To assess the impact of using the combined measles, mumps, rubella, and varicella vaccine (MMRV) instead of the measles, mumps, and rubella vaccine and the varicella vaccine separately (MMR+V) on uptake of vaccines recommended by the Advisory Committee for Immunization Practices (ACIP) in young children.

Electronic immunization records of children 12 to 15 months of age who received either MMR+V (n = 29,716) or MMRV (n = 29,711) between November 2003 and June 2007 while enrolled in Kaiser Permanente Southern California were evaluated. Proportions of children who received either MMRV or MMR+V and other ACIP-recommended vaccines either simultaneously or during recommended age intervals were compared.

Concomitant administration of DTaP (76.3 vs 75.6%), PCV7 (89.1 vs 78.6%), Hib (92.3 vs 91.3%), and hepatitis A vaccine (HAV) (44.2 vs 0.1%) and on-time administration of PCV7 (91.7 vs 78.7%), Hib (93.9 vs 93.2%), and HAV (86.8 vs 24.3%) were greater with MMRV than with MMR+V. On-time administration of DTaP (91.4 vs 90.5%) and IPV (91.3 vs 90.0%) was greater with MMR+V than with MMRV.

Concomitant and on-time administration of most ACIP-recommended vaccines was greater with MMRV than with MMR+V despite the addition of HAV to the recommended schedule after introduction of MMRV. However, the modest compliance increases suggest that focusing on other barriers to compliance may be important to increasing adherence to future vaccination recommendations.

Vaccination compliance improved slightly following the introduction of MMRV combination vaccine in place of the MMR and varicella vaccines given separately.

		This increase was detected in spite of addition of another vaccine to the immunization schedule at the same time that MMRV was introduced.

		The increase in compliance was small, suggesting that other barriers to vaccination compliance may need to be addressed in order to optimize adherence to future vaccinationrecommendations.

		Combination vaccines will continue to be important to maintaining vaccine complianceas new vaccines are added to the recommended immunization schedule.

Childhood immunizations have markedly reduced an increasing number of vaccine-preventable diseases.

 However, vaccination rates among children remain less than optimal, with nearly 30% of children failing to receive all recommended vaccines by 2 years of age

 and as many as 90% of children failing to receive all recommended doses at age-appropriate intervals.

In an attempt to improve childhood vaccination coverage rates, the standards for pediatric immunization practices direct providers to administer simultaneously all vaccines for which children are eligible at each visit.

 However, concerns of parents and providers about the pain and psychological trauma associated with the administration of multiple injections result in a reluctance to administer more than 2 to 3 injections simultaneously.

This frequently results in vaccine deferral,

 which substantially reduces vaccine coverage rates.

The importance of simultaneous administration of vaccines will grow as the number of vaccines included in the childhood vaccine schedule increases. Between 1991 and 2000, the number of injections necessary to provide all recommended vaccinations for children through age 23 months increased from 11 to 17, assuming the use of all combination vaccines then licensed in the United States.

However, development of combination vaccines may decrease the number of injections required to provide the increasing number of recommended vaccines, potentially improving immunization rates.

ProQuad (MMRV; Merck and Co, Inc, Summit, New Jersey), a combination of measles, mumps, and rubella (MMR) and varicella vaccines, was developed primarily to increase varicella vaccine coverage in children. However, we hypothesized that decreasing the number of injections required for children at 12 to 15 months of age, when the recommended vaccination schedule is particularly injection dense,

 might increase the simultaneous uptake of other childhood vaccines recommended by the Advisory Committee for Immunization Practices (ACIP). The objective of this study was to assess the impact of combining the MMR and varicella vaccines on the uptake of other vaccines given at the same time as MMRV or the MMR vaccine and varicella vaccine separately (MMR+V) in children 12 to 15 months of age.

Subjects for this study had been included in a phase-IV post-licensure retrospective cohort study that was conducted by Kaiser Permanente Southern California (KPSC) to evaluate the safety of a combined MMRV vaccine licensed in the United States in 2005 as previously described.

 Briefly, children 12 to 60 months of age who received a first dose of MMRV between February 2006 and June 2007 or MMR+V between November 2003 and January 2006 in KPSC were included in the study. Subjects who received MMRV were optimally matched without replacement on age, sex, and month and day of vaccination to children who received MMR+V. Children were excluded if they had a history of measles, mumps, rubella, or varicella disease, or a history of vaccination for any of these diseases. To allow validation of these criteria, subjects were required to be continuously enrolled in the Kaiser Foundation Health Plan from age 6 months until 90 days following vaccination. We compared the proportions of children aged 12 to 15 months who received either MMRV or MMR+V and the vaccinations recommended in the routine childhood vaccination schedule

 either simultaneously (concomitantly) or during recommended age intervals (on time). Study subjects were members of KPSC, the largest managed care organization in Southern California, serving 3.4 million members who are broadly representative of the diverse racial, ethnic, and socioeconomic background of the source population in Southern California. Vaccinations may be administered to members without charge at any visit including no-cost nurse visits. Immunizations are tracked by KPSC through the Kaiser Immunization Tracking System, one of the largest immunization tracking systems in the United States.

Several changes in the schedule of recommended childhood vaccines occurred during the study that affected measurements of compliance with vaccination recommendations (

). Due to a nationwide shortage of pneumococcal conjugate vaccine (PCV7), the fourth and the third PCV7 doses were suspended for low-risk children in February 2004 and March 2004, respectively; as the shortage resolved, these doses were reinstituted in July 2004 and September 2004, respectively.

Therefore, compliance with recommendations to administer PCV7 dose 4 in the MMR+V recipients was corrected by excluding children who received MMR+V between February 2004 and September 2004 when PCV7 dose 4 was suspended.

The recommendation to administer hepatitis A vaccine (HAV) at 12 months of age was made in January 2006

 just prior to introduction of MMRV at KPSC on February 1, 2006.

