The effects on population health status of using dedicated property taxes to fund local public health agencies

<p>Abstract</p> <p>Background</p> <p>In the United States, a dedicated property tax describes the legal authority given to a local jurisdiction to levy and collect a tax for a specific purpose. We investigated for an association of locally dedicated property taxes to fund local public health agencies and improved health status in the eight states designated as the Mississippi Delta Region.</p> <p>Methods</p> <p>We analyzed the difference in health outcomes of counties with and without a dedicated public health tax after adjusting for a set of control variables using regression models for county level data from 720 counties of the Mississippi Delta Region.</p> <p>Results</p> <p>Levying a dedicated public health tax for counties with per capita income above $28,000 is associated with improved health outcomes of those counties when compared to counties without a dedicated property tax for public health. Alternatively, levying a dedicated property tax in counties with lower per capita income is associated with poor health outcomes.</p> <p>Conclusions</p> <p>There are both positive and negative consequences of using dedicated property taxes to fund public health. Policymakers should carefully examine both the positive association of improved health outcomes and negative impact of taxation on poor populations before authorizing the use of dedicated local property tax levies to fund public health agencies.</p

What Can a Pilot Congestive Heart Failure Disease Management Program Tell Us about Likely Return on Investment?: A Case Study from a Program Offered to Federal Employees

In 1999, the Blue Cross and Blue Shield Federal Employee Program (FEP) implemented a pilot disease management program to manage congestive heart failure (CHF) among members. The purpose of this project was to estimate the financial return on investment in the pilot CHF program, prior to a full program rollout. A cohort of 457 participants from the state of Maryland was matched to a cohort of 803 nonparticipants from a neighboring state where the CHF program was not offered. Each cohort was followed for 12 months before the program began and 12 months afterward. The outcome measures of primary interest were the differences over time in medical care expenditures paid by FEP and by all payers. Independent variables included indicators of program participation, type of heart disease, comorbidity measures, and demographics. From the perspective of the funding organization (FEP), the estimated return on investment for the pilot CHF disease management program was a savings of $1.08 in medical expenditure for every dollar spent on the program. Adding savings to other payers as well, the return on investment was a savings of$1.15 in medical expenditures per dollar spent on the program. The amount of savings depended upon CHF risk levels. The value of a pilot initiative and evaluation is that lessons for larger-scale efforts can be learned prior to full-scale rollout. (Disease Management 2005;8:346-360)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63402/1/dis.2005.8.346.pd

Demographic and indication-specific characteristics have limited association with social network engagement: evidence from 24,954 members of four health care support groups

Background: Digital health social networks (DHSNs) are widespread, and the consensus is that they contribute to wellness by offering social support and knowledge sharing. The success of a DHSN is based on the number of participants and their consistent creation of externalities through the generation of new content. To promote network growth, it would be helpful to identify characteristics of superusers or actors who create value by generating positive network externalities. Objective: The aim of the study was to investigate the feasibility of developing predictive models that identify potential superusers in real time. This study examined associations between posting behavior, 4 demographic variables, and 20 indication-specific variables. Methods: Data were extracted from the custom structured query language (SQL) databases of 4 digital health behavior change interventions with DHSNs. Of these, 2 were designed to assist in the treatment of addictions (problem drinking and smoking cessation), and 2 for mental health (depressive disorder, panic disorder). To analyze posting behavior, 10 models were developed, and negative binomial regressions were conducted to examine associations between number of posts, and demographic and indication-specific variables. Results: The DHSNs varied in number of days active (3658-5210), number of registrants (5049-52,396), number of actors (1085-8452), and number of posts (16,231-521,997). In the sample, all 10 models had low R2 values (.013-.086) with limited statistically significant demographic and indication-specific variables. Conclusions: Very few variables were associated with social network engagement. Although some variables were statistically significant, they did not appear to be practically significant. Based on the large number of study participants, variation in DHSN theme, and extensive time-period, we did not find strong evidence that demographic characteristics or indication severity sufficiently explain the variability in number of posts per actor. Researchers should investigate alternative models that identify superusers or other individuals who create social network externalities

Health care expenditure disparities in the European Union and underlying factors: a distribution dynamics approach

This paper examines health care expenditure (HCE) disparities between the European Union countries over the period 1995-2010. By means of using a continuous version of the distribution dynamics approach, the key conclusions are that the reduction in disparities is very weak and, therefore, persistence is the main characteristic of the HCE distribution. In view of these findings, a preliminary attempt is made to add some insights into potentially main factors behind the HCE distribution. The results indicate that whereas per capita income is by far the main determinant, the dependency ratio and female labour participation do not play any role in explaining the HCE distribution; as for the rest of the factors studied (life expectancy, infant mortality, R&D expenditure and public HCE expenditure share), we find that their role falls somewhat in between