Outcomes and cost effectiveness of treating type 2 diabetes with a nurse case manager following treatment algorithms versus primary care physicians

Background. Diabetes places a significant burden on the health care system. Reduction in blood glucose levels (HbA1c) reduces the risk of complications; however, little is known about the impact of disease management programs on medical costs for patients with diabetes. In 2001, economic costs associated with diabetes totaled $100 billion, and indirect costs totaled $54 billion. ^ Objective. To compare outcomes of nurse case management by treatment algorithms with conventional primary care for glycemic control and cardiovascular risk factors in type 2 diabetic patients in a low-income Mexican American community-based setting, and to compare the cost effectiveness of the two programs. Patient compliance was also assessed. ^ Research design and methods. An observational group-comparison to evaluate a treatment intervention for type 2 diabetes management was implemented at three out-patient health facilities in San Antonio, Texas. All eligible type 2 diabetic patients attending the clinics during 1994–1996 became part of the study. Data were obtained from the study database, medical records, hospital accounting, and pharmacy cost lists, and entered into a computerized database. Three groups were compared: a Community Clinic Nurse Case Manager (CC-TA) following treatment algorithms, a University Clinic Nurse Case Manager (UC-TA) following treatment algorithms, and Primary Care Physicians (PCP) following conventional care practices at a Family Practice Clinic. The algorithms provided a disease management model specifically for hyperglycemia, dyslipidemia, hypertension, and microalbuminuria that progressively moved the patient toward ideal goals through adjustments in medication, self-monitoring of blood glucose, meal planning, and reinforcement of diet and exercise. Cost effectiveness of hemoglobin AI, final endpoints was compared. ^ Results. There were 358 patients analyzed: 106 patients in CC-TA, 170 patients in UC-TA, and 82 patients in PCP groups. Change in hemoglobin A1c (HbA1c) was the primary outcome measured. HbA1c results were presented at baseline, 6 and 12 months for CC-TA (10.4%, 7.1%, 7.3%), UC-TA (10.5%, 7.1%, 7.2%), and PCP (10.0%, 8.5%, 8.7%). Mean patient compliance was 81%. Levels of cost effectiveness were significantly different between clinics. ^ Conclusion. Nurse case management with treatment algorithms significantly improved glycemic control in patients with type 2 diabetes, and was more cost effective. ^

THE MULTIPLE LOCATION TIME WEIGHTED INDEX: USING PATIENT ACTIVITY SPACES TO CALCULATE PRIMARY CARE SERVICE AREAS

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering “free or low cost visits”, meeting “all of the patient’s health care needs”, and seeing “the patient quickly” were all ranked higher than geographic reasons. Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts.

Consumer satisfaction with primary care provider choice and associated trust

Trust is important in medical relationships and for the achievement of better health outcomes. Developments in managed care in the recent years are believed to affect the quality of healthcare services delivery and to undermine trust in the healthcare provider. Physician choice has been identified as a strong predictor of provider trust but has not been studied in detail. Consumer satisfaction with primary care provider (PCP) choice includes having or not having physician choice. This dissertation developed a conceptual framework that guided the study of consumer satisfaction with PCP choice as a predictor of provider trust, and conducted secondary data analyses examining the association between PCP choice and trust, by identifying factors related to PCP choice satisfaction, and their relative importance in predicting provider trust. The study specific aims were: (1) to determine variables related to the factors: consumer characteristics and health status, information and consumer decision-making, consumer trust in providers in general and trust in the insurer, health plan financing and plan characteristics, and provider characteristics that may relate to PCP choice satisfaction; (2) to determine if the factors in aim one are related to PCP choice satisfaction; and (3) to analyze the association between PCP choice satisfaction and provider trust, controlling for potential confounders. Analyses were based on secondary data from a random national telephone survey in 1999, of residential households in the United States which included respondents aged over 20 and who had at least two visits with a health professional in the past two years. Among 1,117 eligible households interviewed (response rate 51.4%), 564 randomly selected to respond to insurer related questions made up the study sample. Analyses using descriptive statistics, and linear and logistic regressions found continual effective care and interaction with the PCP beyond the medical setting most predictive of PCP choice satisfaction. Four PCP choice satisfaction factors were also predictive of provider trust. Findings highlighted the importance of the PCP's professional and interpersonal competencies for the development of sustainable provider trust. Future research on the access, utilization, cognition, and helpfulness of provider specific information will further our understanding of consumer choice and trust. ^

