To assess the usefulness and cost-effectiveness of pneumococcal polysaccharide vaccine (PPV) among healthcare workers compared with nonuse of PPV during an influenza pandemic.

Markov modeling was used to estimate the cost-effectiveness of PPV in previously unvaccinated healthcare workers during an influenza pandemic.

Invasive pneumococcal disease (IPD) incidence rates were incorporated into the model, which assumed that IPD events occurred at twice the usual rate during a year of pandemic influenza. Societal and hospital perspectives were examined. Assumptions were that pneumococcal disease transmission from healthcare worker to patient did not occur, heightened IPD risk occurred for only 1 year, and PPV did not prevent noninvasive pneumonia, all of which potentially bias against vaccination.

From a societal standpoint, PPV of healthcare workers during an influenza pandemic is economically reasonable, costing $2935 per quality-adjusted life-year gained; results were robust to variation in multiple sensitivity analyses. However, from the hospital perspective, vaccinating healthcare workers was expensive, costing $1676 per employee absence day avoided, given an IPD risk that (although increased) would remain less than 1%.

Vaccinating all healthcare workers to protect against pneumococcal disease during a pandemic influenza outbreak is likely to be economically reasonable from the societal standpoint. However, PPV is expensive from the hospital perspective, which might prevent implementation of a PPV program unless it is externally subsidized.

A cause of extended morbidity and mortality in previous influenza pandemics has been invasive pneumococcal disease.

In an influenza pandemic, healthcare resources will be strained as increased numbers of patients seek care and healthcare workers fall ill and cannot work.

A potential means of preventing healthcare worker illness and absence from work is to vaccinate them with pneumococcal polysaccharide vaccine.

From a societal perspective, this strategy is cost-effective, costing $3.14 per healthcare worker and $2935 per quality-adjusted life-year gained.

From the hospital perspective, vaccinating healthcare workers was expensive, costing $1676 per employee absence day avoided.

As governmental, scientific, medical, and public health communities responded to the 2009 influenza pandemic, many strategies were considered to reduce the possibility of widespread morbidity and mortality. Primary, secondary, and tertiary prevention measures were devised, including the development of priority groups for pandemic influenza immunization.

 In a pandemic, during which demand for healthcare would inevitably increase, the stability of healthcare systems  would be threatened if healthcare workers became ill in sufficient numbers, a possible scenario given their heightened exposure to a highly infectious illness. Therefore, the Centers for Disease Control and Prevention (CDC)1 listed healthcare workers among tier 1 groups for priority influenza vaccination.

Secondary pneumococcal infections caused as much as 20% of mortality during the 1918-1919 pandemic, as demonstrated in a review of autopsy series showing that 27% of blood cultures were positive for Streptococcus pneumoniae.

 This led to recommendations that pandemic preparedness plans comprise more than just influenza vaccination and include, for example, vaccination against pneumococcal disease.

Vaccinating healthcare workers against pneumococcal disease is a strategy with significant potential, but it is not without countervailing arguments. For example, based on what is known about the efficacy of pneumococcal polysaccharide vaccine (PPV) against invasive pneumococcal disease (IPD), is this a cost-effective solution? Given the worldwide nature of a pandemic, potential costs associated with prevention measures must be considered. Furthermore, concerns about hyporesponsiveness (ie, immunologic interference from the first dose, limiting gains from a subsequent dose)

 raise the question whether vaccination of healthy healthcare workers during a pandemic merely shifts the burden of pneumococcal disease to later years, without changing the cumulative incidence of disease. The objective of this study was to use Markov modeling to assess the cost-effectiveness of PPV among healthcare workers compared with nonuse of PPV during an influenza pandemic. The results of these analyses will help health systems to anticipate needs and costs in their pandemic preparedness planning.

The cost-effectiveness of 23-valent PPV use among healthcare workers was compared with nonuse of PPV during an influenza pandemic using a Markov model, shown  schematically in 

 (available at www.ajmc.com). The model was programmed using available software (TreeAge Pro Suite; TreeAge Software Inc, Williamstown, MA). In the model, identical hypothetical cohorts of previously unvaccinated healthcare workers can remain well or develop IPD. Those developing IPD can recover completely, become disabled, or die of IPD. Risks of death from other causes for individuals in any health state can be determined  from US life table data.

 Patients with disabilities due to IPD will die at higher rates. We model increases in IPD rates that might occur as a result of influenza but do not model influenza itself.

Three model assumptions were made that potentially bias it against PPV use in healthcare workers. The first assumption is that pneumococcal disease transmission from healthcare worker to patient does not occur. This assumption is based on pneumococcal disease resulting as an influenza complication in an individual already colonized with pneumococci and the unlikely possibility of simultaneous transmission of influenza and pneumococcus.6 The second assumption  is that heightened IPD risk due to pandemic influenza occurs for only 1 year, as the duration of any influenza pandemic is unknown. The third assumption is that PPV does not prevent noninvasive pneumonia, as PPV protection against noninvasive pneumonia is unclear and controversial.

Analyses were performed from societal and hospital perspectives. In the societal perspective analysis, the time horizon was 15 years with costs and benefits discounted at 3% per year, following the recommendations of the US Panel on Cost-Effectiveness in Health and Medicine.

 Effectiveness was valued in quality-adjusted life-years (QALYs) to account for changes in life span and quality of life due to PPV. Quality-adjusted life-years are the product of the time spent in a health state and the quality-of-life utility associated with that state summed over time; utilities are a measure of preference for a health state, ranging from 0 (death) to 1 (perfect health). In all analyses, costs were estimated in 2006 US dollars. In the hospital perspective analysis, the effectiveness term was employee absence day avoided over a 5-year time horizon, and the following 3 additional assumptions were made: (1) all direct medical costs are covered by insurance, (2) all healthcare workers with IPD miss work for 10 days, and (3) 2 months are required to replace healthcare workers who are disabled due to IPD or who die of IPD.

 In the base-case analysis, we assumed that IPD cases and deaths occurred at twice the usual rate during the year of pandemic influenza in healthcare workers with a mean age of 45 years. This age was derived using US Bureau of Health Professions data for registered nurses and licensed practical nurses, who comprise the bulk of healthcare workers.

 We averaged the published age distributions of these workers to calculate the usual IPD rate for this cohort, 13.7 cases per 100,000, using CDC Active Bacterial  Core surveillance data (eAppendix C available at www.ajmc.com).

 Using similar methods, we aged the cohort 15 years and calculated an IPD risk of 24.4 cases  per 100,000 and then used linear interpolation to estimate IPD risk between years 1 and 15. We used the general population risk because there are no specific data on IPD risk in healthcare workers. Therefore, IPD rates were varied from 50% to 150% of their base-case levels in a separate sensitivity analysis.

