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Introduction:Whether the focus of population-health improvement efforts, the measurement of health outcomes, risk factors, and interventions to improve them are central to achieving collective impact in the population health perspective. And because of the importance of a shared measurement system, appropriate measures can help to ensure the accountability of and ultimately integrate the efforts of public health, the health care delivery sector, and other public and private entities in the community to improve population health. Yet despite its importance, population health measurement efforts in the United States are poorly developed and uncoordinated. Collaborative Measurement Development: To achieve the potential of the population health perspective, public health officials, health system leaders, and others must work together to develop sets of population health measures that are suitable for different purposes yet are harmonized so that together they can help to improve a community's health. This begins with clearly defining the purpose of a set of measures, distinguishing between outcomes for which all share responsibility and actions to improve health for which the health care sector, public health agencies, and others should be held accountable. Framework for Population Health Measurement: Depending on the purpose of the analysis, then, measurement systems should clearly specify what to measure-in particular the population served (the denominator), what the critical health dimensions are in a measurement framework, and how the measures can be used to ensure accountability. Building on a clear understanding of the purpose and dimensions of population health that must be measured, developers can then choose specific measures using existing data or developing new data sources if necessary, with established validity, reliability, and other scientific characteristics. Rather than indiscriminately choosing among the proliferating data streams, this systematic approach to measure development can yield measurement systems that are more appropriate and useful for improving population health. [ABSTRACT FROM AUTHOR]

Meta-analysis has increasingly been used to identify adverse effects of drugs and vaccines, but the results have often been controversial. In one respect, meta-analysis is an especially appropriate tool in these settings. Efficacy studies are often too small to reliably assess risks that become important when a medication is in widespread use, so meta-analysis, which is a statistically efficient way to pool evidence from similar studies, seems like a natural approach. But, as the examples in this paper illustrate, different syntheses can come to qualitatively different conclusions, and the results of any one analysis are usually not as precise as they seem to be. There are three reasons for this: the adverse events of interest are rare, standard meta-analysis methods may not be appropriate for the clinical and methodological heterogeneity that is common in these studies, and adverse effects are not always completely or consistently reported. To address these problems, analysts should explore heterogeneity and use random-effects or more complex statistical methods, and use multiple statistical models to see how dependent the results are to the choice of models. [ABSTRACT FROM AUTHOR]

• Case-fatality varied from 14.5% in the first to 0.8% in the fourth pandemic wave. • Vaccines drastically reduced COVID-19 hospitalizations and deaths in Italy. • Before vaccines, non-pharmaceutical interventions contained and mitigated the virus. Over the course of the COVID-19 pandemic in Italy, different response measures were taken to contain the spread of the virus. These include a variety of non-pharmaceutical interventions and a mass vaccination campaign. While not definitive, epidemiological measures provide some indication of the impact of such measures on the dynamics of the pandemic and lessons to better prepare for future emergencies. To describe the impact of vaccine rollout and health policies on the evolution of the COVID-19 pandemic in Italy from March 2020 to October 2021 using a set of epidemiological indicators. We performed a time-trend analysis of new confirmed COVID-19 cases, patients in hospital, and deaths. Using line charts, we informally assessed the relationship of these indicators with the immunization campaign and other health policies. Daily aggregate data were gathered from GitHub repositories of certified data from Italy's Government and Civil Protection. The immunization coverage increased starting in March 2021, with a parallel decrease in COVID-19 infections, hospitalizations, and deaths. Despite different implementation approaches, the vaccine coverage growth rate had a similar pattern across regions. A comprehensive approach including measures such as requiring face masks and a "Green Pass" to enter indoor places also helped contain the pandemic. The vaccine rollout had a major effect on COVID-19 in Italy, especially on hospitalizations and deaths. Before the vaccine was available, however, other non-pharmaceutical interventions also helped to contain the spread of the virus and mitigate its effect on the population. [ABSTRACT FROM AUTHOR]

As an alternative to standard quality improvement approaches and to commonly used after action report/improvement plans, we developed and tested a peer assessment approach for learning from singular public health emergencies. In this approach, health departments engage peers to analyze critical incidents, with the goal of aiding organizational learning within and across public health emergency preparedness systems. We systematically reviewed the literature in this area, formed a practitioner advisory panel to help translate these methods into a protocol, applied it retrospectively to case studies, and later field-tested the protocol in two locations. These field tests and the views of the health professionals who participated in them suggest that this peer-assessment approach is feasible and leads to a more in-depth analysis than standard methods. Engaging people involved in operating emergency health systems capitalizes on their professional expertise and provides an opportunity to identify transferable best practices. [ABSTRACT FROM AUTHOR]

ABSTRACT BACKGROUND The 2009 H1N1 vaccination campaign was the largest in US history. State health departments received vaccines from the federal government and sent them to local health departments ( LHDs) who were responsible for getting vaccines to the public. Many LHD's used school-based clinics to ensure children were the first to receive limited vaccine supplies, but the success of school-based distribution strategies varied in different locations. The goal of this project was to identify and learn from high-performing school-based vaccination clinics in order to share successes and improve performance in future school-based vaccination campaigns. METHODS We used a combination of process mapping and comparative analysis to identify and derive lessons from positive outlier cases observed during 2009 H1N1 school-based vaccination clinic implementation. We created process maps to identify the activities of LHDs conducting school-based vaccinations and used them as the basis for in-depth interviews of LHD staff. We asked interviewees to describe their activities during the 2009 H1N1 pandemic ( pH1N1) school-based vaccination campaign with a focus on successful processes. RESULTS We identified positive deviants, that is, those that performed better than expected, and categorized qualitative data from in-depth interviews with 13 successful LHDs according to the process maps. Key mechanisms for school-based vaccination success included having a relationship with local school authorities, communicating effectively with parents, and ensuring clinic logistics allowed for an easy flow of students through the vaccination process. CONCLUSIONS Utilizing rigorous methodology, we defined and learned lessons from successful LHDs when conducting school-based vaccination clinics, which can be applied to future school-based vaccination campaigns. [ABSTRACT FROM AUTHOR]