Surveys for Urban Equity: getting, and using, the data needed to respond to neglected non-communicable diseases in urban areas in LMICs

非传染性疾病（NCD）是南亚地区的一个主要公共卫生问题，占死亡人数的60%。城市化是一个关键驱动因素，增加了精神压力和伤害以及吸烟、饮食不良和体育活动不足等风险因素。中低收入国家的公共卫生决策者需要公正、准确和易于获取的数据，为公平的城市发展提供信息。具有国家代表性的调查是用于为中低收入国家的决策提供信息的主要数据来源，例如，90多个中低收入国家采用了人口与健康调查和STEPS非传染性疾病风险因素监测。这种方法有许多问题：它排除了非正式的贫困住区和行政边界以外的城郊地区;“家庭”的定义忽视了城市贫困人口的多重居住;目前的财富排名方法对城市贫困不敏感;目前在这些调查中评估的非传染性疾病不包括伤害和精神疾病，这些疾病对城市贫困人口的影响不成比例。这些方法上的限制使决策者无法理解或应对低收入国家城市地区的非传染性疾病负担和不平等现象。此外，目前以冗长的报告形式提供数据的方式损害了决策者为规划和管理卫生方案寻找和解释数据的能力。我们将利用现有的合作伙伴关系并开发新的合作伙伴关系，以i）确定和测试有关心理健康和伤害的问题，ii）测试廉价有效的新方法，以减少城市调查中的偏见iii）探索定义家庭，测量和排名财富的替代方法iv）开发数据可视化工具，以支持决策者使用调查数据。我们将在尼泊尔、孟加拉国和越南开展这一基础工作，因为它们不同的城市特点使我们的工程适用于其他中低收入国家，而且我们已有强大的合作伙伴关系。我们将举行两次会议，促进与国际和国家机构的持续互动，以分享专业知识，并在新的调查方法，财富和非传染性疾病的措施，数据可视化和使用数据解决城市不平等问题方面建立能力。我们将对问卷进行系统的搜索，并利用我们自己的工作评估伤害和心理健康，以确定最适合LMIC城市地区的问题。我们将与政府利益相关方合作，在检查城市贫民窟的可理解性后，完成调查问卷。我们将在加德满都试点我们的新调查抽样和绘图方法，这些方法的最初试点因2015年地震而被削减。我们的新方法使用卫星图像，移动的电话和人口普查数据，以确定整个城市人口，包括那些没有被人口普查捕获。一旦抽样，我们将使用基于网络的应用程序来准确地绘制社区地图。将在河内和达卡对这些方法和编制的问卷进行规模较小的可行性研究。我们将探讨城市贫民窟的家庭类型并确定财富指标。利用我们的试点数据，我们将探索家庭消费、资产和收入之间的联系，以制定一个适当的城市财富衡量标准。在每个国家，我们将通过与决策者的访谈，评估数据在城市规划和管理中的使用程度。通过在市政当局内建立实践社区，我们将共同制作适当的数据可视化工具，以支持规划和管理。这笔基金会赠款有可能i）改变所有低收入国家使用的调查方法，从而能够更准确地评估城市地区的卫生不平等，ii）提供有关心理健康和伤害的问题，供低收入国家的其他调查使用，iii）确保数据为低收入国家卫生部门应对非传染性疾病提供信息。

Non-communicable diseases (NCD) are a major public health issue in South Asia (S Asia), accounting for 60% of deaths. Urbanisation is a key driver, increasing mental stress and injuries and risk factors such as tobacco use, poor diet and inadequate physical activity. Public health decision-makers in LMICs need unbiased, precise and accessibly-presented data to inform equitable urban development. Nationally representative surveys are the main data source used to inform decision-making in LMICs, e.g. Demographic and Health Surveys (DHS) and STEPS NCD Risk Factor Surveillance are used in over 90 LMICs. This approach has many problems: it excludes unofficial poor settlements and peri-urban areas beyond administrative boundaries; definitions of a 'household' overlook multiple occupancy by poor urbanites; current methods for ranking wealth are not sensitive to urban poverty; and NCDs currently assessed in these surveys do not include injuries and mental ill-health, conditions that disproportionately affect the urban poor. These methodological constraints leave policy makers with no way of understanding or responding to the NCD burden and inequities found within urban areas in LMICs. Furthermore, the way data is currently presented - in long wordy reports - undermines decision-makers' ability to find and interpret data for planning and managing health programmes. We will capitalise on existing partnerships and develop new ones, to i) identify and test questions on mental health and injuries, ii) test cheap and efficient novel methods to reduce bias in urban surveys iii) explore alternative approaches to defining households, measuring and ranking wealth iv) develop data visualisation tools to support decision-makers' use of survey data. We will conduct this foundation work in Nepal, Bangladesh and Vietnam as their different urban characteristics increase our works applicability to other LMICs and we have existing strong partnerships. We will hold two meetings and facilitate ongoing interaction with international and national bodies to share expertise and build capabilities on the novel survey methods, measures of wealth and NCDs, data visualisation and use of data to address urban inequities. We will conduct a systematic search of questionnaires and draw on our own work assessing injuries and mental health to identify the best questions for LMIC urban areas. We will finalise the questionnaire in collaboration with government stakeholders after checking its comprehensibility in urban slums.We will pilot our novel survey sampling and mapping methods in Kathmandu, where our initial pilot of these methods was curtailed by the 2015 earthquake. Our novel methods use satellite imagery, mobile phone and census data, to identify the entire urban population including those not captured by the census. Once sampled, we will use a web-based app to accurately map communities. Smaller feasibility studies of the methods and developed questionnaire will be conducted in Hanoi and Dhaka.We will explore household types and identify wealth indicators in urban slums. Using our pilot data, we will explore associations between household consumption, assets and income to develop an appropriate measure of urban wealth. In each country, we will assess the extent to which data is used in urban planning and management through interviews with decision-makers. Through the establishment of communities of practice within municipalities, we will co-produce appropriate data visualisation tools to support planning and management. This foundation grant has the potential to i) transform survey methods used in all LMICs, enabling more accurate assessment of health inequities in urban areas ii) provide questions on mental health and injuries for use in other surveys in LMICs, and iii) ensure data informs the health-sector response to NCDs in LMICs.