The concept of increased pneumococcal disease rates is based on autopsy studies

 demonstrating pneumococcal bacteremia and empyema in the 1918-1919 pandemic and bacterial lung infection in the 2009 pandemic. Because the specific effect of influenza on IPD is unknown, we arbitrarily set IPD rates at twice  the usual rate and varied the relative risk of IPD from 1 (no change in IPD rates) to 6 (6 times the usual IPD rates) in sensitivity analyses. The probability that IPD was caused by a pneumococcal serotype contained in the PPV was derived from CDC Active Bacterial Core surveillance data

. Costs related to vaccination and vaccination adverse events were also obtained from the literature,

 and age-specific IPD costs were obtained from the 2006 National Inpatient Sample (

 A panel of pneumococcal disease experts estimated duration-specific protection from PPV for susceptible pneumococcal serotypes,

 basing their estimates on data by Shapiro et al

). Invasive pneumococcal disease—related meningitis incidence was used as  a proxy for the probability of IPD-related disability.

For all variables, the base-case value and range of values examined in 1-way sensitivity analyses are given in Table 1. Two-way sensitivity analyses were conducted to examine the combined effect of age and IPD rate variation on model results. To test the robustness of model results, parameter values were varied simultaneously in a probabilistic sensitivity analysis in which variables were assigned distributions and values were chosen from each distribution 5000 times. Uniform distributions were used for utility values, triangular distributions were used for hospital perspective costs, and clinical trial data and CDC data were assigned distributions based on data characteristics and skewness. Triangular distributions were also used to model estimates of PPV efficacy using the time-specific base-case estimates given in Table 2 as the most common value and the low-range and high-range efficacy estimates as the lower and upper bounds of these distributions.

. For healthcare workers with a mean age of 45 years and twice the usual IPD incidence and mortality rates during the pandemic influenza year, the per-patient total cost (including vaccination and IPD costs) for the PPV strategy was $3.14 greater than no vaccination, while gaining 0.00107 QALY (about 0.4 day). Therefore, the incremental cost-effectiveness ratio of vaccination compared with no vaccination was $2935 per QALY gained.

We estimated that 284 cases and 32 deaths per 100,000 healthcare workers would occur without PPV and that 171 cases and 19 deaths per 100,000 healthcare workers would occur with PPV. Therefore, a PPV strategy prevented about 40% of IPD cases and deaths in healthcare workers.

In 1-way sensitivity analyses in which parameter values were individually varied, results were most sensitive to healthcare worker age, with incremental cost-effectiveness ratios exceeding $100,000 per QALY for healthcare workers 29 years or younger (

). Conversely, pandemic-based PPV was cost saving for healthcare workers 49 years or older under base-case assumptions. Results were less sensitive to variation of IPD relative risk (eAppendix D available at  

). In a 2-way sensitivity analysis, we varied the healthcare worker age and the relative risk of IPD during an influenza pandemic year (

). Using an acceptability threshold of $100,000 per QALY gained, PPV would be favored for workers 29 years or older when the relative IPD risk is 1 or higher or for all workers (regardless of age) when the relative risk is 3.4 or higher.

When the base-case variables were used with a heightened IPD risk related to pandemic influenza lasting 2 years instead of 1 year, PPV costs $398 per QALY gained. If no increase in IPD rates occur with an influenza pandemic or if a pandemic occurs after PPV loses effectiveness (ie, after 15 years), PPV costs $6199 per QALY. While there is no standard criterion for cost-effectiveness, interventions that cost less than $100,000 per QALY gained are generally acceptable and are comparable to other commonly used interventions.

 Interventions that cost less than $20,000 per QALY gained are typically thought to be a “good buy” and to represent strong evidence for adoption.

 Evidence suggests higher acceptability thresholds for interventions related to occupational health,

 making our results even more favorable from that standpoint. The CDC Active Bacterial Core surveillance IPD risk data are risks for the general population; it is unclear if this risk applies to healthcare workers. Using 50% to 150% of the general public’s risk as a way to assess varying risk for healthcare workers, PPV in healthcare workers costs $31,600 per QALY if their IPD risk is 50% of the general population risk, and PPV is cost saving if healthcare worker risk is 150% of the general population risk.

In a probabilistic sensitivity analysis, all variable values listed in Table 1 were simultaneously varied. Vaccinating all healthcare workers was favored more than 91% of the time using an acceptability threshold of $20,000 per QALY and more than 99% of the time using an acceptability threshold of $100,000 per  QALY.

From a hospital perspective, the PPV strategy had a total cost per vaccinated worker that was $20.71 greater than no vaccination, while avoiding 0.01236 absence day per worker, for a cost-effectiveness ratio of $1676 per employee absence day avoided (Table 3). This figure could be an overestimate given the assumption of no vaccine efficacy for noninvasive pneumonia, and that does not account for lost revenue due to reduced hospital manpower and capacity, as well as the intangible costs of negative public perception if hospital services were to be curtailed. Pneumococcal polysaccharide vaccination in healthcare workers costs $4103 per employee absence day avoided if their IPD risk is 50% of the general population risk and costs $867 per employee absence day avoided if their IPD risk is 150% of the general population risk. In a probabilistic sensitivity analysis, the 95% probability range for vaccination compared with no  vaccination was $20 to $10,800 per employee absence day avoided.

In this analysis, PPV is an economically reasonable strategy to be considered for healthcare workers during an influenza pandemic from a societal perspective, with robust results in 1-way sensitivity analyses and a high likelihood of cost-effectiveness in a probabilistic sensitivity analysis. Our results are comparable to findings of a recent analysis by the CDC examining PPV use in critical infrastructure personnel (eg, healthcare workers and utility  workers).

 The investigators estimated 35,000 invasive and non-IPD cases in a population of 20 million, or an attack rate of 175 per 100,000. Using their age-specific hospitalization rates and assuming that 25% of workers are 50 years or older, the pneumococcal disease hospitalization rate was about 25.8  per 100,000. If half of those hospitalized had IPD, then the IPD rate was 12.9 cases per 100,000, slightly less than our calculated IPD rate in healthcare workers, 13.7 cases per 100,000. Using 0.942 (ie, 12.9 of 13.7), the IPD relative risk derived from their study, in our analysis produces an incremental cost-effectiveness ratio of $6419 per QALY gained. This value falls within the range of their discounted life-years saved result of $37,320 (95% confidence interval, $5865-$80,359). Unlike our analysis, the CDC study did not use utilities or account for disability or PPV effects beyond the pandemic year.

