In this paper, we propose a Bayesian factor analysis model with the purpose of serving as an alternate approach to calculating the UNDP's Human Development Index, as well as providing a general methodology which can be used to augment existing indices or build new ones. In addition to addressing several potential issues of the official HDI, we also estimate an alternative "green HDI" index by adding a new environmental variable, and build a novel MDG index as an example of constructing a new index with a more complex variable structure. Under our methodology, we find the "living standard" dimension provides a greater proportional contribution to human development than it is assigned by the official HDI while the "longevity" dimension provides a lower proportional contribution. The results also show considerable levels of general disagreement when compared to the ranks of the official HDI. We show that incorporating an environmental variable increases the amount of disagreement between model based ranks and the official HDI, but decreases the amount of uncertainty associated with model ranks. In addition, we report the sensitivity of our methods to the choice of functional form and data imputation procedures.
Both the prevalence and importance of rankings have grown considerably in cases where a characteristic cannot be captured using a single variable. A prime candidate for the use of rankings is county-level health in the United States. Given county health's multidimensional nature, researchers, policymakers, and other stakeholders regularly use rankings to combine information from multiple sources into a single outcome. The most commonly used county health rankings (CHRs) are the University of Wisconsin Population Health Institute's (UWPHI's) CHRs.Since 2010, the CHRs have been used to allocate scarce resources, design health policy, and demonstrate need for health improving interventions. In 2012, the San Bernardino County Health department used the CHRs to support their Healthy Communities initiatives program, offering $100,000 dollar seed grants to participating counties (County Health Rankings and Roadmaps, 2012a). In Lima Ohio, Active Allen County relied on the CHRs to engage with the Ohio Department of Public Health and local stakeholders to improve health in their county, receiving a $1.2 million Community Transformation grant from the Centers for Disease Control and Prevention (County Health Rankings and Roadmaps, 2013). The Greater Flint Health Coalition implemented a 10-year County Health Rankings Action Plan to
an anonymous reviewer, and the participants of The 2nd International Workshop in Memory of Yair Mundlak for their valuable comments and suggestions. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
Background: The COVID-19 pandemic has led to disruptions in the provision of care at substance use disorder (SUD) treatment facilities. Stresses associated with the pandemic could also negatively impact treatment outcomes for clients. The aim of this study is to evaluate how SUD treatment facilities in Mississippi changed their operations following the start of the pandemic. The change in client success rates at the facilities is also assessed. Methods: An online survey was completed by 12 SUD treatment facilities in Mississippi between February and May 2021. Results: Generally, the facilities’ capacity to provide treatment to clientele was moderately affected by the pandemic. Facilities in the sample also adapted a variety of policies to limit the spread of COVID-19. Changes in the services provided by facilities was observed in the survey responses. For client success rates reported by the facilities, there was a decrease in the number of facilities stating that more than 80% of their clients completed treatment across the pre- and post-pandemic periods. However, the number of facilities with more than 80% of their clients successfully finishing treatment has increased in recent months. Conclusions: To continue serving their clientele during the pandemic, facilities enacted COVID-19-related policies and began offering new services such as telehealth. Although client success rates decreased at the beginning of the pandemic, they have returned to pre-pandemic levels in recent months. Our results indicate that SUD treatment facilities and clients have improved in terms of giving and receiving care as the pandemic has progressed.
We would like to thank Charles Courtemanche, Ian McDonough, Erik Nesson, workshop participants at IUPUI, and participants at the 2019 Western Economics Association International annual conference. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
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