Chien-Wen Tseng, MD, MPH

 To determine if poor well-child care (WCC) visit adherence is associated with increased risk for Ambulatory Care—Sensitive Hospitalizations (ACSHs) among young children in an integrated healthcare delivery system.

This was a retrospective observational study.

We used claims and administrative data for children aged 2 months to 3.5 years enrolled at Group Health Cooperative from 1999 to 2006. Our main independent variable was timely WCC visits based on Group Health’s 2000 recommended schedule. We used Cox proportional hazard regression models to determine the association between WCC visit adherence and risk for a child’s first ACSH.

 Of the 20,065 children, 797 (4%) had an ACSH. Children with lower WCC visit adherence had increased hazard ratios (HRs) of 1.4-2.0 for ACSH (adherence 0-25%: HR 2.0, 95% confidence interval [CI]: 1.6-2.6, P <.001; adherence 26-50%: HR 1.4, 95% CI: 1.1-1.8, P <.05). Of the 2196 children with >1 chronic disease, 189 (9%) had an ACSH. Children with >1 chronic disease and with lower WCC visit adherence also had increased HRs for ACSH (adherence 0-25%: HR 3.2, 95% CI: 1.8-5.6, P <.001; adherence 26-50%: HR 1.9, 95% CI: 1.2-3.2, P <.05).

: For young children, poor WCC visit adherence was associated with increased risk for ACSH in this integrated healthcare delivery system.

Resources should continue to be directed at encouraging attendance at well-child care visits, especially among children with chronic disease.

		Prior literature has not evaluated this relationship in integrated healthcare delivery systems.

		This supports healthcare reform policies that provide preventive services free of costs.

		Well-child care visit adherence may be a useful marker to identify children that might benefit from a case management intervention

Children less than 5 years old have higher hospitalization rates than older children.

 Finding approaches to minimize morbidity for these children (eg, decreasing asthma exacerbations) may reduce hospitalizations. Regular attendance at well-child care (WCC) visits may prevent unnecessary hospitalizations through timely immunizations and/or chronic disease management. WCC visits likely have a higher impact for young children due to the greater frequency of recommended WCC visits in the first 3 years of life (13 visits) compared with older children (1 visit per year through age 18 years).

Prior studies evaluating the association between WCC visit adherence and hospitalizations have been mixed, as 3 studies found that higher WCC visit adherence was associated with decreased ambulatory care—sensitive hospitalizations (ACSHs) while 2 studies found no association.

 Only 3 of these studies accounted for the timeliness of WCC visits.

 No study has evaluated this relationship in integrated healthcare delivery systems. Unlike other healthcare systems, integrated healthcare delivery systems have greater control in standardizing and improving the care patients receive (ie, population-based care), which can decrease practice variation.

In addition, integrated healthcare delivery systems often place high importance on preventive care and therefore encourage and provide these services at no charge. Evaluating WCC visit adherence and risk of hospitalization in this setting provides a unique opportunity to understand this relationship where all patientsreceive similar WCC visit content.

The goal of this study was to understand whether parents’ timely adherence to WCC visits was associated with risk of ACSHs  among young children enrolled in an integrated healthcare delivery system. ACSHs represent hospitalizations which are potentially preventable with adequate access to and/or provision of outpatient care.

 We hypothesized that poor WCC visit adherence would be associated with increased risk for ACSHs in this integrated healthcare delivery system.

This was a retrospective cohort study conducted in collaboration with Group Health Cooperative, an integrated healthcare delivery system with ~640,000 members in Washington and Idaho. Claims from both the Group Health delivery system and contracted network physicians were included. This study was approved by both the Seattle Children’s Research Institute and Group Health Research Institute’s Institutional Review Boards.

We focused on children younger than 3.5 years because this age group is at higher risk for hospitalizations than older children,

because they have higher contact rates with healthcare providers, and because Group Health encourages 1 WCC visit between the ages of 2 and 3.5 years. Children were eligible for the study if they were enrolled by 2 months of age at GroupHealth between January 1, 1999, and December 31, 2006. Participants entered the study on either January 1, 1999 (ie, already a plan member), or on their first day of enrollment during the study period. When determining a child’s complete enrollment information, we considered gaps in enrollment of <45 days as continuous enrollment based on the Healthcare Effectiveness and Data Information Set definition.

 In addition, children needed to be enrolled on or prior to a recommended  WCC visit and through at least 1 of the subsequent recommended WCC visits in order to calculate adherence and minimize misclassification (

International Classification of Disease, Ninth Revision

) codes to identify children with chronic disease.13 Children were classified as having at least 1 chronic disease (ie, “

1 chronic disease”) if they had 1 or more claims matching an

 diagnosis included in the validated list. Children were classified as “healthy” otherwise. Since diagnosing asthma in children less than 4 years old can be challenging,

 were required for a child to be classified as having asthma. The top 5 chronic disease categories for our study were: asthma (38%), congenital heart disease (15%), congenital musculoskeletal anomalies (14%), failure to thrive (7%), and hereditary and acquired hemolytic anemias (6%).

A WCC visit was identified if an outpatient claim billed by a primary care provider (pediatrician, family physician, general  practitioner, osteopath, or nurse practitioner) contained the standard WCC visit 

 codes (eg, V20.2) in any diagnosis field. Adherence was based on Group Health’s 2000 WCC visit schedule, which was identical to the AAP’s schedule for the first 6 months and then differed from this schedule by requiring the following WCC visits through 3.5 years old: 10 months, 15 months, 2 years, 3.5 years (ie, the AAP’s 9-month, 12-month, 18-month, and 3-year visits are not required). WCC visits were included in the adherence calculation (numerator) if the WCC visit was “timely.” For example, a “4-month WCC visit” was allowed to occur on or after a child turned 4 months old and up to less than 6 months old (ie, the “4-to-6 month” WCC interval). To allow for an appropriate window of acceptability for when WCC visits might reasonably occur, we counted a “duplicate WCC visit” in 1 interval as the WCC visit for the following interval if the duplicate visit was <14 days (visits before 15 months of age) or 

30 days(visits after 15 months of age) prior to the start of a WCC visit interval where no visit had occurred. Otherwise only the first WCC visit was counted for WCC visit intervals with multiple WCC visits.

WCC visit adherence (range 0% to 100%) was a timevarying variable that was updated at the end of each WCC interval. For WCC intervals that overlapped completely with a child’s study period enrollment, WCC visit adherence was calculated by dividing a child’s total number of “timely” WCC visits by the total number of recommended WCC visits from start of enrollment through the end of each age-specific WCC interval (ie, a child enrolled for “X” number of WCC visit intervals would have “X” number of WCC visit adherence values for our regression analysis). We examined WCC visit adherence using a priori categories to facilitate interpretation and comparison with our prior study as follows: 0% to 25%, 26% to 50%, 51% to 74%, and 75% to 100% (referent).