On the other hand, from the hospital perspective, PPV of healthcare workers costs $1676 per employee absence day avoided, which most hospitals would consider  a steep premium to pay for a small expected return. Our analysis highlights the dilemma many hospital systems face in trying to do the right thing from a societal standpoint, while at the same time attempting to remain fiscally sound. Managed care faces this tension of hospital versus societal perspectives as it seeks to maximize health in a cost-effective way. Even with a heightened risk of pneumococcal disease during an influenza pandemic, the risk of IPD would probably remain less than 1% for healthcare workers, and this fact might lead hospital systems to defer PPV for their workers unless some subsidy to defray vaccination costs was available. We did not include the possibility that healthcare workers would infect other workers or patients or that hospitals would be  unable to meet staffing needs due to absenteeism; therefore, our estimates are conservative. Maintenance of appropriate staffing levels is a priority in managed care. Although expensive from the hospital perspective, vaccination may lead workers to perceive themselves as better protected and to be more willing to work in the face of a pandemic. Given that PPV is less expensive than many other occupationally indicated vaccines, it may be reasonable to consider PPV as a means to allay worker concerns about complications of pandemic influenza.

From a societal standpoint, PPV administration to healthy healthcare workers at the onset of a pandemic has pros and cons. Vaccination is likely to reduce IPD

 and in healthy workers may reduce pneumococcal pneumonia.

 However, concerns exist about vaccine efficacy against noninvasive pneumonia and about hyporesponsiveness to subsequent vaccination, also known as tolerance.

 If hyporesponsiveness occurs following PPV, it might simply shift the burden of pneumococcal disease from the time of the pandemic to later in the life of the healthcare worker, without changing the cumulative incidence. Repeat doses of PPV later in life are generally safe

; therefore, the concern is not safety at the time of vaccination or of repeat vaccination years later but of potential  hyporesponsiveness. Another potential limitation of our analysis is that recent PPV recommendations

 have added smoking and asthma to the list of comorbid conditions for which vaccination before age 65 years is recommended and could further lessen the effect of healthcare worker vaccination given the considerable proportion of workers falling into these categories.

Because of the current absence of clinical trial data and for the foreseeable future, we used a Markov model to synthesize available data. We also used a series of conservative estimates and assumptions, including PPV effectiveness against IPD, not pneumococcal pneumonia, although some data suggest that PPV may have effectiveness against pneumonia in healthy adults.

 Despite these assumptions, vaccination of healthcare workers with PPV was cost-effective from the societal perspective. Limitations include the unknown increased IPD risk in a pandemic and the inability to address PPV effect on hyporesponsiveness to future pneumococcal vaccines, either polysaccharide or conjugate, the latter of which may be licensed for adults in the future. We used longer time horizons than prior analyses; this has the advantage of not being limited to pandemic effects but has the disadvantage of not accounting for future new vaccines and changes in epidemiology. Because we used a 15-year time frame and a single PPV in those who were previously unvaccinated and because current CDC recommendations only call for PPV revaccination at age 65 years for those vaccinated before that age, we did not include revaccination as part of our analyses of current workers. If a longer time horizon was examined or if hyporesponsiveness occurred, greater costs per QALY gained would result.

In conclusion, vaccinating all healthcare workers to protect against pneumococcal disease during a pandemic influenza outbreak is likely to be economically reasonable according to a societal perspective in an analysis biased against vaccination. However, when analyzed from a hospital perspective, PPV is expensive, and the small risk of illness might prevent hospital implementation unless vaccination is externally subsidized.

From the Section of Decision Sciences and Clinical Systems Modeling (KJS, MSR), the Department of Family Medicine and Clinical Epidemiology (MR, MPN, RKZ), and the Department of Behavioral and Community Health Sciences (RKZ), University of Pittsburgh, Pittsburgh PA.

This study was supported by grant R01AI076256 from the National Institute of Allergy and Infectious Diseases. Its contents are the responsibility of the authors and do not necessarily reflect the official views of the National Institutes of Health.

Drs Nowalk and Zimmerman report receiving grants from Merck & Co, Inc, and MedImmune. The other authors (KJS, MR, MSR) 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 (KJS, MSR, RKZ); acquisition of data (KJS, MR, RKZ); analysis and interpretation of data (KJS, RKZ); drafting of the manuscript (KJS, MPN, RKZ); critical revision of the manuscript for important intellectual content (KJS, MPN, MSR); statistical analysis (MR); obtaining funding (KJS, RKZ); and administrative, technical, or logistic support (MR).

Kenneth J. Smith, MD, MS, Section of Decision Sciences and Clinical Systems Modeling, University of Pittsburgh, 200 Meyran Ave, Ste 200, Pittsburgh, PA 15213. E-mail: 

1. National Center for Immunization and Respiratory Diseases, CDC; Centers for Disease Control and Prevention (CDC). Use of influenza A (H1N1) 2009 (monovalent) vaccine: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2009. MMWR Recomm Rep. 2009;58(RR-10):1-8.

2. Morens DM, Taubenberger JK, Fauci AS. Predominant role of bacterial pneumonia as a cause of death in pandemic influenza: implications for pandemic influenza preparedness. J Infect Dis. 2008;198(7):962-970.

3. Brundage JF. Interactions between influenza and bacterial respiratory pathogens: implications for pandemic preparedness. Lancet Infect Dis. 2006;6(5):303-312.

4. O’Brien KL, Hochman M, Goldblatt D. Combined schedules of pneumococcal conjugate and polysaccharide vaccines: is hyporesponsiveness an issue? Lancet Infect Dis. 2007;7(9):597-606.

5. Arias E. United States life tables, 2002. Natl Vital Stat Rep. 2004;53(6):1-38.

Articles

FOCUS ON VACCINES

Pneumococcal polysaccharide vaccination of healthcare workers during an influenza pandemic is cost-effective from a societal perspective but not from a hospital perspective without external subsidy.


Cost-Effectiveness of Pneumococcal Polysaccharide Vaccine Among Healthcare Workers During an Influenza Pandemic

To quantify rates of stand-alone e-prescribing (SEP) adoption and use among primary care physicians (PCPs) participating in a SEP initiative and to determine which physician and patient characteristics were associated with higher rates of each.

Using records from an insurer-led SEP initiative, we compared the characteristics of 297 PCPs who adopted SEP through the initiative with the characteristics of 1892 eligible PCPs who did not. Among 297 adopters, we studied the extent of SEP use.

Dependent variables included each physician’s adoption of SEP and his or her e-prescribing use ratio (the ratio of electronic prescriptions to pharmacy claims in the same period). Independent variables included characteristics of PCPs (specialty, practice size, and prescribing volume) and their patients (patient age, sex, race/ethnicity, and household income).

Solo practitioners, pediatricians, and physicians with more patients from predominantly African American zip codes were underrepresented among SEP adopters. The mean (SD) e-prescribing use ratio among adopters was 0.23 (0.28). Twenty percent of physicians maintained e-prescribing use ratios above 0.50. Available physician characteristics explained little of the variance in use, but physicians in smaller practices had greater use (P = .02).

Certain categories of physicians may need more tailored incentives to adopt SEP. On average, adopters used the SEP system for only about one-quarter of their prescriptions. Some adopters achieved high levels of SEP use, and further research is needed to elucidate the factors that enabled this.

e-Prescribing is seen as a critical technology for improving medication use.

