Individuals\u27 Lifetime Use of Nursing Home Services: A Dynamic Microsimulation Approach

Despite the projected growth in the number of older Americans who will use nursing home services as the baby boom generation ages, there is little information about the total amount of time we can expect people to reside in nursing homes. I estimate individuals’ lifetime use of nursing home services using data from the 1984-1990 Longitudinal Study of Aging and the 1982, 1984, and 1989 National Long-Term Care Survey. A Markov model of functional status was used to estimate monthly functional status transition probabilities. Discrete-time hazard models were estimated to determine characteristics that were associated with nursing home use. Microsimulation techniques were employed to impute monthly values of functional status, incorporate monthly information about individuals’ functional status into the models that predict nursing home use, and examine the life-cycle implications of the nursing home use estimates. I find that substantially more women use nursing home services during their lifetimes than men (36 percent versus 18 percent). Nonwhite males use nursing homes significantly less than white males. Women who reside in states that have generous nursing home supply and demand factors use substantially more nursing home services than women living in states without these policies. With the projected large increase in the older population, these findings have important fiscal planning implications for the delivery of long-term care services

Stochastic Modeling of Active Life and Its Expectancy

The concept of “active” (or “disability-free”) life, and its average value, has proven to be a useful index of public health and of quality of life for populations. A question of great interest in recent years is whether recent trends towards longer life expectancy have been accompanied by comparable increases in active life expectancy. Past research on patterns and trends of active and “inactive” life has focused almost exclusively on the expectancy—or, the average value—of years spent with and without disability. This measure is useful for actuarial calculations, for example analysis of the insurance value of programs that provide long-term care services. However, when considering broader issues of equity and efficiency in the financing and provision of services, or of targeting of programmatic resources, it is also useful to analyze the full frequency distribution of time spent in each activity status, in addition to the average values of each. Nevertheless, to our knowledge no past research has attempted to trace out the frequency distribution that underlies the calculations of Active Life Expectancy (ALE). Similarly, the uncertainty (or, the “margin of error”) in our calculations of active life expectancy traceable to sampling error has received little attention. This paper addresses two related phenomena: variability in active life, which is to say the relative likelihood that someone will spend an additional 0, 1, 2, ... or more of his or her years in various functional statuses such as “active” or “inactive;” and uncertainty about the average value of additional years spent in each such status. Our concern with both phenomena leads us to present our findings in the form of intervals, or measures of dispersion, as well in the more conventional form of point estimates. Linking the two areas of analysis is a recognition of several sources of randomness, or stochasticity, that are inevitably present when analyzing the dynamics of functional status. In general, we find that the variability in years of active life is substantial. This variability is obscured in analyses that address only the expected value of active life. In contrast, uncertainly related to sampling error appears to be quite small, at least for the combination of survey data and model specification employed here

Association of type of birth attendant and place of delivery on infant mortality in sub-Saharan Africa

Objective: To examine the association between type of birth attendant and place of delivery, and infant mortality (IM).Methods: This cross-sectional study used self-reported data from the Demographic Health Surveys for women in Ghana, Kenya, and Sierra Leone. Logistic regression estimated odds ratios (ORs) and95% confidence intervals.Results: In Ghana and Sierra Leone, odds of IM were higher for women who delivered at a health facility versus women who delivered at a household residence (OR=3.18, 95% confidence interval, CI: 1.29-7.83, p=0.01 and OR=1.62, 95% CI: 1.15-2.28, p=0.01, respectively). Compared to the use of health professionals, the use of birth attendants for assistance with delivery was not significantly associated with IM for women in Ghana or Sierra Leone (OR=2.17, 95% CI: 0.83-5.69, p=0.12 and OR=1.25, 95% CI: 0.92-1.70, p=0.15, respectively). In Kenya, odds of IM, though nonsignificant, were lower for women who used birth attendants than those who used health professionals to assist with delivery (OR=0.85, 95% CI: 0.51-1.41, p=0.46), and higher with delivery at a health facility versus a household residence (OR=1.29, 95% CI: 0.81-2.03, p=0.28).Conclusions: Women in Ghana and Sierra Leone who delivered at a health facility had statistically significant increased odds of IM. Birth attendant type-IM associations were not statistically significant.Future research should consider culturally-sensitive interventions to improve maternal health and help reduce IM.Keywords: birth attendant, infant mortality, sub-Saharan Afric

Association of type of birth attendant and place of delivery on infant mortality in sub-Saharan Africa.