Ambulatory Care—Sensitive Hospitalizations

As defined by the Agency for Healthcare Research and Quality, a hospitalization was classified as an ACSH if the primary diagnosis was one of those included in the predetermined list of ACSH diagnoses. The only exceptions to this were for the diagnoses of dehydration or iron deficiency anemia where the ACSH diagnosis could be either primary or secondary.

we excluded “adult” conditions (chronic obstructive pulmonary disease, congestive heart failure, hypertension, pelvic inflammatory disease, skin grafts with cellulitis, and angina). We also excluded “congenital syphilis” as these hospitalizations are unlikely to be affected by WCC visits due to the fact that these infections begin in utero or at birth. Additional hospitalization diagnoses were included based on their clinical importance to children less than 3½ years old(“pneumococcal meningitis” [

 320.2], and “septicemia due to H. influenza” [

 038.41]). Hospitalizations prior to the age of 2 months were excluded. Children could only have 1 ACSH for analysis since we were interested in whether WCC visit adherence was associated with the time to the child’s first ACSH.

Bivariate analyses consisted of Student’s 

 test for comparisons of continuous variables and the Pearson’s χ

 test for categorical variables. We used a time-varying Cox proportional hazards regression model to determine the association between WCC visit adherence and time from enrollment to the first ACSH. Children were censored when they had their first ACSH, reached the end of the study (December 31, 2006), disenrolled, or turned 3.5 years old, whichever came first. Potential confounders were included a priori in multivariate analyses based on the existing literature: patient age at start of enrollment,

 Age at start of enrollment was modeled as a continuous variable. The reference categories were male (vs female), no chronic disease, and never on Medicaid for our categorical variables of gender, chronic disease, and Medicaid enrollment, respectively. The proportional hazard  assumption was examined using STATA10. Statistical significance was determined at the 

We also conducted 2 sensitivity analyses. First, we used propensity scores in an attempt to control for self-selection bias (eg, children at greatest risk for ACSH may also be less likely to attend WCC visits).

 We predicted WCC visit adherence  (categorical) propensity score probabilities with multinomial logistic regression using age at start of enrollment, chronic disease status, and whether the child was ever enrolled in Medicaid. This sensitivity analysis (data not shown) did not change our results or conclusions. Second, although we could not adjust for an appropriate measure of continuity of care for this healthcare setting (ie, “clinic level”), we adjusted for continuity of care (COC) at the “provider level” based on the importance of this potential confounder from prior studies.

 We used Bice and Boxerman’s COC index (range 0 to 1) to measure how often children saw the same provider.

 We analyzed COC as a time-varying categorical variable that was updated at the end of each agespecific WCC interval. The magnitude of associations and confidence intervals (CIs) were similar to our primary findings (data not shown).

Of the 23,579 children that enrolled in the health plan prior to age 2 months, 20,065 (85%) children were eligible for analyses (Figure). Nearly a quarter of our population was on Medicaid and children with 

1 chronic disease accounted for approximately 11% of our study population (

). Based on when children exited the study, approximately threefourths of children fell into the highest WCC visit adherence category (75-100%, Table 1). WCC visit adherence was calculated based on at least 4 recommended WCC visits for 69% of children (median 5, interquartile range: 3-6).

During the study period, 797 (4%) children had at least 1 ACSH (Table 1). The median age at time of hospitalization was 13 months (interquartile range [IQR] 8-20 months) and 70% of all ACSHs were accounted for by the top 4 diagnoses (

). For healthy children, the top 5 ACSHs accounted for 65% of their ACSH (17% pneumonia, 15% dehydration, 14% asthma, 10% gastroenteritis, and 9% urinary tract infections). For children with 

1 chronic disease, the top 5 ACSHs accounted for 79% of their ACSHs (31% asthma, 15% pneumonia, 12% seizures, 11% gastroenteritis, and 10% dehydration). The median time from enrollment until first ACSH increased with increasing WCC visit adherence categories (

). The unadjusted rates of ACSH for children with one of our top 5 chronic disease categories were 9% for asthma, 11% for congenital heart disease, 8% for congenital musculoskeletal anomalies, 8% for failure to thrive, and 8% for hereditary and acquired hemolytic anemias. Lower WCC visit adherence was significantly associated with increased hazards for ACSH after controlling for presence of chronic disease, gender, enrollment in Medicaid, and age at start of enrollment (

). Compared with children with higher WCC visit adherence (75-100%), children who missed half or more of the recommended WCC visits were at 1.4 to 2 times the hazard foran ACSH (adherence 0-25%: hazard ratio (HR) 2, 95% CI:1.6-2.6, 

 <.001; adherence 26-50%: HR 1.4, 95% CI: 1.1-1.8, 

1 chronic disease who missed half or more of the recommended WCC visits were 1.9 to 3.2 times the hazard for an ACSH (adherence 0-25%: HR 3.2, 95% CI: 1.8-5.6, 

 <.001; adherence 26-50%: HR 1.9, 95% CI: 1.2-3.2, 

 <.05) compared with those in the highest WCC visit adherence category.

In this study evaluating the relationship between parents’ timely adherence to WCC visits and hospitalizations in an integrated healthcare delivery system, we found that young children who had lower WCC visit adherence (

50% adherence) had up to 2 times the hazard for ACSH compared with children with higher WCC visit adherence (>75% adherence). This relationship

was stronger for children with chronic disease and was surprisingly similar to our prior study in a traditional fee-for-service system.

This study adds to the limited and mixed evidence supporting WCC visit adherence and confirms our Hawaii study.

 In Hawaii, children typically are cared for by solo practitioners, which can increase the variability of care betweenhealthcare providers. In comparison, Group Health has greater control of variability through its population-based model of standardizing the care patients receive. Therefore, timely WCC visit adherence still appears to be an important component of reducing hospitalizations even in a system where patients are more likely to receive similar care for the same services.

Timely WCC visits, in theory, can reduce hospitalizations by providing opportunities to administer immunizations to prevent serious infections, proactively manage a child’s chronic disease, or enhance the primary care relationship. Previous studies have found that children with asthma who received better asthma management plans (eg, inhaled corticosteroids or asthma action plans) had lower rates of hospitalizations for asthma.

This may be particularly important since children with asthma have approximately 2.1 hospitalizations for every 100 children.

Unfortunately, a recent study found that only 46% of children with asthma received appropriate asthma care.

It is also possible that low WCC visit adherence may simply be a marker for other factors (eg, poor access, parental healthcare utilization beliefs, sociodemographic characteristics, having a trusting, well-established relationship with their child’s PCP) that are associated with increased risk for hospitalizations.

