2012 State of the Science Congress on Nursing Research

“Relationships between Moderators of Access and Clinical Preventive Services Use in Older Adults with

Near Universal Health Coverage”

Sandra Bibb, DNSc., RNAssociate Professor and Associate Dean for Faculty Affairs

Graduate School of NursingUniformed Services University

• 1 in every 8 Americans (40.4 million) is 65 or older. The 65 and older population will increase to 55 million by the year 2020; 5.5 million adults are 85 and older; 6.6 million will be 85 or older by 2020. (Administration of Aging, 2011).

• The incidence of chronic diseases increases with age; 6 of the 7 leading causes of death in older adults are due to chronic diseases.(Federal Interagency Forum on Aging Related Statistics, 2010).

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Improving access to high quality clinical preventive services (CPS) for

a growing population of older adults is a major population health

(PH) concern in the United States.

Chronic diseases negatively affect PH but can be identified and managed through the use of CPS.

(Federal Interagency Forum on Aging Related Statistics, 2010)

Current Characterizations of the Relationships between Access and CPS Use are Inadequate in

EXPLAINING Continuing Variations

• Adults, 65 and older, with Medicare and a Medicare supplement are the only group in the United States with near universal health coverage (health insurance and a regular place of care), yet variations in the utilization of CPS continue to exist in older adults. (Agency of Healthcare Research and Quality [AHRQ] 2011) 3

Purpose

To identify the relationships between potential moderators (population characteristics, health status/ risks/behaviors) of access (financial,

structural, personal) and utilization of clinical preventive services (CPS) in a homogeneous

(race, income, education, place of residence) sample of older adults with near universal

health coverage (health insurance, regular place of care).

Multiple Factors Determine the Health of Older Adults

Population Economics Health Status Health Risks & Behaviors

Health Care

# of older adults

Poverty Life expectancy Vaccinations Use of health care services

Racial & ethnic composition

Income Mortality Mammography Health care expenditures

Education attainment

Source of income Chronic health conditions

Dietary Quality Prescription drugs

Living arrangements

Net worth Sensory impairments & oral health

Physical activity Sources of health insurance

Older veterans Participation in labor force

Memory impairment Obesity Out of pocket health care expenditures

Housing expenditure

Depressive symptoms

Cigarette smoking

Sources of payment for health care services

Disability Air quality Veterans health careRespondent assessed health status

Nursing home utilization

Residential servicesCare giving and assistive device use

Source: Older Americans Update. (2008 & 2010) Federal Interagency Forum on Aging Related Statistics5

Access is Multifaceted and the Health of Older Adults has Multiple Determinants

• Population Health Framework for this study was based on concepts and definitions outlined in:

– Baron & Kenny. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations,

– Healthy People 2010: Understanding and Improving Health (DHHS, 2000),

– The 2007and 2008 Behavioral Risk Factor Surveillance System (BRFSS) survey, and

– Older Americans Update 2008: Key Indicators of Well-being (Federal Interagency Forum on Aging Related Statistics [FIFARS], 2008).

Theorized Relationships between Potential Moderators and Utilization of Clinical Preventive Services in Older Adults

with Universal Health Coverage

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Tertiary

Prevention

Primary

Prevention

Potential Moderators

Population

Characteristics

Health Status

Health Risks

Health Behaviors

Clinical Preventive Services

Secondary Prevention

Near Universal Health Coverage (Financial, Structural, Personal Access)

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Variable Name Conceptual Definition Measures Financial access Related to health insurance coverage and

financial capacity Insurance coverage; Income; Cost prevented use of services

Structural access

Related to availability of health services Availability of primary services, preventive services, and Veterans health care

Personal Access Related to cultural differences, language barriers, knowledge of health need, and concerns about discrimination

Knowledge of health need related to diabetes, high blood pressure, heart disease, weight management, sensory impairment, arthritis, and oral health; Concerns about discrimination

Population Characteristics

Demographic information related to age, race, ethnicity, marital status, education

