Integrated Urban Health Data: Processes and Prospects for Strengthening Urban Health Systems in Nepal

Integrated Urban Health Data: Processes and Prospects for Strengthening Urban Health Systems in Nepal | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Urban Health Data: Processes and Prospects for Strengthening Urban Health Systems in Nepal Sampurna Kakchapati, Neelu Sharma, Jijeebisha Baral, Shirish Maharjan, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8497897/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban populations in low- and middle-income countries (LMICs) are growing rapidly, bringing diverse socio-economic groups, extensive informal settlements, and frequent migration into cities. Local governments in cities are central to policies and programmes to protect and improve the health of their urban populations. Yet, many struggle to access, integrate and utilise data across the multiple sectors that influence health, plurality of health providers and to do this at a granular spatial level that supports local decision making. These trends have led to persistent health challenges and stark disparities in access to care and health outcomes, as resource allocation and planning often lag behind dynamic urban need. This protocol describes a mixed-methods implementation study in two wards of Budhanilkantha municipality of Nepal to assess existing municipal data systems, collect geospatial data and household data, and build an integrated urban health data portal. We will conduct stakeholder interviews, community focus groups, and policy reviews to understand data use and gaps. We will then carry out social mapping, a door-to-door household census, and health facility assessments to generate detailed ward-level data on population, infrastructure, and health care services. These data will be geocoded and linked via common identifiers to existing routine health and administrative databases. We will co-create an interactive portal with local government users, featuring real-time dashboards, GIS maps, and analytic tools to support local planning and equity-focused decision-making. The expected outcome is a proof-of-concept framework showing how combined routine and primary data can inform resilient, multisectoral urban health governance. Figures Figure 1 1. Introduction Rapid urbanization is one of the defining trends of the 21st century, especially in low- and middle-income countries (LMICs) (1). The United Nations projects that by 2050 nearly 68% of the world’s population will live in urban areas, with most growth occurring in South Asia and Africa [ 1 ]. While cities can offer economic opportunities and access to services, unplanned growth often outpaces infrastructure, leading to slums and informal settlements with poor living conditions [ 1 , 2 ]. In Nepal and similar settings, municipal planning structures face new pressures: heterogeneous populations, mobile labor forces, complex social dynamics and the impact of non-health sectors such as transport, housing, planning on population health and well-being [ 3 , 4 ]. These factors contribute to urban health challenges such as overcrowding, environmental hazards, infectious disease transmission, and rising communicable and non-communicable diseases [ 2 , 5 ]. Critically, rapid change can exacerbate inequalities: marginalized groups (the urban poor, migrants, women, and ethnic minorities) often experience limited access to health care and worse outcomes compared to wealthier residents [ 2 , 4 , 5 ]. Rapid migration swells cities, pushing rural and cross-border migrants into informal settlements with little planning. Informal settlements, often invisible in official records, face overlapping deficits—unsafe housing, poor sanitation, no clean water, inadequate services, and unpaved roads—while also bearing environmental hazards like flooding, waste pollution, and air contamination. Such clustered risks drive diarrheal disease, mosquito-borne infections, and respiratory illness, yet without detailed local data, municipalities cannot target resources or improve urban health [ 6 , 7 ]. Despite the acute need, robust data on urban health status and the determinants that influence health in LMIC settings are often lacking. Routine health information systems (e.g. clinic records, vital registration) are usually designed for national or rural contexts and rarely cover the plurality of providers within urban areas or capture the granularity needed at city or ward levels. They may omit informal settlements entirely or fail to disaggregate by socio-economic status and migration status [ 8 ]. At the same time, periodic surveys (e.g. DHS, household surveys) provide snapshots but are infrequent and may not cover slum areas and urban issues adequately [ 2 , 3 , 8 , 9 ]. Furthermore, ward-level or community-level data are scarce. This lack of granuality undermines equity-focused planning, as it obscures where health needs are greatest. For example, a slum cluster in one ward might have high child mortality or lack safe water, but without ward-level data these problems remain “hidden” in aggregate urban statistics [ 2 , 8 ]. Data fragmentation is a final, critical challenge. Planners and health managers frequently work with fragmented information: different sectors such as health, education, water, maintain separate databases with no interoperability. Health, census, urban planning and utility data are often held in separate silos or updated infrequently, so city officials cannot link data to understand the wider risks to health or to identify system bottlenecks [ 7 , 10 ]. In many LMICs, multiple agencies collect divers and potentially valuable information on for example, clinic visits, vital events, migration estimates, but the differing formats or geographical coverage undermine interoperability. A recent review found that a major barrier to effective use of health data for planning, monitoring, and improving service delivery is data fragmentation, where different databases use inconsistent coding and formats [ 7 , 10 , 11 ]. Likewise, a multi-city study of health information systems noted the data quality and interoperability of various separate systems as the greatest challenges [ 10 , 11 ]. Fragmented data mean municipalities lack a coherent evidence base with maps of disease incidence, population density and sanitation rarely overlapping, so planning immunization campaigns or new clinics can miss the neediest areas. The rationale for an integrated urban health data approach is to create a comprehensive, interoperable information system that combines all relevant data for local action. Linking routine administrative data with new primary data