For example, parents of children who are less adherent to the WCC visit schedule may also be less adherent to other aspects of healthcare (eg, less likely to follow treatment regimens). If so, then improving WCC visit adherence may not necessarily reduce hospitalizations. However, WCC visit adherence could be used as a marker to identify children who may benefit from a case management intervention. WCC visit adherence is a relatively easy metric to monitor even in systems that only have access to claims data. Besides ensuring that children attend the recommendedWCC visits, health plan or primary care team case  managerscould discuss with parents whether they have the appropriate support for all of their child’s healthcare needs (eg, adequate transportation). From a population-based perspective, case managers can help health plans and providers ensure that their patients receive appropriate care.

Given the association found between increased timely WCC adherence and more favorable outcomes in this and prior studies,

 we should continue encouraging attendance at WCC visits. Similar to our study,

Hakim and Bye found that children up-to-date (vs not up-to-date) with WCC visit adherence had decreased hazards of avoidable hospitalizations.3 The only other study which accounted for timeliness of WCC visit adherence found no association with ACSH, which may be due to the relatively small number of ACSHs (N = 115).

 Although not statistically significant, these authors found that more children without an ACSH (63%) were up-to-date with WCC visits than those with an ACSH (53%, 

 One way of increasing WCC visit adherence might be to provide preventive services at no cost. Studies have found that adults with copays were less likely to receive recommended preventive care than those without copays.

During our study period, the majority of visits for WCC at Group Health did not require a copay. This might explain why WCC visit adherence, particularly for children with chronic disease, was higher in this integrated healthcare delivery system than in our prior study of children enrolled in a traditional fee-forservice

The limitations of this study are that we studied members enrolled in a single integrated healthcare delivery system and that children had high WCC visit adherence and were likely from higher-income families. Therefore our results may not be generalizable to other systems or populations. In this study, we could not adjust for some important potential confounding factors (eg, income, education, race/ ethnicity) due to limitations of available data. Future studies should attempt to directly  account for these potential confounders. We were also unable to control for continuity of care for this healthcare setting at the clinic level due to the limitations of the available data; during the study period, Group Health encouraged continuity at both the provider and clinic level. Therefore the magnitude of our findings may be biased away from the null. Due to the small number of children with chronic disease, these findings should be interpreted with caution. ACSHs are only “potentially preventable” and  therefore may not always be avoidable even with adequate access to and/or provision of outpatient care. In addition, some ACSHs may be related more to ambulatory specialty care (eg, seizures) than primary care. Our study design only suggests associations that cannot be inferred as causal. Although we attempted to control for self-selection bias, our available variables and the size of our study population may have limited our ability to adequately conduct propensity score analysis. Our study excluded 3514 (15%) children, although these children are unlikely representative of Group Health’s general population due to their very short duration of enrollment (median 1 month, IQR 1-2).

In this integrated healthcare system, timely WCC visit adherence was associated with reduced ACSHs. When compared with our prior study, our findings may help support healthcare reform policies that require preventive care free of costs. To better understand if this association could be causal, we recommend that future investigations rely on utilization data that are collected from electronic health records so that data from providers’ notes could be extracted. Our study findings could also be enhanced by qualitative data to further explore reasons why parents did not bring their child in for a WCC visit and survey data to collect important confounders (eg, income). In addition, future studies may want to evaluate emergency department visits as a mediator between low WCC visit adherence and hospitalizations or include diagnoses for acute injuries and poisonings, similar to a prior study,3 as these topics are often discussed at WCC visits. Until this association is clarified, poor WCC adherence can only be regarded as a potentially important risk factor for hospitalization among young children.

 Kaiser Permanente Center for Health Research (JOT), Honolulu, HI; Department of Pediatrics (JOT, RM-S, DCG), Department of Health Services (RM-S, DCG), University of Washington, Seattle, WA; Center for Child Health, Behavior, and Development (RM-S, CS), Seattle Children’s Research Institute, Seattle, WA; Group Health Research Institute (DCG), Seattle, WA; Department of Family Medicine and Community Health (C-WT), University of Hawaii, John A. Burns School of Medicine, Honolulu, HI; Pacific Health Research and Education Institute (C-WT), Honolulu, HI.

This study was supported by a Health Resources and Services Administration grant (T32HP10002-21), Group Health Cooperative, and Group Health Research Institute. Our sponsors did not have any role in the following: study design; the collection, analysis, and interpretation of data; the writing of the report; and the decision to submit the manuscript for publication.

 Dr Grossman reports that he is a shareholder in the Group Health Permanente Medical Group, which  contracts exclusively to provide medical care for Group Health members. Dr Tseng reports that she was supported by the Hawaii Medical Service Association Chair for Health Care Services Quality Research. The authors (JOT, RM-S, CS) 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 (JOT, RM-S, DCG, C-WT); acquisition of data (DCG); analysis and interpretation of data (JOT, RM-S, DCG, CS, C-WT); drafting of the manuscript (JOT, RM-S); critical revision of the manuscript for important intellectual content (JOT, RM-S, DCG, C-WT); statistical analysis (JOT, CS); obtaining funding (JOT, DCG); administrative, technical, or logistic support (JOT, DCG); and supervision (RM-S, DCG).

 Jeffrey O. Tom, MD, MS, Kaiser Permanente Center for Health Research, Hawaii, 501 Alakawa St, Ste 201, Honolulu, HI96817. E-mail: jeffrey.o.tom@kp.org.1. HCUPnet: A tool for identifying, tracking, and analyzing national hospital statistics. http://hcupnet.ahrq.gov/. Accessed April 2, 2012.

2. AAP Recommendations for Preventive Pediatric Health Care (Periodicity Schedule). http://practice.aap.org/content.aspx?aid=1599.Accessed April 2, 2012.

3. Hakim RB, Bye BV. Effectiveness of compliance with pediatric preventive care guidelines among Medicaid beneficiaries. Pediatrics. 2001;108(1):90-97.

4. Pittard WB 3rd, Laditka JN, Laditka SB. Early and periodic screening, diagnosis, and treatment and infant health outcomes in Medicaidinsured infants in South Carolina. J Pediatr. 2007;151(4):414-418.

Articles

Timely well-child care visits may play a role in decreasing unnecessary hospitalizations in children in integrated healthcare delivery systems.

Well-Child Care Visits and Risk of Ambulatory Care-Sensitive Hospitalizations

To examine whether physicians' use of information technology (IT) was associated with better knowledge of drug costs.

A 2007 statewide survey of 247 primary care physicians in Hawaii regarding IT use and self-reported knowledge of formularies, copayments, and retail prices.

