spss23出现数据消失
District Health Information Software, or DHIS2, is one of the most important sources of health data in low- and middle-income countries (LMICs). Used by 72 different LMIC governments, DHIS2 is a web-based open-source platform that is used to collect, manage, and analyze routine and critical health data. It is the backbone for national health information systems in these countries and a vital resource for monitoring program and policy performance.
区卫生信息软件，或DHIS2 ，是在低收入和中等收入国家(低收入国家)的健康数据的最重要来源之一。 DHIS2由72个不同的LMIC政府使用，是一个基于Web的开源平台，用于收集，管理和分析常规和重要的健康数据。 它是这些国家国家卫生信息系统的骨干，也是监控计划和政策绩效的重要资源。
Since developing DHIS2, the Health Information Systems Program at the University of Oslo has worked tirelessly to improve the platform to respond to user needs and support training in the platform. The global health community has simultaneously invested in initiatives to improve DHIS2 data quality and encourage the use of DHIS2. However, there has been less attention towards improving the capacity to visualize data within DHIS2. I’ve previously described how often in global health, dashboards can be treated as go-to solutions without essential reflection on user interpretability and key messages; DHIS2 reflects this same quandary.
自开发DHIS2以来，奥斯陆大学的健康信息系统计划一直在不懈地努力，以改进该平台以响应用户需求并支持该平台中的培训。 全球卫生界同时投资于改善DHIS2数据质量和鼓励使用DHIS2的计划。 但是，很少有人关注提高DHIS2中的数据可视化能力。 前面我已经描述了在全球健康状况中， 仪表板可以被视为首选解决方案，而不必对用户的可解释性和关键信息进行实质性的思考。 DHIS2反映了同样的难题。
Given the widespread use of DHIS2, it has become a key resource for COVID-19 surveillance in LMICs. The recent Digital Solutions for COVID-19 report by the Johns Hopkins University’s Global mHealth Initiative identified DHIS2 as one of the two most standout platforms for COVID-19 surveillance based on maturity, flexibility, and large-scale deployment. DHIS2’s virtually irreplaceable role in COVID-19 surveillance highlights not just the importance, but also the urgency, of considering how to improve data visualization within the platform. In 2018, Aprisa Chrysantina and Johan Ivar Sæbø from University of Oslo conducted a study to assess the quality of user-created DHIS2 dashboards in Indonesia. The team has kindly shared some insights from their study with me below as they make a case for investing in data visualization within global health.
鉴于DHIS2的广泛使用， 它已成为LMIC中COVID-19监视的关键资源 。 约翰·霍普金斯大学(Johns Hopkins University)的全球移动医疗计划 ( Global mHealth Initiative)最近发布的COVID-19数字解决方案报告指出，基于成熟度，灵活性和大规模部署，DHIS2是用于COVID-19监视的两个最出色的平台之一。 DHIS2在COVID-19监视中几乎不可替代的作用不仅凸显了考虑如何改善平台内数据可视化的重要性，而且凸显了其紧迫性。 2018年，奥斯陆大学的Aprisa Chrysantina和Johan IvarSæbø进行了一项研究，以评估印度尼西亚用户创建的DHIS2仪表板的质量 。 小组在下面与我分享了他们的研究中的一些见识，为他们在全球卫生领域投资数据可视化提供了依据。
Tricia Aung: Why is data visualization important to DHIS2?
Tricia Aung：为什么数据可视化对DHIS2很重要？
Aprisa Chrysantina and Johan Ivar Sæbø (DHIS2): Visualisation is a big part of the platform. The users can visualise and analyse their data using pivot tables, all kinds of charts from bar, pie, line, to speedometer, and also GIS functionality. The DHIS2 team believes that the end point of having data is to use it (albeit, correctly) and it will not be possible without ability to visualise the data.
Aprisa Chrysantina和Johan IvarSæbø(DHIS2)： 可视化是平台的重要组成部分。 用户可以使用数据透视表，从条形图，饼形图，折线图到速度计的各种图表以及GIS功能来可视化和分析其数据。 DHIS2团队认为拥有数据的目的是使用它(尽管正确)，并且没有可视化数据的能力是不可能的。
How has DHIS2 been used in Indonesia?
