Public Health, Nova Southeastern University, 3200 South involves heterogeneous markers. Therefore, the identifi- University Dr., Health Professions Division, Ft. Lauderdale, FL 33328, USA cation of more homogeneous subgroups of individuals Full list of author information is available at the end of the article 2013 Hardigan et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the CreativeCommons Attribution License which permits unrestricted use, distribution, andreproduction in any medium, provided the original work is properly cited.
Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60 and the refinement of the measurement criteria are typ- or no fall) and second what covariates increase or decrease ically inter-related research goals. Appropriate statistical the likelihood of this occurrence. The four specific aims of applications, such as latent class analysis, have become the study are to identify items that indicate classes, available for researchers to model the complex hetero- estimate class probabilities, relate the class probabilities to covariates, and predict a distal outcome (fall/no-fall) based Latent class models are used to cluster participants.
on class membership. We model this process through the This type of model is adequate if the sample consists of application of latent class analysis (Figure ).
different subtypes and it is not known before-handwhich participant belongs to which of the subtypes The latent categorical variable is used to model hetero- geneity. In the classic form of the latent class model, A convenient retrospective database consisting of a ran- observed variables within each latent class are assumed dom sample of 3,293 elderly patients was used to develop to be independent, and no structure for the covariances a model to predict the likelihood of falling among individ- of observed variables is specified uals aged 65 years or older. Due to the retrospective LCA is one of the most widely used latent structure nature of this study, this study was granted an exemption models for categorical data LCA differs from more in writing by the Nova Southeastern University's IRB.
well-known methods such as K-means clustering which This is a poof-of-concept analysis so it should be noted apply arbitrary distance metrics to group individuals that this data set was not designed for an LCA, therefore, based on their similarity LCA derives clusters additional medical variables which may predict falling based on conditional independence assumptions applied were not available. For this study an elderly person was to multivariate categorical data distributed as binomial defined as someone aged 65 years or older. Descriptive or multinomial variables ,Using statistical distri- data were as follows (Table ): The average age of patients butions rather than distance metrics to define clusters was 77 years old; 32 percent of the subjects had fallen in helps in evaluating whether a model with a particular the last 30 days; falling patients were taking an average of number of clusters is able to fit the data, since tests can five prescription medications while non-fallers were con- be performed to observed (ni) versus model expected suming two; and 75 percent of the subjects were female.
values (mi), using exact methods as recommended ,].
Research demonstrates that about 22% of community- This comparison gives rise to a χ2 test of global model fit, dwelling elderly persons fall each year; 10% of these in which significant values indicate lack of fit ]. Here "fallers" have multiple episodes []. This research was ap- lack of fit means deviation of (model) predicted (m) proved by Nova Southeastern University's Institutional frequencies from observed frequencies (n) Review Board for human subjects research.
Latent class analysis assumes that each observation is a member of one and only one latent class (unobservable)and that the indicator (manifest) variables are mutually in-dependent of each other []. The models are expressedin probabilities of belonging to each latent class. Forexample, seven manifest variables can be expressed as: where πX denotes the probability of being in a latent class (t = 1,2,…,T) of latent variable X; πA X denotes the condi- tional probability of obtaining the ith response from itemA, from members of class t, i = 1,2,…,I; and πBjXπCjXπDjX , j = 1,2,…,j k = 1,2,…,k l = 1,2,…,l m = 1,2,…,m n = 1,2,…,n O = 1,2,…,O are the corresponding conditionalprobabilities for items B,C,D,E,F, and G respectively.
We are testing the hypothesis that a two-class distal Figure 1 Proposed fall model for the latent class analysis. Yi are relationship (fall/no fall) can explain the relationship the observed categorical medical indicators on the latent classes C.
among the biomedical, pharmacological and demographic Drug Measure is the correspondence analysis derived drug score for variables. Proper analysis of this data requires the under- each subject. Age is the age of patient. # Rx is the number of standing of two interdependent outcomes.2 First, the prescriptions taken by each subject. Gender is the subjects reportedgender. Falling is the distal outcome.
binary outcome is whether or not the event occurred (fall Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60 Table 1 Descriptive statistics cerebral ischemia. Data from both principal and secondary diagnosis fields within a patient record.