In order to estimate the impact of combining MMR and varicella vaccines on varicella vaccine uptake and to estimate the relative compliance of the source population for each cohort, we compared the uptake of MMRV with the uptake of MMR+V in 1-year samples of children drawn from the source population from which the cohorts were drawn for the phase-IV postlicensure study described previously.

 Vaccinated children were required to have had continuous membership from 6 months of age to the time of vaccination. The percentage of children born between March 1, 2005, and February 28, 2006, who received the first dose of MMRV at age 12 to 15 months  was compared with the percentage of children born between March 1, 2003, and February 28, 2004, who received the first dose of MMR+V at age 12 to 15 months.

Race and ethnicity data were available from health plan files for 82% of members included in this study and were complete for 90% after surname analysis and geocoding imputation.

Chi-square tests were used to compare the percentages. All tests were conducted using SAS software (SAS Enterprise Guide, version 4.1, SAS Institute Inc, Cary, North Carolina). This study was approved by the KPSC Institutional Review Board.

Of the 31,298 children each in the MMRV and MMR+V cohorts,18 29,711 and 29,716 were between 12 to 15 months of age, respectively. The demographics of the cohorts were similar (

Compliance Among MMRV and MMR+V Recipients

The on-time and concomitant uptake of recommended childhood vaccines among children who received MMRV or MMR+V between 12 and 15 months of age is shown in 

. Concomitant administration of the booster doses of diphtheria, tetanus, and pertussis (DTaP), PCV7, and H

(Hib) was greater with MMRV than with MMR+V (

 <.04). However, most differences were small (0.7%-10.5%). Concomitant administration of inactivated polio vaccine (IPV) dose 3 was low for MMRV and MMR+V (1.83%-1.92%). Similarly, on-time administration of booster doses of PCV7 and Hib vaccines was greater with MMRV than with MMR+V, though the differences were small (0.7%-13.0%). On the other hand, on-time administration of DTaP booster and IPV dose 3 was greater with MMR+V than MMRV (

<.001), but the differences were small (0.8%- 1.3%). The hepatitis A vaccine was given more often both concomitantly and on-time with MMRV than with MMR+V (

Combined on-time administration of all recommended vaccines (DTaP, PCV7, Hib, and IPV) was greater with use of MMRV than with MMR+V. This result held both with inclusion and exclusion of HAV from analysis (64.6% vs 18.5% and 74.1% vs 60.5%, respectively; both 

Uptake of MMRV vs MMR+V in Young Children

In 1-year samples of children from the cohort source populations, 81.4% of children who were born between March 1, 2005, and February 28, 2006, received MMRV at 12 to 15 months of age and 81.7% of children born between March 1, 2003, and February 28, 2004, received MMR+V at 12 to 15 months of age (

The recommended vaccine schedule has become increasingly injection dense, particularly at the 1-year visit. Strategies to address this situation have included giving vaccines at other ages and developing combination vaccines. However, many vaccines cannot be given at less than 12 months of age due to diminished efficacy.

 In addition, deferring vaccination results in decreased uptake.

 Combination vaccines make it possible to administer vaccines on schedule while reducing the number of injections given to patients.

 In this study, we evaluated the potential effect of combining MMR and varicella vaccines on the uptake of other recommended vaccines in children 12 to 15 months of age. While an increase was seen in the combined uptake of vaccines given concomitantly as well as on time with MMRV compared with MMR+V, the statistically significant but small increase in uptake of individual vaccines noted in this study is likely of limited clinical significance.

There are several possible reasons why a greater increase in vaccines given with MMRV compared with MMR+V was not seen. First, the recommendation to give HAV at the 12-month visit was made nearly simultaneously with the use of MMRV instead of MMR+V. Therefore, the number of injections recommended at the 12- to 15-month visit for nearly the entire MMRV cohort was same as that recommended for the MMR+V cohort. This is likely why on-time uptake of all recommended vaccines, including HAV, when MMRV was used (64.6%) was similar to on-time uptake of all recommended vaccines, not including HAV, when MMR+V was used (60.5%). However, while the number of injections received by each cohort was the same (n = 6), MMRV recipients were protected against 1 additional illness.

In addition, we compared 2 populations that received MMR-containing vaccine, making them more likely to be compliant with receipt of other vaccines.

 A slight reduction in the number of injections administered to compliant cohorts may have had less impact than it would have had in populations more reluctant to accept vaccines. Furthermore, the high compliance seen in both cohorts in this managed care setting may have had a ceiling effect, limiting further achievable increases in compliance. Moreover, vaccines are provided within KPSC without copayment and at no-cost visits. Hence, the reduced cost to caregivers of 1 less injection resulting from the use of MMRV rather than MMR+V seen in some settings was not a factor in this study setting.

 For these reasons, the results of this study conducted in a single managed care organization may not be generalizable to other populations and more diverse settings.

Finally, school and day care vaccine requirements may have increased compliance in both cohorts, thereby blunting any increase in compliance due to a decreased number of doses of vaccines that might otherwise have been seen in the MMRV cohort.

Combination vaccines have been found to increase compliance with vaccine administration recommendations.

 However, the minimal increase in uptake of individual vaccines and the continued suboptimal combined on-time uptake of all recommended vaccines seen following the introduction of MMRV in place of MMR+V suggest that other barriers to vaccination compliance need to be addressed. In addition, complications may arise as a result of combining vaccines. Shortly after completion of this study, ACIP withdrew its recommendation to administer MMRV preferentially as the first dose of MMR-containing vaccine in young children as a result of an increased incidence of febrile seizures following administration of MMRV compared with MMR+V.

Articles

WEB EXCLUSIVE

Although vaccination compliance increased slightly following use of combination MMRV instead of separate MMR and varicella vaccines, additional barriers to improved compliance warrant future evaluation.

Impact of MMRV Combination Vaccine on Childhood Vaccination Compliance

To assess the impact of a simple education program on adherence to influenza vaccination.