Development and formative evaluation of an obesity prevention intervention for the pediatric primary care setting: Helping HAND (Healthy Activity and Nutrition Directions)

As the obesity epidemic continues to increase, the pediatric primary care office setting remains a relatively unexplored arena to offer obesity prevention interventions for children. The increased risk for adult obesity among 10 to 14 year-old children who are overweight, suggests obesity prevention programs should be introduced just before this age or early in this age period. Research is also accumulating on the importance of targeting parents along with children, since parents are in charge of the home environment for children. Therefore, the aim of this project was to develop an obesity prevention program called Helping HAND (Healthy Activity and Nutrition Directions) based on Social Cognitive Theory and authoritative parenting techniques for the pediatric primary care setting and conduct one-on-one interviews with parents as the initial formative evaluation of the intervention material for the obesity prevention intervention. A secondary aim of the project was to determine the feasibility of identifying appropriate subjects for the intervention, and conducting qualitative evaluations of the materials through recruitment through pediatric primary care settings. ^

The multiple location time weighted index: Using patient activity spaces to calculate primary care service areas

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. ^ During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. ^ Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering "free or low cost visits", meeting "all of the patient's health care needs", and seeing "the patient quickly" were all ranked higher than geographic reasons. ^ Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts. ^

Depression care for the old-old in a primary care setting

Objective. Although those age 75 and older are the fastest growing age group in the U.S., few studies focus on the course and treatment of depression in this age group. This study examines the differences between the young-old (age 60 to 74) and the old-old (age 75 and older) in regards to their response to a collaborative care model for depression in primary care. We hypothesized that old-old participants would have more severe depression and have a lower rate of treatment response compared to young-old participants. ^ Methods. The sample consisted of 906 participants (n = 606 young-old; n = 300 old-old) who were randomized to receive the intervention with a depression care manager in the IMPACT trial. This study compared young-old and old-old patients on process of care and outcome variables to identify potential differences between the two age groups. Process of care was determined by the type of treatment and level of stepped care received. Clinical outcomes included SCL-20 depression scores, treatment response (defined as a ≥50% decrease in SCL-20 score from baseline) and complete remission (defined as a SCL-20 score <0.5) at 3-, 6-, and 12-months follow-up. ^ Results. The process of care variables did not differ between the two age groups. SCL-20 depression scores did not significantly differ between the two age groups at all follow-up intervals. Treatment response was significantly different between young-old and old-old participants at 6- and 12-months. Complete remission rates were significantly different between the two age-groups at 12-months follow-up. ^ Conclusions. Young-old and old-old patients have a similar clinical response to initial collaborative depression care in a primary care setting, but old-old patients may have lower rates long-term treatment response and complete remission. These findings will help guide future clinical and public health approaches to treat old-old patients with depression. ^