In a health plan—sponsored e-prescribing initiative, the mean e-prescribing rate of participating primary care physicians (PCPs) was 1 prescription per 4 pharmacy claims, but some PCPs achieved high use.

Given this low use, future initiatives may need to consider more resources to increase e-prescribing use.

Efforts should be made to ensure that all patient demographics benefit from e-prescribing.

Higher e-prescribing use among physicians in smaller practices suggests that e-prescribing may be an appropriate manner of extending health information technology to these physicians, who traditionally are reluctant users of such technology. 

There is evidence that in some settings health information technology (HIT) can improve patient outcomes and reduce healthcare costs.

 Most of this evidence comes from 4 healthcare organizations at which academic physicians and employees are usually required to use homegrown electronic medical records (EMRs).

 However, few physicians practice in these types of environments. More than 75% of physicians practice in groups of 5 or fewer.

 Unfortunately, the structure of these small community private practices is not conducive to providing the financial and time investment necessary for EMR adoption.

 As a result, only 9% to 14% of these practices have adopted EMRs compared with 23% to 50% of larger practices.

Stand-alone e-prescribing (SEP) has been proposed as a possible method of transitioning community physicians toward EMR functionality without the initial investments required for a full EMR system.

 Indeed, recent legislation promises to increase Medicare reimbursement for e-prescribing physicians in the short term and to decrease Medicare reimbursement for paper prescribers in the long term.

We are aware of only 1 prior study that evaluates adoption and use of commercial SEP systems by community physicians. Fischer et al

 examined use and adoption of the PocketScript system, which was offered without cost to high-volume outpatient prescribers in Massachusetts. A striking finding of their analysis was the low use of e-prescribing, which (although increasing throughout the period studied) was less than 30% of all eligible prescriptions 1 year after adoption. The authors cite anecdotal evidence of increased e-prescribing since that period but present data only through early 2005. 

To further characterize experiences with SEP from other states using another e-prescribing system and in a more recent period, we quantified the rates of e-prescribing adoption and use that occurred when Horizon Blue Cross Blue Shield of New Jersey (Horizon BCBSNJ) offered SEP to community physicians participating in their health maintenance organization (HMO) and preferred provider organization network. Our primary study objectives were to quantify rates of SEP adoption and use and to determine which physician and patient characteristics were associated with higher rates of each.

Horizon BCBSNJ, New Jersey’s largest health insurer, provides coverage for 3.2 million members. In late 2004, Horizon BCBSNJ launched an initiative offering its physicians Caremark’s iScribe SEP software (Caremark is Horizon BCBSNJ’s pharmacy benefits manager). The program installed SEP systems for individual physicians rather than for practices as a whole. All features of the program, including the target population, recruitment, and incentives provided, were determined by Horizon BCBSNJ for purposes of improving care delivery. Our analysis of physicians’ SEP adoption and use was subsequently designed to use secondary data from the program and from other sources.

Of approximately 14,250 physicians in the Horizon BCBSNJ provider network, about 5890 were eligible for the program based on prescribing activity that resulted in at least 500 Horizon BCBSNJ pharmacy claims annually (this cutoff was determined by Horizon BCBSNJ for purposes of program feasibility). Eighty-seven percent of these eligible physicians were in practices containing 5 or fewer physicians. An initial wave of recruitment focused on the highest-volume prescribers (>2500 filled prescriptions per year), and subsequent phases targeted incrementally lower-volume prescribers. By the time the allocated resources were expended, 4457 physicians had received the e-prescribing offer (

Study Population and Dependent Variable for Physician Adoption Analysis

We retrospectively constructed 2 cohorts, one of physicians who adopted the offered SEP system and another of physicians who did not. Physician adoption was then used as the dependent variable in our adoption analysis. Physicians were also characterized based on their specialty, practice size, prior Horizon BCBSNJ pharmacy claims volume, and assigned primary care patient panel. Horizon BCBSNJ ensured that all patients in their HMO and point-of-service insurance plans had an assigned primary care physician (PCP), whereas all patients with other Horizon BCBSNJ insurance plans did not. To focus our analysis on PCPs, physicians without any assigned Horizon BCBSNJ primary care patients were excluded from the analysis. However, physicians with few Horizon BCBSNJ—assigned primary care patients were included in the analysis, with the expectation that they probably also provided primary care to many non–managed care patients insured by Horizon BCBSNJ.

Dependent Variable for e-Prescribing Use Analysis

The second major goal of our project was to study physician use of SEP among adopters. We calculated an “e-prescribing use ratio” (range, 0-1) by dividing the number of SEP prescriptions the physician wrote for Horizon BCBSNJ patients by the number of Horizon BCBSNJ pharmacy claims attributable to the physician during the same quarter. Each physician’s ratio was calculated for each quarter of the e-prescribing use evaluation period (January 1, 2006, to June 30, 2006). Because pharmacy claims may be generated for prescriptions written before a given quarter, it was possible for a PCP’s ratio to exceed 1; this occurred in particular when total prescription denominators were low. Therefore, all eprescribing use ratios were capped at 1 for the analysis. The ratio numerator included all prescriptions generated through the SEP system whether printed or electronically transmitted. Because we wanted to understand physician behavior rather than patient behavior, electronic renewals (physician behavior) were counted in the ratio numerator, and renewal claims were counted in the ratio denominator. Refills (patient behavior) of existing prescriptions were excluded from the ratio numerator and denominator.

After all PCPs were assigned e-prescribing use ratios, some physicians were also classified as “never having used” the system if records did not show any electronic prescriptions after the day of activation (when test prescriptions were often transmitted). Other PCPs were classified as having “quit e-prescribing” if they had initially used the system but later stopped synchronizing their personal digital assistant and sending any electronic prescriptions by the last quarter of the use evaluation period.

Independent Variables for PCP Characteristics and Patient Panel Data

Caremark provided physician specialty and practice size information, which was available only in previously determined groupings (1, 2-5, 6-10, and >10 physicians). Each physician’s total pharmacy claims volume was provided by Horizon BCBSNJ for 2003 (the calendar year before the start of program recruitment) in categories (0-250, 251-500, etc), which we aggregated into approximate “high,” “medium,” and “low” tercile categories. Physicians were categorized as low-volume prescribers (<1750 Horizon BCBSNJ pharmacy claims in 2003, which represented the 35th percentile), mid-volume prescribers, and high-volume prescribers (>3500 Horizon BCBSNJ pharmacy claims in 2003, which represented the 71st percentile).

We used zip codes to estimate the racial/ethnic makeup of the neighborhoods from which the PCPs’ patients were drawn. First, patients living in zip codes with more than 50% African American residents (per 2000 US Census data) were categorized as living in majority African American neighborhoods; those from zip codes with more than 40% Hispanic residents were categorized as living in Hispanic plurality neighborhoods. An analysis of the studied zip codes showed that these predominantly African American and Hispanic neighborhood categorizations were mutually exclusive more than 99% of the time. The PCPs were then categorized based on having at least 10% of their patients living in majority African American and Hispanic plurality neighborhoods (which represented the 80th and 87th percentiles, respectively).