The education program group (EPG) received an education program via surface mail, consisting of information regarding the effectiveness and safety of the influenza vaccine. The no-program group (NPG) received no intervention. All patients in 13 primary care practices in the city of Castellón, Spain, were included if they were 60 years or older on the first day of the 2009 seasonal influenza vaccination campaign.

There were a total of 2402 participants (mean age 70.4 [± 7.1] years); 1338 (55.7%) were women. In 2009, 950 participants (39.5%) received the vaccine, including 900 of the same participants (37.5%) who had received it in 2008 (

 = .14). Of those vaccinated in 2009, 501 (52.7%) belonged to the EPG and 449 (47.3%) to the NPG (

 = .01). In a logistic regression analysis we found an association between adherence to vaccination in 2009 and both vaccination in the previous year and receiving the educational intervention. Influenza-related morbidity was similar in both groups during the 7 months after vaccination.

A mailed reminder program was effective to improve influenza vaccination rate, but only to a limited extent. Vaccination the previous year was the main predictor of adherence to vaccination. More programs are needed to improve vaccination rates in the study community.

An education program delivered via surface mail was effective in improving the influenza vaccination rate in Castellón, Spain.

		The education program consisted simply of information regarding the effectiveness and safety of the influenza vaccine.

		Among individuals vaccinated in 2009, 501 (52.7%) received the educational intervention and 449 (47.3%) did not (

		However, vaccination in the previous year was the main predictor of adherence to vaccination.

Influenza is a highly contagious infection that causes outbreaks and epidemics throughout the world, generally during the winter season. Every year the disease is responsible for thousands of deaths and substantial costs for health systems worldwide. Although rates of influenza are highest among children, serious illness and death are particularly common in patients with chronic medical conditions and in older individuals, as both groups are especially vulnerable to complications of the disease.

Influenza vaccination of seniors has been found to be effective in reducing hospitalization for heart disease, cerebrovascular disease, pneumonia, and influenza, and also in reducing overall risk of death.

Despite those benefits, signifï»¿icant proportions of patients for whom the vaccine is recommended fail to receive it. In recent years, vaccination rates have progressively increased in most developed countries, but efforts to improve the rates are still pressingly needed.

Strategies to improve vaccination rates can be classified into 4 different categories: (1) personalized education programs, communicated via letters, phone calls, or other means, designed to increase perceptions of susceptibility to influenza, vaccine effectiveness, and vaccine safety; (2) enhancement of access to vaccination, for example by providing more clinics and better hours, vaccinating during home visits, offering free vaccine, and decreasing administrative barriers to vaccination; (3) interventions to ensure that healthcare workers have adequate information about influenza and the vaccine, as well as strategies to increase these workers’ motivation and willingness to vaccinate patients; and (4) social intervention, including administrative frameworks and campaigns to inform the population about influenza and vaccination, implementation of policies that suggest changing from risk-based to age-based vaccination targeting, remuneration of healthcare workers for increasing vaccination rates or achieving targets, and so forth

Studies assessing those strategies are scant, and most of them have been carried out in the United States.

So many uncertainties remain regarding the efficacy of those strategies, especially in countries with healthcare systems different from the US system. We undertook this study to assess (1) the impact of a simple education program on the rate of adherence to seasonal influenza vaccination, (2) the morbidity experienced by patients during the 7 months after vaccination, and (3) the factors associated with adherence with seasonal influenza vaccination.

This was a randomized open controlled study to assess the efficacy of an education program designed to improve the rate of seasonal influenza vaccination in patients 60 years and older.

The study was done in Centro de Salud Rafalafena, a health center situated in the city of Castellón, Comunidad Valenciana, Spain, associated with the Agencia Valenciana de Salud of the National Health Service. Participants belonged to the practices of 13 family physicians. All patients in those practices were included if they were 60 years or older on the first day of the 2009 seasonal influenza vaccination campaign (ie, September 28, 2009). Patients were excluded if they had an egg allergy or had been diagnosed with Guillain-Barré syndrome within 6 weeks of influenza vaccination in previous years. Participants were included and excluded with the help of Abucasis II, the intranet application used by the Agencia Valenciana de Salud for clinical follow-up of all of its patients.

 We used a computer random-number generator and a 1:1 ratio to randomly assign participants to 1 of 2 groups: the education program group (EPG) or the no-program group (NPG). The study was open for participants but blinded for the healthcare workers responsible for caring for the patients.

The program was delivered to participants by means of a personalized letter sent via surface mail in the first days of September 2009, a few weeks before initiation of the official seasonal influenza vaccine campaign. Postal addresses were obtained from Abucasis II, where patient data are permanently updated in order to provide medical assistance. The letter was written in Spanish on a DIN A4 sheet of paper and included basic information about the clinical manifestations and possible complications of influenza, and about the efficacy of the vaccine to prevent the disease, according to recommendations of the Centers for Disease Control and Prevention

 and the local authorities of the Comunidad Valenciana.

 The content and style of the mailing were intended for patients. The letter addressed common concerns about the flu shot and was written in easy-to-understand language. The mailing was sent once to eligible participants.

For every participant, the following variables were obtained: sex, age on the day of initiation of the 2009 seasonal influenza vaccination campaign, nationality, race, labor status (worker or retired), code of the primary care physician responsible for medical attention, district or town of residence, seasonal influenza vaccine received in 2009 or not, week of the campaign when the vaccine was received, seasonal influenza vaccine received in 2008 or not, visit to the Department of Health hospital emergency department after initiation of the 2009 seasonal influenza vaccination campaign or not, admission to the Department of Health hospital after initiation of the 2009 seasonal influenza vaccination campaign or not, reason for the visit to the emergency department (respiratory disease, heart disease, cardiovascular disease other than heart disease, other nonsurgical disease, surgical disease, or other reason), and reason for admission (respiratory disease, heart disease, cardiovascular disease other than heart disease, other nonsurgical disease, surgical disease, other reason).