Number of Medications  Type of prescription medication—type of prescription medication was taken from patient  Number of prescription medications—was taken The data set was taken from the State of Florida's Elder from patient records.
Affairs Office. All variables were physician diagnosed andrecorded in an electronic dataset using appropriate ICD-9 Demographic variables codes. Variables included in the database were:  Age—was taken from patient records.
 Gender—Self reported male or female taken from patients' record.
 Arthritis—defined as a person diagnosed with osteoarthritis (OA) and/or rheumatoid arthritis (RA). Presence or absence of arthritis was based onresponses to questions on the basis of ICD-9 714.0,  Falling—was defined as "an event which results in 715.× -716.×, from both principal and secondary the person coming to rest inadvertently on the diagnosis fields within a patient record.
ground or other lower level, and other than as a  High Blood Pressure (HBP)—defined as a person consequence of sustaining a violent blow." Falling diagnosed with hypertension. HBP was identified on was taken from both principal and secondary the basis of ICD-9 codes 401–405, from both diagnosis fields within a patient record.
principal and secondary diagnosis fields within apatient record.
A two-step modeling approach was employed. First, it  Diabetes—defined as a person diagnosed with was necessary to reduce the number of different medica- diabetes mellitus. Diabetes was identified on the tions (N = 121). Initially, a licensed geriatric pharmacist basis of ICD-9 codes of 250.0×–250.5× and 250.7×– (PharmD) reviewed the medication list for accuracy and 250.9× from both principal and secondary diagnosis to remove medications that have not been shown to im- fields within a patient record.
pact the probability of falling. Using correspondence  Heart Disease (HD)—defined as a person diagnosed analysis (CA) the medications were converted to con- with coronary artery disease. HD was identified on tinuous scores. CA is an exploratory technique related the basis of ICD-9 codes 414.0x, from both principal to principal components analysis which finds a multidi- and secondary diagnosis fields within a patient mensional representation of the association between the row and column categories of a multi-way contingency  Foot Disorders (FD)—defined as a person diagnosed table ]. This technique finds scores for the row and with peripheral neuropathy, foot wounds, peripheral column categories on a small number of dimensions vascular disease, or Charcot arthropa. FD was which account for the greatest proportion of the chi2 for identified on the basis of ICD-9 codes 356.9, 892.0- association between the row and column categories, just 892.2, 443.9, and 713.5 from both principal and as principal components account for maximum variance secondary diagnosis fields within a patient record.
These scores were then used in the latent class ana-  Parkinson's Disease (PD)—defined as a person lysis. Similar to other data reduction techniques, CA can diagnosed with Parkinson's Disease. PD was be used to transform data identified on the basis of ICD-9 code 332.0 from Second, we looked for the optimal number of latent clas- both principal and secondary diagnosis fields within ses for the seven binary indicators: (1) arthritis, (2) high a patient record.
blood pressure, (3) diabetes, (4) heart disease, (5) foot disor-  Stroke—defined as a person diagnosed with ders, (6) Parkinson's disease, and (7) stroke; along with the occlusion and stenosis of precerebral arteries patients' medication "score" and three covariates (age, gen- including basilar artery, carotid artery, and vertebral der, and number of medications). The appropriate latent artery, etc.; occlusion of cerebral arteries including class structure, with the covariates, were modeled on the cerebral thrombosis and Cerebral embolism; distal outcome (fall/no fall). The default estimator was max- unspecified cerebral artery occlusion; and transient imum likelihood with robust standard errors. The Pearson

Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60 Table 2 List of medications and correspondence scores Based on patient charts forty-one different medications were used in the latent class analysis (Table To reduce this to a manageable number, correspondence analysis (CA) was employed and the CA values were saved for use in the latent class analysis. Due to missing data this reduced the number of subjects in the final model to 2,814. The higher the CA score the more likely the medication will induce a fall. CA scores are given by the following formula  P is the matrix of counts divided by the total  r and c are row and column sums of P the Ds are diagonal matrices of the values of r and c A plot of the values indicates that an elderly person with a score of 0.40 has a 50% chance of falling (Figure For this manuscript CA values are averaged for each person and referred to as the drug falling measure (singular value = 25%, inertia = 6% chi-square = 164.63).