collection, we can develop detailed community profiles and dynamic monitoring tools. Such a system can support evidence-informed decision-making by municipal authorities, health officials, and civil society, enabling timely responses to emerging issues and addressing inequities. In this model, data become a shared public good rather than siloed departmental assets. This study aims to design, implement, and document an integrated urban health data system in selected municipalities of Nepal. Specifically, we will: assess existing municipal data sources and systems to identify gaps and limitations; conduct social mapping to measure and map key health and socio-demographic indicators at the ward level; carry out a complete household census to capture urban health issues, complemented by health facility assessments in two wards of municipality to address data gaps; and develop an interactive dashboard and GIS platform that integrates routine and newly collected data, enabling real-time visualization and analysis to support evidence-based local planning. These wards have diverse populations, including dense informal settlements and migratory populations, and are manageable for intensive fieldwork. Lessons learned will inform broader rollout to other wards and cities in Nepal and similar LMIC contexts. 2. Methods This mixed-method study will be conducted in one municipality of Kathmandu, Budhanilkantha using qualitative methods and primary quantitative data collected in two wards of the municipality. Secondary data from routine data from municipality department will be used. We will adopt a pragmatic approach to understand and improve data systems in context. The study will have two phases: qualitative formative research to engage stakeholders and assess existing data systems; and quantitative primary data collection and technical development of the urban data portal. Stakeholders include municipal health officials, infrastructure and environment authorities, education officers, and community representatives. 2.1 Formative Research We will conduct a range of activities to gather insights before designing the data collection tools: Stakeholder interviews : Key informant interviews will be conducted among municipal officials across various departments including; Health, Education, Civil registration system, Disaster, Animal, Agriculture, Environment, Infrastructure, Information Technology (IT), and Women, Children & Inclusion. We will explore existing data source, data workflows, priority health issues, and perceived barriers to data use. Interviews will be semi-structured, recorded (with consent), and thematically analyzed. Policy and document review : Municipal plans, health policies, census reports, and previous survey findings will be reviewed to identify what data exist at ward/municipality level and any official strategies for urban health. These formative activities will map the “information ecosystem” and guide the design of data collection instruments. They will enhance established relationships and buy-in with local stakeholders, which is crucial for co—creation of the portal and for ensuring data will be used in future. We will compile an inventory of data sources currently accessible to the municipality: Routine data : This includes the national health information system (e.g. Health Management Information System), civil registration system and municipal education records (school enrollment, attendance). We will request aggregate data at the ward level if available. Non-routine data : Past survey or research data (if any), program evaluations (e.g. NGO-led health projects), and any GIS layers (roads, utilities, administrative boundaries). Based on the findings of the formative assessment and in agreement with the municipal team, we will identify key routine and non-routine data sources and assess their suitability to be integrated into the urban health data portal. We will identify key limitations, such as fragmentation across departments, lack of geolocation for data points, and absence of data on vulnerable subpopulations (e.g. migrants, urban poor). We will integrate routine municipal data from each department into the portal. Routine data sources include health facility records, vital registration (births and deaths), waste management, water supply, education, and infrastructure databases. These datasets will be inventoried and standardized to ensure consistency in formats, variable names, and indicator definitions. Paper-based records will be digitized where necessary, with data cleaning and validation to improve quality. Automated data pipelines through APIs or periodic batch uploads will transfer routine data into the portal on a regular schedule (e.g. monthly or quarterly). 2.2 Primary Data Collection Routine municipal information systems in Nepal, such as the Health Management Information System, civil registration, and administrative records, will provide essential but incomplete data for local planning. These datasets are expected to lack ward-level granularity, omit informal settlements, and fail to capture key household, environmental, and service readiness indicators. During the formative phase, municipal officials are anticipated to identify these data gaps as a major barrier to evidence-based and equitable planning. To address this, primary data collection including social mapping, a comprehensive household census, and a public health facility assessment will be conducted as a one-time, strategic investment to generate a detailed baseline for integration with routine data sources. These activities will be co-designed with local government users to ensure that the tools, indicators, and outputs directly address their information needs and strengthen institutional capacity for data use. While such data collection may entail higher initial costs and logistical demands, it will be essential to fill critical gaps left by routine systems, validate existing data, and reinforce local data ecosystems. The integration of these primary datasets with routine municipal information within a unified urban health data portal will ensure both immediate utility and long-term sustainability. As routine data systems are strengthened, the portal will increasingly rely on continuous data flows from municipal departments, transitioning from intensive primary data generation toward a sustainable, routine-driven data maintenance model that supports equity-focused decision-making. Therefore, we will conduct three interlinked primary data collection activities in two wards of municipality: social mapping, a household census, and a public health facility assessment. 