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Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population
 Patrick C Hardigan^{1}Email author,
 David C Schwartz^{2} and
 William D Hardigan^{3}
https://doi.org/10.1186/147269471360
© Hardigan et al.; licensee BioMed Central Ltd. 2013
Received: 8 August 2012
Accepted: 13 May 2013
Published: 25 May 2013
Abstract
Background
Falls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique 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 for the prediction of falls among community dwelling elderly.
Methods
Using a retrospective dataset a twostep LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients’ prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chisquare, likelihood ratio chisquare, BIC, LoMendellRubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons.
Results
A review of the model fit indices with covariates shows that a sixclass solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good 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 atrisk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data.
Background
Latent Class Analysis (LCA) is a statistical method for finding subtypes of related cases (latent classes) from multivariate categorical data [1]. The most common use of LCA is to discover case subtypes (or confirm hypothesized subtypes) based on multivariate categorical data [1–4]. LCA is well suited to many health applications where one wishes to identify disease subtypes or diagnostic subcategories [1–4]. LCA models do not rely on traditional modeling assumptions (normal distribution, linear relationship, homogeneity) and are therefore, less subject to biases associated with data not conforming to model assumptions [1–4]. In this paper, we demonstrate the utility of LCA for the prediction of falls among community dwelling elderly.
Falls among the elderly are a major public health concern. Research on falls and fallrelated behavior among the elderly has found that falls are the leading cause of injury deaths among individuals who are over 65 years of age [5–11]. Research has shown that sixty percent of fallrelated deaths occur among individuals who are 75 years of age or older [5–11]. Demography research estimates that by 2030, the population of individuals who are 65 years of age or older will double and by 2050 the population of individuals who are 85 years of age or older will quadruple [5–11].
Predicting elderly falling can be complex and often involves heterogeneous markers. Therefore, the identification of more homogeneous subgroups of individuals and the refinement of the measurement criteria are typically interrelated research goals. Appropriate statistical applications, such as latent class analysis, have become available for researchers to model the complex heterogenous measurements.
Latent class models are used to cluster participants. This type of model is adequate if the sample consists of different subtypes and it is not known beforehand which participant belongs to which of the subtypes [2]. The latent categorical variable is used to model heterogeneity. In the classic form of the latent class model, observed variables within each latent class are assumed to be independent, and no structure for the covariances of observed variables is specified [2].
LCA is one of the most widely used latent structure models for categorical data [12]. LCA differs from more wellknown methods such as Kmeans clustering which apply arbitrary distance metrics to group individuals based on their similarity [13–15]. LCA derives clusters based on conditional independence assumptions applied to multivariate categorical data distributed as binomial or multinomial variables [16, 17]. Using statistical distributions rather than distance metrics to define clusters helps in evaluating whether a model with a particular number of clusters is able to fit the data, since tests can be performed to observed (ni) versus model expected values (mi), using exact methods as recommended [18, 19]. This comparison gives rise to a χ ^{2} test of global model fit, in which significant values indicate lack of fit [20]. Here lack of fit means deviation of (model) predicted (m) frequencies from observed frequencies (n) [16].
where ${\mathrm{\pi}}_{\mathrm{t}}^{\mathrm{X}}$ denotes the probability of being in a latent class (t = 1,2,…,T) of latent variable X; ${\mathrm{\pi}}_{\mathrm{it}}^{\mathrm{A}\mathrm{X}}$ denotes the conditional probability of obtaining the i th response from item A, from members of class t, i = 1,2,…,I; and ${\mathrm{\pi}}_{\mathrm{jt}}^{\mathrm{B}\mathrm{X}}{\mathrm{\pi}}_{\mathrm{kt}}^{\mathrm{C}\mathrm{X}}{\mathrm{\pi}}_{\mathrm{lt}}^{\mathrm{D}\mathrm{X}}{\mathrm{\pi}}_{\mathrm{mt}}^{\mathrm{E}\mathrm{X}}{\mathrm{\pi}}_{\mathrm{nt}}^{\mathrm{F}\mathrm{X}}{\mathrm{\pi}}_{\mathrm{ot}}^{\mathrm{G}\mathrm{X}}$, 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 conditional probabilities for items B,C,D,E,F, and G respectively.
Methods
Descriptive statistics
Variable  Statistic  NoFall  Fall 

N = 1906  N = 908  
Age  Mean ± SD  77.47 ± 6.91  77.98 ± 7.41 
Number of Medications  Mean ± SD  2.30 ± 5.57  5.10 ± 10.10 
Gender  Male  27%  22% 
Female  73%  78% 
The data set was taken from the State of Florida’s Elder Affairs Office. All variables were physician diagnosed and recorded in an electronic dataset using appropriate ICD9 codes. Variables included in the database were:
Biomedical

Arthritis—defined as a person diagnosed with osteoarthritis (OA) and/or rheumatoid arthritis (RA). Presence or absence of arthritis was based on responses to questions on the basis of ICD9 714.0, 715.× 716.×, from both principal and secondary diagnosis fields within a patient record.

