Data Decision Research Articles

Using the Concept of Risk to Plan Drug Use Intervention Programs

The concept of risk is integral to the field of epidemiology. Risk can be defined as the probability of an event (including drug use) occurring in a population of interest. If drug prevention and intervention programs are to be successful, they must be targeted at high-risk population groups. This article proposes a two-step, multilevel, multivariate analysis strategy to estimate high-risk population groups in high-risk geographic areas. Its successful use could result in better data for decision makers to make informed judgments about developing intervention programs.

Decisional data and the principle of maximum efficiency estimation

In 1978 Charnes, Cooper, and Rhodes introduced Data Envelopment Analysis (DEA) as a method to rate the efficiencies of decision making units by imputing weights to observed inputs and outputs in order to form an efficiency ratio. Based on this work, a three step procedure is presented which may be used to estimate the values of parameters such as unknown costs. In the presentation a method called the Principle of Maximum Efficiency Estimation (MEE) is introduced. An application to the estimation of inventory cost parameters is given.

The First Year of Life

<h3>ABSTRACT</h3> <h3>Background:</h3> Three African Health Initiative (AHI) partnership projects in Ethiopia, Ghana, and Mozambique implemented strategies to improve the quality and evaluation of routinely collected data at the primary health care level and stimulate its use in evidence-based decision making. We compare how these programs designed and carried out data for decision-making (DDM) strategies, elaborate on barriers and facilitators to implementation success, and offer recommendations for future DDM programming. <h3>Methods:</h3> Researchers from each project collaboratively wrote a cross-country protocol based on these objectives. By adapting the Consolidated Framework for Implementation Research (CFIR) through a qualitative theme reduction process, they harmonized lines of inquiry on the design of the respective DDM strategies and the barriers and facilitators of effective implementation. We conducted in-depth interviews and focus group discussions with stakeholders from the primary health care systems in each country, and we carried out multistage, thematic analyses using a deductive lens. <h3>Results:</h3> Effective implementation of DDM depended on whether implementers felt that DDM was adaptable to context, feasible to trial, and easy to introduce and maintain. The prevailing policy and political environment in the wider health system, learning climate and absorptive capacity for evidence-based change in DDM settings, engagement of external change agents and internal change leaders, and promotion of opportunities and means for team-based reflection and evaluations of what works influenced the success or failure of DDM strategies. <h3>Conclusion:</h3> Opportunities for team-based capacity building and individual mentorship led to effective DDM programming. External policies and associated incentives bolstered this but occasionally led to unintended consequences. Leadership engagement and availability of resources to act on recommendations; respond to capacity-building needs; and facilitate collaborations between peers, within hierarchies, and across the local health system proved crucial to DDM, as was encouraging adaptation and opportunities for iterative on-the-job learning.

Decision Makers' Judgments

Evaluators often assume that outcome studies assessing agency effectiveness should pro vide the most relevant data for decision makers who must form judgments about treat ment in order to make policy, program, and clinical decisions. Yet evaluators have found that decision makers often fail to use results of evaluation studies. To shed light on the utilization problem, the research reported here was undertaken to learn about the criteria, information sources, and beliefs decision makers in the environment of nine drug treat ment agencies used to form judgments about treatment programs before evaluative data concerning outcomes became available. The results showed that decision makers in different occupations tended to use similar information sources (primarily informal verbal exchanges) yet used different evaluative criteria to judge treatment programs. The impli clations of these results for the design of evaluation studies and utilization of evaluation results are discussed.