2.3 Social Mapping We will use GIS-based social mapping to create detailed maps of two wards of the Budhanilkantha municipality which were selected by the municipal authorities as they represent a mix of low- and middle-income areas, including both planned neighborhoods and informal settlements. Based on discussions with the municipality team, agreed health risk indicators guided the mapping process. Local community members including youth volunteers, ward officials, will work with the research team to sketch ward boundaries and key landmarks on large printouts or digital map platforms. Locations of both public and private health providers and infrastructure (such as hospitals, health posts, basic health care center, NGO clinics, pharmacies), water sources (wells, taps), education facilities (schools, colleges), markets, transportation routes, and high-risk areas (garbage dumps, flood zones) will be marked. Informal settlements, densely populated clusters, open spaces, green spaces, and blue spaces will also be delineated, along with shops selling meat, tobacco sales and liquor as these locations are relevant to understanding environmental health risks, food safety, and behavioral health determinants within the wards. Social mapping will feed into the urban health data portal by visually pinpointing underserved areas, clustering of health risks, and environmental hazards at the ward level. Using the community knowledge with geotagged data, it will create interactive GIS layers that allow users to compare wards, track changes, and overlay health, infrastructure, and environmental indicators making gaps clear and guiding evidence-based local planning. 2.4 Household Census Household census data is the backbone of an effective Urban Health Data Portal, providing a detailed and dynamic picture of the community’s health landscape. It reveals who lives where, their health status, behaviors, and the environmental conditions shaping their well-being. This granular insight enables targeted, evidence-based interventions by highlighting vulnerable groups, service gaps, and environmental risks. When geotagged and integrated, household data transforms into powerful maps that pinpoint hotspots and track changes over time, driving smarter resource allocation and timely responses. Beyond data, it empowers communities and health workers by making health information transparent and actionable, ultimately fueling healthier, more equitable urban futures. With this importance, the household census will be conducted among household heads, mothers of children under two, and all family members in two wards of the municipality. We will also adopt a citizen science approach by involving local residents in data collection and fieldwork, which improved data accuracy through their local knowledge and enhanced community understanding of the research process. Household heads or the most knowledgeable members will provide data on household composition, socio-demographics, assets, mortality, healthcare access, environmental conditions, and waste management. Adults aged 18 and above will reported on risk behaviors, including tobacco and alcohol use, diet, physical activity, and the prevalence of communicable and non-communicable diseases. Mothers of young children will provide information on maternal health, antenatal and postnatal care, child immunization, and HPV vaccination coverage among adolescent girls aged 10–19 years, who also self-reported on HPV vaccination status. Household members involved in agriculture or livestock will provide details on land use and livestock ownership. Anthropometric measurements—height, weight, BMI, and blood pressure—will be recorded for all adults present, while mid-upper arm circumference assessed nutritional status in children under five years. The questionnaire will be developed in Nepali (and local languages as needed), pretested in a similar setting, and refined accordingly. Field enumerators will use tablets with electronic data entry forms (e.g., Open Data Kit) to minimize errors and enable daily uploads to a secure database. A physical “family health folder” with a unique ID, summarizing key health indicators, will be printed for each household to engage families and support local health staff in monitoring. Data quality will ensure through daily reviews, GPS tracking of enumerator routes, and random spot-checks by supervisors. The household census provides ward-level population data and identifies community-level patterns such as clusters of vulnerability. We will identify the key indicators from the census will be integrated into the Urban Health Data Portal (see Table 1 ). Table 1 List of key indicators from household census that can be integrated into urban health data portal Section Indicators Socio-Demographic Data Household composition (age, gender, education, employment, family structure) Socio-economic status and assets Health Status and Disease Data Prevalence of communicable and non-communicable diseases (NCDs) Maternal health indicators (antenatal, postnatal care) Child health indicators (immunization status, including HPV vaccination) Health-seeking behaviors and access to healthcare facilities Risk Behaviors and Lifestyle Data Tobacco and alcohol use Dietary habits and physical activity levels Sedentary behavior Environmental Health Data Sanitation and waste management practices Exposure to air pollution Housing quality and access to safe drinking water Anthropometric and Clinical Measurements Height, weight, BMI of household members Blood pressure readings for adults Mid-upper arm circumference (MUAC) for children under five Geospatial Data Household locations linked to ward boundaries Clusters of informal settlements and densely populated areas Agriculture and Livestock Data Land use for agriculture Livestock and poultry ownership and business activities Insert Table 1 here Quantitative data from the household census and facility survey will be analyzed to compute descriptive statistics (demographic profiles, prevalence of key health and socio-economic indicators). We will use GIS analysis to identify spatial patterns (e.g. clustering of poverty, distance to health services). The integrated portal itself will serve as a living analysis tool, allowing users to generate tables and charts in real time. We will conduct a preliminary evaluation of the portal’s usability (e.g. through user acceptance testing with municipal staff) and document any implementation challenges. 