High Blood Pressure (HBP)—defined as a person diagnosed with hypertension. HBP was identified on the basis of ICD9 codes 401–405, from both principal and secondary diagnosis fields within a patient record.

Diabetes—defined as a person diagnosed with diabetes mellitus. Diabetes was identified on the basis of ICD9 codes of 250.0×–250.5× and 250.7×–250.9× from both principal and secondary diagnosis fields within a patient record.

Heart Disease (HD)—defined as a person diagnosed with coronary artery disease. HD was identified on the basis of ICD9 codes 414.0x, from both principal and secondary diagnosis fields within a patient record.

Foot Disorders (FD)—defined as a person diagnosed with peripheral neuropathy, foot wounds, peripheral vascular disease, or Charcot arthropa. FD was identified on the basis of ICD9 codes 356.9, 892.0892.2, 443.9, and 713.5 from both principal and secondary diagnosis fields within a patient record.

Parkinson’s Disease (PD)—defined as a person diagnosed with Parkinson’s Disease. PD was identified on the basis of ICD9 code 332.0 from both principal and secondary diagnosis fields within a patient record.

Stroke—defined as a person diagnosed with occlusion and stenosis of precerebral arteries including basilar artery, carotid artery, and vertebral artery, etc.; occlusion of cerebral arteries including cerebral thrombosis and Cerebral embolism; unspecified cerebral artery occlusion; and transient cerebral ischemia. Data from both principal and secondary diagnosis fields within a patient record.
Pharmacological variables

Type of prescription medication—type of prescription medication was taken from patient records.

Number of prescription medications—was taken from patient records.
Demographic variables

Age—was taken from patient records.

Gender—Self reported male or female taken from patients’ record.
Outcome

Falling—was defined as “an event which results in the person coming to rest inadvertently on the ground or other lower level, and other than as a consequence of sustaining a violent blow.” Falling was taken from both principal and secondary diagnosis fields within a patient record.
A twostep modeling approach was employed. First, it was necessary to reduce the number of different medications (N = 121). Initially, a licensed geriatric pharmacist (PharmD) reviewed the medication list for accuracy and to remove medications that have not been shown to impact the probability of falling. Using correspondence analysis (CA) the medications were converted to continuous scores. CA is an exploratory technique related to principal components analysis which finds a multidimensional representation of the association between the row and column categories of a multiway contingency table [22]. This technique finds scores for the row and column categories on a small number of dimensions which account for the greatest proportion of the chi ^{ 2 } for association between the row and column categories, just as principal components account for maximum variance [22]. These scores were then used in the latent class analysis. Similar to other data reduction techniques, CA can be used to transform data [23].
Second, we looked for the optimal number of latent classes for the seven binary indicators: (1) arthritis, (2) high blood pressure, (3) diabetes, (4) heart disease, (5) foot disorders, (6) Parkinson’s disease, and (7) stroke; along with the patients’ medication “score” and three covariates (age, gender, and number of medications). The appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chisquare, likelihood ratio chisquare, (BIC), LoMendellRubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons.
Results
Data reduction
where:

P is the matrix of counts divided by the total frequency

r and c are row and column sums of P

the Ds are diagonal matrices of the values of r and c
List of medications and correspondence scores
Level  Number  Score 

