This study examined the predictors of total nurse and registered nurse (RN) staffing hours per resident day separately in all free-standing California nursing homes (1,555), using staffing data from state cost reports in 1999. This study used a two-stage least squares model, taking into account nursing turnover rates, resident case mix levels, and other factors. As expected, total nurse and RN staffing hours were negatively associated with nurse staff turnover rates and positively associated with resident case mix. Facilities were resource dependent in that a high proportion of Medicare residents predicted higher staffing hours, and a higher proportion of Medicaid residents predicted lower staffing hours and higher turnover rates. Nursing assistant wages were positively associated with total nurse staffing hours. For-profit facilities and high-occupancy rate facilities had lower total nurse and RN staffing hours. Medicaid reimbursement rates and multifacility organizations were positively associated with RN staffing hours.

Objective. To examine the relationship between nursing staffing levels in U.S. nursing homes and state Medicaid reimbursement rates. Data Sources. Facility staffing, characteristics, and case-mix data were from the federal On-Line Survey Certification and Reporting (OSCAR) system and other data were from public sources. Study Design. Ordinary least squares and two-stage least squares regression analyses were used to separately examine the relationship between registered nurse (RN) and total nursing hours in all U.S. nursing homes in 2002, with two endogenous variables: Medicaid reimbursement rates and resident case mix. Principal Findings. RN hours and total nursing hours were endogenous with Medicaid reimbursement rates and resident case mix. As expected, Medicaid nursing home reimbursement rates were positively related to both RN and total nursing hours. Resident case mix was a positive predictor of RN hours and a negative predictor of total nursing hours. Higher state minimum RN staffing standards was a positive predictor of RN and total nursing hours while for-profit facilities and the percent of Medicaid residents were negative predictors. Conclusions. To increase staffing levels, average Medicaid reimbursement rates would need to be substantially increased while higher state minimum RN staffing standards is a stronger positive predictor of RN and total nursing hours.

A two-group randomized clinical trial was used to test the hypothesis that patients with myocardial infarction (MI) who receive both written instructions and a videotape to view at home will have greater knowledge, better quality of life, less anxiety, greater sexual satisfaction, and will resume sexual activity more quickly than will those who receive written instructions alone. The participants, 115 patients diagnosed with an MI, were pretested in the hospital and followed at home at 1, 3, and 5 months. The intervention was an educational videotape on return to sexual activity. Significant improvements in knowledge were found for the experimental group at 1 month. The videotape intervention provides an alternative method for education to facilitate recovery post-MI.

Trends in the average nurse staffing levels are reported for certified nursing facilities in the United States from 1991 through 1995. Data from the federal On-Line Survey Certification and Reporting system show a small overall increase in the staffing levels for registered nurses (RNs), licensed vocational and licensed practical nurses (LVNs/LPNs), and nursing assistants over the 5 years, but there are substantial variations across states and regions. A two-stage least squares panel analysis examined predictors of nurse staff levels in states. States with higher resident case mix levels had higher RN and LVN/LPN hours. States with higher percentages of large facilities had lower RN and LVN/LPN levels and states with higher percentages of for-profit facilities had lower RN staff levels. States with a higher percentage of Medicaid residents had higher LVN/LPN staff levels. These findings indicate the need for more studies of staff variations and public policies that affect staffing.