Our analysis consisted of 2 components. In the first component (SEP adoption analysis), we compared the physician characteristics of adopting PCPs versus control PCPs using t test, χ2 test, and multivariate logistic regression. The second component measured use among adopting PCPs via a 2-part model. The first part was a logistic regression model in which the dependent variable was “never having used” or “quit e-prescribing” (as already described) versus having some evidence of e-prescribing use into the last quarter of the observation period. The objective was to identify factors that predispose physicians to stop e-prescribing. The second part was a multivariate linear regression model that examined the association between physician characteristics and extent of SEP use among the subsample of physicians who had started e-prescribing and did not quit. We excluded “never having used” and “quit eprescribing” physicians to examine ongoing use barriers among those physicians who continued to try to use SEP.

Both regression models were constructed by beginning with all available predictor variables included, and then model fit was manually reassessed after elimination of each nonassociated (P >.05) predictor variable. Model fit was assessed using R2 for the linear regression and C statistic for the logistic regression. Colinearity was assessed using correlation coefficients between all variables remaining in each of the final models. Only complete cases were analyzed in the regression models. All analyses were performed using statistical software (SAS, release 9.1; SAS Institute, Inc, Cary, NC).

PCP Characteristics Associated With SEP Adoption

Of 4457 physicians invited to join the SEP program, 427 adopted the SEP system during 2005 (Figure 1). An additional 64 physicians who adopted the SEP system in 2006 (part way through the use evaluation period) were excluded from our analyses. Of 427 adopting physicians, 297 (69.6%) were classified as PCPs based on their having any primary care patients assigned from a Horizon BCBSNJ managed care plan as of January 1, 2006. There were 3966 control physicians; 1892 (47.7%) had primary care patients assigned to them. Therefore, PCPs participated at a higher rate than non-PCPs. The control and adoption cohorts had medians of 68 and 69 assigned primary care patients, respectively.

 compares the adoption and control cohorts of PCPs and their patients. Practice size distribution differed between the cohorts (

 <.001). Pharmacy claims volume and specialty of the cohorts were not significantly different, nor were sex, age, and household income of the PCPs’ patients. The PCPs in the adoption cohort were less likely to have more than 10% of their patient panel live in majority African American neighborhoods (

Multivariate logistic regression analysis showed that the association of SEP adoption with practice size and neighborhood racial/ethnic makeup persists after controlling for other PCP characteristics (

). No association was found between SEP adoption and PCP pharmacy claims volume or patient panel age, sex, or household income. χ2 Test for physician specialty showed no statistically significant difference between the 2 groups of physicians. When adjustments were made for practice size and race/ethnicity, the regression model indicated that pediatricians were significantly less likely to adopt e-prescribing (odds ratio, 0.61; 95% confidence interval, 0.41-0.92; P = .02). None of the distinct variables in the final model had a correlation coefficient exceeding 0.15.

PCP Characteristics Associated With Extent of e-Prescribing Use

Among 297 SEP-adopting PCPs, the mean (SD) e-prescribing use ratio was 0.23 (0.28) (interquartile range, 0.00-0.39), which represents 23 electronic prescriptions per 100 pharmacy claims (

). Thirty-four PCPs (11.4%) never used the system after it was installed, and another 22 PCPs (7.4%) quit using it after at least some initial use. Among the remaining 241 PCPs, e-prescribing use ratios were less than 0.25 in 141 (58.5%), 0.25 to 0.50 in 51 (21.2%), and greater than 0.50 in 49 (20.3%).

In the first part of our 2-part model, we used logistic regression to estimate the association of physician and patient characteristics with being in the “never having used” or “quit e-prescribing” categories. However, these classifications were not significantly associated with any of our independent variables. In the second part of our 2-part model (

), physician practice size was significantly associated with e-prescribing use ratio (omnibus 

 = .02), although this factor accounted for little of the variance (R2 = 0.01). Among those using e-prescribing at all, PCPs in larger practices (6-10 and >10 physicians) tended to use their e-prescribing systems for fewer of their prescriptions. None of the distinct variables in the final model had a correlation coefficient exceeding 0.15.

SEP has been proposed as an entry-level HIT accessible to most US physicians who work in small practices. Because small practices have been slow to adopt other forms of HIT,

 several initiatives are promoting SEP to this group of physicians.

 Despite the interest, there are limited data about which physicians actually adopt SEP and which patient populations might be affected.

 Furthermore, levels of use are particularly important to understand for SEP because physicians can easily revert to paper prescribing.

Our adoption analysis found that PCPs who adopted SEP were more likely to be in practices with 2 to 10 physicians and were less likely to be pediatricians or have patients living in majority African American neighborhoods. A prior study

 of SEP adoption reported the practice size and specialty of adopting physicians, but these physicians were not compared with nonadopters. Other studies

 of EMR adoption found that adoption increases steadily with increasing practice size.  In contrast, we found that physicians in practices of 2 to 5 physicians and 6 to 10 physicians were equally likely to adopt SEP and that both groups had 1.8 times the odds of adopting SEP compared with solo practice physicians. Because our sample underrepresented large group practices, our confidence interval for this group is large, and we cannot draw any conclusions about the relative likelihood of these physicians to adopt SEP. Nonetheless, our results from small and midsize physician groups suggest that SEP is being adopted by physicians who might otherwise have been unlikely to use HIT.

MANAGERIAL

In a health plan–sponsored e-prescribing initiative, participating PCPs' mean e-prescribing rate was 1 prescription per 4 pharmacy claims, but some PCPs achieved high use.

Adoption and Use of Stand-Alone Electronic Prescribing in a Health Plan-Sponsored Initiative

 To assess the effect of a telephone intervention to improve quality of life among patients with congestive heart failure (CHF).

 Single-site recruitment of 458 patients using Veterans Health Administration care into a randomized controlled trial with a 1-year preintervention data collection period and a 1-year intervention and follow-up period. To compensate for imbalanced study groups, propensity scores were included in adjusted models of quality of life, satisfaction with care, inpatient utilization, survival, and costs of care.

 Patients aged 45 to 95 years participated in the study; 22% were of Hispanic race/ethnicity, and 7% were African American. All but 5 were male, consistent with the older population among veterans. At baseline, 40% were in Goldman Specific Activity Scale class I, 42% were in class III, 6% were in class II or IV, and 12% were unclassified. Patients scored a mean (SD) of 14 (1.5) points below the norm on the physical component score. After the yearlong intervention, no differences in clinical outcomes were noted between the intervention group and the control group. The CHF-related costs were higher for the intervention group, as were overall costs that included the cost of the intervention. Intervention group patients reported better compliance with weight monitoring and exercise recommendations.

