To determine the association between preexisting characteristics and current health and the cost of different types of advanced human immunodeficiency virus (HIV) care.

Treatment-experienced patients failing highly active antiretroviral treatment (ART) in the United States, Canada, and the United Kingdom were factorial randomized to an antiretroviral-free period and ART intensification. Cost was estimated by multiplying patient-reported utilization by a unit cost.

A total of 367 participants were followed for a mean of 15.3 quarters (range 1-26). Medication accounted for most (61.8%) of the $26,832 annual cost. Cost averaged $4147 per quarter for ART, $1981 for inpatient care, $580 for outpatient care, and $346 for other medications. Cost for inpatient stays, outpatient visits, and other medications was 171% higher (

 <.01) when cluster of differentiation 4 (CD4) count was <50 cells/μL compared with periods when CD4 count was >200 cells/μL. Some baseline characteristics, including low CD4 count, high viral load, and HIV from injection drug use with hepatitis C coinfection, had a sustained effect on cost.

The association between health status and cost depended on the type of care. Indicators of poor health were associated with higher inpatient and concomitant medication costs and lower cost for ART medication. Although ART has supplanted hospitalization as the most important cost in HIV care, some patients continue to incur high hospitalization costs in periods when they are using less ART. The cost of interventions to improve the use of ART might be offset by the reduction of other costs.

Healthcare costs were measured in a clinical trial of alternatives for advanced human immunodeficiency virus (HIV):

Antiretroviral drugs accounted for most (61.8%) of the $26,832 annual cost of care for advanced HIV.

Costs were higher during AIDS-defining illnesses.

Costs were especially high in patients infected with HIV from injection drug use and in patients coinfected with hepatitis C.

Antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, but hospitalization remains the predominant cost in especially advanced disease. The cost of interventions to improve the use of antiretroviral treatments could be offset by reduced use of other services.

Human immunodeficiency virus (HIV) care costs $20,000 per year in industrialized countries.

 Highly active antiretroviral treatment (ART) accounts for approximately half of this expense.

 The annual cost of caring for persons with HIV continues to grow and now exceeds $13 billion worldwide.

Newer ART medications have markedly improved treatment outcomes.

 Models have been used to estimate the cost effectiveness of these new drugs,

 and the effect of patent expiration on the cost effectiveness of older medications.

 Cost-effectiveness models may be hindered by the lack of more contemporary data on the cost of HIV care.

HIV care is more costly when the disease is more severe, as indicated by a low cluster of differentiation (CD4) level (<50 cells/μL),

 Results of other studies of the cost of specific services have showed that hospital costs are lower for patients who receive more ART,

 and that the cost of medication for opportunistic infections is lower for patients with higher CD4 count.

Cost is higher for patients with psychiatric comorbidities, 

 in patients presenting later for antiretroviral treatment,

 and for those infected by injection drug use (IDU).

 Other studies have showed baseline characteristics to be unimportant predictors of cost after current health status, as reflected by CD4 count, is considered.

Multivariate analyses have looked at the effect of Medicaid eligibility

 on total cost while controlling for disease severity. These studies employed ordinary least squares regression, a method that requires assumptions that are usually inappropriate for cost data, which have a highly skewed distribution. The more robust general linear models have been used only in the most recent studies of HIV cost.

We report the cost of care received by participants enrolled in OPTIMA (OPTions In Management with Antiretrovirals), the largest randomized clinical trial of treatment strategies in advanced HIV. We applied advanced multivariate methods to address gaps in the knowledge about the cost of advanced HIV care: whether preexisting factors have a sustained effect on cost, and the effect of these factors and current health status on the different types of cost, including hospital services, combination ART medication, medications for opportunistic infections, and outpatient care.

The OPTIMA trial studied 2 interventions: an intended 12-week ART-free period and intensification of ART to a target of 5 or more drugs.

 A 2-by-2 factorial design evaluated both interventions simultaneously. Participants were randomized to ART interruption, ART intensification, both interventions, or neither intervention.

The trial enrolled patients who had been taking their current ART regimen for at least 3 months, had failed 2 or more prior regimens involving all 3 available ART classes in use at the time, and had evidence of current treatment failure (HIV viral load ≥2500 copies/mL and CD4 count of ≤300 cells/μL), while excluding those with an ongoing opportunistic infection.

 Consent was obtained according to the human subjects regulations of the 3 countries where participants were enrolled: the United States, Canada, and the United Kingdom.

Participants were assessed every 12 weeks and a case report form was used to record inpatient stays, outpatient visits, and changes in antiretroviral medication regimens. Unit costs for HIV care were derived from analysis of costs of HIVpositive patients at the US Veterans Health Administration (VHA), where 78% of study participants were enrolled. Inpatient costs were derived from a regression method.

 Outpatient estimates were based on the Medicare reimbursement for the procedure codes used in visits of patients with HIV.

 Cost for all participants was determined by multiplying the utilization recorded on case report forms by these unit costs: $5004 per day of hospital stay in intensive care, $1782 per day of other medical-surgical care, $768 per day of nursing home care, $201 per visit to an infectious disease clinic, and $254 per visit to other medical clinics. All costs were adjusted to 2007 US dollars using the consumer price index.

The dates that each ART medication was initiated and stopped were recorded on a case report form. Estimates of medication use assumed the guideline-recommended dosage and were adjusted by participant-reported adherence. We characterize ART medications by categories defined by the year in which the drug was approved by the US Food and Drug Administration (FDA). We identified periods in which participants received 1 of the 5 new ART medications: etravirine, tipranavir, darunavir, maraviroc, or raltegravir.

The unit costs of medications were estimated as 64% of the average wholesale price, which is the net cost of brand name medications to Medicaid.