Approximately 8 in 10 physicians regularly used IT in clinical care: 60% Internet, 54% e-prescribing, 43% electronic health records (EHRs), and 37% personal digital assistants (PDAs). However, fewer than 1 in 5 often knew drug costs when prescribing, and more than 90% said lack of knowledge of formularies and copayments remained a barrier to considering drug costs for patients. In multivariate analyses adjusting for sex, practice size, years in practice, number of formularies, and use of clinical resources (eg, pharmacists), use of the Internet—but not e-prescribing, EHRs, or PDAs&mdash;was associated with physicians reporting slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04). No type of IT was associated with better knowledge of formularies or retail prices.

 Despite high rates of IT use, there was only a modest association between physicians’ use of IT and better knowledge of drug costs. Future investments in health IT should consider how IT design can be improved to make it easier for physicians to access cost information at the point of care.

In this statewide survey, high rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.

Approximately 8 in 10 physicians regularly used health information technology in clinical care; however, only 1 in 4 often knew drug costs.

In multivariate analyses, Internet use (but not e-prescribing, electronic health records, or personal digital assistants) was associated with slightly better knowledge of copayments (adjusted predicted percentage of 23% vs 11%; P = .04).

Future investments in health information technology should consider how to improve its design so that physicians can easily access cost information at the point of care.

The United States is preparing to invest billions of dollars in a major reform of its healthcare system.

 A critical step is increasing the use of health information technology (IT).

 Although prior studies have focused on the ability of IT to make clinical information (eg, medical history, labs) available at the point of care,

 IT systems also can be designed to facilitate physicians’ and patients’ access to accurate and timely cost information.

 This access is especially important as policymakers, insurers, physicians, and patients alike grow increasingly concerned with containing healthcare costs.

In this study, we examined the association between physicians’ use of IT and their knowledge of drug costs. Nationally, 26% of patients report nonadherence to medications because of cost.

 Although physicians want to help, they often are hampered by their lack of awareness of drug costs.

 Health IT can improve physicians’ access to cost information. A 2003 survey found that computerized prescription order entry was associated with physicians having greater knowledge of drug costs,

 but since then information regarding formularies, copayments, and retail prices has increasingly become available via other forms of IT such as the Internet and personal digital assistants (PDAs)10 (see www.epocrates.com, www.uptodate.com, www.medicalletter.com, www.medicare.gov, www.drugstore.com, www.costco.com, and www.walmart.com). Understanding whether different types of IT are currently associated with better knowledge of drug costs can highlight if physicians are already successfully obtaining cost information using health IT, or if further improvements in IT design are needed.

The study was a 2007 cross-sectional survey of community physicians in Hawaii who provided adult primary care, regularly saw patients, and were likely to face more than 1 formulary in their clinical practice. We initially identified 819 general internists, family physicians, and general practitioners through the Hawaii Medical Association (which records all licensed Hawaii physicians) and local health plans’ lists of participating physicians. We called physicians’ offices to exclude specialists, Kaiser and military physicians (ie, those employed by staff model institutions with a single formulary), hospitalists, academic teaching faculty, residents in training, retired physicians, and physicians without accurate contact information based on phone directory and Internet searches. A total of 460 physicians remained, similar to the number identified in a separate unpublished study using claims from Hawaii’s largest insurer, Hawaii Medical Services Association, which captures approximately 60% of Hawaii’s residents and contracts with the vast majority of local physicians. (These independent claims analyses identified 415 adult primary care physicians who were the main providers for >20 Hawaii Medical Services Association members with diabetes.) We mailed these physicians a 10-minute, 27-item survey with no financial incentives, followed by 3 mailings to nonrespondents. Of 281 participants (61% response rate), 34 were ineligible (eg, saw <20 patients per week); therefore, final analyses were based on 247 participants. The institutional review board at the University of Hawaii approved this study.

Physicians were asked, “Do you regularly use the following IT in clinical care (yes/no)?” for the Internet, electronic health records (EHRs), e-prescribing, and PDAs.

 This question referred to both software (Internet, EHRs, e-prescribing) and hardware (PDAs) forms of IT often used by physicians in clinical settings. Because potential overlaps can exist between different IT types, we allowed physicians to answer yes to all functionalities that applied. For example, physicians who used EHRs with e-prescribing functionality could respond yes to use of both EHR and e-prescribing. We did not ask physicians about the specific functionalities of the IT types that they reported using.

To assess physicians’ use of clinical resources when prescribing, we asked, “Do you regularly use any of these resources to help you choose a medication for your patient (yes/no): peers/consultants, health plans, pharmaceutical representatives, pharmacists, and medical journals?” 

 With respect to knowledge of drug costs, physicians reported how often they knew copayments, formularies, or retail prices, and how often they checked formularies when unsure of coverage or discussed drug costs with patients (almost always, most of the time, some of the time, rarely/never). Physicians also were asked, “How important is it for you to consider the cost of a medication to your patient?” and “How many of your patients do you think have difficulty paying for their medications?” Lastly, physicians reported demographic (age, sex, specialty) and practice (eg, number of formularies, solo vs group practice) characteristics.

 The survey was tested for ease of understanding and question comprehension in a convenience sample of 5 physicians.

To examine the association between different types of IT use and drug cost knowledge, we used multivariate logistic regression to predict 3 main outcomes: (1) usually or always knew copayments, (2) usually or always knew retail prices, and (3) had no difficulty in determining formulary coverage. We used the 4 separate IT types as our main predictors: Internet, EHR, e-prescribing, and PDA. In sensitivity analyses, we created a composite IT score by assigning 1 point for each type of IT used and summing these points. The composite score (0 to 4) was not statistically significant in predicting any of the 3 outcomes, and detailed results are not presented.

We adjusted our model for sex, solo versus group practice, years in practice, number of formularies, and use of clinical resources. We excluded age and practice setting because they were highly correlated (r >0.40) with years in practice and solo versus group practice. We excluded provider type because it did not approach significance in bivariate analyses. Results are presented as adjusted predicted percentages with 95% confidence intervals for greater ease of understanding.

Most of the 247 respondents were general internists (55%) and family physicians (36%) in solo (43%) or small practices (48%), and 70% faced 6 or more formularies (

). Nearly all reported that their patients had difficulty paying for medications (99%) and that considering drug costs for patients was important (98%). The majority of respondents (65%) said they discussed medication costs with patients most of the time (49%) or always (16%).  The remainder said they discussed these costs sometimes (31%) or rarely (4%).

Approximately 8 in 10 physicians used at least 1 type of IT regularly in clinical care (Table 1): Internet (60%), e-prescribing (54%), EHRs (43%), and PDAs (37%). Nearly two-thirds (62%) regularly used 2 or more IT types. With respect to using clinical resources when choosing medications, physicians reported using information from pharmacists (69%), followed by peers (56%), pharmaceutical representatives (48%), medical journals (46%), and health plans (45%).