Variables related to personal data or vaccination were recovered from Abucasis II, and variables related to visits to the emergency department or admissions to the Department of Health hospital were recovered from the center general database, where visits and admissions are coded according to the

 International Classification of Diseases, Ninth Edition, Clinical Modification

No variables related to pandemic influenza, which also occurred in 2009, are included in our study.

The study was carried out according to the good clinical practice principles set out in the Declaration of Helsinki in 1964 and subsequent updates.

The study was approved by the Clinical Research Committee of the Department of Health of Castellón, Spain. Participants’ identifying details were replaced by codes, which were used throughout all phases of the study.

On the basis of the percentage of participants vaccinated in 2008 and results of previous studies,

 we calculated that a sample size of 1187 participants in each group was needed to find a vaccination rate difference of at least 5% between the EPG and the NPG (42.5% and 37.5%, respectively), with a level of significance of

 = .05 and a power of 80%. Discrete variables are reported as absolute values and frequencies, and continuous variables are reported as mean and standard deviation if normally distributed or as median and interquartile range if not normally distributed. The following bivariate analyses were used: the 

² test for discrete variables, independent samples, or repeated measures;

 tests for normally distributed continuous variables; and the Mann-Whitney 

 test, Wilcoxon signed rank test, or Kruskal-Wallis H test for nonnormally distributed continuous variables. A multivariate logistic regression analysis was carried out to assess the possible association of the most representative variables in the study (independent variables) with getting or not getting an influenza vaccination in 2009 (dependent variable). In all analyses an alpha level of <.05 was used, 1 tailed if a unidirectional hypothesis on the results existed or 2 tailed if such hypothesis did not exist.

A total of 2402 patients were included. All data on all variables, except for labor status, were available for 2241 participants (93.0%), and the code of the primary care physician was available for 2125 participants (88.5%). None of the sent letters was returned to sender. No patients were excluded because of egg allergy or previous diagnosis of the Guillain-Barré syndrome.

A total of 1338 participants (55.7%) were women. The mean (± standard deviation) age of participants was 70.4 (±7.1) years, while their median (interquartile range) age was 69 (64-76) years. A total of 1891 participants (78.7%) were retired. Almost all participants belonged to the same sociodemographic group: 2396 (99.8%) lived in the province of Castellón, 2386 (99.3%) were Spaniards, and 2380 (99.1%) were white; most (ie, 2360 [98.2%]) lived in the city of Castellón. The main characteristics of the EPG and NPG groups are shown in 

; no significant differences existed between the groups with respect to those characteristics.

The proportion of participants cared for by each primary care physician (PCP) varied substantially. The physician with the largest number of participants was in charge of 268 patients (11.2%), and the physician with the smallest number was in charge of 105 patients (4.4%). A total of 277 patients (11.5%) were not assigned to any physician.

Of all the study patients, a total of 950 (39.5%) were vaccinated in 2009; 900 (37.5%) of those same patients were vaccinated in 2008 (

 = .14). A total of 752 patients (31.3%) were vaccinated in both 2008 and 2009.

displays the number of patients vaccinated every week during the seasonal influenza vaccine campaign of 2009. A total of 49 patients (5.1%) received the vaccine outside the campaign period.

Table 1 shows the vaccination rates for EPG and NPG participants. The rates did not differ in 2008, but did differ in 2009, with a higher vaccination rate in EPG patients. In 2009, 41.7% of EPG participants were vaccinated compared with 37.4% of NPG participants. Vaccinations were given to 52 more EPG participants than NPG participants, for an 11.6% increase in the vaccination rate. That means that 23 education program letters must be sent to gain 1 vaccinated person. Comparison of vaccination rates in both years showed that 43 more EPG patients were vaccinated in 2009 than in 2008 (a 9.4% increase), whereas only 7 more NPG patients were vaccinated in 2009 compared with 2008 (a 1.6% increase) (

We did a logistic regression analysis to assess the possible association between the dependent variable (vaccination status in 2009) and 5 independent variables (vaccination status in 2008, sex, age, EPG or NPG membership, and living in the city of Castellón or not). A test of the whole model was significant (

 <.01), meaning that the independent variables were useful to predict the dependent variable. The variation in the dependent variable explained by independent variables was fair, with a Cox and Snell R

of 0.55. The model correctly predicted vaccination status in 89.8% of participants who did not receive the vaccine, and in 79.2% of those who did receive the vaccine. 

shows the regression coefficients, the Wald test, significance of the Wald test, the odds ratios, and the confidence intervals for every independent variable. As can be seen, vaccination in 2009 was predicted by vaccination in 2008 and by belonging to EPG.

The vaccination rate was 46.0% in retired participants and 15.7% in worker participants (

<.01). Among the sets of patients assigned to the different PCPs, the highest rate of vaccination for a set was 62.6% and the lowest rate was 34.1% (

 <.01). The rate of vaccination was only 4.0% in the set of patients not assigned to any particular PCP.

A simple education program was effective in improving the influenza vaccination rate, although vaccination in the previous year was the main predictor of adherence.

Impact of Education Program on Influenza Vaccination Rates in Spain

To assess the impact of the MDVIP model of personalized preventive care on hospital utilization rates over a 5-year period.

This study was a comparative hospital utilization analysis between MDVIP members and nonmembers using the Intellimed database from 5 mandatory reporting states (New York, Florida, Virginia, Arizona, and Nevada) from 2006 to 2010.

Hospital discharge rates per 1000 persons were calculated and comparisons were made between members and nonmembers by age (Medicare [>65 years] vs non-Medicare [35-64 years]) and year.