Age, Gender, Race, Ethnicity; Educational level; Marital status; Veteran status

Health Status Related to chronic health conditions, sensory impairment, oral health, disability, and health related quality of life, perceived and self reported health status

Perceived health status; Quality of life; Mental and Physical disability; Chronic health conditions (diabetes, heart disease, high blood pressure, asthma, arthritis); Sensory and memory impairment

Health Risks and Behaviors

Related to risks and behaviors that influence health (dietary quality, physical activity, tobacco use, alcohol use, obesity, awareness of signs & symptoms of cardiovascular disease)

Awareness of signs and symptoms of cardiovascular disease; High cholesterol; Tobacco use; Tobacco cessation; Alcohol use BMI (calculated using height and weight); Taking prescribed medications; Dietary choices; Physical activity; Adherence to disease and condition management recommendations

Primary Prevention

Prevention of disease and health promotion (fostering awareness, influencing attitudes & behaviors to improve health)

Length of time since primary care visit; Length of time since visit to dentist; Length of time since visit to ophthalmologist; Influenza immunization; Pneumococcal immunization

Secondary Prevention

Detection of disease Cholesterol screening; Mammogram screening; Prostate cancer screening; Colorectal screening

Tertiary Prevention

Management of health conditions/diseases

Length of time since visit for disease management (asthma & diabetes)

Measures adapted from DHHS, CDC: 2007 and 2008 Behavioral Risk factor Surveillance System survey.

Methodology

• Design– Descriptive, exploratory– Human Subjects/Institutional Review Board Approval

• Setting/Sample– Convenience sample;202 older adult residents of two

privately owned/managed military independent living retirement communities

– Community residents were: 1) 65 years of age or older; 2) eligible for Medicare; 3) likely to have health insurance coverage including pre-paid plans (as the result of military/government retirement) or Medicare and a supplement; 4) likely to have had health insurance coverage for at least 12 months; 5) had a regular source of income; and 6) lived in and maintained individual homes

Methodology• Instrumentation

– Behavioral Risk Factor Surveillance System (BRFSS) questions are in the public domain; permission to use questions is granted on the BFRSS web site.

– The BRFSS Questionnaire has three parts: 1) the core component; 2) optional modules; and 3) state-added questions. The core includes queries about current health-related perceptions, conditions, and behaviors, as well as demographic questions.

– Most of the core questions have been identified as at least moderately reliable and valid, and many have been identified as highly reliable and valid.

Methodology• Data Collection

– Data were collected:• across two years (2007 to 2009); each participant was

interviewed once. • on site and without identifiers in a private room or in the

participants’ homes. – Data collectors were part of the research team and

were trained in data collection techniques by the principal investigator.

– Each question on the questionnaire, was read out loud, to each participant.

– Interviews averaged 45 to 60 minutes in length.

Methodology• Data Analysis

– Completed questionnaires were coded.

– Nominal, ordinal, and scale level data entered into statistical spreadsheet. SPSS version 16.0 was used to conduct all analyses.

– Descriptive statistics (frequencies, measures of central tendency) were used to describe and summarize the variables.

Methodology• Data Analysis

– Exploratory bivariate analyses…conducted to examine the relationships between moderating factors; and between moderating factors and utilization of CPS.

– Hierarchical logistic regression…conducted on all personal access and moderating factor variables to explore the extent to which interactions between personal access variables and theorized moderating factor variables affected utilization of CPS .

– Categorical independent and moderator variables… dummy coded for regression analysis. The dependent variables were coded as a dichotomous.

Methodology• Sample Size

– All residents at both communities were invited to participate in this exploratory study.

• A total of 202 of 596 or 34% of the residents across both communities participated in the study.

– For hierarchical logistic regression…models with one personal access variable and one moderating factor, were considered, yielding one interaction term for a total of three independent variables in each logistic regression model.

• To achieve stable estimates, at least 10 “events” are required per independent variable where an event is defined as “absence” of the characteristic being measured. Our sample size estimates were based on the assumption that 30% of the observations would correspond to an event. The required sample size was 10x3/.3=100 (Peduzzi, et al. 1996).