Fewer than 1 in 5 physicians often knew copayments or retail prices when prescribing, and 68% reported difficulty knowing formulary coverage (

). Only 1 in 4 physicians regularly checked formularies when unsure whether a drug was covered. Nearly all said that difficulty knowing formulary coverage (94%) and copayments (91%) were significant barriers to considering drug costs for patients.

In analyses adjusting for sex, solo versus group practice, years out from training, number of formularies, and use of clinical resources, physicians who regularly used the Internet in clinical care were slightly more likely to report that they usually knew copayments (adjusted predicted percentage of 23% vs 11%; P = .04) (

). Neither EHR, e-prescribing, nor PDA use was significantly associated with physicians reporting better knowledge of copayments. For formulary coverage and retail prices, none of the 4 types of IT were significantly associated with better knowledge.

Physicians who faced 6 or more formularies in clinical care were less likely to know copayments (15% vs 26%; P = .048) and formulary coverage (26% vs 46%; P = .006). However, physicians who used information from health plans reported better knowledge of copayments (25% vs 13%; P = .01) and formulary coverage (40% vs 26%; P =.03). With respect to retail prices, the use of pharmacists approached but did not reach statistical significance in predicting physicians having better knowledge of prices (24% vs 12%; P = .07).

In this statewide survey of primary care physicians, we were surprised that although 8 in 10 participants said they regularly used at least 1 type of health IT in clinical care, we detected only a modest association between IT use and better knowledge of drug costs. Use of the Internet was associated with slightly better knowledge of copayments (23% vs 11%; P = .04). However, even among those who regularly used the Internet, the majority of physicians still reported poor knowledge of drug costs.

We are aware of only 1 prior study that assessed multiple types of IT use and physicians’ knowledge of drug costs. In this 2003 study, Shrank et al surveyed physicians (49.6% response rate) identified from the California Medical Association, whose members represented one-third of California physicians.

 In contrast with the results from our more recent survey, the authors found that “computer order entry” (not further defined) but not Internet use was associated with greater awareness of drug costs.

 Neither that study nor ours represent national samples, but our findings should alert policymakers, insurers, and IT providers to reevaluate, as additional money is spent on health IT,

 the ability of current and future IT systems to successfully provide cost information to physicians at the point of care.

Our findings should not discount the potential future usefulness of certain types of IT in increasing physicians’ access to drug cost information.

 Although our participants who reported regularly using e-prescribing did not report better knowledge of drug costs, the integration of formulary information support into prescribing software has been shown to decrease both out-of-pocket and total drug costs.

 Fischer et al analyzed claims for 2 Massachusetts insurers 18 months after the implementation of an e-prescribing system with formulary decision support, and found that physicians were more likely to prescribe lower-cost drugs.

 McMullin et al found that use of an e-prescribing system with integrated clinical and cost information decreased the likelihood of physicians prescribing higher-cost drugs from 35.8% (control group) to 17.5% (intervention group).

At the time of our study, an informal review indicated that several health plans in Hawaii made copayment and formulary information available via the Internet, but not necessarily via EHR, e-prescribing, or PDA. A highly promoted type of e-prescribing software in Hawaii contained formulary information only for the single health plan that sponsored its adoption.

 Thus, we speculate that this may have been why use of the Internet, but not other IT types, was associated with slightly better knowledge of copayments.

This study indicates that improving physicians’ knowledge of drug costs will require more than simply increasing physicians’ use of health IT. Given the financial burden of drug costs on patients, there is a critical need to determine if the problem is that drug cost information is not adequately available via various types of IT, physicians are unaware of its availability, or physicians find such cost information too difficult to use. We did not ask physicians who reported using health IT why they still experienced difficulty accessing drug cost information. However, other studies indicate the issue may be the variability among formularies and out-of-pocket costs from patient to patient, as well as the need for further availability of drug cost information via IT.

 Nearly all of our participants recognized the financial burden of drug costs on patients and the importance of considering out-of-pocket drug costs. However, unless health IT is designed to make the costs of drugs (and other medical services) automatically available at the point of care, physicians and patients will likely continue to be hampered in obtaining healthcare that is appropriate from both a cost and a quality perspective.

Among our participants, no single type of IT was used by more than 60% of physicians. National estimates of physicians’ use of different types of IT for clinical care are generally lower than 50%: 41% use the Internet for computerized decision support,

 Thus, cost information will need to be accessible via multiple IT types to achieve a wider impact in improving physicians’ knowledge of drug costs.

There are several important limitations to this study. We sampled physicians from a single state, and the impact of IT use on physicians’ knowledge of drug costs will vary depending on how well cost information is integrated into local IT systems. Our findings, however, show that in at least 1 state, even physicians with very high levels of IT use still experience substantial problems accessing drug costs, and this issue warrants policy intervention. Our results are based on self-report, and physicians could have overreported or unnderreported their actual use of IT. Physicians reported only whether they regularly used IT in general clinical care; we did not ask specifically about their use of IT to retrieve drug cost information. Further studies are needed to determine whether physicians use IT specifically to access drug cost information and if so, how. If they do not use IT to access drug cost information, the reasons why must be investigated. Physicians also self-reported their knowledge of drug costs, and actual knowledge is likely to be even poorer.

 We focused on busy, community-based primary care physicians, and our results cannot be generalized to specialists or physicians who see fewer patients.

Despite high rates of health IT use in clinical care, there was only a modest association between IT use and physicians reporting better knowledge of drug costs. Policymakers and insurers should examine the integration of cost information into future health IT systems and its usability by community physicians at the point of care.

We would like to thank Ms Allison Imamura for her invaluable help with the project.

From the Department of Family Medicine and Community Health (CWT, ALH), University of Hawaii, Honolulu, HI; Pacific Health Research Institute (CWT, RC), Honolulu, HI; RAND Corporation (RHB, EBK, CMM), Santa Monica, CA; Departments of Medicine and Health Services (RHB, CMM), UCLA, Los Angeles, CA; Department of Medicine (GCA), University of Chicago, Chicago, IL; Department of Pharmacy Practice (GCA), University of Illinois at Chicago, Chicago, IL; Department of Family Medicine (EAJ), University of Connecticut, Hartford, CT; and Department of Medicine (RAD), University of California, San Francisco, CA.

Dr Tseng’s work on this study was funded by the Robert Wood Johnson Foundation Generalist Physician Faculty Scholars Program. Dr Dudley’s work was funded by an Investigator Award in Health Policy from the Robert Wood Johnson Foundation. Dr Mangione received support from the Resource Centers for Minority Aging Research/Center for Health Improvement of Minority Elderly (RCMAR/CHIME) funded by the National Institutes of Health/National Institute on Aging (P30 AG021684). The sponsors had no role in study conduct, data analyses, or manuscript preparation.