2.5 Health Facility Assessment Health facilities are the heartbeat of an Urban Health Data Portal, delivering real-time, actionable data that brings the city’s health landscape into sharp focus. This integration will inform targeted interventions to improve health service coverage and equity in the municipality. They provide crucial insights into service availability, staffing, equipment, and medicine stocks highlighting where resources meet needs and where gaps persist. Understanding patient flow and utilization patterns further reveals emerging health trends and demand spikes, supporting timely, data-driven interventions. In response to the municipality’s growing demand for evidence-based planning and equitable health service delivery, we will conduct a comprehensive assessment of all public health facilities serving Wards 4 and 7 of Budhanilkantha Municipality. Public facilities are prioritized because they represent the primary access point for essential health services for the majority of residents particularly low-income and marginalized groups and their performance directly reflects municipal accountability for health outcomes. The assessment will cover 11 public facilities, including one public hospital, four primary health care centers, and six health posts. Data will be collected using the structured assessment tools developed for this study to capture detailed information on facility infrastructure, service readiness, equipment, and logistics. The survey will also assess the availability of 18 tracer drugs and collect data on health care providers, including their education, years of experience, and training received. Additionally, secondary data from Nepal’s Minimum Service Standards (MSS), a nationally endorsed framework of the Ministry of Health and Population will be used to benchmark performance across domains such as human resources, infrastructure, and service delivery capacity. GPS coordinates of each facility will be captured to support spatial analysis. Facility data will be linked with household census data using GIS and patient flow information to identify service gaps relative to community health needs and geographic distribution. Table 1 summarizes the diverse data sources that will be used to develop the Integrated Urban Health Data System. The table highlights how each dataset contributes unique and complementary information needed to generate a comprehensive understanding of urban health, service delivery, and population characteristics in Nepal. The Household Census provides the most detailed demographic, socioeconomic, health and housing information, forming the foundational dataset for constructing population and health indicators. Social mapping adds a spatial dimension by visualizing settlements, services, and environmental risks at the ward level, enabling geographic targeting and resource allocation. The Health Facility Assessment offers systematic information on service availability, infrastructure, and readiness, which is essential for evaluating health system capacity. Routine health information from HMIS/DHIS2 complements this by supplying monthly data on maternal and child health, disease burden, and service utilization trends. Civil Registration/Vital Events, which track births, deaths, and marriages, and the Women, Children, and Social Inclusion system, which records data on disability, single mothers, and vulnerable groups strengthen the system’s ability to capture population dynamics and equity-related dimensions of urban health. Administrative insights from municipal records help contextualize local governance, planning, and service delivery functions. Together, these datasets ensure that the Urban Health Data System integrates demographic, health, administrative, and social inclusion perspectives, enabling municipalities to plan, monitor, and improve urban health services in a more informed and evidence-based manner. Insert Table 2 here Table 2 Data Sources Included in the Development of the Urban Health Data System Data Source Type of Data Frequency Purpose in the Study Expected Output Household Census Primary household-level demographic, socioeconomic, and housing data Periodic To obtain baseline information on population, housing, and SES Base dataset for indicator construction Social Mapping Spatial data on settlements, services, and environmental risks Periodic To map community structure, service distribution, and hazards Ward-level spatial layers Health Facility Assessment Facility readiness, service delivery, infrastructure Periodic To assess availability, readiness, and quality of health services Facility readiness index HMIS / DHIS2 Routine health service data (MCH, NCDs, diseases) Monthly To extract key health indicators and service utilization trends Health service indicator set Municipal Records Administrative and program data Continuous To understand municipal service delivery, planning, and governance Municipal administrative profiles Civil Registration (Vital Events) Birth, marriage, death registration Continuous To incorporate vital statistics into municipal-level demographic and health profiles Birth rate, death rate, marriage statistics Women, Children, and Social Inclusion Data on disability, single mothers, vulnerable households, inclusion indicators Periodic To integrate social inclusion and vulnerability characteristics into urban health profiles Disability indicators, vulnerable household profiles Municipal Records Administrative and program data Continuous To understand municipal service delivery, planning, and governance Municipal administrative profiles 3. Data Integration and Portal Development The final phase focuses on integrating all collected data and developing the urban health data portal framework. We will geocode all mapped locations households, health facilities, and infrastructure assigning each household a unique ID to enable linkage with facility records (with consent) and standardize administrative codes across datasets. This allows seamless cross-referencing, such as connecting household health indicators to nearby facility readiness. Data cleaning and standardization will harmonize variables and ensure quality through checks for inconsistencies and duplicates. Routine government data will be incorporated and aligned with ward boundaries using common identifiers, supported by a comprehensive data dictionary. In collaboration with municipal stakeholders, we will co-create a user-friendly, web-based, multilingual portal accessible via computers and mobile devices. In collaboration with municipal stakeholders, we will co-create a user-friendly, web-based, multilingual portal accessible via computers and mobile devices. We will adopt a user-centered design (UCD) approach, which emphasizes iterative engagement with end users throughout all stages of design, testing, and refinement. Municipal health officials, and community representatives will be actively involved through