Overall, MDVIP members were approximately 42%, 47%, 54%, 58%, and 62% less likely to be hospitalized relative to nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively. By 2010, MDVIP hospital discharges for the Medicare population were 79% lower than the nonmember Medicare population, and this difference was shown to be trending up since 2006 (70% to 79%). A similar trend was seen in the non- Medicare population (49% to 72%). In addition, elective, non-elective, emergent, urgent, avoidable, and unavoidable admissions were all lower in the MDVIP members compared with nonmembers for each year.

The MDVIP model of personalized preventive care allows the physician to take a more proactive, rather than reactive, approach; we believe this increased physician interaction is the reason for the lower hospital utilization and ultimately lower healthcare costs seen here.

The MDVIP personalized preventive care model which allows for increased physician interaction results in better health outcomes and ultimately lower healthcare costs.

		 MDVIP hospital discharges for both the Medicare and Commercial populations were lower than those for the nonmembers over 5 years, and this difference was shown to be trending up from 2006 to 2010.

		 MDVIP members were readmitted 97%, 95%, and 91% less frequently for acute myocardial infarction, congestive heart failure, and pneumonia, respectively.

		The estimated cost savings to the system from the 5 states was $119.4 million for 2010, representing a $2551 savings per patient.

The high and growing cost of healthcare is a significant issue in the United States, with hospital care representing the largest component (31.1%) of national healthcare expenditures.

 In 2008, there were an estimated 39.9 million hospital discharges (131 per 1000 persons) in the United States, with an estimated total aggregate cost of $364.7 billion.2 Medicare was the expected primary payer for the largest number of discharges (14.9 million, 46.2%), followed by private insurance (14.1 million, 32.2%).

Some hospitalizations for ambulatory care—sensitive conditions, such as diabetes and chronic cardiac and respiratory diseases, can potentially be prevented with timely and effective ambulatory care. Diabetes with complications was in the top 20 most expensive conditions requiring hospitalization for Medicare in 2008.

 In addition, coronary artery disease and acute cerebrovascular disease (stroke) ranked among the top 10, while congestive heart failure, heart attack, pneumonia, and respiratory failure ranked among the top 20 most expensive conditions for both Medicare and private insurance in 2008.3 In 2006, hospital costs for potentially preventable (ie, ambulatory care—sensitive) conditions were approximately $30.8 billion (1 of every 10 dollars of total hospital expenditures), and as many as 4.4 million hospital stays could possibly have been prevented with better ambulatory care, improved access to effective treatment, and/ or implementation of healthy behaviors.

 Therefore, reducing the frequency of potentially preventable hospitalizations would be an effective strategy for lowering costs while improving quality of care and outcomes.

In 2001, the Institute of Medicine issued its vision for reinventing healthcare in the 21

century to foster innovation and improve the delivery of care, which included patient-centered, responsive, and timely approaches to care.

 One new model that has emerged in the United States that seeks to address this issue is personalized preventive care, which focuses on prevention and wellness while delivering a high quality of care. This model is based upon consumer/patient empowerment, greater focus on preventive services, increased physician availability, and access to other healthcare resources. MDVIP (MD — Value in Prevention) is the first company to establish a network of primary care practices that delivers personalized healthcare in this new way. The MDVIP model of comprehensive management focuses on personalized preventive healthcare by delivering a host of screenings (eg, depression, anxiety, sleep, nutrition, sexual function, vision, and hearing) and diagnostics (eg, expanded testing for diabetes, bone disease, and cardiovascular disease) for a membership fee of $125 to $150 per month. MDVIP practices limit the number of members in each practice (

600 patients per physician) to be able to logistically deliver the service, thus allowing physicians to provide more personalized attention to manage all relevant health issues including acute concerns, chronic disease, and preventive screening, not just the diagnosis and treatment of illness.

 Traditional practices have over 2000 patients

 and do not have the time to provide many preventive services.

 MDVIP founders calculated the amount of time it would take to deliver a comprehensive preventive wellness program yearly and be able to follow up with chronic issues, thus capping the number of patients at 600 for each physician. As a result of the smaller practice sizes required to be able to provide the services, MDVIP members also receive same-day or next-day appointments for urgent and nonurgent care and the ability to reach their primary care physician (PCP) 24 hours a day. This model is not a third-party payer, and the fee covers only the extended prevention and wellness services provided by the PCP; therefore, members still need traditional health insurance to cover the costs of inpatient and outpatient visits, services provided by specialists, and other medical services (eg, laboratories, Xrays).6 In this model, MDVIP believes that the focus on prevention and wellness and the additional attention and time (ie, high quality of care) will lead to lower hospital utilization and ultimately lower healthcare costs. In an early effort to assess the impact of the MDVIP model on hospital utilization, we compared the hospital inpatient discharge rates from MDVIP members from 5 mandatory reporting states to nonmembers within the same states over a 5-year period.

Comparative Utilization Methodology and Definitions

Hospital inpatient utilization data were purchased from Intellimed, an industry provider of hospital data. Intellimed reports utilization by state and payer (ie, for Medicare and non-Medicare). They also provided population data from the US Census Bureau and Claritas system, which were used to construct the comparator (nonmember) population against which the hospital utilization of MDVIP members was compared. In addition, members were identified by physician provider information since individual patient identifiers are not provided in the database.

Hospital utilization data were classified within the Intellimed database as being elective/ non-elective, emergent/urgent, and avoidable/unavoidable admissions. An elective admission was predefined in the IntelliMed database as the condition permitting adequate time to schedule the availability of a suitable accommodation. A non-elective admission was predefined in the IntelliMed database as the condition not permitting adequate time to schedule the availability of a suitable accommodation. Furthermore, non-elective admissions were classified as emergent or urgent. Emergent and urgent admissions were also predefined in the IntelliMed database. Emergent admissions were those that required immediate medical intervention as a result of a severe, life-threatening, or potentially disabling condition, and urgent admissions were those that required attention for the care and treatment of a physical or mental disorder. An avoidable admission was defined and validated by MDVIP physicians as an admission for inpatient care of chronic illnesses that could be averted if the patient had good quality outpatient care (eg, ruptured appendix, asthma, cellulitis, congestive heart failure [CHF], diabetes, gangrene, hypokalemia, immunizable conditions, malignant hypertension, pneumonia, pyelonephritis, perforated or bleeding ulcer) and included the following

 International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes: 032, 033, 037, 045, 055, 072, 250.1, 251.0, 276.8, 401.0, 402.0, 403.0, 404.4, 428, 437.2, 481, 482, 483, 485, 486, 493, 531.0, 532.0, 533.0, 540.0, 590.0, 681, 682, 785.4, inclusive of all sub-codes; whereas, an unavoidable admission could not be averted with good-quality outpatient care.