 Medicaid is an important sponsor of US HIV care, with costs that are similar to those of the US AIDS Drug Assistance Program, another important sponsor. Since we wanted study findings to be generalizable to the US healthcare system, we did not use the lower cost paid by VHA. Data on prescription medications used to prevent or treat opportunistic infections were obtained from the pharmacy benefit management system of VHA. Although data on ART medications was available for participants who enrolled at sites in Canada and the United Kingdom, information on these concomitant medications was not available at these sites. Cost and utilization were assigned to each 3-month period following randomization. These quarterly observations were matched to clinical data indicating if the patient had an AIDS-defining event (ADE).

We further characterized health using the mean CD4 count and viral load tests conducted during the quarter. If no laboratory test was conducted in the quarter, we used the weighted average of tests that preceded and followed the quarter. Weights were inversely proportional to the number of days the test preceded the beginning of the quarter and the number of days the test followed the end of the quarter.

The association of patient characteristics with cost was assessed with Generalized Linear Model (GLM). These Take-Away Points Healthcare costs were measured in a clinical trial of alternatives for advanced human immunodeficiency virus (HIV): n Antiretroviral drugs accounted for most (61.8%) of the $26,832 annual cost of care for advanced HIV. n Costs were higher during AIDS-defining illnesses. n Costs were especially high in patients infected with HIV from injection drug use and in patients coinfected with hepatitis C. n Antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, but hospitalization remains the predominant cost in especially advanced disease. The cost of interventions to improve the use of antiretroviral treatments could be offset by reduced use of other services. regressions avoid the inappropriate assumption of normal distribution and homoscedastic errors.

 A log link function was determined appropriate based on a Box-Cox regression.

 The gamma distribution was selected based on a modified Park test. Standard errors were corrected for the correlation between multiple observations from each trial participant. Independent regressors were chosen based on significance in univariate tests. Final models were chosen based on predictive fit, with attention to the extremes of the distribution. To express the effect of current clinical status in the original units of analysis—dollars cost&mdash; we used the regression parameters to predict log cost for each observation given the participant’s baseline characteristics and a set of current status variables (indicators of CD4 count, viral load, and presence of AIDS-defining illness). We exponentiated these predicted log values without correction for retransformation bias. This correction is not needed with GLM regressions. We reported the mean cost in dollars of predicted values for each set of clinical status variables, and the associated standard error.

Participants were enrolled between 2001 and 2006 and followed until 2007. This maximized study power, but there were fewer observations representing periods more distant from randomization. Bias from this design was corrected by applying an inverse probability weight, the Kaplan-Meier estimator for the probability of administrative censoring.

There were 367 participants with cost data (excluding 1 participant with no follow-up data). 

 describes participant characteristics at randomization. Participants were immunologically compromised, with 32.2% having baseline CD4 counts below 50 cells/μL. Most (78%) participants enrolled at VA sites, with the balance enrolling at sites in Canada (11%) and the United Kingdom (11%). HIV infection was attributed to men having sex with men (MSM) (47.6%), heterosexual transmission (23.4%), IDU (14.1%), and infection from blood products (9.5%). There were 7 participants (1.9%) who had both MSM and IDU risk factors.

Participants were randomized to care as usual (n = 106), structured treatment interruption followed by treatment intensification (n = 78), structured treatment interruption without treatment intensification (n = 85), or treatment intensification without treatment interruption (n = 98). Participants from the United Kingdom were not randomized to structured treatment interruption. Baseline characteristics did not differ by treatment group.

 provides the unweighted means of 5612 quarterly observations of cost, utilization, and clinical status. Participants were followed for a mean of 15.3 quarters (range 1-26). Quarterly cost averaged $4147 for ART, $1981 for inpatient care, $580 for outpatient care, and $346 for concomitant medications (medications used for prophylaxis and treatment of opportunistic infections). Information on concomitant medications was available only for US participants. Total cost was $6708 per quarter. Participant CD4 count was below 50 cells/μL during 22.7% of follow-up quarters, and viral load exceeded 100,000 copies/mL during 22.1% of follow-up quarters. There was modest correlation between the costs in all categories of care, with 1 exception. Inpatient costs were negatively correlated with the cost of ART medication (r = −.068; 

As previously reported, neither ART interruption nor ART intensification reduced the time to the composite end point of ADE or death.

Neither intervention had a significant effect on cost, except a transitory difference during the first 3 months of the trial when participants randomized to ART interruption had a $4216 lower cost of highly active antiretroviral treatment (HAART) (

 <.001) and $434 additional cost for other medications (P <.001) compared with participants randomized to no ART interruption.

. Medications approved by the FDA after 2001 (the year the trial began) accounted for 36% of the duration and 49% of the costs of ART. Participants reported at least 90% adherence during 78.6% of the quarters of follow-up. They received an average of 3 or more ART active ingredients during 79.7% of the quarters. At least 1 of the newest ART medications (which includes those approved after 2005) was received during 19.7% of the quarters.

presents regression results. Each column represents a regression with a different cost as the dependent variable. Independent regressors were all indicator variables. The parameters represent the effect of the characteristic relative to its absence (eg, quarters in which an AIDS-defining illness was present compared with quarters in which the illness was not present) controlling for all other factors in the regression. Baseline characteristics were associated with higher cost. Patients who were coinfected with hepatitis C virus (HCV) whose HIV was a result of IDU had significantly higher hospital cost (

 <.001). HIV infection from IDU without HCV coinfection was associated with higher outpatient cost (

 <.01) and higher cost of ART medications (

Higher viral load at baseline was associated with higher ART cost (

 <.001). Low CD4 count at baseline was associated with higher cost of medications used to prevent or treat opportunistic infections (

 <.001). In multivariate analyses, other baseline characteristics including age, employment status, and diagnosis of coronary artery disease were not associated with higher cost in any category of care, and those variables were not included in the models.