Community referral for promoting physical activity among primary care patients

Introduction. There is a need for physical activity interventions based in primary care clinics that take advantage of community resources. The purpose of this randomized controlled trial was to compare the effects of two physical activity interventions: (1) physical activity prescription by a primary care provider plus referral to community physical activity resources and (2) physical activity prescription only. ^ Methods. Sedentary adult patients recruited from a general medicine clinic were randomized to receive a physical activity prescription, delivered by the primary care provider, plus referral to community physical activity resources (n=38) or physical activity prescription only (n=32). Outcomes were use of community resources (exercise facility and personal trainers), physical activity levels (self-report questionnaire and pedometer), and attitudes regarding physical activity assessed at 8 weeks. ^ Results. Three of 38 (7.9%) subjects referred to the community resources and none of the 32 subjects in the prescription only group used the community resources during the 8 week trial. Sixteen of 32 subjects in the prescription plus referral group and 19 of 38 in the prescription group completed the self-report follow-up forms at 8 weeks. For minutes of moderate- or vigorous-intensity physical activity per week, there were no between-group differences at baseline, follow-up, or change from baseline to follow-up. However, for moderate- and vigorous-intensity physical activity, there were significant improvements from baseline to follow-up within each group. For attitudes related to physical activity, there were no between-group differences at baseline, follow-up, or change from baseline to follow-up; neither were there any within-group changes. ^ Discussion. Physical activity prescription delivered by a healthcare provider in the context of a routine primary care visit can improve physical activity levels, with no additional improvement gained by referring to community resources. The intervention was feasible for primary care providers to deliver, but only 50% of subjects returned the self-report physical activity questionnaire at the 8 week assessment. ^

Demographics of having a gun in the home in Columbia, SC as reported by women primary care patients

Objectives. To determine demographic correlates of having one or more guns in the household of women primary care patients in the southern USA. ^ Methods. All participants in this cross-sectional study were women aged 18-65 who were insured by either Medicaid or a managed care provider and had ever had an intimate sexual relationship with a male partner that lasted at least three months. Prevalence rate ratios and 95% confidence intervals were calculated using stratified analyses for having a gun in the home and the following demographic factors: age, race, educational attainment, marital status, employment status, and alcohol/drug use. ^ Results. Twenty six percent of households had at least one gun and 6.5% had 3 or more guns. The following demographic characteristics of women were associated with having a gun in the household: age (>40) (prevalence rate ratio [PRR] = 1.4; 95% confidence interval [CI] = 1.1–1.8); White race (PRR = 1.89; 95% CI = 1.61–2.27); currently being employed (PRR = 1.72; 95% CI = 1.22–2.44); higher education; and being insured by an HMO (PRR = 1.92; 95% CI = 1.47–2.50). Neither the partner's unemployment nor his substance use was associated with having a gun. While White households were more likely to have a gun, the same correlates of gun ownership held for both White and African-American households; being married or living as married and higher socio-economic status (i.e. HMO insurance and being employed) were strongly correlated with gun in the household. The following were correlated with having multiple guns in the household: White race (p < 0.0001); increased age (p = 0.005); being currently married or living as married (p < 0.0001); and HMO insured status (p < 0.0001). Among those households with at least one gun, White race and married or currently living as married were associated with having 2 or more guns relative to one gun in the household. ^ Conclusions. Currently living with a man and being of higher socio-economic status were strong correlates of household gun ownership among both Whites and African-Americans. Substance use was not associated with household gun ownership. ^

A qualitative analysis of leadership styles and the selection of quality improvement in primary care practice

In the Practice Change Model, physicians act as key stakeholders, people who have both an investment in the practice and the capacity to influence how the practice performs. This leadership role is critical to the development and change of the practice. Leadership roles and effectiveness are an important factor in quality improvement in primary care practices.^ The study conducted involved a comparative case study analysis to identify leadership roles and the relationship between leadership roles and the number and type of quality improvement strategies adopted during a Practice Change Model-based intervention study. The research utilized secondary data from four primary care practices with various leadership styles. The practices are located in the San Antonio region and serve a large Hispanic population. The data was collected by two ABC Project Facilitators from each practice during a 12-month period including Key Informant Interviews (all staff members), MAP (Multi-method Assessment Process), and Practice Facilitation field notes. This data was used to evaluate leadership styles, management within the practice, and intervention tools that were implemented. The chief steps will be (1) to analyze if the leader-member relations contribute to the type of quality improvement strategy or strategies selected (2) to investigate if leader-position power contributes to the number of strategies selected and the type of strategy selected (3) and to explore whether the task structure varies across the four primary care practices.^ The research found that involving more members of the clinic staff in decision-making, building bridges between organizational staff and clinical staff, and task structure are all associated with the direct influence on the number and type of quality improvement strategies implemented in primary care practice.^ Although this research only investigated leadership styles of four different practices, it will offer future guidance on how to establish the priorities and implementation of quality improvement strategies that will have the greatest impact on patient care improvement. ^