For example, a person may be using three different medications with CA values of -0.30, 0.20, and 1.20; so their drug falling measure is 0.37. A higher drug falling measure is associated with a higher probability of falling (r = .19, p < 0.00).
chi-square, likelihood ratio chi-square, (BIC), Lo-Mendell- Figure 2 Proposed fall model for the latent class analysis. This Rubin Adjusted Likelihood Ratio test and the bootstrap is a plot of the probability of falling by correspondence analysisderived drug score.
likelihood ratio test were used for model comparisons.
Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60 Table 3 Basic latent class structure Four class solution Five class solution Six class solution Seven class solution Number of parameters Latent class analysis Group I. Seventeen percent of the sample is For the latent class analysis, a review of the model fit in- classified into latent class one (Table ). The dices shows that a six-class solution was preferred classification accuracy is 95%; the misclassified (Table . The six-class solution provided a lower Bayesian elderly were all placed into class four (Table Information Criteria--BIC (lower is better), much smaller Subjects in class one have a 47% chance of falling.
chi-square values, and as indicated by the procedures The odds ratio indicate that a person in class one is (Lo-Mendell-Rubin likelihood ratio test--LMR and 4.41 times more likely to fall than a person in class bootstrap likelihood ratio test--BLRT), non-significant six: Healthy Group II (Tables and ).
p-values. Age, number of medications, and gender were  Class two is also affected by all measured medical shown to have a significant impact on falling. Females, conditions (Figure The average age of this class is older patients, and the more prescription drugs an elderly 76.89 ± 7.02, the average number of medications is person takes, the greater the probability that they will fall.
7.5, and the drug falling measure is 0.017. This is Table provides a comparison of fit indices for four-class, defined as the Poorest-Health Group II. Twenty- five-class, six-class and seven-class solutions. The six class eight percent of the sample is placed into latent structure, with covariates is interpreted as follows: class two (Table ). The classification accuracy is89% (Table misclassified elderly were placed into  Class one is most likely to be affected by all medical class three. Subjects in class two have a 46% chance conditions (Figure The average age of this class is of falling. The odds ratio indicate that a person in 77.78 ± 7.01, the average number of medications is class two is about 4.67 times more likely to fall than a 4.7, and the average drug falling measure is 0.016.
person in class six: Healthy Group II (Tables and Latent class one is defined as the Poorest-Health  Class three is generally unaffected by all medical markers (Figure ). The average age of this class is78.83 ± 6.63, the average number of medications is 7.8, and the drug falling measure is 0.006. We definethis as the Healthy Group I. Seventeen percent of the sample is classified class three (Table ). The classification accuracy for latent class three is 84% (Table ). Misclassified elderly were placed into class two, indicating some overlap between the two latent classes. Subjects in class three have a 16% chance of falling. There is no significant difference in the likelihood of falling between class three and class Medical Condition six: Healthy Group II (Tables and ).
 Class four is primarily affected by arthritis; therefore, this is defined as the arthritis group(Figure ). Twenty-percent of the sample fell into Figure 3 Overlay plot of latent classes by medical condition.
latent class four (Table The average age of this Arthritis = Arthritis. HBP = High Blood Pressure. DB = Diabetes.
class is 78.69 ± 7.32, the average number of HD = Heart Disease. FD = Foot Disorders. PD = Parkinson's medications is 2.6, and the drug falling measure is Disease. Stroke = Stroke.