The authors (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD) 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 (CWT, ALH, RAD); acquisition of data (CWT); analysis and interpretation of data (CWT, RHB, GCA, ALH, EBK, RC, RAD); drafting of the manuscript (CWT, RHB, RC, RAD); critical revision of the manuscript for important intellectual content (CWT, RHB, GCA, ALH, EBK, CMM, RC, EAJ, RAD); statistical analysis (CWT, EBK, RC); obtaining funding (CWT, CMM); administrative, technical, or logistic support (CWT, EAJ); and supervision (RHB, CMM).

High rates of health information technology use by physicians were only modestly associated with better knowledge of drug costs.


Health Information Technology and Physicians' Knowledge of Drug Costs

Generic-only drug benefits are a way to provide some coverage, as opposed to no coverage. 

To examine how switching from brand name to generic-only drug coverage affected seniors' medication use and financial burden. 

Data are from a 2002 cross-sectional survey conducted for a separate study on benefit caps. Participants belonged to a Medicare managed care plan in one state and had $2000 capped brand name benefits in 2001 but generic-only benefits in 2002. 

Participants reported their cost-cutting strategies before and after the change to generic-only coverage, the medications affected, and their financial burden. We conducted bivariate and multivariate analyses of cost-cutting strategies and financial burden before and after implementation of generic-only benefits. 

Among 611 participants (63% response), rates of switching medications increased after discontinuation of brand name coverage (27% vs 8%, 

< .001). Switches were from brand name drugs to generic equivalents (14%) (eg, Prozac to fluoxetine hydrochloride), to nonequivalent generics (26%) (eg, Paxil to fluoxetine), and to different brand name drugs (45%) (eg, Paxil to Zoloft). Ninety percent of brand name switches remained in the same therapeutic class (eg, selective serotonin reuptake inhibitors). After discontinuation of brand name coverage, participants reported higher rates of decreased medication use (28% vs 17%) and greater difficulty paying for prescriptions (65% vs 37%) (

Changing from brand name benefits to generic-only drug coverage led many participants to switch to less expensive medications but also decreased medication use and increased financial burden. Insurers need to actively help patients adjust to a discontinuation of brand name coverage. 

Policy makers and insurers increasingly view generic-only drug benefits as a way to provide at least some drug coverage, as opposed to no coverage, and to emphasize generic drugs as effective cost-saving medication choices for patients.

Before implementation of the Medicare Part D drug benefit plan, generic-only coverage comprised 60% of Medicare managed care drug coverage in 2003, compared with 18% in 2001.

Some Medicare drug benefit plans are using limited generic-only coverage to bridge the $2250 to $5100 "doughnut hole" in total annual drug expenditures (ie, the gap between partial coverage up to a limit and catastrophic coverage), during which patients typically would be 100% responsible for their drug costs.

 With generic-only coverage, patients must pay for all brand name drugs out of pocket even if no generic drugs are available within the treatment class. Because 38% of Medicare beneficiaries are expected to have high enough drug expenditures to fall into the Medicare drug benefit coverage gap,

it is important to understand how switching from brand name/generic to generic-only benefits can affect seniors. 

used pharmacy claims to show that an involuntary switch from brand name to generic-only coverage led to increased out-of-pocket drug costs for patients and to decreased use of important medications (eg, angiotensin-converting enzyme inhibitors for heart failure). Our study adds to that evidence using data from patient surveys (1) to adjust for patient characteristics (eg, income and health status) and to describe which seniors may be most affected by brand name coverage discontinuation, (2) to ask about financial burden, (3) to describe whether seniors adopted other cost-cutting strategies besides decreasing medication use (eg, switching drugs and using samples), and (4) to evaluate how discontinuation of brand name coverage affected medication use for a broader range of treatment classes (eg, nonsedating antihistamines and antihypertensives). 

Our data are from seniors who were surveyed in 2002 for a separate study

on the effect of benefit caps on medication use. These participants had $2000 capped brand name drug benefits in 2001 but were involuntarily switched by their plan to unlimited generic-only benefits in 2002. We took advantage of this natural experiment to ask seniors about their cost-cutting strategies in 2001 and 2002 (ie, before and after the switch from brand name benefits to generic-only benefits), the medications affected, and their reported financial burden from drug costs. 

Participants belonged to a Medicare managed care plan in one state with 438 802 members and purchased at least 1 prescription during 2001. At the time of the single cross-sectional survey (March through July 2002), participants had recently been involuntarily switched by their plan from capped brand name benefits in 2001 to unlimited generic-only coverage in 2002. Although the survey was designed to study the effect of benefit caps, we added questions to ask participants about their cost-cutting strategies in 2001 (during brand name/generic coverage) and in 2002 (after the change to generic-only benefits). Our participants were not sampled from among all plan members but were restricted to a subset of seniors based on inclusion criteria for the study on benefit caps. Therefore, participants were sampled from among plan members (1) who had $2000 capped brand name benefits in 2001 but who did not exceed their cap (ie, the plan's share of cost was less than $2000) and (2) had 2001 total drug expenditures ranging from $1277 to $5042 (mean, $2483). These criteria were set so that participants could act as a control group for seniors who had exceeded their caps in 2001 ($750 or $1200 caps for 75-120 days) by being similar in distribution of total drug expenditures. In our resulting sample, two thirds of the participants had high enough total drug expenditures in 2001 to reach the $2250 to $5100 coverage gap in the 2006 Medicare Part D prescription drug benefit. Our participants' drug expenditures also placed them in the highest quartile (77th-99th percentiles) of plan members with $2000 caps in 2001, although this was not part of the sampling criteria. 

Data were collected from a single cross-sectional survey conducted from March through July 2002 (3-7 months after the change from brand name/generic drug benefits in 2001 to generic-only drug benefits in 2002). We mailed potential participants information on the study, contacted nonrefusers by telephone (=3 attempts), and mailed questionnaires to those who refused telephone surveys or who could not be contacted by telephone (=3 mailings). Participants were eligible if they were aged 65 years or older, not covered by Medicaid, continuously enrolled in 2001, and were plan members at the time of the 2002 survey. Participants who were cognitively impaired or were unable to complete the survey in English were ineligible for the study. 

All participants had the same formulary with $7 to $8 generic and $25 brand name copayments in 2001 and with $9 generic copayments in 2002. The plan informed members in advance of the change to generic-only benefits. Brand name drugs remained available at discounted prices by mail order. All of the plan's Medicare managed care enrollees in the state were changed to generic-only coverage, so no comparison group was available in 2002. 

