Medicaid DSRIP in New Jersey: Trade-offs between Broad Hospital Participation and Safety Net Viability.

The Delivery System Reform Incentive Payment (DSRIP) program, an increasingly utilized payment strategy to foster population health management by hospitals and outpatient providers, may sometimes generate financial and operational hardships for safety net hospitals (SNHs). The authors utilized a hospital survey and stakeholder interviews to examine impacts of the New Jersey DSRIP program, particularly focusing on its participatory structure that extended eligibility to all hospitals, and specific effects on SNHs. They found that the New Jersey DSRIP fulfilled its primary objective of conditioning receipt of Medicaid supplementary payments on quality and reporting of care by hospitals. It also provided an impetus to ongoing hospital-directed initiatives and introduced new areas of focus, including behavioral health and obesity. However, stakeholders reported that program implementation was not sensitive to specific constraints, priorities, and resource needs of SNHs. Some of the policies relating to outpatient partnerships, reporting of quality metrics, and monitoring low-income populations were perceived to have placed disproportionate burdens on SNHs. Despite appearing to meet its primary goals, the New Jersey DSRIP experience reveals a critical need to be responsive to problems faced by SNHs so as to limit their short-term transition costs and maintain financial viability for serving their patient populations.

Implications of the growing use of wireless telephones for health care opinion polls.

OBJECTIVE: To assess the effect of wireless telephone substitution in a survey of health care reform opinions. DATA SOURCE: Survey of New Jersey adults conducted by landline and wireless telephones from June 1 to July 9, 2007. STUDY DESIGN: Eighty-one survey measures are compared by wireless status. Logistic regression is used to confirm landline-wireless gaps in support for coverage reforms, controlling for population differences. Weights adjust for selection probability, complex sample design, and demographic distributions. PRINCIPAL FINDINGS: Significant differences by wireless status were found in many survey measures. Wireless users were significantly more likely to favor coverage reforms. Higher support for government-sponsored universal coverage, income-related state coverage subsidies, and an individual mandate remain after adjustment for demographic variables. CONCLUSIONS: Opinion polls excluding wireless users are likely to understate support for coverage reforms.

The adequacy of household survey data for evaluating the nongroup health insurance market.

OBJECTIVE: To evaluate the accuracy of household survey estimates of the size and composition of the nonelderly population covered by nongroup health insurance. DATA SOURCES/STUDY SETTING: Health insurance enrollment statistics reported to New Jersey insurance regulators. Household data from the following sources: the 2002 Current Population Survey (CPS)-March Demographic Supplement, the 1997 and 1999 National Surveys of America's Families (NSAF), the 2001 New Jersey Family Health Survey (NJFHS), a 2002 survey of known nongroup health insurance enrollees, a small 2004 survey testing alternative health insurance question wording. STUDY DESIGN: To assess the extent of bias in estimates of the size of the nongroup health insurance market in New Jersey, enrollment trends are compared between official enrollment statistics reported by insurance carriers to state insurance regulators with estimates from three general population household surveys. Next, to evaluate possible bias in the demographic and socioeconomic composition of the New Jersey nongroup market, distributions of characteristics of the enrolled population are contrasted among general household surveys and a survey of known nongroup subscribers. Finally, based on inferences drawn from these comparisons, alternative health insurance question wording was developed and tested in a local survey to test the potential for misreporting enrollment in nongroup coverage in a low-income population. DATA COLLECTION/EXTRACTION METHODS: Data for nonelderly New Jersey residents from the 2002 CPS (n=5,028) and the 1997 and 1999 NSAF (n=6,467 and 7,272, respectively) were obtained from public sources. The 2001 NJFHS (n=5,580 nonelderly) was conducted for a sample drawn by random digit dialing and employed computer-assisted telephone interviews and trained, professional interviewers. Sampling weights are used to adjust for under-coverage of households without telephones and other factors. In addition, a modified version of the NJFHS was administered to a 2002 sample of known nongroup subscribers (n=1,398) using the same field methods. These lists were provided by four of the five largest New Jersey nongroup insurance carriers, which represented 95 percent of all nongroup enrollees in the state. Finally, a modified version of the NJFHS questionnaire was fielded using similar methods as part of a local health survey in New Brunswick, New Jersey, in 2004 (n=1,460 nonelderly). PRINCIPAL FINDINGS: General household sample surveys, including the widely used CPS, yield substantially higher estimates of nongroup enrollment compared with administrative totals and yield estimates of the characteristics of the nongroup population that vary greatly from a survey of known nongroup subscribers. A small survey testing a question about source of payment for direct-purchased coverage suggests than many public coverage enrollees report nongroup coverage. CONCLUSIONS: Nongroup health insurance has been subject to more than a decade of reform and is of continuing policy interest. Comparisons of unique data from a survey of known nongroup subscribers and administrative sources to household surveys strongly suggest that the latter overstates the number and misrepresent the composition of the nongroup population. Research on the nongroup market using available sources should be interpreted cautiously and survey methods should be reexamined.