Objective: To examine the association between type of birth attendant and place of delivery, and infant mortality (IM). Methods: This cross-sectional study used self-reported data from the Demographic Health Surveys for women in Ghana, Kenya, and Sierra Leone. Logistic regression estimated odds ratios (ORs) and95% confidence intervals. Results: In Ghana and Sierra Leone, odds of IM were higher for women who delivered at a health facility versus women who delivered at a household residence (OR=3.18, 95% confidence interval, CI: 1.29-7.83, p=0.01 and OR=1.62, 95% CI: 1.15-2.28, p=0.01, respectively). Compared to the use of health professionals, the use of birth attendants for assistance with delivery was not significantly associated with IM for women in Ghana or Sierra Leone (OR=2.17, 95% CI: 0.83-5.69, p=0.12 and OR=1.25, 95% CI: 0.92-1.70, p=0.15, respectively). In Kenya, odds of IM, though nonsignificant, were lower for women who used birth attendants than those who used health professionals to assist with delivery (OR=0.85, 95% CI: 0.51-1.41, p=0.46), and higher with delivery at a health facility versus a household residence (OR=1.29, 95% CI: 0.81-2.03, p=0.28). Conclusions: Women in Ghana and Sierra Leone who delivered at a health facility had statistically significant increased odds of IM. Birth attendant type-IM associations were not statistically significant.Future research should consider culturally-sensitive interventions to improve maternal health and help reduce IM

Association between community health center and rural health clinic presence and county-level hospitalization rates for ambulatory care sensitive conditions: an analysis across eight US states

<p>Abstract</p> <p>Background</p> <p>Federally qualified community health centers (CHCs) and rural health clinics (RHCs) are intended to provide access to care for vulnerable populations. While some research has explored the effects of CHCs on population health, little information exists regarding RHC effects. We sought to clarify the contribution that CHCs and RHCs may make to the accessibility of primary health care, as measured by county-level rates of hospitalization for ambulatory care sensitive (ACS) conditions.</p> <p>Methods</p> <p>We conducted an ecologic analysis of the relationship between facility presence and county-level hospitalization rates, using 2002 discharge data from eight states within the US (579 counties). Counties were categorized by facility availability: CHC(s) only, RHC(s) only, both (CHC and RHC), and neither. US Agency for Healthcare Research and Quality definitions were used to identify ACS diagnoses. Discharge rates were based on the individual's county of residence and were obtained by dividing ACS hospitalizations by the relevant county population. We calculated ACS rates separately for children, working age adults, and older individuals, and for uninsured children and working age adults. To ensure stable rates, we excluded counties having fewer than 1,000 residents in the child or working age adult categories, or 500 residents among those 65 and older. Multivariate Poisson analysis was used to calculate adjusted rate ratios.</p> <p>Results</p> <p>Among working age adults, rate ratio (RR) comparing ACS hospitalization rates for CHC-only counties to those of counties with neither facility was 0.86 (95% Confidence Interval, CI, 0.78–0.95). Among older adults, the rate ratio for CHC-only counties compared to counties with neither facility was 0.84 (CI 0.81–0.87); for counties with both CHC and RHC present, the RR was 0.88 (CI 0.84–0.92). No CHC/RHC effects were found for children. No effects were found on estimated hospitalization rates among uninsured populations.</p> <p>Conclusion</p> <p>Our results suggest that CHCs and RHCs may play a useful role in providing access to primary health care. Their presence in a county may help to limit the county's rate of hospitalization for ACS diagnoses, particularly among older people.</p