participatory workshops, prototype testing sessions, and feedback loops to ensure that the portal’s structure, visualizations, and functionalities align with their practical needs and digital capacities. This approach reflects established principles of user-centered design, which highlight the importance of engaging users to enhance system usability, acceptability, and long-term adoption [ 12 , 13 ]. Key features will include interactive dashboards displaying health indicators by ward, GIS mapping layers for household density, infrastructure, WASH coverage, and health outcomes, as well as basic analytics tools to identify hotspots and generate custom reports. Role-based access will ensure appropriate data security and privacy. To promote sustainability, the portal will be built on the open-source platforms DHIS2 enhanced with GIS modules, with thorough documentation and training provided to municipal IT staff. Evidence shows that integrated, geo-referenced urban health data enables cities to identify inequities and allocate resources effectively. Without such systems, informal settlements and migrant populations often remain invisible, limiting targeted interventions. Interconnecting population dynamics, environmental risks, and service coverage, this integrated data approach can empower LMIC cities to reduce slum health burdens and create more equitable, resilient urban health systems. 4. Stakeholder Engagement and Cocreation Throughout the project, we will maintain active stakeholder engagement, guided by principles of user-centered design to ensure that end users such as municipal officials, and community representatives are meaningfully involved in shaping the system’s functionality, usability, and relevance. This participatory approach will promote ownership, enhance user experience, and support the long-term sustainability of the portal. At key milestones including planning, social mapping, household census, and portal development we will hold co-creation workshops with municipal officials, the mayor, deputy mayor, department focal persons, and ward representatives, fostering collaborative decision-making and strong support for each activity. During the household census, we will discuss and identify innovative approaches tailored to the community’s needs such as developing and distributing pamphlets to households and broadcasting jingles through local taxis to inform residents about the purpose and benefits of the census and to encourage active participation. These workshops will play a vital role in supporting the Urban Health Data Portal by bringing together diverse stakeholders to collaboratively shape its design, functionality, and implementation. Through open dialogue and shared decision-making, the portal will be tailored to local needs and priorities, fostering ownership among municipal officials, health workers, and community members. Co-creation will help identify relevant data sources, prioritize key health indicators, and develop user-friendly features suited to different users. Moreover, these workshops will be designed to facilitate capacity building by involving stakeholders directly in the process, increasing their ability to effectively use and maintain the portal. This collaborative approach aims to promote transparency, trust, and sustained engagement ultimately leading to a more responsive, inclusive, and impactful urban health data system. 5. How Integrated Urban Health Data Can Help An integrated urban health data system – linking demographic, environmental, and service data at neighborhood scales can help municipalities tackle these challenges. Such systems combine maps of populations (even in slums) with layers on health facilities, water networks, and socioeconomic indicators. For instance, new geospatial methods (satellite imagery, mobile data, surveys) can now estimate key SDG indicators at high resolution. Recent projects have “modeled dozens of household survey indicators” (e.g. poverty, stunting, literacy) at 1×1 km or finer across entire countries [ 6 , 14 ]. In practice, these data allow city planners to see precisely which informal neighborhoods lack clinics, sanitation or parks, and to monitor changes as slums expand. Area-level “health determinants” can include access to quality health facilities, traffic density, level of informality, pollution and social exclusion [ 12 , 15 ] all measurable by integrating diverse datasets. “Vital registration data such as records of births, deaths, marriages, and migration are examples of routine data collected continuously. Municipalities can utilize this routine data to strengthen their health information systems, improve population monitoring, and enhance planning and service delivery. Aggregating this information helps cities anticipate health service demand as people relocate. During outbreaks or seasonal epidemics, integrated urban data enable quicker, localized responses. For example, in western Kenya a digital HDSS used tablets and a central database (OpenHDS) to collect malaria data in real time [ 16 ]. This system ensured “quickly and effectively” gathered data with built-in quality checks, saving time and cost and demonstrating feasibility of electronic urban health surveillance [ 16 ]. Similarly, GIS-based dashboards (overlaying clinic cases, environmental risks and mobility patterns) have proven powerful in targeting vector control or immunization in Latin American slums and elsewhere. However, maintaining such a hub and keeping the data up to date presents significant challenges, including ensuring timely data collection, managing data quality, and integrating multiple data sources effectively. 6. Ethical Considerations Ethical approval has been obtained from the Nepal Health Research Council (NHRC). All participants in interviews and the household census will provide informed consent. The purpose of data collection w explained, and participation is voluntary. Personal identifiers will be removed from analysis datasets; household survey data will be de-identified and stored securely. We will comply with NHRC guidelines and the principles of the Helsinki Declaration. Community consent will also be obtained from ward leaders, and we will share findings in an understandable format with the communities involved. 