 A risk-stratified intervention for patients with CHF resulted in potential behavioral improvements but no survival benefit. A high-cost high-intensity intervention may be required to improve survival for patients with CHF. Inclusion of the costs of interventions is recommended for future researchers.

In future investigations, the cost of interventions for congestive heart failure (CHF) should be included in cost analyses to provide clinical and policy decision makers with a clear picture of the economic implications of novel interventions.

This study showed evidence of positive effects of an intervention of variable intensity for CHF on healthcare utilization, self-care behaviors, and possibly quality of life. 

This study found no survival benefits of the intervention; future research should attempt to determine what components and level of intensity are required to affect survival.

Healthcare systems may need to balance intervention cost increases against multiple benefits to patients.

Several randomized trials of telephone interventions for patients with congestive heart failure (CHF) have shown improvement in patient outcomes. In a study of 49 patients by Spaeder et al,

 equivalent β-blocker titration doses were achieved using telemedicine to monitor dose titration compared with in-clinic monitoring, but telemonitoring resulted in a shorter time to the final dosage. Jerant et al

 found reduced readmission rates associated with telenursing strategies for postdischarge care of 37 patients with CHF. Using an intensive telephone disease management intervention among more than 1000 patients, Galbreath and colleagues

 achieved a survival benefit averaging 76 days. Hopp and colleagues

 studied a small sample of Veterans Health Administration (VA) patients with CHF and noted greater improvements in self-reported mental health status (health-related quality of life and the mental health component) for patients receiving telemedicine (video monitoring and feedback) versus patients in usual care. Approaches vary with respect to video monitoring versus telephone versus delayed report, as well as the outcomes assessed, but findings support the use of more frequent symptom monitoring to improve care. Less persuasive evidence of improved outcomes is reported, and systematic review teams such as Maric and colleagues

 note the ongoing need for large-scale randomized trials. Replicability of results showing survival benefit should be verified before implementation of interventions for these resource-intensive patients.

In a randomized controlled trial among 458 VA patients with CHF, we tested a telemedicine intervention designed to improve patients’ health-related quality of life and clinical outcomes with minimal effect on costs of care among VA patients with CHF. We hypothesized that, compared with control group patients receiving usual care, patients receiving the intervention would have (1) improved physical and mental health-related quality of life and (2) equivalent costs of care. As secondary objectives, we examined the effects of the intervention on survival over a 1-year period and on compliance and satisfaction with care.

Patients receiving care at a large southwestern medical center in the VA were recruited into a trial of an intervention to improve outcomes and quality of life among patients with CHF. Approval of the study was granted by the institutional review board before study initiation. Subjects participated in the informed consent process and granted consent in writing.

Eligibility criteria included the following: age at least 18 years, diagnosis of CHF (

International Classification of Diseases, Ninth Revision, Clinical Modification 

] codes 425 and 428), receipt of inpatient care or urgent care or more than 12 outpatient visits in the past 12 months, and speaker of English or Spanish.

Of 539 patients identified, 81 (15%) were ineligible. Reasons for ineligibility included the following: (1) end-stage renal disease or congestive dialysis, (2) prior heart transplant, (3) end-stage or terminal illness in addition to CHF (such as metastatic malignant neoplasm or AIDS) with anticipated life expectancy less than 6 months, (4) residence in a nursing home, (5) participation in another research or care management program, (6) refusal to participate in the study, (7) relocation out of the area, and (8) severe dementia.

The study used a prospective randomized controlled design, with a 1-year intervention and data collection period. Retrospectively, 1-year preintervention data were collected to provide baseline assessments. In June 2005, the intervention team began contacting identified patients with CHF for enrollment in a 1-year home-based telephone disease management CHF program. Recruited patients were assigned to treatment or control status based on the last digit of their social security number (odd vs even). Enrollment occurred over a 6-month period from mid-June through December 1, 2005. All veterans enrolled in this telephone disease management program continued to receive comprehensive CHF care from the VA. Registered nurses conducted a telephone survey at intake, with reassessments at 6 and 12 months querying participants’ knowledge, behavior, and health status. The study was registered at clinicaltrials.gov (identifier NCT00141856). A CONSORT (Consolidated Standards of Reporting Trials) flowchart in the 

When patients agreed to enroll, the intervention team (McKesson Corporation) created a participant-specific selfmanagement plan using the primary care physician’s therapeutic plan, the intensity of which varied per a standardized risk stratification algorithm. Risk stratification was determined using a combination of review of the medical record, patient admission history, and self-report at baseline. Factors included the Goldman Specific Activity Scale,

 The copyrighted algorithm used Boolean logic and sorted patients into 3 risk categories that determined the frequency of scheduled telephone interactions over the course of the year (low risk [2 calls], medium risk [7 calls], and high risk [16 calls]). Full details are available from the developer.

During the scheduled telephone interactions, disease management nurse interventions included education and coaching for behavior change based on guidelines established by the American Heart Association

 and using motivational interviewing principles.

 Interventions occurred more frequently in the first 6 months of the program, and high-risk participants were called on average every 3 to 4 weeks, medium-risk participants every 8 weeks, and low-risk participants every 6 months. The mean intervention length was 30 to 40 minutes and focused on the participant-specific self-management plan derived from the participant admission history and based on the program focus. The program focus was participant education and behavior change for fluid weight management, medication adherence, diet, early treatment for escalating symptoms, discussion of recent laboratory values, and vital signs monitoring.

The intervention included access to a nurse advice line for symptoms and counseling 24 hours a day 7 days per week, medication compliance reminders, vaccination reminders, and printed literature, including action plans, workbooks, and postassessment letters, in addition to the scheduled nurse education and motivational interviewing sessions. The participant’s physician was provided with faxed alerts about signs and symptoms of decompensation, as well as notification of gaps between participant-reported practice and guideline recommendations. In addition, communication to physicians occurred through the VA electronic medical record system regularly and was customary after each scheduled call, providing information on the patient’s condition. Physicians were encouraged to communicate to the disease management registered nurses through the electronic medical record system, providing their recommendations for further counseling topics or clarification of subject-reported information.

During the study, intervention nurses averaged 157 patient telephone interventions per month for the first 12 months and 121 interventions per month overall for 19 months. Altogether, 89% of interventions were completed within the first 13 months. Fewer than 75 interventions per month were completed after the first 13 months.