A review of the published literature on interventions to increase compliance with follow-up outpatient medical appointments in primary care practice

Effective strategies for patient follow-up compliance in family practice are essential for the prevention and early detection of disease with the consequences of decreasing morbidity and mortality. With effective appointment reminder systems in place, physicians can better manage the overall health of their patients by providing preventive care as well. This literature review examines intervention strategies used by the authors, the compliance rate of appointment adherence using these techniques, as well as theories relating to study outcomes. The findings of this study may be used as an educational tool by practices to suggest which intervention strategies might be the most effective for their clinic.^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

Comparing experience of diabetes care with chronic illness care in the primary care clinic using the Patient Assessment of Chronic Illness Care (PACIC)

Purpose. The overall purpose of the study was to evaluate the patient experience relevant to the Chronic Care Model as measured by the PACIC. Chronic illness care of patients with diabetes was compared to those with other chronic illnesses. In addition, chronic illness care of Hispanics was compared to those of other race/ethnicity. ^ Methods. The setting of this study was 20 primary care practices located in San Antonio, TX. The subjects in this study were consecutive adult patients age >18 yrs. Data was collected via a survey (PACIC) administered to 40-60 consecutive adult patients in each primary care clinic who presented for a scheduled appointment. ^ Results. Patient experience of the Chronic Care Model is different among those with diabetes than those with other chronic diseases: those with diabetes report a higher PACIC score. (P = 0.012) Although Hispanic patients report a higher PACIC score, patient experience of the Chronic Care Model among Hispanic patients is not significantly different than that of patients of other race/ethnicity regardless of chronic disease. (P = 0.053) After controlling for the patient characteristics of age, education, health status, and race/ethnicity, the diabetes status of the patient remains significantly associated with the outcome, the PACIC score. (P = 0.033) ^ Conclusions. Diabetes is associated with a greater experience of the Chronic Care model. Contributing factors to diabetes patients’ greater experience of the Chronic Care Model include the greater heath care use and higher self-care needs unique to individuals with diabetes. Special consideration must be given to the specific needs diabetic patients to ensure effective interventions, higher patient education, greater patient compliance, and lower health care costs. ^

Has implementation of integrated mental health services in primary care programs positively impacted health outcomes among the mentally ill in developing countries?

Objective. The World Health Organization (WHO) estimates that nearly 450 million people suffer from a mental disorder in the world. Developing countries do not have the health system structure in place to support the demand of mental health services. This study will conduct a review of mental health integration in primary care research that is carried out in low-income countries identified as such from the World Bank economic analysis. The research follows the standard of care that WHO has labeled appropriate in treatment of mental health populations. Methods. This study will use the WHO 10 principles of mental health integration into primary care as the global health standard of care for mental health. Low-income countries that used these principles in their national programs will be analyzed for effectiveness of mental health integration in primary care. Results. This study showed that mental health service integration in primary care did have an effect on health outcomes of low-income countries. However, information did not lead to significant quantitative results that determined how positive the effect was. Conclusion. More ethnographic research is needed in low-income countries to truly assess how effective the program is in integrating with the health system currently in place.^ ^