Of the 40 states and the District of Columbia in which MDVIP does business, only 5 states provide physician-specific data that could be used for this analysis. Therefore, MDVIP members were identified from MDVIP-affiliated physician provider information from those 5 mandatory reporting states: New York, Florida, Virginia, Arizona, and Nevada.

Since there is a lack of patient-level data within the Intellimed data set utilized for this analysis, MDVIP members were identified as any admission where an MDVIP physician was the attending physician. We established the comparator population using both inclusion and exclusionary measures. Using Claritas population data and collateral information available from the US Census Bureau, we established a comparison population matched by state of residence, age, and third-party payer coverage. We excluded from the comparator population individuals with attributes not found within the MDVIP cohort (ie, individuals under the age of 35 years were excluded from the commercial payer group, and individuals under the age of 65 years from the Medicare comparison group). We also excluded from the comparison group all payer groups not represented within the MDVIP membership (Medicaid, Medicaid HMO, CHAMPUS, Charity, Kid Care, state/ local government, self pay, underinsured, unknown, Veteran’s Administration, worker’s compensation, and other). In order to further ensure valid comparison, we excluded from the comparison group admission counts any admission types not represented in the population of MDVIP members (ie, newborn/ newborn premature, observation, unknown [including “info” and “NA”], semi-urgent, and trauma). MDVIP members were also excluded if their physician had a start date with the MDVIP network within the reporting quarter.

Hospital inpatient discharge rates per 1000 persons were used as the measure of comparison, calculated as: (discharges/ [days in reporting period/365])/(population/1000). Sensitivity analyses were also performed to assess the robustness of the data and to gain better understanding of systematic influences on the utilization differences between MDVIP and nonmember populations.

Additionally, the rate of readmission for MDVIP Medicare members between January 1, 2008, and December 31, 2010, with a primary diagnosis (

code) of acute myocardial infarction (MI), CHF, or pneumonia was calculated using IntelliMed as the data source. Even though individual patient identifiers are not available in the IntelliMed database, readmissions were defined as another hospital discharge within 30 days of a preceding discharge for the same

 code and the same physician provider for a patient with the same age and sex. Thus, if any patient’s date of birth fell in between the first admission and the readmission, then the readmission was not captured, since only age is provided in the IntelliMed database. Also, if a patient was readmitted by another physician provider, then the readmission was not captured. MDVIP Medicare member average readmission rates from 2008 to 2010 for the 3 medical conditions were then compared with readmission rates for non-MDVIP Medicare members within the same 5 states for the same 3 conditions based on the 2009 Medicare readmission rates available from the Dartmouth Atlas Project.

 shows the number of hospital inpatient discharge cases and the base population used in the analysis by health plan for each of the 5 years. From 2006 to 2010, the base population of MDVIP members more than doubled due to the increase in enrollment in the network over time. Approximately 45% of the MDVIP member base was commercial, and approximately 55% was Medicare per year.

Overall (combining commercial and Medicare), MDVIP members were approximately 42%, 47%, 54%, 58%, and 62% less likely to be hospitalized relative to the nonmember population for the years 2006, 2007, 2008, 2009, and 2010, respectively. 

 show the discharges/ 1000 by member population and year for the commercial and Medicare health plan populations, respectively. By 2010, hospital discharges for the MDVIP Medicare population were 79% lower than the nonmember population in this study, and this difference was shown to be trending up since 2006 (Figure 2). A similar trend was seen in the commercial population (49% in 2006 to 72% in 2010) (

In addition, relative hospital utilization tended to decrease as MDVIP membership increased over time. The estimated utilization savings for just 2010 in these 5 states was calculated to be $109.2 million for Medicare and approximately $10.2 million for commercial members, totaling $119.4 million, which was derived from the 2009 inpatient cost per admission value from the US Department of Health and Human Services (since the 2010 value was not available).

For elective admissions, the overall MDVIP member discharges/ 1000 was approximately 59% lower than nonmembers for both 2006 and 2007. For 2008, 2009, and 2010, the relative reductions in elective admissions were 71%, 77%, and 83%, respectively.  For non-elective admissions, the overall MDVIP member discharges/1000 were approximately 37%, 43%, 49%, 52%, and 56% lower than nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively.

Of the non-elective admissions, the overall MDVIP member discharges/1000 for urgent admissions were approximately 17%, 35%, 42%, 40%, and 42% lower than nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively. For emergent admissions, the overall MDVIP member discharges/ 1000 for urgent admissions were approximately 41%, 45%, 51%, 54%, and 58% lower than nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively.

MDVIP members experienced approximately 23%, 31%, 38%, 47%, and 49% fewer avoidable admissions than nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively. Furthermore, MDVIP members experienced approximately 45%, 49%, 56%, 59%, and 63% fewer unavoidable admissions than nonmembers for the years 2006, 2007, 2008, 2009, and 2010, respectively.