Outcomes Variables and Statistical Analysis

We calculated each participant's 2001 total drug expenditures from pharmacy claims by summing costs paid by the patient and by the plan. We examined the availability of generic equivalents for brand name drugs in 2001 from a field in the claims indicating whether drugs were generics (eg, fluoxetine hydrochloride), brand name drugs with generic equivalents (eg, Prozac and fluoxetine), or brand name drugs without generic equivalents (eg, Zoloft). We divided the number of brand name drugs with generic equivalents by the total number of unique drugs to calculate each participant's percentage of brand name drugs with generic equivalents. For the top 10 medications affected by decreased use, we examined whether generics were available within the same treatment class in 2001. 

In the survey, we asked participants if they had adopted any of 7 cost-cutting strategies during the following 2 periods: (1) in 2001, during brand name/generic coverage, and (2) in 2002 (up to the time of the survey), after the change to generic-only coverage. The 7 possible strategies were as follows: (1) switched to less expensive medications, (2) used current medications less often than wanted or prescribed, (3) stopped medications altogether, (4) did not start newly prescribed medications, (5) used free samples, (6) used others' medications, or (7) bought medications from outside of the United States. Participants were asked whether they adopted strategies only in 2001, only in 2002, or in both 2001 and 2002. We asked all participants to answer yes only if cost was the primary reason for adopting the strategy. We defined participants as having decreased medication use if they used less medications, stopped medications, or did not start medications. 

Participants reported their age, education, race/ethnicity, marital status, household size, annual household income, self-rating of health, functional independence in intermediate activities of daily living, and presence of any of 10 common medical conditions (hypertension, diabetes mellitus, heart disease, depression, stroke, cancer, peptic ulcer disease, hypercholesterolemia, osteoporosis, and bronchitis/asthma). We used the median income and household size of participants who reported their actual income (43% of the sample) to impute the incomes of participants who gave only their income ranges (42% of the sample) or who withheld their income information (15% of the sample). 

To test the effect of the change from brand name/generic to generic-only coverage on adoption of cost-cutting strategies and financial burden, we conducted bivariate McNemar tests of correlated proportions to determine if participants reported higher rates of cost-cutting strategies and greater difficulty paying for medications in 2002 than in 2001. To determine which participants were most at risk for decreasing medication use after losing brand name coverage, we conducted multivariate logistic regression analyses. We included participant-level random effects (xtlogit in STATA

) to account for repeated measures, because time points were nested within participants (ie, patients reported cost-cutting strategies for 2 periods [2001 and 2002]). Our dependent variable was decreased medication use (ie, used less, stopped, or did not start) in 2001 vs 2002. We included a variable representing brand name/generic vs generic-only benefit (ie, benefit type) as a covariate so that we could test the effect of independent variables on cost-cutting strategies after accounting for drug benefit type. Our independent variables included patients' sex, age, race/ethnicity, education, marital status, annual household income, supplemental drug insurance coverage, self-rating of health, and functional independence in intermediate activities of daily living, as well as whether they completed a telephone or mail survey, the month they were surveyed in 2002, and the number of health conditions reported at the time of the survey. The significance level was set at 2-sided 

Using their 2001 medication lists, we assisted the two thirds of participants who completed telephone surveys (n = 407) with recalling which medications they switched or decreased use of. For these telephone participants, we described whether their brand name switches in 2002 were to generic equivalents (eg, Prozac to fluoxetine), to nonequivalent generics (eg, Zoloft to fluoxetine), or to different brand name drugs (eg, Paxil to Zoloft). We also characterized whether switches remained within the same therapeutic class (eg, from one selective serotonin reuptake inhibitor to another) or went outside of the original therapeutic class (eg, from selective serotonin reuptake inhibitor to tricyclic antidepressant). We also determined which therapeutic classes were most affected by decreased medication use by classifying all unique drugs into therapeutic classes and by ranking from highest to lowest the number of times seniors reported decreased medication use in each class. 

We measured participants' financial burden from drug costs before and after the change to generic-only coverage by their reported difficulty paying for medications. We also assessed whether it affected their abilities to do enjoyable activities, to pay rent or bills, and to obtain other medical care. 

From a sample of 1172 potentially eligible participants, we excluded 199 because of death, disenrollment in 2001, self-reported illness that hindered participation, or inability to complete the survey in English. Of the remaining 973 participants, 407 completed telephone surveys and 204 completed mail surveys (overall response, 611 [63%], 222 [23%] refused to participate, and 140 [14%] could not be contacted). 

Of the 611 respondents, patients were on average 75.8 years old, 69% were women, 89% were of white race/ethnicity, and 84% had high school degrees or higher (

). Seventy-one percent had annual household incomes of $30 000 or less. Many had hypertension (77%), hypercholesterolemia (65%), and coronary artery disease (46%), and 38% reported having fair or poor health. Participants' 2001 total drug expenditures averaged $2484 (5th-95th percentile range, $1599-$3344), and out-of-pocket drug costs averaged $865 (5th-95th percentile range, $452-$1428). Telephone vs mail respondents had similar 2001 total drug expenditures but were slightly younger, were more likely to be female, were more educated, had higher income, and reported more health conditions, but telephone respondents had less disability in intermediate activities of daily living and had better self-rated health. For nonrespondents, only data on age, sex, and total drug expenditures were available. Nonrespondents were slightly older (76.3 vs 75.8 years, 

< .01) and had higher total drug expenditures ($2568 vs $2484, 

= .02) than respondents but were similar in gender distribution. 

Generic Availability Before Generic-only Coverage

Participants used on average 12.2 unique medications in 2001 (when they had brand name/generic coverage). Seven percent of these medications were brand names with generic equivalents, 45% were brand name drugs without generic equivalents, and 48% were generic drugs. Sixty-four percent of participants' brand name drugs that had generic equivalents available in 2001 comprised the following 6 drugs: Lanoxin (digoxin), Levothyroid (levothyroxine sodium), Tiazac (diltiazem hydrochloride slow release), Adalat CC (nifedipine slow release), Coumadin (warfarin sodium), and Synthroid (levothyroxine). More than 99% of participants had 1 or more (mean, 5.2) brand name drugs without generic equivalents. 

Based on McNemar tests, participants reported significantly higher rates of switching medications (27% vs 8%, 

< .001) and of decreasing medication use because of cost (28% vs 17%, 

). After discontinuation of brand name coverage, participants were more likely to use less medications (15% vs 9%, 

< .001), not start medications (10% vs 5%, 

 < .001), and buy medications from outside of the United States (6% vs 3%, 

= .01) in 2002 vs 2001. The frequencies of using free samples (26% vs 27%) and taking others' medications (3% vs 2%) because of cost remained similar in 2002 vs 2001. 