PHENOBARBITAL  8  −0.690 
CLOMIPRAMINE  8  −0.690 
METHADONE  7  −0.690 
IMIPRAMINE  17  −0.423 
MORPHINE  36  −0.423 
PRIMIDONE  43  −0.405 
HYDROCODONE  46  −0.262 
DIAZEPAM  128  −0.234 
CHLORDIAZEPOXIDE  45  −0.225 
RBAMAZEPINE  37  −0.187 
OXAZEPAM  23  −0.155 
MIRTAZAPINE  86  −0.124 
AMITRIPTYLINE  180  −0.095 
ALPRAZOLAM  1297  −0.094 
CLONAZEPAM  368  −0.077 
BUSPIRONE  124  −0.072 
OXYCODONE  206  −0.054 
GABAPENTIN  210  −0.051 
DIGOXIN  272  −0.038 
MEPROBAMATE  44  −0.010 
LORAZEPAM  572  0.009 
DISOPYRAMIDE  9  0.023 
NEFAZODONE  6  0.023 
PHENYTOIN  35  0.023 
TEMAZEPAM  1470  0.030 
ESTAZOLAM  179  0.045 
PAROXETINE  338  0.049 
CHLORPROMAZINE  11  0.165 
TRIAZOLAM  16  0.227 
FLUOXETINE  310  0.241 
BACLOFEN  25  0.379 
DESIPRAMINE  6  0.379 
HYDROMORPHONE  7  0.379 
HALOPERIDOL  11  0.379 
CITALOPRAM  60  0.414 
TRAZODONE  234  0.455 
BUPROPION  20  0.498 
DOXEPIN  83  0.580 
PERPHENAZINE  19  0.593 
AMOXAPINE  7  1.449 
THIORIDAZINE  8  1.449 
Latent class analysis
For the latent class analysis, a review of the model fit indices shows that a sixclass solution was preferred (Table 3). The sixclass solution provided a lower Bayesian Information CriteriaBIC (lower is better), much smaller chisquare values, and as indicated by the procedures (LoMendellRubin likelihood ratio testLMR and bootstrap likelihood ratio testBLRT), nonsignificant pvalues. Age, number of medications, and gender were shown to have a significant impact on falling. Females, older patients, and the more prescription drugs an elderly person takes, the greater the probability that they will fall. Table 2 provides a comparison of fit indices for fourclass, fiveclass, sixclass and sevenclass solutions. The six class structure, with covariates is interpreted as follows:

Class one is most likely to be affected by all medical conditions (Figure 3). The average age of this class is 77.78 ± 7.01, the average number of medications is 4.7, and the average drug falling measure is 0.016. Latent class one is defined as the PoorestHealth Group I. Seventeen percent of the sample is classified into latent class one (Table 4). The classification accuracy is 95%; the misclassified elderly were all placed into class four (Table 4). Subjects in class one have a 47% chance of falling. The odds ratio indicate that a person in class one is 4.41 times more likely to fall than a person in class six: Healthy Group II (Tables 5 and 6).

Class two is also affected by all measured medical conditions (Figure 3). The average age of this class is 76.89 ± 7.02, the average number of medications is 7.5, and the drug falling measure is 0.017. This is defined as the PoorestHealth Group II. Twentyeight percent of the sample is placed into latent class two (Table 4). The classification accuracy is 89% (Table 4); misclassified elderly were placed into class three. Subjects in class two have a 46% chance of falling. The odds ratio indicate that a person in class two is about 4.67 times more likely to fall than a person in class six: Healthy Group II (Tables 5 and 6).

Class three is generally unaffected by all medical markers (Figure 3). The average age of this class is 78.83 ± 6.63, the average number of medications is 7.8, and the drug falling measure is 0.006. We define this as the Healthy Group I. Seventeen percent of the sample is classified class three (Table 4). The classification accuracy for latent class three is 84% (Table 4). 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 six: Healthy Group II (Tables 5 and 6).

Class four is primarily affected by arthritis; therefore, this is defined as the arthritis group (Figure 3). Twentypercent of the sample fell into latent class four (Table 4). The average age of this class is 78.69 ± 7.32, the average number of medications is 2.6, and the drug falling measure is 0.003. The classification accuracy is 96% (Table 4). Misclassified elderly were placed into class one. Subjects in class three have a 26% chance of falling. The odds ratio indicate that a person in class four is approximately 2.07 times more likely to fall than a person in class six: Healthy Group II (Tables 5 and 6).

Class five is primarily affected by high blood pressure,diabetes, heart disease and foot disorders (Figure 3). This group is defined as the diabetesheart disease group. Eight percent of the sample fell into latent class five (Table 4). The average age of this class is 77.53 ± 7.04, the average number of medications is 3.1, and the drug falling measure is 0.009. The classification accuracy is 95% (Table 4). Misclassified elderly were placed into either class one (Unhealthy Group I) or six (Healthy Group I). Subjects in class five have a 29% chance of falling. The odds ratio indicates that a person in class five is 2.24 times more likely to fall than a person in class six: Healthy Group II (Tables 5 and 6).

