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Purpose: Mortality data are important tools for research requiring vital status information. We reviewed the major mortality databases and mortality ascertainment services available in the United States, including the National Death Index (NDI), the Social Security Administration (SSA) files, and the Department of Veterans Affairs databases.Methods: The content, reliability, and accuracy of mortality sources are described and compared. We also describe how investigators can gain access to these resources and provide further contact information.Results: We reviewed the accuracy of major mortality sources. The sensitivity (i.e., the proportion of the true number of deaths) of the NDI ranged from 87.0% to 97.9%, whereas the sensitivity for the VA Beneficiary Identification and Records Locator System (BIRLS) ranged between 80.0% and 94.5%. The sensitivity of SSA files ranged between 83.0% and 83.6%. Sensitivity for the VA Patient Treatment File (PTF) was 33%.Conclusions: While several national mortality ascertainment services are available for vital status (i.e., death) analyses, the NDI information demonstrated the highest sensitivity and, currently, it is the only source at the national level with a cause of death field useful for research purposes. Researchers must consider methods used to ascertain vital status as well as the quality of the information in mortality databases. [ABSTRACT FROM AUTHOR]

Context: Although home-based health care has grown over the past decade, its effectiveness remains controversial. A prior trial of Veterans Affairs (VA) Team-Managed Home-Based Primary Care (TM/HBPC) found favorable outcomes, but the replicability of the model and generalizability of the findings are unknown.Objectives: To assess the impact of TM/HBPC on functional status, health-related quality of life (HR-QoL), satisfaction with care, and cost of care.Design and Setting: Multisite randomized controlled trial conducted from October 1994 to September 1998 in 16 VA medical centers with HBPC programs.Participants: A total of 1966 patients with a mean age of 70 years who had 2 or more activities of daily living impairments or a terminal illness, congestive heart failure (CHF), or chronic obstructive pulmonary disease (COPD). Intervention Home-based primary care (n=981), including a primary care manager, 24-hour contact for patients, prior approval of hospital readmissions, and HBPC team participation in discharge planning, vs customary VA and private sector care (n=985).Main Outcome Measures: Patient functional status, patient and caregiver HR-QoL and satisfaction, caregiver burden, hospital readmissions, and costs over 12 months.Results: Functional status as assessed by the Barthel Index did not differ for terminal (P=.40) or nonterminal (those with severe disability or who had CHF or COPD) (P=.17) patients by treatment group. Significant improvements were seen in terminal TM/HBPC patients in HR-QoL scales of emotional role function, social function, bodily pain, mental health, vitality, and general health. Team-Managed HBPC nonterminal patients had significant increases of 5 to 10 points in 5 of 6 satisfaction with care scales. The caregivers of terminal patients in the TM/HBPC group improved significantly in HR-QoL measures except for vitality and general health. Caregivers of nonterminal patients improved significantly in QoL measures and reported reduced caregiver burden (P=.008). Team-Managed HBPC patients with severe disability experienced a 22% relative decrease (0.7 readmissions/patient for TM/HBPC group vs 0.9 readmissions/patient for control group) in hospital readmissions (P=.03) at 6 months that was not sustained at 12 months. Total mean per person costs were 6.8% higher in the TM/HBPC group at 6 months ($19190 vs $17971) and 12.1% higher at 12 months ($31401 vs $28008).Conclusions: The TM/HBPC intervention improved most HR-QoL measures among terminally ill patients and satisfaction among non-terminally ill patients. It improved caregiver HR-QoL, satisfaction with care, and caregiver burden and reduced hospital readmissions at 6 months, but it did not substitute for other forms of care. The higher costs of TM/HBPC should be weighed against these benefits. [ABSTRACT FROM AUTHOR]

This paper describes the Department of Veterans Affairs (VA) home care program and compares it to community-based programs. Structure and process data were collected on hospital based home care programs in VA facilities nationwide (n=75). Supplemental data were obtained on staffing and patient attributes. Although the VA provides program guidelines, some variability was noted. The characteristics of VA programs and patients were then compared to National Center for Health Statistics survey data. This comparison revealed that VA programs provide a more comprehensive array of services to patients including physician home visits than most community-based programs. [ABSTRACT FROM AUTHOR]

Objectives: To examine the role of physicians in the Veteran Affairs (VA) home-based primary care (HBPC) program and to identify variables that predict whether physicians make home visits and volume of home visits made., Design: Descriptive and regression analyses of responses from a mail survey., Participants: Forty-five physicians affiliated with VA HBPC programs. MAIN SURVEY TOPICS: Self-reported work load, attitudes toward home care, reasons for home visits, administrative policies regarding physicians' role in patient care management, and time commitment to home care., Results: A majority of physicians believed strongly in the importance of home care and made home visits for reasons consistent with their training. Physician attitude toward home care and preoccupation with office or hospital practice were related to whether or not physicians made home visits. Degree of preoccupation with office practice and amount of salary support from VA HBPC were significant predictors of the number of visits made (R2 = 0.44)., Conclusions: These findings indicate that most physicians will make home visits if they believe that home care is valuable and if their time commitment is supported financially. Managed care plans that own and operate home care programs and have the capacity to transfer primary care management to physicians who derive financial support from the programs should find this information particularly relevant.