Several indicators of poor health status in the quarter were associated with higher cost. An AIDS-defining event was associated with significantly higher inpatient cost (

 <.001) and with significantly higher concomitant medication cost (

<.05). CD4 below 50 cells/μL was associated with higher inpatient cost (

 <.001) and higher concomitant medication cost (

<.001). CD4 in the range of 50 cells/ μL to 200 cells/μL was also associated with greater inpatient cost (

 <.05). High viral load was associated with higher concomitant medication cost (

 <.01). Death in the quarter was associated with higher inpatient cost (

 <.001) and lower cost of ART and concomitant medications; the individual did not survive to fill all prescriptions.

The results of log regressions are parameters that are not easily interpreted. To provide a result in dollars of cost, we used these parameters to estimate the cost of different health states. 

presents the mean and standard error of the quarterly cost of patients in health states defined by CD4 count, viral load, and the presence of an AIDS-defining illness, given the range patient character istics at baseline. Although some scenarios have a low probability of occurring (eg, a period with an ADE, high CD4 count, and low viral load), all of these combinations occurred during the trial.

The cost of inpatient stays, outpatient visits, and concomitant medications was greater when health was poor. These costs were 327% higher when there was an AIDSdefining illness than they were when one was not present (

<.001). Compared with periods in which CD4 count was below 200 cells/μL, these costs were 171% higher when CD4 count was below 50 cells/μL (

 <.001), and 67% higher when the CD4 count was between 50 cells/μL and 200 cells/μL (

 <.05). Costs were 31% higher when viral load was greater than 100,000 cells/mL compared with periods when viral load was below this threshold (a difference that was not statistically significant; P = .07). When health was the most compromised—when there was an AIDS-defining illness, a CD4 count of less than 50 cells/μL, and viral load greater than 100,000 cells/mL&mdash;quarterly cost for care, excluding ART medications, was $20,494. This was 15 times higher than the $1353 cost when health level was good; or when the participant did not have an AIDS-defining illness, had a low CD4 count, or had a high viral load.

The cost of ART was lower when health was poor. Compared with periods during which CD4 count was below 200 cells/μL, ART cost was 32% lower when CD4 count was below 50 cells/μL (

 <.001) and 14% lower when CD4 count was between 50 cells/μL and 200 cells/ μL (

 <.05). ART cost was 20% lower when viral load was greater than 100,000 cells/mL (

 <.01). ART cost was 9% higher during quarters in which there was an AIDS-defining illness (not statistically significant;

 = .35). ART costs were $4664 per quarter during periods in which health level was good, 59% greater than the $2774 cost of ART in when health was the most compromised. ART accounted for 77% of the total cost during the periods in which health level was good and for 12% of the total cost of during periods in which health was the most compromised.

Participants in this clinical trial incurred $6708 in quarterly costs. This represents $26,832 in annual healthcare costs, 34% more than the $20,000 mean annual cost of HIV care in industrialized countries which was reported in a recent review.

OPTIMA participants differed in important ways. First, they were all receiving ART. Other HIV cohorts include patients not yet receiving treatment. A study of privately insured HIV patients found that 63% were receiving ART.

 OPTIMA participants had a history of treatment failure, and may have progressed to more expensive medications. Antiretroviral drugs and concomitant medications accounted for 61% of the total healthcare cost for OPTIMA participants. In the United States, pharmacy accounted for 54% of the cost of HIV care in 1997 and for 52% of the cost of care for privately insured patients with HIV.

We found that ART made up a smaller percentage of total costs during periods in which health was poor. ART accounted for 12% of total costs during periods in which health was the most compromised (when patients had an AIDS-defining illness, low CD4 count, and high viral load) and for 77% of total costs during the periods when none of these factors were present. These findings are consistent with those of other studies. A recent study of patients in HIV clinics found that ART made up 23% of total costs in patients with CD4 counts below 50 cells/μL, and two-thirds of total costs for those with CD4 counts above 200 cells/μL.

 A model of HIV costs estimated that ART accounted for 38% of total costs during months when CD4 counts were below 50 cells/μL and 77% of total costs during months when CD4 counts were above 300 cells/μL.36 The findings from these studies are not exactly comparable, as each used a different definition of poor health. We found that the difference in the proportion of costs attributable to ART was not only due to the higher cost of non-ART care in patients in poor health, but also because of their lower utilization of ART.

Previous studies of the cost of HIV care have found that individuals with CD4 counts below 50 cells/μL incurred 3 times the annual cost of individuals with CD4 counts above 350 cells/μL.

 Our findings were similar. We found that the cost of inpatient stays, outpatient visits, and concomitant medications was 171% higher when CD4 counts were below 50 cells/μL than they were when CD4 counts were above 200 cells/μL. At the same time, we found that patients with low CD4 counts had 32% lower ART cost. One limitation of some HIV cost studies is the failure to distinguish the different types of cost, such as medication, hospitalization, and ambulatory care.

 Our findings illustrate why this distinction can be important: clinical status may have a different effect on ART cost than on others costs. We found that AIDS-defining illness was associated with higher costs, even after controlling for CD4 count. The cost of inpatient stays, outpatient visits, and concomitant medications was 327% greater during periods with such an illness. Future cost studies may want to include this health status measure.

We found that some baseline characteristics, including low CD4 count, high viral load, and HIV from IDU with HCV coinfection, had a sustained effect on cost.

We acknowledge study limitations. The use of standard inpatient and outpatient unit cost understates cost variation. The use of US unit costs reduces the applicability outside the United States, especially with respect to medication cost. Data on concomitant medications were available only for US sites. Also, our sample included few women.

The clinical trial protocol may have affected utilization, but the trial interventions had little effect on cost and no effect on outcomes. The protocol specified that a follow-up interval of 3 months is not excessive for patients with advanced HIV. OPTIMA participants were similar to other patients with advanced HIV in the VA healthcare system.