-0.003. The classification accuracy is 96% (Table ).
Hardigan et al. BMC Medical Informatics and Decision Making 2013, 13:60 Table 4 Most likely latent class membership Misclassified elderly were placed into class one.
Subjects in class three have a 26% chance of falling.
This paper demonstrated the utility of LCA in the meas- The odds ratio indicate that a person in class four urement of falling among community-dwelling elderly.
is approximately 2.07 times more likely to fall than The basic idea underlying LCA is that variables differ a person in class six: Healthy Group II (Tables across previously unrecognized subgroups These subgroups form the categories of a categorical latent  Class five is primarily affected by high blood variable. Given the potential for confounding among the pressure,diabetes, heart disease and foot disorders study variables, latent class analysis holds great promise.
(Figure ). This group is defined as the diabetes- The six-class solution was statistically sound and pro- heart disease group. Eight percent of the sample fell vided a relatively straightforward interpretable number into latent class five (Table ). The average age of of classes. The interpretation of a LCA relies on both this class is 77.53 ± 7.04, the average number of the statistical indices and the practical interpretation of medications is 3.1, and the drug falling measure is -0 the classes. In our example, the statistical indices .009. The classification accuracy is 95% (Table strongly point toward a six factor model. The classifica- Misclassified elderly were placed into either class tion accuracy for the model was very good. Furthermore, one (Unhealthy Group I) or six (Healthy Group I).
we were able to define each latent class, which provides Subjects in class five have a 29% chance of falling.
researchers and practitioners practical implications of The odds ratio indicates that a person in class five is the analysis.
2.24 times more likely to fall than a person in class Medication usage helped differentiate the latent clas- six: Healthy Group II (Tables and ).
ses. Subjects in latent class one have higher probabilities  Class six is least affected by the medical conditions of possessing the seven medical conditions than subjects and is defined as healthy group II (Figure ). Ten in latent class two; yet, subjects in latent class two pos- percent of the sample fell into latent class six sess similar rates of falling. This may be explained by the (Table The average age of this class is 78.87 ± number of medications that class two is taking (7.5 vs.
7.48, the average number of medications is 4.3, and 4.7). Similarly latent class three and six are both defined the drug falling measure is -0.012. The classification as the healthy groups. Differentiating the two groups is accuracy is 97% (Table ). Subjects in class three the number of medications taken by subjects in latent have a 15% chance of falling. Misclassified elderly class three vs. latent class six (7.8 vs. 4.3).
were placed into class five: the diabetes-heart disease It also true that the type of medications subjects are tak- group (Tables and ).
ing is impacting their probability of falling. This can bedemonstrated for latent class one. Holding age and Table 5 Most likely latent class membership Table 6 Odds ratios Note: Base or comparison group is class six or the "healthy group II" group.
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Competing interestsThe authors declare that they have no competing interests.
doi:10.1186/1472-6947-13-60Cite this article as: Hardigan et al.: Using latent class analysis to model Authors' contributions prescription medications in the measurement of falling among a PH, DS and WH participated in the design, coordination, project planning community elderly population. BMC Medical Informatics and Decision and data collection. PH performed the statistical analysis and drafted the Making 2013 13:60.
manuscript. All authors read and approved the final manuscript.
Author details1Department of Public Health, Nova Southeastern University, 3200 SouthUniversity Dr., Health Professions Division, Ft. Lauderdale, FL 33328, USA. 2TheElderCare Companies, Inc, 2517 State Rt. 35, Bldg. J Ste. 203, Manasquan, NJ08736, USA. 3College of Pharmacy, Nova Southeastern University, 3200 SouthUniversity Dr., Health Professions Division, Ft. Lauderdale, FL 33328, USA.
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