7. Discussion Urbanization in low- and middle-income countries (LMICs) like Nepal presents complex health challenges characterized by rapid population growth, migration, and the expansion of informal settlements [ 2 , 7 , 17 ]. Addressing these challenges requires data systems that transcend traditional siloed approaches, integrating diverse data sources to capture the multifaceted determinants of urban health [ 11 , 18 ]. This study describes an ambitious effort to develop an integrated urban health data system in a Nepalese municipality by combining routine administrative data with participatory mapping and household surveys. Our goal is to fill critical data gaps and facilitate equity-oriented urban health planning aligned with both local and global commitments such as the Sustainable Development Goals (SDGs), particularly SDG 11 on sustainable cities and communities [ 18 , 19 ]. The Importance of Integrated Urban Health Data Systems Integrated data systems are increasingly recognized as foundational for “healthy cities” initiatives, enabling timely, evidence-informed decisions that improve health equity and service delivery [ 15 , 20 ]. Unlike traditional top-down health surveillance systems, which often rely on aggregated or incomplete data, our approach adopts a bottom-up, cross-sectoral methodology. This reflects the complex social, environmental, and infrastructural determinants that shape urban health outcomes [ 17 , 21 ]. An integrated health data portal enhances municipal capacity to identify underserved populations and allocate resources more fairly. For example, our preliminary data reveal that Ward 7 in Budhanilkantha municipality hosts a high density of informal settlers but is served by only one under-resourced clinic. Such insights prompt targeted investment and resource mobilization, crucial for addressing inequities [ 12 , 22 ]. Real-time dashboards enable continuous monitoring of health indicators, such as spikes in diarrheal disease following floods, facilitating rapid response and containment [ 13 , 14 , 23 ]. This “making visible” of the urban poor addresses equity concerns by ensuring that marginalized groups are not overlooked in data-driven planning. Addressing Equity and Service Gaps Equity remains central to our project, as urban health disparities are often compounded by factors like migration status, informal settlement residence, and lack of insurance coverage [ 5 , 24 ]. By capturing detailed data on these populations, we quantify service gaps, such as the proportion of children in Ward 4 who are not enrolled in school or remain unvaccinated. Furthermore, linking household-level data to health facility readiness exposes spatial mismatches areas with high need but inadequate services highlighting inefficiencies that would otherwise remain hidden [ 8 , 15 , 25 ]. Such granular, disaggregated data are indispensable for aligning local interventions with national and global policy goals. The project’s focus on equity supports Nepal’s commitment to universal health coverage and the SDGs, emphasizing inclusive urban growth and access to quality health services for all [ 18 , 24 , 26 ]. Policy and Planning Implications The integrated data portal directly informs municipal decision-making processes. Ward officials can utilize it to draft health budgets, plan the location of new clinics, and monitor progress toward local health goals. Additionally, the portal provides robust evidence to support proposals to regional and national authorities, strengthening advocacy for equitable urban health investments [ 14 , 27 ]. By demonstrating the feasibility and utility of such a system in a pilot municipality, this project lays the groundwork for scaling integrated health data systems nationally. Dissemination of policy briefs and targeted forums will engage policymakers, facilitating institutional adoption and alignment with broader urban development agendas [ 12 , 28 ]. Scalability and Adaptability Although this study focuses on two wards within Budhanilkantha municipality, the data architecture built on common unique identifiers and GIS layers is designed to be scalable and adaptable. The framework can be extended to additional wards and municipalities, with flexible mapping indicators that other cities can tailor to their specific contexts, such as air quality sensors or traffic accident hotspots [ 23 , 29 ]. Documenting implementation lessons around staffing, technology requirements, and costs will provide practical guidance for replication and scaling, addressing a common gap in urban health data initiatives [ 24 , 30 ]. Future Directions Post-implementation, the portal aims to integrate additional data layers including environmental sensors monitoring air and water quality, as well as urban mobility data to capture broader determinants of health. Incorporation of advanced analytics, such as predictive modeling for forecasting disease outbreaks or service demand, could further enhance decision-making capabilities [ 8 , 21 , 31 ]. The portal also envisions evolving into a community-facing platform enabling public health alerts and citizen reporting via mobile apps, fostering community engagement and real-time feedback [ 13 , 32 ]. Long-term, integration with national health information systems will improve continuity of care for migrants and facilitate policy evaluation at granular administrative levels, critical for assessing program impact and refining health strategies [ 18 , 33 ]. Conclusion This study will produce a detailed protocol for developing equitable urban health data systems through an innovative mix of routine data integration and primary data collection. Key contributions include: (i) demonstrating the feasibility of combining participatory mapping, household census, and facility surveys with existing municipal data; (ii) outlining the technical framework for an integrated data portal (dashboards, GIS maps, analytics) to support ward-level planning; and (iii) providing evidence on urban health inequities to inform inclusive governance. We will offer the following recommendations: First, national and municipal governments should invest in local health data infrastructure, including GIS capacity and interoperable information systems. Second, multisectoral collaboration is essential; data from health, water, education, and other sectors must be pooled for a complete picture of urban well-being. Third, this model should be scaled up: pilot results will guide expansion to more wards and cities, with adaptations as needed. 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The urban health penalty and the sustainability challenge. Environ Health Perspect. 2005;113(7):1067–74. Mberu B, Haregu T, Kyobutungi C, Ezeh AC. Urban health in developing countries: convergence of urbanization and globalization. Glob Health Action. 2016;9(1):329–41. Few R. Health and climatic hazards: framing social research on vulnerability, response, and adaptation. Glob Environ Change. 2007;17(2):281–95. Wan G, Zhang X, Zhao M. Urbanization can help reduce income inequality. NPJ Urban Sustain. 