Sources of data included patient self-report in the structured interviews, a VA resource allocation center for cost estimates, a medical record review of the patient’s electronic medical record, and administrative extracts of the medical record. Extracts from the VA’s all-electronic medical record are transmitted nightly to a central data repository according to a nationally standardized protocol for inclusion in administrative databases, which may be accessed for research purposes. The VA databases provided demographics, vital statistics, VA priority status diagnoses, procedures and dates of care for inpatient and outpatient health services, and medication prescriptions and selected laboratory results.

 code 425 or 428. Comorbidity was measured by indicators for diagnosis of chronic obstructive pulmonary disease (COPD) (codes 490-496, 500-505, and 506.4), hypertension (codes 401-405), and diabetes (code 250). In addition, we calculated the modified Charlson-Deyo comorbidity index, a weighted score assessing 18 conditions (CHF, myocardial infarction, peripheral vascular disease, stroke, chronic lung disease, peptic ulcerative disease, rheumatologic disease, dementia, hemiplegia, diabetes, diabetes with complications, mild liver disease, moderate-to-severe liver disease, renal disease, cancer, metastatic cancer, AIDS, and human immunodeficiency virus without AIDS).

 These measures were assessed for both years (preintervention and postintervention).

Demographics from the medical record extracts included age at baseline, race/ethnicity (white, African American, or other or missing), sex, and marital status (married vs other status). Priority status for care in the VA is assigned by the VA during enrollment in the healthcare system based on a veteran’s military service and service-related medical conditions. Values range from 1 to 8. Priority 1 veterans have a service-connected condition that is 50% to 100% disabling. Priority status was extracted from enrollment files and was recoded as high-priority versus any lower-priority status. High-priority veterans did not have copayments for care or medications, whereas lower-priority veterans had copayments.

Additional data were collected by medical review, including baseline medications, lipid levels, blood pressure, moderate-to-severe ejection fraction, and vaccination status. Lipids included total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides and were averaged across all baseline-year values. Medication use assessed prescriptions each year for angiotensin-converting enzyme inhibitors, antihypertensives, β-blockers, diuretics, and cardiac glycosides. Blood pressure was the highest outpatient value recorded in the baseline year. High blood pressure was defined as exceeding 140 mm Hg systolic or 90 mm Hg diastolic for patients without diabetes and as exceeding 130 mm Hg systolic or 80 mm Hg diastolic for patients with diabetes. Moderate-tosevere ejection fraction denoted an indication of moderate or severe systolic dysfunction recorded from any of the following test results noted in the medical record: cardiac catheterization, transthoracic echocardiogram, multiple gated acquisition scan, or persantine thallium stress test.

Outcomes were inpatient use per year, including admissions (total and CHF-related, where CHF was the primary diagnosis) and 30-day readmissions. Estimated costs of care were provided by the VA cost allocation resource center and were tailored to each patient’s date of enrollment. The following 4 measures were analyzed: prescription medication costs, outpatient care costs, total costs attributable to CHF, and total costs of care. To the total costs of care for intervention patients, we added the mean per-patient cost of the intervention. Intervention costs of care were provided by the intervention developer. Costs of the intervention included staff time, the largest component, plus profit margin and office overhead. We calculated survival in the study period using dates of birth and death. All measures except survival captured the 12 months before enrollment (baseline) and the 12 months initiated by enrollment (year 1). Difference scores reflected year 1 minus baseline values. For the regression models, year 1 cost measures were logged, as these values were highly skewed and did not meet linear regression assumptions.

Patient satisfaction was assessed by 5 questions querying the quality of the information, reassurance, and care provided by the healthcare team, assessed at 12 months (no preintervention assessment). The 5 satisfaction-with-care items formed a single factor when subjected to factor analysis with Cronbach α of 0.91. This summary score of satisfaction was analyzed. An additional 4 questions (check weight daily, follow diet, take medications, and exercise regularly as directed) assessed compliance with recommendations (

). The 6-point response sets ranged from “always or daily” to “never,” which were dichotomized to always or daily versus any other response.

Health-related quality of life was assessed by the 8-Item Short Form Health Survey, scored as the physical component score and the mental component score. The component scores are normalized at 50 with an SD of 10, such that group means less than 50 identify patients with health deficits compared with normal populations. A difference of 5 points is considered clinically significant.

All analyses were conducted in an intent-to-treat fashion. A criterion α level of .05 was used.

Intervention and control group patients were compared using χ2 test or t test as appropriate to the measurement level of the variables.

Difference-in-difference calculations compared outcomes of the program period versus the baseline period between the intervention and control groups. The first difference was between the program period and the baseline period for each group. That is, among intervention group patients, we calculated the program period mean or percentage less their baseline period mean or percentage, and among control group patients, we calculated their program period mean or percentage less their baseline period mean or percentage. The second difference was between the intervention and control groups (intervention group first difference less control group first difference). t Test was used to compare intervention changes with control changes.

After baseline differences were detected between the intervention and control groups, propensity scores were estimated using logistic regression predicting intervention status (intervention vs control).

 The regression model included 50 baseline measures; the estimated probability of treatment was captured as the propensity score. Covariate balance was verified in post hoc models assessing differences by intervention status and the interaction of intervention by propensity score quintile; no significant differences were found between treated and untreated subjects except cardiac glycoside use, for which an interaction between treatment status and quintile persisted (

 = .02). Regression analyses then assessed differences in year 1 outcomes by treatment group assignment, controlling for propensity score.

 Logistic regression analysis modeled dichotomous outcomes. For the count variables (admissions, CHF-related admissions, and 30-day readmissions), generalized linear modeling with a log link to model a negative binomial distribution was chosen. A Cox proportional hazards model estimated the hazard rate ratio of treatment associated with survival. For all other outcomes variables, ordinary linear regression was applied. Analyses were completed using SAS (version 9.2, SAS Institute, Cary, NC). Outcomes modeled included admissions, CHF-related admissions, 30-day readmissions, physical component scores, mental component scores, survival, outpatient costs, medication costs, CHF-related costs, total costs over 12 months, patient satisfaction scores, and dichotomized responses (always or daily vs any other response) to compliance with treatment recommendation items.

The mean (SD) age was 70 (11) years (age range, 45-95 years) among 220 patients in the intervention group and 238 patients in the control group. Most patients (99%) were male. Their races/ethnicities were white (50%), Hispanic (22%), or African American (7%), with 22% of other or missing race/ethnicity. One year after study initiation, 7% of patients had died, including 6% of control group patients and 8% of intervention group patients.

At baseline 40% were in Goldman Specific Activity Scale class I, nearly 5% were in class II, 42% were in class III, 3% were in class IV, and nearly 12% were missing. The CHF-related risk stratification for the patients randomized to intervention care resulted in classification of 33% as high risk, 54% as moderate risk, and 13% as low or indeterminate risk. These classifications determined intervention intensity.

The CHF-related costs (and total costs) for the intervention year varied by risk level. High-risk patients had significantly higher mean (SD) costs ($8845 [$17,989] CHF-related cost and $31,732 [$35,131] total cost) than moderate-risk patients ($2606 [$3445] CHF-related cost and $14,320 [$18,312] total cost). The mean (SD) costs of moderate-risk patients were not significantly different from those of low-risk patients ($2344 [$1986] CHF-related cost and $4621 [$3294] total cost).