Costs and quality of medication reconciliation practice in primary care clinics

Medication reconciliation, with the aim to resolve medication discrepancy, is one of the Joint Commission patient safety goals. Medication errors and adverse drug events that could result from medication discrepancy affect a large population. At least 1.5 million adverse drug events and $3.5 billion of financial burden yearly associated with medication errors could be prevented by interventions such as medication reconciliation. This research was conducted to answer the following research questions: (1a) What are the frequency range and type of measures used to report outpatient medication discrepancy? (1b) Which effective and efficient strategies for medication reconciliation in the outpatient setting have been reported? (2) What are the costs associated with medication reconciliation practice in primary care clinics? (3) What is the quality of medication reconciliation practice in primary care clinics? (4) Is medication reconciliation practice in primary care clinics cost-effective from the clinic perspective? Study designs used to answer these questions included a systematic review, cost analysis, quality assessments, and cost-effectiveness analysis. Data sources were published articles in the medical literature and data from a prospective workflow study, which included 150 patients and 1,238 medications. The systematic review confirmed that the prevalence of medication discrepancy was high in ambulatory care and higher in primary care settings. Effective strategies for medication reconciliation included the use of pharmacists, letters, a standardized practice approach, and partnership between providers and patients. Our cost analysis showed that costs associated with medication reconciliation practice were not substantially different between primary care clinics using or not using electronic medical records (EMR) ($0.95 per patient per medication in EMR clinics vs. $0.96 per patient per medication in non-EMR clinics, p=0.78). Even though medication reconciliation was frequently practiced (97-98%), the quality of such practice was poor (0-33% of process completeness measured by concordance of medication numbers and 29-33% of accuracy measured by concordance of medication names) and negatively (though not significantly) associated with medication regimen complexity. The incremental cost-effectiveness ratios for concordance of medication number per patient per medication and concordance of medication names per patient per medication were both 0.08, favoring EMR. Future studies including potential cost-savings from medication features of the EMR and potential benefits to minimize severity of harm to patients from medication discrepancy are warranted. ^

Effect of primary care availability and health insurance coverage on risk and cost of Preventable Hospitalizations in Harris County

Preventable Hospitalizations (PHs) are hospitalizations that can be avoided with appropriate and timely care in the ambulatory setting and hence are closely associated with primary care access in a community. Increased primary care availability and health insurance coverage may increase primary care access, and consequently may be significantly associated with risks and costs of PHs. Objective. To estimate the risk and cost of preventable hospitalizations (PHs); to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs, first alone and then simultaneously; and finally, to estimate the impact of expansions in primary care availability and health insurance coverage on the burden of PHs among non-elderly adult residents of Harris County. Methods. The study population was residents of Harris County, age 18 to 64, who had at least one hospital discharge in a Texas hospital in 2008. The primary independent variables were availability of primary care physicians, availability of primary care safety net clinics and health insurance coverage. The primary dependent variables were PHs and associated hospitalization costs. The Texas Health Care Information Collection (THCIC) Inpatient Discharge data was used to obtain information on the number and costs of PHs in the study population. Risk of PHs in the study population, as well as average and total costs of PHs were calculated. Multivariable logistic regression models and two-step Heckman regression models with log-transformed costs were used to determine the association of primary care availability and health insurance coverage with the risk and costs of PHs respectively, while controlling for individual predisposing, enabling and need characteristics. Predicted PH risk and cost were used to calculate the predicted burden of PHs in the study population and the impact of expansions in primary care availability and health insurance coverage on the predicted burden. Results. In 2008, hospitalized non-elderly adults in Harris County had 11,313 PHs and a corresponding PH risk of 8.02%. Congestive heart failure was the most common PH. PHs imposed a total economic burden of $84 billion at an average of $7,449 per PH. Higher primary care safety net availability was significantly associated with the lower risk of PHs in the final risk model, but only in the uninsured. A unit increase in safety net availability led to a 23% decline in PH odds in the uninsured, compared to only a 4% decline in the insured. Higher primary care physician availability was associated with increased PH costs in the final cost model (β=0.0020; p<0.05). Lack of health insurance coverage increased the risk of PH, with the uninsured having 30% higher odds of PHs (OR=1.299; p<0.05), but reduced the cost of a PH by 7% (β=-0.0668; p<0.05). Expansions in primary care availability and health insurance coverage were associated with a reduction of about $1.6 million in PH burden at the highest level of expansion. Conclusions. Availability of primary care resources and health insurance coverage in hospitalized non-elderly adults in Harris County are significantly associated with the risk and costs of PHs. Expansions in these primary care access factors can be expected to produce significant reductions in the burden of PHs in Harris County.^