Reasons Why Women Do Not Initiate Breastfeeding: A Southeastern State Study

Purpose - Despite the increase in breastfeeding initiation and duration in the United States, only five states have met the three Healthy People 2010 breastfeeding objectives. Our objectives are to study women\u27s self-reported reasons for not initiating breastfeeding and to determine whether these reasons vary by race/ethnicity, and other maternal and hospital support characteristics. Methods - Data are from the 2000-2003 Arkansas Pregnancy Risk Assessment Monitoring System, restricting the sample to women who did not initiate breastfeeding (unweighted n = 2,917). Reasons for not initiating breastfeeding are characterized as individual reasons, household responsibilities, and circumstances. Analyses include the χ2 test and multiple logistic regression. Results - About 38% of Arkansas mothers of live singletons did not initiate breastfeeding. There was a greater representation of non-Hispanic Blacks among those who did not initiate breastfeeding (32%) than among those who initiated breastfeeding (9.9%). Among those who never breastfed, individual reasons were most frequently cited for noninitiation (63.0%). After adjusting for covariates, Hispanics had three times the odds of citing circumstances than Whites (odds ration [OR], 3.07; 95% confidence interval [CI], 1.31-7.18). Women who indicated that the hospital staff did not teach them how to breastfeed had more than two times greater odds of citing individual reasons (OR, 2.25; 95% CI, 1.30-3.91) or reasons related to household responsibilities (OR, 2.27; 95% CI, 1.19-4.36) as compared with women who indicated they were taught. Conclusions - Findings suggest the need for targeting breastfeeding interventions to different subgroups of women. In addition, there are implications for policy particularly regarding breastfeeding support in hospitals

Type 2 diabetes, socioeconomic status and life expectancy in Scotland (2012-2014):a population-based observational study

Aims/hypothesis: The aim of this study was to assess the role of socioeconomic status (SES) in the associations between type 2 diabetes and life expectancy in a complete national population. Methods: An observational population-based cohort study was performed using the Scottish Care Information – Diabetes database. Age-specific life expectancy (stratified by SES) was calculated for all individuals with type 2 diabetes in the age range 40–89 during the period 2012–2014, and for the remaining population of Scotland aged 40–89 without type 2 diabetes. Differences in life expectancy between the two groups were calculated. Results: Results were based on 272,597 individuals with type 2 diabetes and 2.75 million people without type 2 diabetes (total for 2013, the middle calendar year of the study period). With the exception of deprived men aged 80–89, life expectancy in people with type 2 diabetes was significantly reduced (relative to the type 2 diabetes-free population) at all ages and levels of SES. Differences in life expectancy ranged from −5.5 years (95% CI −6.2, −4.8) for women aged 40–44 in the second most-deprived quintile of SES, to 0.1 years (95% CI −0.2, 0.4) for men aged 85–89 in the most-deprived quintile of SES. Observed life-expectancy deficits in those with type 2 diabetes were generally greater in women than in men. Conclusions/interpretation: Type 2 diabetes is associated with reduced life expectancy at almost all ages and levels of SES. Elimination of life-expectancy deficits in individuals with type 2 diabetes will require prevention and management strategies targeted at all social strata (not just deprived groups)

Effects of residence and race on burden of travel for care: cross sectional analysis of the 2001 US National Household Travel Survey

BACKGROUND: Travel burden is a key element in conceptualizing geographic access to health care. Prior research has shown that both rural and minority populations bear disproportionate travel burdens. However, many studies are limited to specific types of patient or specific locales. The purpose of our study was to quantify geographic and race-based differences in distance traveled and time spent in travel for medical/dental care using representative national data. METHODS: Data were drawn from 2001 National Household Travel Survey (NHTS), a nationally representative, cross-sectional household survey conducted by the US Department of Transportation. Participants recorded all travel on a designated day; the overall response rate was 41%. Analyses were restricted to households reporting at least one trip for medical and/or dental care; 3,914 trips made by 2,432 households. Dependent variables in the analysis were road miles traveled, minutes spent traveling, and high travel burden, defined as more than 30 miles or 30 minutes per trip. Independent variables of interest were rural residence and race. Characteristics of the individual, the trip, and the community were controlled in multivariate analyses. RESULTS: The average trip for care in the US in 2001 entailed 10.2 road miles (16.4 kilometers) and 22.0 minutes of travel. Rural residents traveled further than urban residents in unadjusted analysis (17.5 versus 8.3 miles; 28.2 versus 13.4 km). Rural trips took 31.4% longer than urban trips (27.2 versus 20.7 minutes). Distance traveled did not vary by race. African Americans spent more time in travel than whites (29.1 versus 20.6 minutes); other minorities did not differ. In adjusted analyses, rural residence (odds ratio, OR, 2.67, 95% confidence interval, CI 1.39 5.1.5) was associated with a trip of 30 road miles or more; rural residence (OR, 1.80, CI 1.09 2.99) and African American race/ethnicity (OR 3.04. 95% CI 2.0 4.62) were associated with a trip lasting 30 minutes or longer. CONCLUSION: Rural residents and African Americans experience higher travel burdens than urban residents or whites when seeking medical/dental care