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Data collected across Two years 2007 through 2009 (N= 202) # cases

% of total

Population Characteristics Age in years Mean 84.22 SD (5.23) Min. 71 Max. 99 Gender: Female Male

132 70

65.3% 34.7%

White or Caucasian 198 98.0% Marital status: Married Widowed Divorced, separated, never married

100 97 5

49.5% 48.0% 2.5%

Education: Grades 9 – 11 Grade 12 or GED 1-3 years of college College 4 years or more (College graduate)

3

11 48

140

1.5% 5.4%

23.8% 69.3%

Ever served active duty in U.S. Armed Forces (Veteran) 85 42.1% Health Status

Perceived Health Status: Excellent Very good Good Fair or Poor

43 76 58 25

21.3% 37.6% 28.7% 12.4%

Satisfaction with Life: Very satisfied with life Satisfied with life

130 71

64.4% 35.1%

Limitation of activities due to physical, mental, or emotional problem 85 42.3% Summary index of unhealthy days < 14 173 85.6%

Health Risks and Behaviors Mean BMI = 25.69, SD = 8.39 Obese (BMI > 30 kg/m2) adults 30 15.6% Had 5 or more drinks on one occasion in past 30 days 2 <1.0% Eat fruit at least once a day 166 83.0% Eat vegetables at least once a day 172 86.0% No leisure time physical activity over the past month 26 12.9%

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Data collected across Two years 2007 through 2009 (N= 202) # cases

% of total

Financial Access Have health insurance coverage 202 100.0% Annual Household Income $20-25,000 $25-35,000 $35-50,000 $50-75,000 >$75,000

1

15 21 39

118

.5%

7.4% 10.4% 19.3% 58.4%

Structural Access Have primary health care services available 202 100.0% Have a primary care provider 199 98.5%

Have dental care services available 202 100.0%

Personal Access

Have been told he/she has heart disease 32 16.0% Have been told he/she has diabetes 19 9.4% Have been told he/she has high cholesterol 112 56.3% Have been told he/she has high blood pressure 127 62.9% Have been told he/she has arthritis 128 64.6%

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Data collected across Two years 2007 through 2009 (N= 202) #

cases

% of

total

Primary Prevention

Had routine check-up past 1 to 12 months 173 85.6%

Had dentist or dental clinic visit in past 1 to 12 months 191 94.6%

Visited eye care professional in past 1 to 12 months 182 90.1%

Had influenza vaccine injected in past 12 months 193 96.5%

Ever had a pneumococcal vaccine 183 91.0%

Secondary Prevention

Had blood cholesterol checked within the past 5 years (N= 202) 188 96.4%

Ever had a sigmoidoscopy and colonoscopy 188 93.1%

Ever had a Pap test - Women (N=132) 130 98.5%

Ever had a mammogram - Women (N= 132) 131 99.2%

Had mammogram in past 2 years - Women (N= 132) 103 78.0%

Ever had a Prostate-Specific Antigen (PSA) test - Men (N= 70 ) 63 90.0%

Tertiary Prevention

Have been told he/she has diabetes by a Health Professional 19 9.4%

Saw HP for diabetes care at least 2 times in the past 12 months 14 82.4%

Had Hg A1C checked at least 2 times in the past 12 months 12 70.69%

Had eye exam in which pupils were dilated in past 12 months 18 85.7%

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Logistic Regression Models with Statistically Significant (p<.05) Improvement in the Fit by Adding Interaction Terms

Moderator Analysis

Model (Independent, Moderator, & Dependent Variables)

Interaction Terms

( Measures)

Dependent Variable

(Measures)

Pseudo R Square

Statistical Findings (Model Moderator Affect at Step 3)