2022;2:1. Babalola S, Fatusi A. Determinants of use of maternal health services in Nigeria: looking beyond individual and household factors. BMC Pregnancy Childbirth. 2009;9:43. Ministry of Health, Nepal. Nepal Health Sector Strategy 2015–2020. Kathmandu: Ministry of Health; 2020. Paton C, Braa J, Muhire A, Marco-Ruiz L, Kobayashi S, Fraser H, et al. Open source digital health software for resilient, accessible and equitable healthcare systems. Yearb Med Inf. 2022;31(1):67–73. Proctor EK, Landsverk J, Aarons G, Chambers D, Glisson C, Mittman B. Implementation research in mental health services: an emerging science with conceptual, methodological, and training challenges. Adm Policy Ment Health. 2009;36(1):24–34. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q. 2004;82(4):581–629. Williams O, Kiburi S, McLean M, Cairncross S, Singh S, Capron A, et al. Lessons learned from scaling urban health data initiatives. Health Policy Plan. 2019;34(3):202–9. Roda C, Fouque F, Franconi A, Santos R, Guegan JF, Gake B, et al. Data analytics for public health in urban settings: applications and future directions. J Urban Health. 2020;97(2):162–74. Wright J, Williams P, Mullen R, Day R, Biehl M, Gunn M, et al. Citizen engagement and health: a systematic review of mobile app-based public health interventions. J Med Internet Res. 2019;21(6):e13024. World Health Organization. Health Information Systems: A Manual for Developing Countries. Geneva: WHO; 2017. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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13:59:13","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108575,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8497897/v1/5218a6779bcff1ba955bd219.html"},{"id":99713034,"identity":"6645e68d-d7ed-4221-94cb-5123dc392768","added_by":"auto","created_at":"2026-01-07 13:59:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258242,"visible":true,"origin":"","legend":"\u003cp\u003eResearch approach for development of Urban health data Portal\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8497897/v1/b2d1cb8deffcbfa1cf902edd.png"},{"id":101296763,"identity":"09a20c36-88ed-485f-8dbc-67525ce12791","added_by":"auto","created_at":"2026-01-28 09:20:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8497897/v1/90eff943-48e3-4dcf-9d27-fec7e9e3557f.pdf"}],"financialInterests":"","formattedTitle":"Integrated Urban Health Data: Processes and Prospects for Strengthening Urban Health Systems in Nepal","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eRapid urbanization is one of the defining trends of the 21st century, especially in low- and middle-income countries (LMICs) (1). The United Nations projects that by 2050 nearly 68% of the world\u0026rsquo;s population will live in urban areas, with most growth occurring in South Asia and Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While cities can offer economic opportunities and access to services, unplanned growth often outpaces infrastructure, leading to slums and informal settlements with poor living conditions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In Nepal and similar settings, municipal planning structures face new pressures: heterogeneous populations, mobile labor forces, complex social dynamics and the impact of non-health sectors such as transport, housing, planning on population health and well-being [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These factors contribute to urban health challenges such as overcrowding, environmental hazards, infectious disease transmission, and rising communicable and non-communicable diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Critically, rapid change can exacerbate inequalities: marginalized groups (the urban poor, migrants, women, and ethnic minorities) often experience limited access to health care and worse outcomes compared to wealthier residents [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Rapid migration swells cities, pushing rural and cross-border migrants into informal settlements with little planning. Informal settlements, often invisible in official records, face overlapping deficits\u0026mdash;unsafe housing, poor sanitation, no clean water, inadequate services, and unpaved roads\u0026mdash;while also bearing environmental hazards like flooding, waste pollution, and air contamination. Such clustered risks drive diarrheal disease, mosquito-borne infections, and respiratory illness, yet without detailed local data, municipalities cannot target resources or improve urban health [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the acute need, robust data on urban health status and the determinants that influence health in LMIC settings are often lacking. Routine health information systems (e.g. clinic records, vital registration) are usually designed for national or rural contexts and rarely cover the plurality of providers within urban areas or capture the granularity needed at city or ward levels. They may omit informal settlements entirely or fail to disaggregate by socio-economic status and migration status [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. At the same time, periodic surveys (e.g. DHS, household surveys) provide snapshots but are infrequent and may not cover slum areas and urban issues adequately [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, ward-level or community-level data are scarce. This lack of granuality undermines equity-focused planning, as it obscures where health needs are greatest. For example, a slum cluster in one ward might have high child mortality or lack safe water, but without ward-level data these problems remain \u0026ldquo;hidden\u0026rdquo; in aggregate urban statistics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData fragmentation is a final, critical challenge. Planners and health managers frequently work with fragmented information: different sectors such as health, education, water, maintain separate databases with no interoperability. Health, census, urban planning and utility data are often held in separate silos or updated infrequently, so city officials cannot link data to understand the wider risks to health or to identify system bottlenecks [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In many LMICs, multiple agencies collect divers and potentially valuable information on for example, clinic visits, vital events, migration estimates, but the differing formats or geographical coverage undermine interoperability. A recent review found that a major barrier to effective use of health data for planning, monitoring, and improving service delivery is data fragmentation, where different databases use inconsistent coding and formats [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Likewise, a multi-city study of health information systems noted the data quality and interoperability of various separate systems as the greatest challenges [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Fragmented data mean municipalities lack a coherent evidence base with maps of disease incidence, population density and sanitation rarely overlapping, so planning immunization campaigns or new clinics can miss the neediest areas.