Medications approved prior to 2001, when this trial began, accounted for 61% of ART utilization and 51% of the cost of ART incurred by study participants. The patents on many of those medications have expired or will soon be expiring, and the costs for those medications has already or can be expected to decrease. It has been argued that reduced medication costs from patent expiration should be incorporated into cost-effectiveness studies.

 This effect may be of less concern when new branded medications are adopted to respond to viral resistance to older agents.

Although antiretroviral drugs have replaced hospital stays as the most costly component of HIV care, we found that this is not true over all periods for patients with especially advanced HIV disease. Indicators of poor health were associated with lower ART costs and higher costs for inpatient stays and concomitant medications. Treatments that lead to improved use of ART, either by improving adherence or reducing adverse effects of treatment, would at least partially offset their cost by reducing the need

VA Palo Alto Healthcare System, VA Cooperative Studies Program, VA HSR&D Health Economics Resource Center, Menlo Park, CA (PGB, AC, VRJ); Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute and Division of General Internal Medicine, St. Michael’s Hospital, Toronto, Ontario, Canada (AMB); Departments of Medicine and Health Policy, Management and Evaluation, University of Toronto, Ontario, Canada (AMB); Centre for Health Economics, University of York, York, United Kingdom (SCG, MS); VA Palo Alto Healthcare System, Palo Alto, CA (MH, DKO); Center for Primary Care and Outcomes Research, and Center for Health Policy, Stanford University, Stanford, CA (DKO); James J. Peters VA Medical Center, Bronx, NY (STB); Division of Infectious Diseases, University of Ottawa at The Ottawa Hospital, Ottawa, Ontario, Canada (DWC); Royal Free Hospital, London, United Kingdom (MY); CIHR Canadian HIV Trials Network, Vancouver, British Columbia, Canada (HS, AHA); School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada (AHA).

Articles

WEB EXCLUSIVE

Antiretroviral drugs have replaced hospitalization and other services as the most costly component of HIV care, except in patients with especially advanced HIV.

Effect of Management Strategies and Clinical Status on Costs of Care for Advanced HIV

To analyze adherence to antiosteoporosis drugs (AODs) and to assess the influence of patient-related and drug-related factors.

Data on prescriptions for AODs from 2007 through 2008 were retrieved from administrative databases of 10 Italian local health units. Key measurements included compliance and persistence at 1 year. Multivariate regression analyses were performed to estimate adjusted risk ratios for compliance less than 80% and adjusted hazard ratios for no persistence.

Of 40,004 new patients (89.9% women, mean age 69.8 years), 84.0% were treated with bisphosphonates and 74.6% of administration regimens were weekly. Overall, 75.1% of patients had suboptimal levels of compliance and 84.7% were not persistent; almost one-third had only 1 prescription. In regression analyses, younger age, change of drug, and concomitant corticosteroid therapy were significantly associated to compliance and persistence in both genders. In women, weekly and monthly regimens reduced the risk for poor compliance (sex-adjusted relative risks 0.729 [0.697-0.762], 0.846 [0.817-0.876], respectively) and no persistence (sex-adjusted hazard ratios 0.591 [0.541-0.646], 0.508 [0.461-0.560], respectively) compared with a daily regimen.

In our study, 75% of subjects had discontinuous treatment and inadequate drug supply. Age and frequency of administration were strongly associated with adherence. Improvement is urgently needed, and occasional prescriptions represent the main target.

Osteoporosis has become a clinical and public health concern because osteoporotic fractures are one of the most common causes of disability and reduced quality of life, and an important contributor to medical costs in many regions of the world.

 Several medications are currently available for the prevention and treatment of osteoporosis.

 However, the effectiveness observed in trials may not be applicable to daily practice, where 50% to 80% of patients discontinue bisphosphonate use in the first year of therapy.

 Moreover, noncompliance with bisphosphonates has also been reported to be a frequent issue, with rates varying from 35% to 65% of medication possession ratio.

 The full benefits of medications for osteoporosis cannot be reached if compliance is low: poorly compliant patients have a greater risk for fractures than patients who adhere to their prescribed therapy,

 resulting in higher healthcare use and costs.

The aim of this study was to investigate compliance and persistence with antiosteoporosis drugs (AODs) in a sample of new users and to assess the influence of potential determinants.

Data used for this retrospective pharmacoepidemiological study were retrieved from the health service databases of the Local Health Units (LHUs)—provincial-level divisions of the Regional Health Authority&mdash;of Bergamo (Lombardy Region, Northern Italy), Avezzano-Sulmona, Chieti, Lanciano- Vasto, Teramo (Abruzzo Region, Southern Italy), Avellino, Benevento, Caserta, Napoli Nord, Salerno (Campania Region, Southern Italy), with a total population of about 5.5 million people, entirely covered by the National Health Service (NHS).

Prescription data contain dispensed drug name (commercial and international common denomination), Anatomical Therapeutic Chemical (ATC) classification category, dose, number of packs, and date of dispensation. We used the demographic database to retrieve demographic information about patient (gender and age). In compliance with Italian law on privacy, Health Authorities converted patient personal codes to anonymous codes.

AIFA, which is the Italian Medicines Agency and national authority responsible for drug regulation in Italy, has approved 3 bisphosphonates (alendronate, ibandronate, and risedronate) and 4 other drugs (raloxifene, teriparatide, strontium ranelate, and parathyroid hormone) for the treatment of osteoporosis. Oral bisphosphonates are available for daily (alendronate, risedronate), weekly (alendronate, risedronate), or monthly (ibandronate, risedronate) dosing. The Italian government grants reimbursement for AODs in cases of osteoporosis diagnosed by computerized bone mineralometry (T score less than —4, or less than –3 in high-risk patients), previous vertebral fractures, or chronic therapy with corticosteroids in subjects older than age 50 years.