DHIS2在印度尼西亚如何使用？
Indonesia has been implementing DHIS2 to integrate health data from different programs such as HIV, TB, and reproductive, maternal, newborn, and child health. Dashboard implementation has been central to the implementation as it allows health staffs to directly visualise the data they have collected. Less than 2 years after the pilot in the country, DHIS2 has been implemented or at least introduced in 127 districts (24.7% of 514 districts) in Indonesia, and has expanded from 6 pilot programs to 17 different work streams, including pharmaceutical and medical devices, human resources, home care, and COVID-19. The application is mostly used to integrate, visualise, analyse, and report aggregate data across various levels, from facility to district to province, and even national levels.
印度尼西亚一直在实施DHIS2，以整合来自不同计划(如HIV，TB和生殖，孕产妇，新生儿和儿童健康)的健康数据。 仪表板实施一直是实施的中心，因为它允许卫生人员直接可视化他们收集的数据。 在印度开展试点活动不到两年的时间，DHIS2已在印度尼西亚的127个地区(514个地区的24.7％)实施或至少引入，并已从6个试点计划扩展到17种不同的工作流程，包括制药和医疗设备，人力资源，家庭护理和COVID-19。 该应用程序主要用于集成，可视化，分析和报告跨各个级别的汇总数据，从设施到地区再到省甚至国家层面。
What inspired your dashboard assessment in Indonesia?
是什么激发了您在印度尼西亚的仪表板评估？
We understood that appropriate and relevant dashboards are not straightforward to make. Users need a certain level of data literacy to create, read, and analyse charts. There was limited evidence in the literature regarding the quality of dashboards created by health staff in the field.
我们知道，适当而相关的仪表板并不容易制作。 用户需要一定水平的数据知识才能创建，读取和分析图表。 关于该领域的卫生人员创建的仪表板质量的文献证据有限。
What led to your decision to use Stephen Few’s dashboard design criteria in the study?
是什么导致您决定在研究中使用Stephen Few的仪表板设计标准的？
When we conducted this study, guidance to create dashboard and data visualisation are often not presented systematically. However, in his book Information Dashboard Design, Few lists common mistakes with dashboard creation, ranging from clutter and inappropriate contextualisation, to outright wrong use of visualisation techniques. We identified that these mistakes could be translated into guiding assessment questions.
当我们进行这项研究时，创建仪表板和数据可视化的指导通常没有系统地提出。 但是，在他的《 信息仪表板设计》一书中，很少有人列出仪表板创建中的常见错误，从混乱和不适当的上下文环境到完全错误地使用可视化技术。 我们发现这些错误可以转化为指导性评估问题。
What do you think are the most important findings? Was there anything that surprised you?
您认为最重要的发现是什么？ 有什么让您感到惊讶的吗？
Aprisa was (ironically) glad that her suspicion of dashboard quality was proven and that we now have evidence that support that. We were surprised that the frequency of the inappropriate data visualisation usage problem was higher than we expected to be. Unfortunately, although we were involved in some of the trainings where these users created the dashboards, we didn’t investigate the reasons nor the creation process specifically for the purpose of the research. Also, we were surprised that these dashboards and charts were created by health information systems (HIS) consultants or government HIS staff which had either Bachelors or Masters degrees in clinical health/public health or IT. This implied that these advanced educational backgrounds did not guarantee data visualisation literacy.
Aprisa(具有讽刺意味的是)很高兴她对仪表板质量的怀疑得到了证明，并且我们现在有证据支持这一点。 我们感到惊讶的是，不适当的数据可视化使用问题的发生频率比我们预期的要高。 不幸的是，尽管我们参与了这些用户创建仪表板的一些培训，但我们并未针对研究目的调查原因或创建过程。 同样，令我们感到惊讶的是，这些仪表板和图表是由具有临床卫生/公共卫生或IT学士学位或硕士学位的健康信息系统(HIS)顾问或政府HIS人员创建的。 这意味着这些高级教育背景不能保证数据可视化素养。
Did you notice any variation in dashboard quality across levels of the health system (e.g. national vs. facility)?