To verify randomization to intervention and control status, demographic and baseline variables were compared as summarized in 

. Statistically significant differences were noted for race/ethnicity and for comorbid COPD.

CLINICAL

An intervention of variable intensity for congestive heart failure showed some improvements but no survival effect, suggesting a tradeoff between intervention cost and intensity and survival benefit.

An Intervention for VA Patients With Congestive Heart Failure

 used prescription claims from mail-order pharmacies and local pharmacies to compare medication possession rates across both sites. Based on this analysis, they concluded that patients using mail-order pharmacies had higher adherence to antiglycemic, antihypertensive, and lipid-lowering medications than patients using local pharmacies.

The authors neglect the fact that mail-order pharmacies typically enroll patients in automatic refill programs in order to compensate for the 10- to 14-day advance notice needed to fill prescriptions. With these programs, the mail-order pharmacy sends prescriptions automatically to patients on a regular basis at a predetermined time interval to ensure the patient receives the medication prior to consuming previous supplies. There is no mention of whether the community pharmacies also provided this service. We are assuming that the majority of them did not because this is not a standard of practice.

Given this difference, it is not surprising that an analysis of mail-order pharmacy claims would provide higher medication possession rates. The authors should have considered comparing mail-order pharmacies with community pharmacies that provide automatic refills. That would have been a more appropriate and fair comparison.

Dr Duru and his team also attempted to associate the possession of medication with adherence by providing 3 citations of previous research that found a correlation between the 2 measures. Two of these citations are 22 and 13 years old, respectively, and the third citation represents research that was completed outside of the United States. All 3 citations were based on local pharmacy claims or were conducted prior to the use of automatic refill programs in mail-order pharmacies. In local pharmacies, patients typically request refills when they have consumed most of their medication. This is why medication possession is highly correlated with adherence. We challenge the use of the medication possession concept as a proxy for medication adherence with mail-order pharmacies, which send the medication regardless of how much medication the patient may have remaining.

The authors’ analysis was based on claims for patients who were in a diabetes registry. Despite the limited study population, the conclusion of the research was applied to all patients without discussion of the generalizability of the study population to a larger group.

In their discussion, Dr Duru and colleagues acknowledge that interventions by pharmacists to address adherence have been proven effective. However, they contend that the expansion of responsibilities and the cost of such programs will be barriers to implementation. This has not been the case. In the Medicare Part D program, pharmacists have been providing medication therapy management services since 2006. A recent study estimates that the provision of these services has produced a drug cost savings of $15 to $40 per month.

It is unfortunate that the above issues limit the usefulness of this research. Instead, it comes off as a thinly veiled attempt to support mail-order pharmacy.

 Mr Brown reports serving as a paid consultant to community pharmacy associations. Dr Rickles reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Todd A. Brown, MHP, Department of Pharmacy Practice, Northeastern University, 360 Huntington Ave, 206 Mugar Bldg, Boston, MA 02115. E-mail: 

1. Duru OK, Schmittdiel JA, Dyer WT, et al. 

Mail-order pharmacy use and adherence to diabetes-related medications. 

Impact on drug cost and use of Medicare part D of medication therapy management services delivered in 2007. 

We appreciate the opportunity to confirm the validity of our methods by clarifying 2 incorrect assumptions made by Mr Brown and Dr Rickles. First, the Kaiser Permanente Northern California (KPNC) mail-order pharmacy did not have an automatic refill program in 2006 and 2007 when our data were collected. Although a few KPNC pharmacies have recently launched pilot auto-refill programs, those programs did not overlap with our study window. In our study, mailed medication refills were delivered only after patients initiated refill requests and provided payment. This is no different than the standard of care at community pharmacies, which therefore allows for a fair comparison of adherence between the 2 pharmacy types.

Second, the KPNC diabetes registry has been in existence since 1994 and is designed to be comprehensive rather than to represent a limited subset of patients. Annual, planwide screening protocols examine pharmacy data, laboratory data, hospitalization records, and recorded outpatient diagnoses to add every new case of diabetes to the registry. Comparisons between the registry and self-reported diabetes on member surveys indicate that registry capture is 99% complete. Therefore, our results reflect the broad membership of a health plan rather than a select subgroup.

We stand behind our published findings—specifically, that the likelihood of good adherence to a new diabetes-related medication is greater when refills are obtained through the mail. Although patients can contact the pharmacy call center with questions, better adherence may very well come with a price if inappropriate medication usage is increased for patients who do not interact with community pharmacists. Patients who do not frequent a local pharmacy may be less likely to receive on-site preventive care such as influenza vaccinations. We are initiating analyses to examine these important issues. Still, we believe that the use of mail-order pharmacies may represent a feasible, economical, and sustainable approach to improve adherence to chronic medications for many patients.

Although outcome evaluation studies are ongoing, we agree with Mr Brown and Dr Rickles that interventions in local pharmacies such as medication therapy management programs may be very effective for select populations. These programs target Medicare Part D beneficiaries with multiple chronic health conditions and high pharmacy utilization. The funding and inclusion criteria for these programs were linked to passage of the Medicare drug benefit.

 Unfortunately, the expense and logistics of developing such programs for the millions of other Americans taking chronic medications will be prohibitive in an era of rapidly escalating healthcare costs and deepening budget deficits.

In general, the research on mail-order pharmacies is funded by organizations with presumed agendas of promoting the career interests of community pharmacists or of increasing business opportunities for pharmacy benefit managers. Our work was funded by the Centers for Disease Control and Prevention and the National Institutes of Health, with the sole agenda of improving medication adherence and health outcomes. We believe that there is an urgent need for additional work examining the use of mail-order versus local pharmacies, but this work must be unbiased and focus primarily on the potential benefits and/or harms to patients.

This study was jointly funded by program announcement 04005 from the Centers for Disease Control and Prevention (CDC) (Division of Diabetes Translation) and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the NIDDK.

See page 39 of AJMC's January 2010 issue to read the complete disclosure statement.

The authors (OKD, JAS, WTD, MMP, CSU, JC, AJK) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

O. Kenrik Duru, MD, MSHS, Division of General Internal Medicine, Department of Medicine, The David Geffen School of Medicine at UCLA, University of California, Los Angeles, 911 Broxton Plaza, Los Angeles, CA 90024. E-mail: 

1. Barnett MJ, Frank J, Wehring H, et al. 

Analysis of pharmacist-provided medication therapy management (MTM) services in community pharmacies over 7 years. 

March 2010

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Adoption and Use of Stand-Alone Electronic Prescribing in a Health Plan-Sponsored Initiative

An Intervention for VA Patients With Congestive Heart Failure

Mail-Order Versus Local Pharmacies on Adherence: Study Methods Make for Unfair Comparison

Risk Adjustment for Medicare Beneficiaries With Alzheimer's Disease and Related Dementias

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