Based on multivariate logistic regression analyses, the participants most at risk for decreasing medication use in association with discontinuation of brand name benefits were those who were younger (9-percentage-point increase in risk with every 10-year decrease in age, 

= .007) and who had lower income (2-percentage-point increase in risk with every $10 000 decrease in income, 

= .04). Sex, education, race/ethnicity, supplemental drug insurance coverage, self-rating of health, functional independence in intermediate activities of daily living, whether they completed a telephone or mail survey, the month they were surveyed in 2002, the number of health conditions reported at the time of the survey, and 2001 total annual drug expenditures were not significant predictors of decreased medication use. 

Twenty-four percent (96/407) of telephone respondents reported switching to less expensive medications in 2002 after the change to generic-only coverage. Almost three fourths (73% [70/96]) named the drugs from which and to which they switched. This resulted in 96 drug-switching events, representing 51 unique drugs and 27 therapeutic classes. Forty percent of switches were from brand name to generic drugs (14% to generic equivalents and 26% to nonequivalent generics), 45% were from brand name to different brand name drugs, and 15% were other switches (eg, generic to generic or switching from a combination pill to 2 drugs). Ninety percent (74/82) of brand name switches remained within the same therapeutic class. Almost all (43/44) brand name to brand name drug switches occurred in therapeutic classes for which no generics were available (eg, angiotensin II receptor blockers). 

Twenty-five percent (103/407) of telephone respondents reported decreased medication use (used less, stopped, or did not start) after the change to generic-only coverage. Most telephone respondents (84% [87/103]) recalled at least 1 drug involved. This resulted in 152 drug-reducing events, representing 89 unique drugs and 47 therapeutic classes. The top 10 therapeutic classes most affected by decreased medication use included drugs for treating hyperlipidemia, ulcer or reflux, pain or inflammation, asthma or emphysema, allergies, depression, stroke, infection, hypertension, and osteoporosis or hormone therapy (

). Half of the drug-reducing events fell into these top 10 classes. In 2001, 7 of these top 10 therapeutic classes had no generics within the class. By 2004, only 3 of these 10 classes had no generics within the class. 

McNemar tests indicated that participants were more likely to report that it was somewhat or very difficult to pay for medications (65% in 2002 vs 37% in 2001, 

< .001) (Table 2) after switching to generic-only coverage. Medication costs also affected their ability to do enjoyable activities (43% vs 34%, 

< .001), pay rent or other bills (24% vs 17%, 

<.001), get other medical care (15% vs 13%, 

= .004), and borrow money or get help paying for medications (13% vs 9%, 

To our knowledge, this is the first study based on seniors' self-reports of how an involuntary switch from brand name/generic to generic-only drug coverage affected their adoption of cost-cutting strategies and financial burden. One in 4 seniors in our study switched medications, and 1 in 4 decreased medication use after their benefits were changed to generic-only coverage. Two thirds of seniors reported financial burden from drug costs. Patients decreased the use of antihyperlipidemia and antihypertensive drugs, confirming findings of the study by Christian-Herman et al

(using claims data) that discontinuation of brand name benefits affects the use of "essential" drugs for treating diseases with significant mortality or morbidity (hypertension, heart disease, and asthma). Our study found that patients decreased the use of potentially "nonessential" medications such as nonsedating antihistamines and proton pump inhibitors, which are often used to treat non-life-threatening symptoms. In our examination of cost-cutting strategies that cannot be easily evaluated from claims data, we found that rates of switching medications increased but that rates of using free medication samples, taking others' medications, and buying medications outside of the United States did not change substantially. 

These high rates of switching and decreasing medication use have important implications for health plans that are considering generic-only benefits as a way to bridge the coverage gap in the Medicare drug benefit. For the 4 in 10 Medicare beneficiaries expected to fall into the coverage gap, having generic-only benefits is better than no coverage. However, because patients who lose brand name coverage frequently engage in cost-cutting strategies that affect the use of essential and potentially nonessential drugs, insurers and physicians need to actively help patients with generic-only coverage choose the best strategies to trade off cost with health benefits. We did not measure the health effects of the change to generic-only drug coverage, but others found an association with increased hospitalization rates.

In our study, younger and lower-income seniors were at greatest risk for decreasing medication use. However, higher total annual drug expenditures and not having supplemental drug insurance were not significant predictors of decreasing medication use. This may be because most of our seniors had high annual drug expenditures (mean, $2483) and because few (7%) had supplemental drug insurance. Discontinuation of brand name coverage led to significant financial burden, with two thirds of participants reporting difficulty paying for medications. It is surprising that seniors reported this high financial burden, despite adopting cost-cutting strategies, but it may be because many drug classes in 2001 did not have generics within the same treatment class. For example, 45% of switches were to brand name drugs, which may be less expensive than brand name drugs previously used by the patient but still would not be covered by generic-only benefits. Physicians must be aware that even patients with drug coverage may have significant out-of-pocket costs or, as in the case of the Medicare drug benefit, may have coverage gaps. Even when the participants in our study had brand name coverage in 2001, 37% reported financial burden from drug costs. 

Our study adds to the current literature by examining the types of medication switching that seniors made. Forty percent of brand name switches were to generic drugs. Fourteen percent of these switches were from brand name to direct generic equivalents and theoretically should not negatively affect health. However, many brand name switches were made to nonequivalent generics (26%) and to different brand name drugs (45%). We do not know how participants and their physicians chose which drugs to switch to, but 90% of these switches were conservative, with treatment kept within the same therapeutic class. In addition, 98% of switches to brand name drugs were made when no generics existed within the same treatment class. Although we did not examine how many participants had brand name drugs with no generics within the same class at baseline, 7 of 10 medications most affected by decreased use because of cost were in classes without generic drugs. Brand name drugs that have no generics within the same treatment class pose financial and clinical difficulties for patients who must pay out of pocket, forgo medications, or switch to another class. 

We assume, but cannot confirm, that medication switches were to lower-cost drugs, because we asked patients to report only those medication changes made because of cost. Such switches could potentially be positive if patients chose lower-cost but potentially equally effective medications. Many therapeutic classes (angiotensin-converting enzyme inhibitors, proton pump inhibitors, and nonsedating antihistamines) have several drugs within the class that are clinically similar but can differ in cost.

For other classes such as statins, generic drugs (eg, lovastatin vs atorvastatin calcium) are less potent than brand name drugs without generics available but can still help patients reach their clinical goals (eg, cholesterol reduction).

For these classes, switching to less expensive medications within the class makes sense with appropriate patient education and follow-up laboratory testing when necessary. 

Chien-Wen Tseng, MD, MPH

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