Erin D. Bouldin

Falls among adult patients hospitalized in the United States: prevalence and trends

Objectives The purpose of this study was to provide normative data on fall prevalence in U.S. hospitals by unit type and to determine the 27-month secular trend in falls before the implementation of the Centers for Medicare and Medicaid Service (CMS) rule, which does not reimburse hospitals for care related to injury resulting from hospital falls. Methods We used data from the National Database of Nursing Quality Indicators (NDNQI) collected between July 1, 2006, and September 30, 2008, to estimate prevalence and secular trends of falls occurring in adult medical, medical-surgical, and surgical nursing units. More than 88 million patient days (pd) of observation were contributed from 6100 medical, surgical, and medical-surgical nursing units in 1263 hospitals across the United States. Results A total of 315,817 falls occurred (rate = 3.56 falls/1000 pd) during the study period, of which, 82,332 (26.1%) resulted in an injury (rate = 0.93/1000 pd). Both total fall and injurious fall rates were highest in medical units (fall rate = 4.03/1000 pd; injurious fall rate = 1.08/1000 pd) and lowest in surgery units (fall rate = 2.76/1000 pd; injurious fall rate = 0.67/1000 pd). Falls (0.4% decrease per quarter, P < 0.0001) and injurious falls (1% decrease per quarter, P < 0.0001) both decreased over the 27-month study. Conclusions In this large sample, fall and injurious fall prevalence varied by nursing unit type in U.S. hospitals. Over the 27-month study, there was a small, but statistically significant, decrease in falls (P < 0.0001) and injurious falls (P < 0.0001).

Characteristics and well-being of informal caregivers: Results from a nationally-representative US survey

Objectives Given ongoing concerns about high levels of burden reported among some informal caregivers, the goal of this study was to characterize their sociodemographics, health, and well-being. Methods Using cross-sectional data from a large nationally representative survey in the United States (N = 438,712) we identified adults who provided informal care to friends or family members with a health problem, long-term illness, or disability. Descriptive statistics and propensity matching were used to characterize caregivers and compare their health and social support to sociodemographically-similar adults who were not caregivers. Logistic regression models examined associations between caregiving and respondents’ mental health, general health, perceived social support, and sleep problems. Results A total of 111,156 (25.3%) participants reported being caregivers, most of whom reported good mental health (90%) good general health (83%), and adequate social support (77%). After adjusting for respondents’ gender, caregivers reported worse mental health than non-caregivers (odds ratio (OR) = 1.35, 95% confidence interval (CI) = 1.31–1.39 for >15 days poor mental health in the past month) but better general health (OR = 0.96, 95%CI = 0.94–0.98 for fair or poor health). Men caregivers reported somewhat worse overall health than non-caregivers (OR = 1.09, 95%CI = 1.05–1.13) whereas women reported better overall health. Discussion Although reporting good overall well-being, caregivers remain vulnerable for worse outcomes than non-caregivers. Caregiving is associated with poor mental health, and may have additional impacts on the physical health of caregiving men.

Testing and Estimating Model-Adjusted Effect-Measure Modification Using Marginal Structural Models and Complex Survey Data

Recently, it has been shown how to estimate model-adjusted risks, risk differences, and risk ratios from complex survey data based on risk averaging and SUDAAN (Research Triangle Institute, Research Triangle Park, North Carolina). The authors present an alternative approach based on marginal structural models (MSMs) and SAS (SAS Institute, Inc., Cary, North Carolina). The authors estimate the parameters of the MSM using inverse weights that are the product of 2 terms. The first term is a survey weight that adjusts the sample to represent the unstandardized population. The second term is an inverse-probability-of-exposure weight that standardizes the population in order to adjust for confounding; it must be estimated using the survey weights. The authors show how to use the MSM parameter estimates and contrasts to test and estimate effect-measure modification; SAS code is provided. They also explain how to program the previous risk-averaging approach in SAS. The 2 methods are applied and compared using data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey to assess effect modification by age of the difference in risk of cost barriers to health care between persons with disability and persons without disability.