While the comparative utilization analysis matched MDVIP and nonmember populations on all available measures, we recognize the possible effects of selection bias, ie, MDVIP members may differ from nonmembers on factors such as attitudes toward health, personal health behaviors, and baseline health status, none of which are discernible from hospital inpatient utilization data. To better understand systematic influences on the utilization differences between MDVIP and non-MDVIP populations, we performed sensitivity analyses. First, we tested for bias introduced from scaling. The baseline comparative utilization analysis used the comparator group population of the MDVIP physician’s state of practice (matched as described previously). This established a comparison population ratio of roughly 425:1 (nonmembers:MDVIP member). We would expect true utilization differences to be robust to a reduction in scaling differential. To test this, we limited the comparator group to the population of matched nonmembers residing in the same zip code as the physician practice, reducing the comparison population ratio to 15:1. Using this scale, across all payers for the 5 reporting states, inpatient utilization was 57% lower for MDVIP members versus nonmembers. From this we can conclude that the utilization difference for MDVIP versus non-MDVIP is robust to population scaling. Second, since year-over-year differences between MDVIP and nonmember populations increased in magnitude for the past several years, we examined the relationship between mean caseload maturity (ie, how long the average person has been an MDVIP member) and the difference in utilization. We found a strong relationship; roughly 86% of the variance in utilization is accounted for by increased caseload maturity. However, due to a relatively small sample size (4 years of data), analysis of variance did not find significance at the 0.05 level (there is roughly a 7% chance that the relationship would be found by chance).

To further understand the dramatic differences between the hospital utilization of MDVIP Medicare members relative to non-MDVIP Medicare members, readmission rates were evaluated. When compared with the 2009 readmission rates for non-MDVIP Medicare members within the 5 same states,9 MDVIP members were readmitted 97%, 95%, and 91% less frequently for acute MI, CHF, and pneumonia, respectively (

We assessed the impact of the MDVIP personalized preventive care model on hospital utilization and found the MDVIP members' rates were substantially lower than nonmembers'.



Personalized Preventive Care Leads to Significant Reductions in Hospital Utilization

 To evaluate the association between social support and medication adherence.

 A search of articles published before November 2010 in peer-reviewed, healthcarerelated journals was conducted using PubMed, EMBASE, and Web of Science, and search terms related to social support (social support OR friend OR family OR agency) and adherence (patient compliance OR medication adherence), yielding 5331 articles.

Articles were included if they directly measured the relationship between medication adherence and some form of social support. Excluded were case studies, studies with participants <18 years of age, and non—English language studies. Four social support categories were reported: structural, practical, emotional, and combination. Medication adherence was reported in the manner in which it was described in each study.

 Fifty studies were included in the final analysis. A greater degree of practical support was most consistently associated with greater adherence to medication; evidence for structural or emotional support was less compelling. However, most studies were limited in size and design, and substantial variability in designs and outcome measurement prohibited pooling of results, necessitating qualitative evaluation of the studies.

 This qualitative analysis found that practical social support was most consistently associated with greater medication adherence. Interventions that use existing contacts (friends or family) to engage patients in the mundane and practical aspects of medication purchasing and administration may be an effective approach to promoting better medication adherence.

This qualitative analysis found that practical social support is consistently associated with higher medication adherence.

		 Medication adherence is most closely associated with having a repertoire of close friends or providers who can consistently provide emotional and practical support that does not leave the patient with the perception of unmet needs.

		 Providing people in the patient’s social network with emotional and practical skills (eg, focused on medication adherence, barriers, support, and confidence building) may have a significant impact on medication adherence.

Nonadherence to chronic medications imposes a substantial clinical and financial burden on the US healthcare system. Studies have repeatedly demonstrated that medication nonadherence is a common source of hospitalizations, morbidity, and mortality in a variety of populations and disease states.

 The financial consequences of medication nonadherence in the United States are tremendous; the cost of care for patients with cardiovascular disease and diabetes are substantially greater in those who do not adhere to therapy than in those who do adhere, and total annual costs of medication nonadherence are estimated at almost $300 billion in the United States.

Despite the importance of medication nonadherence, no simple solutions are available to fix the problem.

 Numerous studies have aimed to improve medication adherence, yet best practices to enhance better medication taking continue to evolve.

 Moreover, little is known about the most cost-effective interventions to encourage better adherence.

 Therefore, new, low-cost approaches to improve medication adherence are needed to promote improved health outcomes and reduce healthcare costs.

The emergence of popular online social networking websites has stimulated interest in the role of social capital or connectedness in promoting health. Greater social support has been shown to be associated with improved health outcomes and healthier behavior.

 Yet the role of one’s social connectedness in medication adherence has not been well defined. Considering that social support connections are wide ranging, encompassing many aspects of the relationship between the patient and the person providing the support, we know even less about how different features of one’s social support system can influence medication use.

Accordingly, we conducted a systematic review of the published literature to evaluate what is known about the association between social support and medication adherence in a variety of disease states, and to explore features of one’s social support that might encourage better behavior. To do so, we categorized social support structures into those that are more functional and those that are predominantly emotional in nature.

 We aimed to evaluate the relative influence of the various types of support on medication adherence, so that appropriate and effective interventions to improve medication adherence can be designed.

With the help of a professional librarian, we performed a search of articles published before November 2010 in peerreviewed, healthcare-related journals using PubMed, EMBASE, and Web of Science. We used search terms related to social support (

). Articles with at least 1 search term from both categories met the criteria for the initial title/abstract review. After screening for duplicate entries, the results from the 3 databases were combined and totaled 5331 articles. Of these abstracts, 169 were included for full review of inclusion and exclusion criteria (

Articles were included if they directly measured the relationship between medication adherence and some form of social connectedness and/or support. There were 131 excluded articles, including case studies, studies with patients less than 18 years of age, non—English language publications, and studies where no medication-specific adherence measure was reported as an outcome (leaving 38 articles). Pediatric studies were excluded because the different mechanism and influence of social support in pediatric versus adult medication adherence would make conclusions difficult. The inclusion or exclusion of each article was determined by 2 reviewers independently, and disagreements were resolved by a third reviewer. All reference lists from the included articles were evaluated, and 12 additional studies (not found in our original search) were included, as determined by consensus. Data on study populations and characteristics, results, and study quality were extracted from each article using a standardized protocol and reporting form. Specific information collected included study population, social support measure, adherence measure, and adherence outcome.

