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BACKGROUND: Micro-premature newborns, gestational age (GA) ≤ 25 weeks, have high rates of mortality and morbidity. Literature has shown improving outcomes for extremely low gestational age newborns (ELGANs) GA ≤ 29 weeks, but few studies have addressed outcomes of ELGANs ≤ 25 weeks. OBJECTIVE: To evaluate the trends in outcomes for ELGANs born in New Jersey, from 2000 to 2018 and to compare two subgroups: GA 23 to 25 weeks (E1) and GA 26 to 29 weeks (E2). METHODS: Thirteen NICUs in NJ submitted de-identified data. Outcomes for mortality and morbidity were calculated. RESULTS: Data from 12,707 infants represents the majority of ELGANs born in NJ from 2000 to 2018. There were 3,957 in the E1 group and 8,750 in the E2 group. Mortality decreased significantly in both groups; E1, 43.2% to 30.2% and E2, 7.6% to 4.5% over the 19 years. The decline in E1 was significantly greater than in E2. Most morbidities also showed significant improvement over time in both groups. Survival without morbidity increased from 14.5% to 30.7% in E1s and 47.2% to 69.9% in E2s. Similar findings held for 501-750 and 751-1000g birth weight strata. CONCLUSIONS: Significant declines in both mortality and morbidity have occurred in ELGANs over the last two decades. These rates of improvements for the more immature ELGANs of GA 230 to 256 weeks were greater than for the more mature group in several outcomes. While the rates of morbidity and mortality remain high, these results validate current efforts to support the micro-premature newborn.

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Many of the failures to replicate clinical findings of treatment efficacy in more realistic field and community settings can be attributed to inappropriate research designs and other methodological shortcomings. In order to increase research designers' awareness of existing methodologies that may be better suited to answer the critical questions inherent in health services research on alcohol-related issues, the National Institute on Alcohol Abuse and Alcoholism (NIAAA) convened an expert conference with three specific goals; (1) to identify the critical issues involved in alcohol services research; (2) to develop a primer that explicated each key area; and (3) to compile the resulting primers into an accessible resource for researchers, policy makers and consumers. The 9 papers in this special supplement are the product of that conference and are organized broadly around three phases of the research process: study design and implementation, data collection and use, and the analysis and interpretation of data. A final summary paper discusses the issues and offers a synthesis of key themes as well as some direction for the future.

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While some aspects of addiction can be studied in laboratory or controlled settings, the study of long-term recovery management and the health services that support it requires going out into the community and dealing with populations and systems that are much more diverse and less under our control. This in turn raises many methodological challenges for the health service researchers studying alcohol and other drug abuse treatment. This paper identifies some of these challenges related to the design, measurement, implementation and effectiveness of health services research. It then recommends 25 strategies (and key primers) for addressing them: (1) identifying in advance all stakeholders and issues; (2) developing conceptual models of intervention and context; (3) identifying the population to whom the conclusions will be generalized; (4) matching the research design to the question; (5) conducting randomized experiments only when appropriate and necessary; (6) balancing methodological and treatment concerns; (7) prioritizing analysis plans and increasing design sensitivity, (8) combining qualitative and quantitative methods; (9) identifying the four basic types of measures needed; (10) identifying and using standardized measures; (11) carefully balancing measurement selection and modification; (12) developing and evaluating modified and new measures when necessary; (13) identifying and tracking major clinical subgroups; (14) measuring and analyzing the actual pattern of services received; (15) incorporating implementation checks into the design; (16) incorporating baseline measures into the intervention; (17) monitoring implementation and dosage as a form of quality assurance; (18) developing procedures early to facilitate tracking and follow-up of study participants; (19) using more appropriate representations of the actual experiment; (20) using appropriate and sensitive standard deviation terms; (21) partialing out variance due to design or known sources prior to estimating experimental effect sizes; (22) using dimensional, interval and ratio measures to increase sensitivity to change; (23) using path or structural equation models; (24) integrating qualitative and quantitative analysis into reporting; and (25) using quasi-experiments, economic or organizational studies to answer other likely policy questions. Most of these strategies have been tried and tested in this and other areas, but are not widely used. Improving the state of the art of health services research and bridging the gap between research and practice do not depend upon using the most advanced methods, but rather upon using the most appropriate methods.

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Like measures of outcome, measures of implementation are most useful and analytically powerful when measured at client-level and are quantitative. However, high-quality, individual-level, quantitative service utilization data can be expensive, intrusive, or otherwise impractical to obtain. Cruder data--for example, presence versus absence of a given service--are often more feasible to collect, as well as more likely to be available. Consequently, evaluators can benefit by finding ways to better exploit such data at the analysis phase to compensate for shortcomings at the collection phase. This article documents one such instance in which this was done. Specifically, it describes how quantitative, client-level implementation scales were derived from qualitative (categorical) data and used to support a cross-site synthesis of implementation and outcome analyses in a multisite evaluation. It also suggests additional scenarios in which quantitative implementation scales might be derived form qualitative services data.

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