Cost as a barrier to dental care among people with disabilities: a report from the Florida behavioral risk factor surveillance system.

Many individuals who have disabilities or complex health conditions do not have adequate access to comprehensive oral health care. An examination of the literature indicates a variety of contributing factors. This study reports on cost of care as a barrier to oral health care. Data from the 2007 Florida Behavioral Risk Factor Surveillance System (BRFSS) were used (n = 33,777). Respondents who reported activity limitation or the use of special equipment were considered to have a disability. Lack of access to dental care due to cost during the past year was assessed. More individuals with a disability reported not seeing a dentist due to cost versus people without disabilities (30% vs. 16%). After adjusting for confounding variables, Floridians with disabilities were 60% more likely to report cost as a barrier to dental care (OR = 1.60, 95% CI 1.32-1.94). Cost of dental care is an access to oral health barrier for Floridians with disabilities. Improving access to dental care for this population will require consideration of financial issues.

Adult informal caregivers reporting financial burden in Hawaii, Kansas, and Washington: Results from the 2007 Behavioral Risk Factor Surveillance System

BACKGROUND Given the unpaid nature of the work, informal caregiving can create a financial burden for caregivers. Little has been done to identify specific predictors of experiencing financial burden. This study investigated demographic and health factors comparing caregivers who reported having or not having financial burden. METHODS Data are derived from adult caregivers (N = 3,317) as part of the 2007 Behavioral Risk Factor Surveillance System in Hawaii, Kansas, and Washington. The adjusted odds ratios for reporting a financial burden were estimated for demographic and other risk factors. RESULTS Caregivers who reported a financial burden were younger, had lower incomes, were more likely to be current smokers, have had a stroke, and rate their health as fair or poor compared to caregivers who did not report a financial burden. Caregivers who were younger (ages 18-34), resided with care recipients, spent 20-39 hours per week providing care, and reported having a disability were at a statistically significantly higher odds of reporting a financial burden. CONCLUSIONS/IMPLICATIONS Given the current economic difficulties faced by many Americans, further insights into the perceived financial burdens experienced by informal caregivers as well as linkages to policy and programs designed to support caregivers are critical for public health professionals to address the expanding needs in states and communities.

Age Group Differences in Healthcare Access for People With Disabilities: Are Young Adults at Increased Risk?

PURPOSE The purpose of this study was to quantify and describe the population of young adults with disability in Florida and to assess correlates of healthcare access in this population in contrast with adults belonging to middle and older age groups. METHODS This study analyzed data of 36,704 respondents obtained from the 2007 Florida Behavioral Risk Factor Surveillance System. A test for homogeneity of the risk difference across the three age groups was conducted using inverse weighting to adjust for confounding and selection bias. RESULTS The adjusted model for risk difference of not being able to see a doctor in the past 12 months because of cost was significantly heterogeneous across age groups (χ(2)(2df)F value = 12.40, p < .01). The risk difference between population of young adults with disability and their age peers decreased significantly across the groups. The risk difference was 15.5% for those aged 18-29, 11.9% for those aged 30-64, and 2.1% for those aged ≥65. CONCLUSIONS This article quantifies the differences in risk and access to health care between young adults with and without disability, using population-based data. It provides indirect evidence of the widely held belief that there is a problem in healthcare transition in the United States warranting continued investigation and intervention.

Lower-limb amputation and body weight changes in men.