Patients were included in this analysis if they received a prescription of AODs between January 1 and December 31, 2007. A retrospective analysis covering the period from January to December 2006 was performed to identify new users, excluding subjects who had been prescribed any osteoporosis treatment during the 12-month period prior to the index prescription. The first claim for an AOD during the study period was considered the patient’s index date. We also obtained information about any prescription of corticosteroids during the period of 2006 to 2008. Each patient was followed up prospectively for 1 year. Subjects were classified into treatment groups based on the study drug first received.

To evaluate treatment compliance and persistence in our cohort, according to International Society for Pharmacoeconomics and Outcomes Research (ISPOR) definitions, 

 the length time with drug available for each refilled prescription was calculated using specific Defined Daily Doses (DDDs).

 Switching products was not considered an interruption.

Compliance (adherence) was measured as medication possession ratio (MPR), calculated as the total number of days of drug supplied in the observation period divided by the total number of days in the observation period (365), calculated as a percentage. Optimal compliance with therapy was defined by MPR of at least 80%.

Persistence was quantified by the number of days covered by a drug from initiation to discontinuation of therapy. Discontinuation occurred when the period between the end of the coverage of a prescription and the date of the refill was longer than the permissible gap of 30 days.

 Patients with at least 1 discontinuation episode were considered nonpersistent, even if they subsequently restarted treatment. A sensitivity analysis was performed in order to determine the influence of the duration of the permissible gap on the results.

MPR was described by mean values and by distribution of patients across MPR classes. Persistence rates were evaluated using Kaplan-Meier survival analysis. Heterogeneity tests across groups were undertaken using the unpaired student’s 

test or a 1-way analysis of variance (ANOVA) for continuous variables, and χ² test for categorical measures, as appropriate. The impact of some sociodemographic and clinical variables on MPR was estimated using multivariate Poisson regression analysis (dependent variable: MPR <80%). As persistence can change over time, we used a Cox proportional hazards model (dependent variable: nonpersistence). Both regression analyses were adjusted for LHUs. All analyses were performed using SPSS 19.0 (SPSS Inc, an IBM Company, Chicago, Illinois).

We identified 40,004 new users of AODs in 2007: 35,956 women (89.9%, mean age [standard deviation] 69.8 years [11.2]), 4048 men (mean age [SD] 69.6 years [13.9]). Of those, 1792 subjects were 50 years or older and on chronic corticosteroid therapy: 1403 women (3.9%) and 389 men (9.6%).

Characteristics of prescriptions are reported in 

. Alendronic acid was the most commonly prescribed drug (with or without colecalciferol, 45.8% women and 53.2% men), followed by risedronic acid and strontium ranelate. Antiosteoporosis therapy was mainly administered weekly (97.2% of all prescriptions of only alendronic acid and 96.6% of all prescriptions of risedronic acid). Generics were used by 6.1% of both women and men. The switch to another drug was more frequent than the change of administration regimen (10.0% vs 6.3%).

. Although suboptimal in 75.1% of our total sample, compliance was better for women. More than 70% of subjects had already interrupted their treatment after 6 months (

At 1 year, persistent women and men were 15.9% and 10.1%, respectively. In these groups, mean compliance was about 100%. Of the nonpersistent population, females (87.7%) and males (92.8%) had suboptimal compliance; about one-third of nonpersistent women and half of nonpersistent men had only 1 prescription in the study period (Table 3). Excluding these subjects, mean MPR levels were 64.3% in women and 58.1% in men. On the other hand, 46.2% and 29.2% of nonpersistent women and men, respectively, showed at least 1 prescription after the interruption (

). The sensitivity analysis with a permissible gap of 60 days showed a persistence rate of 40.5%.

reports adjusted risk ratios for suboptimal compliance and shows that female subjects younger than 50 years and those 75 years or older were at higher risk of suboptimal compliance, while weekly or monthly administrations (women only), change of drug or of frequency of administration, and concomitant use of corticosteroids were associated with a lower risk of noncompliance. Similar patterns can be seen in the adjusted hazard ratios for nonpersistence (Table 4).

Osteoporotic fractures represent one of the most common causes of disability and are associated with enormous healthcare expenditure. Although several specific therapies are available, accumulating evidence suggests that these agents are underused in clinical practice.

 Moreover, even in subjects undergoing therapy, the management of osteoporosis is difficult because of poor compliance.

Several studies showed that a significant proportion of female patients stopped their treatment within 6 months of initiation and more than half did so within 1 year.

 In addition, observational studies, although showing wide variations, reported high rates of suboptimal adherence. 

McCombs and colleagues reported a mean MPR of 68%, 1-year persistence rates of 24.2%, and mean lengths of persistence of 170 days for bisphosphonates.

 Downey and colleagues observed a 12-month prevalence of optimally compliant patients (MPR ≥80%) of less than 60%, with persistence rates of 20%.

 Results of previous studies are not always directly comparable to ours due to a number of methodological differences such as the population selected, the definition of compliance and persistence, the duration of follow-up, analytical techniques used, and differences in settings (populations, practices, and healthcare systems).

Nonetheless, our findings are consistent with data from other studies, highlighting the widespread problem of poor adherence to antiosteoporosis therapy.

In our study, mean MPR was less than 50%; the optimal compliance rate was 25%; and proportion of persistent subjects was about 15%. These values are mainly the consequence of the high percentage of subjects with only the first prescription (almost 1 out of 3 patients with only 1 prescription in the observed year), a finding particularly relevant in the male cohort (48%) and in younger subjects (53% among subjects <50 years). Evaluation of the adherence in patients with at least 2 prescriptions showed significantly higher values (mean MPR was 64.3% in women and 58.1% in men).

As AODs are characterized by side effects (gastrointestinal, musculoskeletal, neurological) and transient symptoms that adversely affect the quality of life of the patient, the properties of these drugs may be responsible, at least in part, for the observed large proportion of users with only 1 prescription.