您是否注意到卫生系统各个级别(例如国家与机构)的仪表板质量有何不同？
We didn’t specifically address this question in our study. However the people we mentioned above created dashboards and/or charts in different scopes, whether it was for facilities or at the national level. Through my field visits and discussions, I also found that many of these dashboards were developed as part of DHIS2 training process with trainer/facilitator(s) being around. But as we didn’t investigate the training process and trainer’s roles in data viz creation in depth, we could not say anything further about it.
在我们的研究中，我们没有专门解决这个问题。 但是，我们上面提到的人员在不同的范围内创建了仪表盘和/或图表，无论是用于设施还是在国家一级。 通过我的实地访问和讨论，我还发现许多仪表板是作为DHIS2培训过程的一部分开发的，培训师/辅导员在身边。 但是，由于我们没有深入研究培训过程和培训者在数据即数据创建中的作用，因此我们无法对此进一步说明。
How have you used the study findings?
您如何使用研究结果？
As the research highlighted that people are making poor chart type selection, we have implemented new dimensions for selection panels in the Data Visualization app in DHIS2. Thus, we expect that it will be easier to choose the data and it will guide the chart selection.
由于研究突出表明人们在选择图表类型方面比较差，因此我们在DHIS2的“数据可视化”应用程序中为选择面板实现了新的尺寸。 因此，我们希望选择数据会更容易，它将指导图表的选择。
The research has become a foundation for us to encourage the use of standardized dashboards that are built around standard operating procedures and shared down to users. This way, users will get more practically useful dashboards.
该研究已成为我们鼓励使用围绕标准操作程序构建并共享给用户的标准化仪表板的基础。 这样，用户将获得更加实用的仪表板。
The dashboard sharing function has been improved as well. We also added descriptions and dashboards items like boxes that will allow dashboard creators to input more instructions and guidance on how to use the dashboards directly on the dashboards.
仪表板共享功能也得到了改进。 我们还添加了描述和仪表板项目(例如框)，使仪表板创建者可以输入更多有关如何直接在仪表板上使用仪表板的说明和指导。
Specifically in Indonesia, we developed data visualisation guidance materials based on this study result that use real-life examples (i.e of common problems). We delivered these materials academic lectures, roll out, and refresher trainings in discussion method.
特别是在印度尼西亚，我们根据该研究结果开发了数据可视化指导材料，这些材料使用了现实生活中的例子(即常见问题)。 我们提供了这些材料的学术讲座，推广和讨论方法方面的进修培训。
In addition, the WHO meta-data packages primarily focuses on “best practice” data analysis through dashboards. Working with the WHO, we also make accessible pre-defined dashboards based on current knowledge on how to manage various health programs. These may or may not be fully compatible with what countries are currently collecting, but we also see that these are used as inspiration to make better dashboards in countries.
此外，世卫组织元数据包主要侧重于通过仪表板进行“最佳实践”数据分析。 我们还与世界卫生组织合作，根据有关如何管理各种卫生计划的最新知识，提供了可访问的预定义仪表板。 它们可能与国家目前正在收集的内容完全兼容，也可能不完全兼容，但是我们也看到这些被用作启发，以在国家中制作更好的仪表板。
This interview has been lightly edited for clarity.
为了清楚起见，对这次采访进行了少量编辑 。
Thank you to Aprisa Chrysantina and Johan Ivar Sæbø for participating in this interview. You can learn more about DHIS2 here. Special thanks to Senthil Natarajan for his editorial support.
感谢 Aprisa Chrysantina 和 Johan IvarSæbø 参加了这次采访。 您可以 在此处 了解有关DHIS2的更多信息 。 特别感谢 Senthil Natarajan 的编辑支持。
Tricia Aung is a Research Associate and Faculty member at Johns Hopkins School of Public Health in the Department of International Health. She leads workshops and research in visualizing global health data for decision-making in low- and middle-income country audiences. She is a Co-Chair for the DVS Diversity Committee.
特里西娅·昂 ( Tricia Aung) 是 国际卫生部 约翰霍普金斯大学公共卫生 学院的研究员和教授 。 她领导着研讨会和研究工作，以可视化全球卫生数据为中低收入国家的受众提供决策依据。 她是DVS多样性委员会的联席主席。
翻译自: https://medium.com/nightingale/improving-the-visualization-of-health-data-on-2-3-billion-people-cfb83a41bba
spss23出现数据消失