Little is known about the relationship between lower-limb amputation (LLA) and subsequent changes in body weight. We conducted a retrospective cohort study using clinical and administrative databases to identify and follow weight changes in 759 males with amputation (partial foot amputation [PFA], n = 396; transtibial amputation [TTA], n = 267; and transfemoral amputation [TFA], n = 96) and 3,790 nondisabled persons frequency-matched (5:1) on age, body mass index, diabetes, and calendar year from eight Department of Veterans Affairs medical care facilities in the Pacific Northwest. We estimated and compared longitudinal percent weight change from baseline during up to 39 mo of follow-up in participants with and without amputation. Weight gain in the 2 yr after amputation was significantly more in men with an amputation than without, and in men with a TTA or TFA (8%-9% increase) than in men with a PFA (3%-6% increase). Generally, percent weight gain peaked at 2 yr and was followed by some weight loss in the third year. These findings indicate that LLA is often followed by clinically important weight gain. Future studies are needed to better understand the reasons for weight gain and to identify intervention strategies to prevent excess weight gain and the deleterious consequences that may ensue.

Physical Activity among Veterans and Nonveterans with Diabetes

Engaging in regular physical activity (PA), with or without a corresponding decrease in weight, is associated with improved health outcomes. The purpose of this study was to quantify the extent to which PA differed between veterans and nonveterans and to determine how diabetes and age influenced this association. Data from the 2009 Behavioral Risk Factor Surveillance System were used in this study. Respondents were classified as having diabetes if they reported ever being diagnosed with diabetes except during pregnancy. Respondents who reported ever serving on active military duty were classified as veterans. Based on self-report, we calculated the average minutes per week of moderate, vigorous, and total activity. After adjusting for sex, race and ethnicity, household income, education level, body mass index (BMI), and recent health checkup, veteran status was associated with a small but significantly larger amount of average weekly moderate PA (2.2 minutes, P = 0.0058) but not average weekly vigorous PA (−0.02 minutes, P = 0.98). Diabetes and prediabetes were associated with significantly lower mean levels of both moderate and vigorous intensity PA, as was increasing age. Consistent with prior research, veterans engaged in more PA than nonveterans. The association between diabetes, age, and physical activity did not differ by veteran status.

Increased Confusion and Memory Loss in Households, 2011 Behavioral Risk Factor Surveillance System

Using data from the 2011 Behavioral Risk Factor Surveillance System (BRFSS), we examined households in 13 states (N = 81,012) in which the respondent or another adult household member experienced increased confusion or memory loss (ICML) in the preceding 12 months. A total of 12.6% of households reported at least 1 adult who experienced ICML, and in 5.4% of households all adults experienced ICML. Based on these results, an estimated 4 million households in these 13 states have a member with ICML, potentially affecting more than 10 million people. This study can inform public health communication campaigns aimed at increasing awareness of the signs and symptoms of cognitive decline and augment community planning efforts so that the needs of households in which 1 or more adults has cognitive decline are considered.

Weight Change Trajectories After Incident Lower-Limb Amputation

OBJECTIVE To characterize weight change after amputation by identifying typical weight trajectories in men with incident lower-limb amputation (LLA) and describing characteristics associated with each trajectory. DESIGN Retrospective cohort study and analyzed using group-based trajectory modeling. SETTING Administrative data. PARTICIPANTS Veterans who were men (N=759), living in the Northwest United States, and who had an incident toe, foot, or leg amputation between 1997 and 2008 and at least 18 months of amputation-free survival thereafter. INTERVENTIONS Not applicable. MAIN OUTCOME MEASURES Postamputation weight and body mass index change. RESULTS The mean weight at baseline was 91.6±24 kg (202±53 lb), and average follow-up was 2.4 years. We identified 4 trajectory groups for weight change: weight loss (13%), stable weight (47%), slow weight gain (33%), and rapid weight gain (7%). Men with a toe or foot amputation most frequently were assigned to the stable weight group (58%), whereas men with transtibial or transfemoral amputations were most commonly assigned to the slow weight gain group (42% each). Men who died during follow-up were more likely to be assigned to the weight loss group (24%) than men who did not die (11%). CONCLUSIONS We identified distinct weight change trajectories that represent heterogeneity in weight change after LLA. An improved understanding of factors predictive of weight gain or loss in people with LLA may help better target rehabilitation and prosthetic prescription. Additional research is needed to fully understand the relation between weight change and health status after amputation.