Browsing by Subject "health care utilization"

Now showing 1 - 4 of 4
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    Association of Anticholinergic Burden with Cognitive Impairment and Health Care Utilization Among a Diverse Ambulatory Older Adult Population
    (Wiley, 2016-11) Campbell, Noll L.; Perkins, Anthony J.; Bradt, Pamela; Perk, Sinem; Wielage, Ronald C.; Boustani, Malaz A.; Ng, Daniel B.; Biostatistics, School of Public Health
    Study Objective To determine the association between Anticholinergic Cognitive Burden (ACB) score and both cognitive impairment and health care utilization among a diverse ambulatory older adult population. Design Retrospective cohort study. Data Source Medication exposure and other clinical data were extracted from the Regenstrief Medical Record System (RMRS), and cognitive diagnosis was derived from a dementia screening and diagnosis study. Patients A total of 3344 community-dwelling older adults (age 65 yrs and older) who were enrolled in a previously published dementia screening and diagnosis study; of these, 3127 were determined to have no cognitive impairment, and 217 were determined to have cognitive impairment. Measurements and Main Results The study followed a two-phase screening and comprehensive neuropsychiatric examination to determine a cognitive diagnosis, which defined cognitive impairment as dementia or mild cognitive impairment. The ACB scale was used to identify anticholinergics dispensed in the 12 months prior to screening. A total daily ACB score was calculated by using pharmacy dispensing data from RMRS; each anticholinergic was multiplied by 1, 2, or 3 consistent with anticholinergic burden defined by the ACB scale. The sum of all ACB medications was divided by the number of days with any medication dispensed to achieve the total daily ACB score. Health care utilization included visits to inpatient, outpatient, and the emergency department, and it was determined by using visit data from the RMRS. The overall population had a mean age of 71.5 years, 71% were female, and 58% were African American. Each 1-point increase in mean total daily ACB score was associated with increasing risk of cognitive impairment (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.004–1.27, p=0.043). Each 1-point increase in mean total daily ACB score increased the likelihood of inpatient admission (OR 1.11, 95% CI 1.02–1.29, p=0.014) and number of outpatient visits after adjusting for demographic characteristics, number of chronic conditions, and prior visit history (estimate 0.382, standard error [SE] 0.113; p=0.001). The number of visits to the emergency department was also significantly different after similar adjustments (estimate 0.046, SE 0.023, p=0.043). Conclusion Increasing total ACB score was correlated with an increased risk for cognitive impairment and more frequent health care utilization. Future work should study interventions that safely reduce ACB and evaluate the impact on brain health and health care costs.
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    Comorbidity Profile and Health Care Utilization in Elderly Patients with Serious Mental Illnesses
    (Elsevier, 2013-12) Hendrie, Hugh C.; Hay, Don; Lane, Kathleen A.; Gao, Sujuan; Purnell, Christianna; Munger, Stephanie; Smith, Faye; Dickens, Jeanne; Boustani, Malaz A.; Callahan, Christopher M.; Department of Psychiatry, IU School of Medicine
    Objectives Patients with serious mental illness are living longer. Yet there remain few studies that focus on health care utilization and its relationship to comorbidities in these elderly mentally ill patients. Design Comparative study. Information on demographics, comorbidities and health care utilization were taken from an electronic medical record system. Setting Wishard Health Services senior care and community mental health clinics. Participants Patients age 65 years and over-255 patients with serious mental illness (schizophrenia, major recurrent depression and bipolar illness) attending a mental health clinic and a representative sample of 533 non-demented patients without serious mental illness attending primary care clinics. Results Patients having serious mental illness had significantly higher rates of medical emergency room visits (p=0.0027) and significantly longer lengths of medical hospitalizations (p<0.0001) than did the primary care control group. The frequency of medical comorbidities such as diabetes, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, thyroid disease, and cancer were not significantly different between the groups. Hypertension was lower in the mentally ill group (p<0.0001). Reported falls (p<0.0001), diagnoses of substance abuse (p=0.02), and alcoholism (p=0.0016) were higher in the seriously mentally ill. The differences in health care utilization between the groups remained significant after adjusting for comorbidity levels, lifestyle factors, and attending primary care. Conclusions Our findings of higher rates of emergency care, longer hospitalizations, and increased frequency of falls, substance abuse, and alcoholism suggest the elderly seriously mentally ill remain a vulnerable population requiring an integrated model of health care.
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    A generalized semiparametric mixed model for analysis of multivariate health care utilization data
    (Sage, 2018-12) Li, Zhuokai; Liu, Hai; Tu, Wanzhu; Biostatistics, School of Public Health
    Health care utilization is an outcome of interest in health services research. Two frequently studied forms of utilization are counts of emergency department (ED) visits and hospital admissions. These counts collectively convey a sense of disease exacerbation and cost escalation. Different types of event counts from the same patient form a vector of correlated outcomes. Traditional analysis typically model such outcomes one at a time, ignoring the natural correlations between different events, and thus failing to provide a full picture of patient care utilization. In this research, we propose a multivariate semiparametric modeling framework for the analysis of multiple health care events following the exponential family of distributions in a longitudinal setting. Bivariate nonparametric functions are incorporated to assess the concurrent nonlinear influences of independent variables as well as their interaction effects on the outcomes. The smooth functions are estimated using the thin plate regression splines. A maximum penalized likelihood method is used for parameter estimation. The performance of the proposed method was evaluated through simulation studies. To illustrate the method, we analyzed data from a clinical trial in which ED visits and hospital admissions were considered as bivariate outcomes.
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    Generational differences in complementary medicine use in young Australian women: Repeated cross-sectional dataset analysis from the Australian longitudinal study on women’s health
    (Elsevier, 2019-04) Steel, Amie; Munk, Niki; Wardle, Jon; Adams, Jon; Sibbritt, David; Lauche, Romy; Health Sciences, School of Health and Rehabilitation Sciences
    Objective Examine the generational differences in complementary medicine (CM) utilisation between young women from the X and Millennial generations. Design Secondary analysis of two cross-sectional surveys from the Australian Longitudinal Study on Women’s Health (ALSWH). Setting Australia. Main outcome measures Differences between young Generation X women (surveyed 1996; aged 18–23 years), and Millennial women (surveyed 2014; aged 19–24 years) regarding consultations with CM practitioners, sociodemographic characteristics, and health. Predictors for CM consultations were analysed via logistic regression analyses. Results Of the 14,247 Generation X women, 19.4% reported consulting CM, compared to 26.8% of the 11,344 Millennial women. CM consultations was predicted in both cohorts by higher age, education beyond primary school, non-urban (vs. urban) residence, and frequent back pain or headaches. Obesity and regular smoking predicted non-use in both. Significant cohort differences were found in physical activity levels (moderate/high levels associated with increased CM consultations in Millennial, but not Generation X women), and health status (Generation X women reporting fair-poor health were more likely to consult CM practitioners, while Millennial women reporting good health were less likely to do so, compared to women with very good/excellent health). Conclusions The increase in CM utilization among young Australian women from Generation X compared to the Millennial generation could indicate different health consumer patterns for future middle-aged and older adult Australian women. Further increases in CM usage may be observed as current young women age into characteristics traditionally linked with higher CM use such as worsening health status and increased disposable income.