The simple distinction between compliant subjects and those exhibiting suboptimal compliance, as well as that between persistent and nonpersistent subjects, provides only a rough description of patient attitude toward drug use, and is strongly influenced by the choice of analysis parameters (an analysis performed with a permissible gap of 60 days showed a persistence rate of 40.5%). In fact, among nonpersistent subjects, 12% had the medication available for more than 80% of the time, thus becoming part of the compliant group despite the presence of occasional therapeutic gaps. Therefore, the proportion of subjects with a discontinuous treatment and insufficient drug supply during 1 year decreased to 75% of the total cohort. These are the patients who may derive a lower-than-expected benefit from therapy.

Indeed, it should be noted that nonpersistence, as defined in this and other studies, is not necessarily equivalent to permanent treatment discontinuation, as patients may lapse and then resume treatment after a “drug holiday” of variable duration. Actually, most publications that have examined persistence to osteoporosis pharmacotherapy have considered only the initial treatment episode. However, an underreported finding is that many patients who discontinue pharmacotherapy return to treatment after a variable gap, a percentage ranging between 30% and 50% of nonpersistent subjects.

 In our study, 46% of nonpersistent women and 29% of nonpersistent men showed at least 1 prescription after the first gap. It is possible that these drug-free intervals could have been avoided through closer monitoring of these subjects by their physician.

Previous studies were all conducted using female cohorts, and thus compliance and persistence data in men are scant. In a retrospective chart-review study of male veterans, Hansen and colleagues observed optimal compliance in 59% of patients.

 Our study adds data showing that compliance and persistence rates in men were lower than those estimated in women, mainly due to a greater proportion of subjects with only 1 prescription. In men, as reported in the literature, osteoporosis is a prevalent problem that is underrecognized and undertreated, and morbidity and mortality following a fracture are also greater than they are for women.

 From this perspective, the observed low levels of adherence have even more clinically relevant implications, requiring more attention by physicians, especially in this population.

A few studies have investigated factors influencing nonadherence, identifying side effects, age, and therapeutic regimen as main determinants.

 In our regression analyses, age was a major determinant of poor adherence and nonpersistence, as both younger age and older age increased risk, probably due to a reduced perception of adverse outcomes from osteoporosis

(which has low prevalence in people under age 50 years) and to the increased comorbidity and concomitant treatments at older ages (which may lead practitioners and patients to favor other therapies).

A major perceived problem with oral bisphosphonates is the inconvenience of the regimen, and the association between complex drug regimens and compliance is well established. 

 Several studies showed that less frequent dosing regimens significantly improved levels of both compliance and persistence.

 This was confirmed in our female sample, supporting a great possibility of improvement from new formulations with delayed administration frequency.

In our analyses, the presence of a therapy change has been shown to reduce the risk of nonadherence and nonpersistence. This evidence may suggest the switch as an indicator of further attention by the physician (to counteract the onset of adverse effects or to meet the patient’s needs), but we should consider that switching to another brand may result in the accumulation of drug units that are not used, which can lead to skewed levels of compliance.

Data about patients who were prescribed bisphosphonates because of chronic corticosteroid therapy are scarce. A study by Curtis and colleagues evaluated persistence and compliance to bisphosphonates at 2 years among corticosteroid users and reported a mean MPR of 73%.

 In our study, compliance levels within corticosteroid therapy were poor (mean MPR 56.4%), although higher than those of other patients (mean MPR 46.1%), and in regression analyses, the use of corticosteroids significantly reduced the risk of suboptimal compliance and of nonpersistence. It is likely that these patients are more aware of the risk of fractures and the importance of antiosteoporosis therapy.

The results of our study should be considered within the context of the study’s limitations, mainly related to the use of administrative registry as a data source. Although this database allows access to information on a large number of patients gathered in real-world conditions and repeated dispensing over regular intervals is a good proxy for actual compliance and persistence to treatment, pharmacy claims do not guarantee the real consumption of medications, nor do they reflect the actual timing or manner of use. In addition, the administrative database does not capture drugs taken during hospitalization and use of over-the-counter calcium and supplements including vitamin D, so we did not cover all antiosteoporosis interventions. Moreover, data on many important clinical variables that may influence compliance and persistence are not available.

On the other hand, the absence of exclusion criteria specific for age, pathology, or concomitant treatment allowed us to evaluate the AOD utilization profile of an unselected population in the general practice setting. The enrollment of both genders into the study provided information about prescriptive patterns and adherence and persistence to therapy also in the male population. In addition, we extended our observation to all medications recommended for osteoporosis treatment by Italian guidelines.

Overall, our data underscore the urgent need to improve the use of antiosteoporosis drugs, and suggest some priorities for intervention to improve compliance and persistence. First, both patients and doctors should be more aware that, after the decision to start a pharmacologic therapy, this cannot be interrupted; and that a scheduled, close follow-up is crucial, at least during the initial months of therapy, to reduce drop-out rate after the first prescription. Second, indications to minimize side effects should be offered to the patients whenever these drugs are prescribed. The risk/benefit ratio for younger and older patients should also be expressed. Finally, weekly or monthly regimens should be the preferential choice. The results of such improvement strategies could be easily monitored using the administrative databases, as showed by the current study.

This study confirms that compliance and persistence to antiosteoporosis drugs is suboptimal in everyday practice, with short periods of persistence and lengthy gaps in therapy, and with 75% of treated patients probably getting no benefit or only partial benefit from therapy. To address these issues, a strategy to detect first prescriptions without any refill should be implemented (such as automated alerts close to the end of drug availability after a prescription is filled), along with adequately planned follow-up to monitor treatment effectiveness and compliance, and selection of a drug regimen that can help patients be more compliant with therapy. This is an example of how administrative databases, when available, can be used to monitor drug use and to identify the areas in which improvement is needed to increase compliance and persistence to therapy.