Class six is least affected by the medical conditions and is defined as healthy group II (Figure 3). Ten percent of the sample fell into latent class six (Table 4). The average age of this class is 78.87 ± 7.48, the average number of medications is 4.3, and the drug falling measure is 0.012. The classification accuracy is 97% (Table 4). Subjects in class three have a 15% chance of falling. Misclassified elderly were placed into class five: the diabetesheart disease group (Tables 5 and 6).
Basic latent class structure
Four class solution  Five class solution  Six class solution  Seven class solution  

Pearson χ ^{2}  2519  2196  2173  2172 
LR χ ^{2}  1171  1156  1143  1107 
χ ^{2} df  478  469  462  454 
Loglikelihood  −12922  −12012  −11672  −11509 
Number of parameters  48  61  74  87 
BIC  24226  23893  23718  23710 
LMR (p value)  .000  .029  .758  .626 
BLRT (p value)  .000  .028  .758  .626 
Entropy  .854  .883  .893  .839 
Most likely latent class membership
Count  Proportion  Class 1  Class 2  Class 3  Class 4  Class 5  Class 6  

Class 1  477  17%  0.95  0.00  0.00  0.04  0.00  0.00 
Class 2  792  28%  0.00  0.89  0.11  0.00  0.00  0.00 
Class 3  486  17%  0.00  0.16  0.84  0.00  0.00  0.00 
Class 4  553  20%  0.04  0.00  0.00  0.96  0.00  0.00 
Class 5  222  8%  0.01  0.00  0.00  0.00  0.95  0.04 
Class 6  284  10%  0.00  0.00  0.00  0.00  0.03  0.97 
Total  2,814  100% 
Most likely latent class membership
Class 1  Class 2  Class 3  Class 4  Class 5  Class 6  

Class 1  .95  .00  .00  .04  .00  .00 
Class 2  .00  .89  .11  .00  .00  .00 
Class 3  .00  .16  .84  .00  .00  .00 
Class 4  .04  .00  .00  .96  .00  .00 
Class 5  .01  .00  .00  .00  .95  .04 
Class 6  .00  .00  .00  .00  .03  .97 
Odds ratios
Class  Class  Odds ratio  PValue  Lower 95% CI  Upper 95% CI  

6  Vs  1  4.41  0.000  3.07  6.46 
6  Vs  2  4.67  0.000  3.32  6.72 
6  Vs  3  1.12  0.574  0.75  1.69 
6  Vs  4  2.07  0.000  1.43  3.04 
6  Vs  5  2.24  0.000  1.45  3.49 
Discussion
This paper demonstrated the utility of LCA in the measurement of falling among communitydwelling elderly. The basic idea underlying LCA is that variables differ across previously unrecognized subgroups [24]. These subgroups form the categories of a categorical latent variable. Given the potential for confounding among the study variables, latent class analysis holds great promise.
The sixclass solution was statistically sound and provided a relatively straightforward interpretable number of classes. The interpretation of a LCA relies on both the statistical indices and the practical interpretation of the classes. In our example, the statistical indices strongly point toward a six factor model. The classification accuracy for the model was very good. Furthermore, we were able to define each latent class, which provides researchers and practitioners practical implications of the analysis.
Medication usage helped differentiate the latent classes. Subjects in latent class one have higher probabilities of possessing the seven medical conditions than subjects in latent class two; yet, subjects in latent class two possess similar rates of falling. This may be explained by the number of medications that class two is taking (7.5 vs. 4.7). Similarly latent class three and six are both defined as the healthy groups. Differentiating the two groups is the number of medications taken by subjects in latent class three vs. latent class six (7.8 vs. 4.3).
It also true that the type of medications subjects are taking is impacting their probability of falling. This can be demonstrated for latent class one. Holding age and number of medications at their means, females with a drug falling measure of 1.50 [i.e., Thioridazine & Amoxapine] have a 80% greater chance of falling than the same subjects with a drug falling measure of 0.50 [i.e., Imipramine & Methadone] (p < 0.05). We stress that the latent classes are composite variables, so one should not look at medications in isolation. As one would expect, the two latent classes with the highest probability of falling also possess the highest drug falling measure and the worst medical conditions.
Conclusion
As was demonstrated in past research correspondence analysis is a useful tool for researchers examining prescription medication data [25]. Combining LCA with CA provides researchers a powerful tool for data reduction analysis. We demonstrated that this approach was effective for finding relevant subgroups with a heterogenous atrisk population for falling. Nevertheless, the results may not be relevant to other countries, with different lifestyles and different socioeconomic status.
LCA and CA possess limitations which make its application to this type of modeling dependent on replication studies. The specific limitations include (1) Classes not known prior to analysis, and (2) Class characteristics not know until after analysis. Both of these problems are related to LCA being an exploratory procedure for understanding data. Furthermore, the items were not designed for a LCA approach. A latent class study designed apriori may offer better solutions. We would also suggest that additional items (medical) be used which have demonstrated to impact falling among elderly community dwellerssuch as eye disease and pain.
Declarations
Authors’ Affiliations
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