For our key explanatory variable, social support, we defined 4 categories: structural, practical, emotional, and combination, which are further defined with examples in the 

. Each social support item abstracted from an included study was placed into 1 of these 4 social support categories by 2 reviewers independently, and disagreements were resolved by a third reviewer. The combination category reflects those social support items or instruments that did not distinguish the precise type of support measured, or that only reported a summary measure that included multiple support types.

Our outcome variable, medication adherence, is reported in the manner it was described in each study (Appendix). Studies that reported more than 1 social support category are represented in more than 1 table in the Appendix.

We included a total of 50 studies; 14 evaluated structural support, 12 practical support, 14 emotional support, and 26 combination support (Figure). Most were conducted in disease-specific populations; the most common were human immunodeficiency virus infection/acquired immunodeficiency syndrome (HIV/AIDS), diabetes, dialysis, tuberculosis, cardiovascular (hypertension and congestive heart failure), asthma, and transplant. Most were cross-sectional observational studies. Sample sizes ranged from 26 to 1198 patients.

Almost all included studies required participants to evaluate their perception of support; 2 interviewed caregivers.

 Approximately half used some variation of a previously validated instrument, and half used study-specific questionnaires. A majority utilized a self-completed survey instrument (n = 28), while fewer relied on interviews (n =16). The remainder utilized a combination of surveys and interviews, or did not describe the data collection. Most included several different types of support, while 17 measured only 1 type. Adherence was largely measured using subjective self-report, while 13 corroborated adherence objectively with pill count, refill information, or urine/serum markers of adherence.

 One study used a nurse assessment of adherence.

We identified 14 studies that assessed the relationship between structural social support and medication adherence; this structural support was cohabitation in 4 studies,

 presence of a spouse or supportive relationship in 7 studies,

In 1 study, structural social support was defined by the size of patients’ social network (Table).

Of all these studies, 6 (43%) identified an association between structural support and medication adherence, but some identified a positive association and others found a negative association. Two identified relationship status as significantly associated with being more adherent, with logistic regression coefficients of 1.2 (

; however, a third study identified having a partner as being associated with lower odds of adherence compared with not having a partner (odds ratio [OR] of adherence 0.97, 

and a fourth study found that adherence was highest in divorced persons.

Living situation (eg, living with someone) had a negative association with adherence in 2 studies (both in an HIV population).

 Only 1 study found that living with a spouse or relative had a significant, but modest, association with adherence.

Of the 8 (57%) studies that did not identify an association between structural support and medication adherence, 

 most evaluated marital status or living status. Two of these studies involved large cohorts of patients with objectively measured medication adherence.

 The 1 study that evaluated social network size did not identify a significant association with adherence, although it evaluated only frequency of contact, but not quality or type of the interaction.

We identified 12 studies that assessed the relationship between practical social support and medication adherence. Practical support was assessed by subjective participant ratings about how much help they received or how satisfied they were with the help in 3 main areas: medication help (reminders to take medication, directive guidance on medications, picking up prescriptions), help with household functions (cooking, cleaning, paying bills, running errands), and help with transportation.

Of these 12 studies, 8 (67%) identified a significant association of help with adherence. Of those, all but 1 identified a significant positive association (either amount of support or satisfaction with support) with medication adherence.

The 1 study that identified a negative association was a small study with an HIV cohort, where the adherence measure was nurse-perceived adherence, a poorly validated measure of actual medication use.

Two studies analyzed the association using a multivariate technique. The first study found the number of sources of practical support was associated with adherence; having 1 source of support was not associated with higher adherence, but 2 or more sources were associated with twice the odds ofadherence compared with the adherence of patients with no sources of support (OR of adherence 2.12, 95% confidence interval [CI] 1.06-4.26; 

In the second study, participants who had an “unmet need” for benefits (eg, Medicaid, Social Security, or prescription help) had an OR of nonadherence of 2.8 (

<.01) compared with patients who did not have this unmet need; 51% of the cohort reported needing help in this area. Other unmet practical needs were not associated with adherence (housing, emergency provisions, legal assistance, or supplemental nutrition), although about two-thirds of the cohort did not report a need in those areas.

Of the 4 (33%) studies that did not identify an association, all were limited by very small sample sizes (58-112 patients), and subjective self-reports of both practical support and medication adherence.

We identified 14 observational studies that assessed the relationship between emotional support and medication adherence. Emotional support was assessed by self-report in a variety of ways, including perceived amount of or satisfaction with social support, number of social support unmet needs, or the number of friends and social support sources.

Of these studies, 6 (42%) identified a significant association between any emotional support measure and adherence. Five found a positive association between emotional support and adherence, and 1 found a mixed association between support and adherence, with a negative association between social attachment and short-term (3-day) medication adherence (OR 0.49, 95% CI 0.34-0.71; 

 <.01), and a positive association between higher reassurance of worth and longer term (30-day) medication adherence (OR 1.3, 95% CI 1.03-1.6; 

 Of the 5 that found a positive association, 2 found a univariate association between medication adherence and emotional support in small samples of HIV patients.

Another small study of renal transplant patients found affectionate support explained 12% of the variance in adherence in multivariable regression.

Medication adherence is most closely associated with emotional and practical support.

December 2012

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Personalized Preventive Care Leads to Significant Reductions in Hospital Utilization

Association Between Different Types of Social Support and Medication Adherence

The Validity of Claims-Based Risk Estimation in Underinsured Populations

Care Coordination in Accountable Care Organizations: Moving Beyond Structure and Incentives

Trends in Viral Hepatitis Cost-Effectiveness Studies

Communication About Diabetes Risk Factors During Between-Visit Encounters

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Prioritized Post-Discharge Telephonic Outreach Reduces Hospital Readmissions for Select High-Risk Patients