 Epidemiology and Preventive Pharmacology Centre (SEFAP), Department of Pharmacological and Biomolecular Sciences, University of Milan, Italy (MC, ALC, ET); IRCCS MultiMedica, Milan, Italy (ALC); Local Pharmaceutical Service, LHU of Bergamo, Bergamo, Italy (RP, LDF, MG); Center of Pharmacoeconomics (CIRFF), University of Naples Federico II, Naples, Italy (EM, VO); Department of Medicine and Aging Sciences, University of Chieti, Chieti, Italy (LM, MEF); Italian Society of General Medicine (SIMG), Florence, Italy (AF).

This analysis was partially performed under the IFI project, funded by Regione Lombardia (according to DGR 8501 March 22, 2002).

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (MC, ALC, AF, ET); acquisition of data (RP, EM, LM, LD, VO, MEF, MG); analysis and interpretation of data (MC, EM, LM, VO, ET); drafting of the manuscript (MC); critical revision of the manuscript for important intellectual content (ALC, EM, LM, VO, AF, ET); statistical analysis (MC); provision of study materials or patients (RP, LD, MG); administrative, technical, or logistic support (RP, LD, MEF, MG); supervision (ALC, MEF, ET).

This analysis of antiosteoporosis therapy shows that 75% of patients have inadequate drug coverage and that adherence is strongly associated with age and administration regimen.

Assessment and Potential Determinants of Compliance and Persistence to Antiosteoporosis Therapy in Italy

Pharmacogenetic testing as a means of guiding treatment decisions is beginning to see wider clinical use in psychiatry. The utility of this genetic information as it pertains to clinical decision making, treatment effectiveness, cost savings, and patient perception has not been fully characterized.

In this retrospective study, we examined health claims data in order to assess medication adherence rates and healthcare costs for psychiatric patients.

Individuals for whom pharmacogenetic testing was ordered (cases) were contrasted with those who did not undergo such testing (controls). Cases and controls were propensity score matched in order to minimize risk of confounding in this nonrandomized study. An initial analysis of 111 cases and 222 controls examined both adherence and healthcare costs. A replication study of 116 cases and 232 controls examined adherence alone, as cost data was not available for this latter cohort.

Overall, individuals with assay-guided treatment were significantly more medication adherent (

 = 1.56 3 10—3; Cohen’s d = 0.511) than patients with standard treatment and demonstrated a relative cost savings of 9.5% in outpatient costs over a 4-month follow-up period, or $562 in total savings.

The data show the utility of pharmacogenetic testing in everyday psychiatric clinical practice, as it can lead to improved patient adherence and decreased healthcare costs.

This research describes the clinical utility of pharmacogenetic testing in psychiatric practice through the analysis of pharmacy and private practitioner medical claims. Patients whose clinicians had access to results of a genetic test to help guide treatment decisions had improved medication adherence as well as decreased overall healthcare costs. This study was able to demonstrate that:

Genetic testing can lead to a relative cost savings of 9.5% in outpatient costs, or $562 per patient over 4 months.

Genetic testing can lead to improved patient medication adherence.

Genetic testing in psychiatric populations could become a useful tool in everyday practice.

Depression and other psychiatric illnesses have substantial costs in human and financial terms, accounting for 4 of the top 10 causes of disability worldwide.

 In the United States, it is estimated that mental health disorders account for 6.2% of the nation’s healthcare spending.

 A major driver of depression costs is treatment-resistant depression (TRD),

 defined as a failure to reach symptomatic remission despite at least 2 adequate treatment trials.

 An estimated two-thirds of patients with depression will not respond to first-line treatment and more than one-third of patients will become treatment resistant.

 These individuals pose an even more striking cost burden, approximately 40% higher, compared with patients without TRD.8,9 Treatment for psychiatric disorders is further complicated by variation in medication response across populations. Known pharmacodynamic and pharmacokinetic variations exist across ethnic groups impacting response to common psychiatric medications.

There is abundant evidence that genetic variations influence drug disposition, metabolism, transport, and response, resulting in changes to efficacy and tolerability of psychotropics.

 An estimated 40% of the interindividual differences in antidepressant response are explained by common genetic variations.

Genetic variations may compound already elevated costs by increasing the risk for intolerable side effects or poor efficacy, which may contribute to nonadherence.

Identifying genetic variations involved in treatment response may facilitate more targeted evidence-based interventions in order to improve the likelihood of remission. While a theoretical basis for expecting benefit from genetic testing exists, the actual impact on outcomes—and potential cost savings in particular&mdash;has not been well established. The use of claims data to establish these benefits provides advantages through the efficiency of data collection, ability to observe effectiveness in real-world clinical practice, and the ability to directly measure costs. While such methods pose a risk of confounding, modern analysis and matching techniques, including propensity score matching, can be used to reduce these potential sources of bias. The objective of the current study was to determine whether patient and clinician access to genetic information during psychiatric treatment selection would influence medication adherence and healthcare costs among psychiatric patients.

This retrospective, observational study used patients’ claims data from September 2010 through September 2012. Patients were divided into 2 groups: (1) cases whose treating clinicians ordered genetic testing and (2) a matched set of controls whose treating clinicians did not have access to genetic information, treating patients as usual.

The genetic test used in this study, the Genecept Assay, analyzed variations in 5 pharmacodynamic and 2 pharmacokinetic genes associated with treatment response, side effects, metabolism, tolerability, and overall efficacy of many psychiatric medications.