Personal Access, Population Characteristic, Primary Prevention

Advised to lose weight x Gender

Check up last 12 months

Cox & Snell - .047 Nagelkerke - .084

X2( 3, N=193 )= 9.376, p=.025*

Personal Access, Health Status, Primary Prevention

Advised to lose weight x Perceived Health Status

Check up last 12 months

Cox & Snell - .049 Nagelkerke -.087

X2( 3, N=193 )= 9.639, p=.022*

Personal Access, Health Risk, Primary Prevention

Advised to lose weight x BMI > 30 kg/m2

Check up last 12 months

Cox & Snell -.062 Nagelkerke - .107

X2( 3, N=183 )= 11.659, p=.009*

Personal Access, Health Risk, Primary Prevention

High Blood Pressure x BMI > 30 kg/m2

Check up last 12 months

Cox & Snell - .041 Nagelkerke - .071

X2( 3, N=191 )= 7.981, p=.046*

Personal Access, Health Status, Secondary Prevention

Advised to lose weight x Perceived Health Status

Had colonoscopy/ sigmoidoscopy

Cox & Snell - .042 Nagelkerke - .104

X2( 3, N=193 )= 8.353, p=.039*

Personal Access, Health Status, Secondary Prevention

High Cholesterol x Perceived Health Status

Had colonoscopy/ sigmoidoscopy

Cox & Snell - .044 Nagelkerke - .121

X2( 3, N=198 )= 9.008, p=.029*

*Significant p<.05

Limitations

• Self Report

• Selection Bias

• Methodological Design

• Limited Generalizability

Conclusions

• Evidence validating the need to differentiate between “having” and “using” access to care in older adults is mounting.

• Findings from this study contribute to this mounting body of evidence and support that exploring the impact of non-socially determined moderators on utilization of CPS may yield more insight into variations between “having” and “using” access than could be explained by measurement of the direct effect of having near universal health coverage.

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Implications

• Primary and secondary analyses studies should be conducted across various types of population groups with varying levels of financial and structural access to care to determine the extent to which the potential moderators identified in this study impact use of clinical preventive services.

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Acknowledgements•  Acknowledge the support of:

– Associate Investigator: Dr. Diane Padden; – Research Associate: Ms. Wakettia Ferguson; and – Research Assistants during Phase II Data Collection

(Students in MSN Program): Jean Barido, Shawn Kelly, Linda Nunn-Pridgen, and Michelle Weddle

• This research was supported by an Intramural Research Grant from the Uniformed Services University (C061JR).

• Bibb, S. C., Padden, D., & Ferguson, W. (2012). Moderators of access and utilization of clinical preventive services in older adults. Journal of Advanced Nursing, 68(2), 335-348.

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QUESTIONS?

Sandra Bibb, DNSc., RNAssociate Professor and Associate Dean for Faculty Affairs

Graduate School of NursingUniformed Services University

Selected References

• Agency for Healthcare Research and Quality (AHRQ). (2011). 2010 National healthcare disparities report. Rockville, MD: Author.

• Baron, R. M. & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations, Journal of Personality and Social Psychology 51(6), pp. 173-1182.

• Department of Health and Human Services, Administration on Aging. (2011). A profile of older Americans. Available at: http://www.aoa.gov/AoARoot/Aging_Statistics/Profile/2011/docs/2011profile.pdf

• Department of Health and Human Services [DHHS], Centers for Disease Control and Prevention (CDC). (2010). Behavioral Risk factor Surveillance System survey. Available at: http://www.cdc.gov/brfss/questionnaires/questionnaires.htm

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Selected References

• Department of Health and Human Services (DHHS). (2000). Healthy People 2010: Understanding and Improving Health (2nd ed.) Washington, DC: U.S. Government Printing Office.

• Federal Interagency Forum on Aging Related Statistics. (2010). Older Americans update 2010: Key Indicators of well-being. Washington, DC: Author. Available at :http://www.agingstats.gov.

• Peduzzi, P., Concato, J., Kemper, E., Holford, T. R., & Feinstein, A. R., 1996. A simulation study of the number of events per variable in logistic regression analysis, Journal of Clinical Epidemiology 49(12), pp. 1373-1379.

• Young, T. K. (2005). Population health: concepts and methods, (2nd ed). New York: Oxford University Press.

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