Objectives: This study examines home medical equipment (HME) receipt for 1,040 veterans considered appropriate for home health services., Methods: HME receipt was monitored for 12 months using the Department of Veterans Affairs' Prosthetics database., Results: Eighty-three percent received at least one item; averaging 7.4 items (SD = 6.8). The most common items included commodes/bath benches (9%), canes/walkers (7%), safety equipment (7%), liquid oxygen (6%), and wheelchairs (6%). Two functional status variables, home care use and race, correctly classified 69% of HME recipients. Logistic regressions were run for specific equipment; c-indices ranged from .64 to .75. Age, race, income, functional status, risk of hospital readmission, and home care use were significant predictors., Discussion: HME accounted for $4.5 billion in sales (16% of total) for medical products in 1996. As the HME market continues to expand, the characteristics of HME recipients are necessary to project future HME needs in a growing, elderly population.

This study tested the cross-sectional relationship between caregiver burden and health-related quality of life (HRQOL) among 1,594 caregivers of veterans identified to qualify for formal home care. A two-stage model found that familial relationship, coresidence, and low income predicted objective burden. Coresidence also predicted subjective burden, whereas being African American was protective. In the full model, spousal relationship, low income, and burden were associated with poor HRQOL scores. Total variance explained in HRQOL ranged from 14% to 29%, with objective burden contributing more than subjective burden. These findings suggest a direct effect of objective burden on caregiver HRQOL, indicating a need among caregivers for assistance in caring for disabled family members.

The U.S. Department of Veterans Affairs (VA) operates and maintains one of the largest health care systems under a single management structure in the world. The coordination of administrative and clinical information on veterans served by the VA health care system is a daunting and critical function of the Department. This article provides an overview of VA Health Services Research and Development Service initiatives to assist researchers in using extant VA databases to study patient-centered health care outcomes. As examples, studies using the VA's Patient Treatment File (PTF) and the Beneficiary Identification and Records Locator System (BIRLS) Death File are described.

Objective: To examine the impact of home care on hospital days., Data Sources: Search of automated databases covering 1964-1994 using the key words "home care," "hospice," and "healthcare for the elderly." Home care literature review references also were inspected for additional citations., Study Selection: Of 412 articles that examined impact on hospital use/cost, those dealing with generic home care that reported hospital admissions/cost and used a comparison group receiving customary care were selected (N = 20)., Study Design: A meta-analytic analysis used secondary data sources between 1967 and 1992., Data Extraction: Study characteristics that could have an impact on effect size (i.e., country of origin, study design, disease characteristics of study sample, and length of follow-up) were abstracted and coded to serve as independent variables. Available statistics on hospital days necessary to calculate an effect size were extracted. If necessary information was missing, the authors of the articles were contacted., Methods: Effect sizes and homogeneity of variance measures were calculated using Dstat software, weighted for sample size. Overall effect sizes were compared by the study characteristics described above., Principal Findings: Effect sizes indicate a small to moderate positive impact of home care in reducing hospital days, ranging from 2.5 to 6 days (effect sizes of -.159 and -.379, respectively), depending on the inclusion of a large quasi-experimental study with a large treatment effect. When this outlier was removed from analysis, the effect size for studies that targeted terminally ill patients exclusively was homogeneous across study subcategories; however, the effect size of studies that targeted nonterminal patients was heterogeneous, indicating that unmeasured variables or interactions account for variability., Conclusion: Although effect sizes were small to moderate, the consistent pattern of reduced hospital days across a majority of studies suggests for the first time that home care has a significant impact on this costly outcome.

This paper is an overview of hospital closure in the United States for the five-year period 1976-1980. We describe the distributional patterns of closings among noncommunity and community hospitals classified according to institutional characteristics such as bed size, control, and location. We also examine the ten percent of community hospitals operating at the beginning of the period which were shown to have combined institutional characteristics strongly associated with closure via a method of regression analysis.

Examination of the distributional patterns and frequencies of the closings and resulting bed losses that occurred in 1977-1981 among urban community hospitals shows that these hospitals are more subject to closure than their non-urban counterparts. Small hospitals, investor-owned hospitals, and hospitals located in very large urban areas in the Pacific and Middle Atlantic regions are especially vulnerable.

Closure of U.S. community hospitals during the years 1976-1980 is analyzed by describing the distributional patterns of closings among hospitals classified by certain institutional characteristics: bed size, number of facilities and services, ownership, teaching status, location in a Standard Metropolitan Statistical Area (SMSA) or in a non-SMSA, and location in one of the nine United States Census Divisions. The method of Cox regression analysis is used to show that each of these institutional characteristics is independently, and significantly, associated with closure, and to estimate the strength of the association. On the basis of this analysis, a theoretic model is constructed for estimating the relative probability of closure for any community hospital with a given set of these institutional characteristics.