 Pharmacokinetic genes included cytochrome P450 2D6 (

). Pharmacodynamic genes included the serotonin transporter (

), dopamine receptor subtype (DRD2), catechol- O-methyl transferase (

), and methylenetetrahydrofolate reductase (

The saliva-based test was administered to cases at their clinician’s office. Clinicians were provided with instructions and telephone support. Samples were sent to a Clinical Laboratory Improvement Amendments (CLIA) certified lab for analysis. After genotyping via polymerase chain reaction (PCR) Taqman, a results report was provided to clinicians with a summary of clinical interpretation and implications for each variant supported by peer-reviewed published literature. An algorithm that was designed based on the functional significance of each variant and the associated effects on treatment outcomes was applied to generate a unique report for each patient. Professionals with experience in medical genetics and psychopharmacology were available to assist clinicians with interpretation of results.

Healthcare utilization and cost data were extracted from IMS Health longitudinal patient-level databases which included pharmacy claims, private practitioner medical claims, and hospital detail charge master records. Available data included all claims submitted to third-party payers including commercial insurers, Medicare and Medicaid via Centers for Medicare & Medicaid Services (CMS) 1500 forms and others (eg, Tricare) in all 50 US states, and cash claims. Prescriptions were analyzed using the National Council for Prescription Drug Programs claim forms, which included approximately 2 billion dispensed claims per year within the database. Patients were assigned a synthetic identifier, and the databases were certified as being compliant with the Health Insurance Portability and Accountability Act. This study was deemed exempt from additional consent requirements by Chesapeake IRB (Pro00008398), as data were completely deidentified.

Cases were included if their practitioner received their test results between May 1, 2011, and March 31, 2012. The index date for cases was defined as the date the Genecept Assay results were available to clinicians. Additional inclusion criteria for cases required a psychotropic prescription be dispensed between 5 and 60 days postindex (

) as well as pharmacy activity between 5 and 8 months pre-index, as evidenced by claims data. Cases were required to have a psychiatric diagnosis listed in the claims database (

). Additional selection criteria were applied to create a final pool of cases utilized in propensity score matching (PSM) (

The pool of controls was created by selecting patients available in the claims database with matching year of birth, sex, and psychiatric condition to any cases. The index date for the corresponding case was assigned to all matching controls. The matching controls must have received any dispensed psychotropic agent within +/— 7 days from the date of a case’s in both the pre- and postindex periods. Additional selection criteria were applied to the controls to further limit the pool utilized in PSM (Table 1). Figure A shows a temporal depiction of inclusion characteristics. PSM was performed on the refined pools of cases and controls, matching 2 controls to every case.

PSM was computed using a logistic regression model that adjusted for covariates of patient age, sex, payer type, US Census region, all psychiatric condition(s), all medication type(s), Charlson Comorbidity Index score, and treating practitioner’s specialty.

 Binary variables were assigned for presence or absence of each central nervous system (CNS) diagnosis of interest as determined by the International Classification of Diseases, Ninth Revision, supplied at the time of testing for cases or the diagnosis code associated with private practitioner visits between 4 months pre to 60 days postindex (Appendix B). Separate binary variables were assigned for each pharmacologic category of interest (Appendix A). A standard mean difference between the 2 samples of less than 10% pre-post matching was considered indicative of good balance.

 Figure B shows a temporal depiction of matching.

A separate replication analysis of medication adherence, which included different cases and controls, was conducted using the same selection criteria, with the exception of observation within medical claims following index date (Table 1). This criterion was necessary for cost evaluation in the primary analysis, but did not impact inclusion in a replication analysis regarding medication adherence. Adherence Methods Adherence was calculated using the medication possession ratio (MPR).

 MPR was calculated at the active ingredient or generic drug level for each psychiatric product the patient filled during the respective periods: (1) preindex included dispensed scripts between 4 and 8 months pre-index (each product was tracked for 120 days from the time the first script was filled within this period); and (2) postindex included dispensed scripts between days 5 and 120 postindex. Dispensed dates that did not allow for a 4-month observation window were excluded. At the generic molecule and patient level, the days of supply was summed over the respective observation period to represent the MPR numerator. The MPR denominator was the observation period duration of 120 days. Figure 1B shows a temporal depiction of pharmacy claims data utilized to determine MPR.

Overlapping days were considered early fills. Any prescription’s days of supply that extended beyond the 120- day observation period was truncated to end on the 120th day of observation. MPR was calculated for all distinct drugs at the generic level, and the patient’s maximum MPR among all products was used to represent the overall MPR for that period. Within sample comparison of the mean MPR 4-month pre-index versus 4-month postindex analysis was conducted. Additionally, a case versus control approach was utilized.

Statistical analyses were conducted using The Comprehensive R Network and a significance level of 0.05. χ² tests were conducted for categorical variables and Welch 2-sample 2-sided t tests for differences in means, assuming unequal variances; pre-post comparisons within a sample were calculated using 2-sided paired t tests. Propensity score matching utilized the MatchIt package.

 Combined meta-analysis of the primary and replication cohorts was done using the weighted z-score method.

Resource Utilization and Associated Costs Methods

Mean resource utilization and associated costs were calculated for each cohort. The total number and cost of dispensed prescriptions for all medications and the total number and cost of all outpatient medical visits were calculated for the 4-month pre- and postindex periods. Pharmacy costs and total cost data were analyzed as component and as composite utilization for cases and controls. Costs were calculated by using standard costs for the actual services and quantities received to control for variable costs within the data, and the inability to incorporate health plan—allowed amounts into the matching. Dispensed prescription cost was calculated using average wholesale unit price for the given national drug code (NDC) and quantity dispensed; medical costs were assessed using the 2012 CMS medical physician fee schedule, including the procedure’s relative value units (work, practice expense, and malpractice) offset by geographic practice cost index (GPCI) based on the physician’s location. Where a physician’s zip code was missing or invalid, the national level GPCI of “1” was used.

Pharmacy and medical claims data showed that patients whose clinicians had access to pharmacogenetic test results had increased adherence and overall cost savings.

May 2014

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