\u003c/p\u003e \u003cp\u003eThe rationale for an integrated urban health data approach is to create a comprehensive, interoperable information system that combines all relevant data for local action. Linking routine administrative data with new primary data collection, we can develop detailed community profiles and dynamic monitoring tools. Such a system can support evidence-informed decision-making by municipal authorities, health officials, and civil society, enabling timely responses to emerging issues and addressing inequities. In this model, data become a shared public good rather than siloed departmental assets.\u003c/p\u003e \u003cp\u003eThis study aims to design, implement, and document an integrated urban health data system in selected municipalities of Nepal. Specifically, we will: assess existing municipal data sources and systems to identify gaps and limitations; conduct social mapping to measure and map key health and socio-demographic indicators at the ward level; carry out a complete household census to capture urban health issues, complemented by health facility assessments in two wards of municipality to address data gaps; and develop an interactive dashboard and GIS platform that integrates routine and newly collected data, enabling real-time visualization and analysis to support evidence-based local planning. These wards have diverse populations, including dense informal settlements and migratory populations, and are manageable for intensive fieldwork. Lessons learned will inform broader rollout to other wards and cities in Nepal and similar LMIC contexts.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThis mixed-method study will be conducted in one municipality of Kathmandu, Budhanilkantha using qualitative methods and primary quantitative data collected in two wards of the municipality. Secondary data from routine data from municipality department will be used. We will adopt a pragmatic approach to understand and improve data systems in context. The study will have two phases: qualitative formative research to engage stakeholders and assess existing data systems; and quantitative primary data collection and technical development of the urban data portal. Stakeholders include municipal health officials, infrastructure and environment authorities, education officers, and community representatives.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Formative Research\u003c/h2\u003e \u003cp\u003eWe will conduct a range of activities to gather insights before designing the data collection tools:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStakeholder interviews\u003c/b\u003e: Key informant interviews will be conducted among municipal officials across various departments including; Health, Education, Civil registration system, Disaster, Animal, Agriculture, Environment, Infrastructure, Information Technology (IT), and Women, Children \u0026amp; Inclusion. We will explore existing data source, data workflows, priority health issues, and perceived barriers to data use. Interviews will be semi-structured, recorded (with consent), and thematically analyzed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePolicy and document review\u003c/b\u003e: Municipal plans, health policies, census reports, and previous survey findings will be reviewed to identify what data exist at ward/municipality level and any official strategies for urban health.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese formative activities will map the \u0026ldquo;information ecosystem\u0026rdquo; and guide the design of data collection instruments. They will enhance established relationships and buy-in with local stakeholders, which is crucial for co\u0026mdash;creation of the portal and for ensuring data will be used in future. We will compile an inventory of data sources currently accessible to the municipality:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRoutine data\u003c/b\u003e: This includes the national health information system (e.g. Health Management Information System), civil registration system and municipal education records (school enrollment, attendance). We will request aggregate data at the ward level if available.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNon-routine data\u003c/b\u003e: Past survey or research data (if any), program evaluations (e.g. NGO-led health projects), and any GIS layers (roads, utilities, administrative boundaries).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eBased on the findings of the formative assessment and in agreement with the municipal team, we will identify key routine and non-routine data sources and assess their suitability to be integrated into the urban health data portal. We will identify key limitations, such as fragmentation across departments, lack of geolocation for data points, and absence of data on vulnerable subpopulations (e.g. migrants, urban poor). We will integrate routine municipal data from each department into the portal. Routine data sources include health facility records, vital registration (births and deaths), waste management, water supply, education, and infrastructure databases. These datasets will be inventoried and standardized to ensure consistency in formats, variable names, and indicator definitions. Paper-based records will be digitized where necessary, with data cleaning and validation to improve quality. Automated data pipelines through APIs or periodic batch uploads will transfer routine data into the portal on a regular schedule (e.g. monthly or quarterly).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Primary Data Collection\u003c/h2\u003e \u003cp\u003eRoutine municipal information systems in Nepal, such as the Health Management Information System, civil registration, and administrative records, will provide essential but incomplete data for local planning. These datasets are expected to lack ward-level granularity, omit informal settlements, and fail to capture key household, environmental, and service readiness indicators. During the formative phase, municipal officials are anticipated to identify these data gaps as a major barrier to evidence-based and equitable planning. To address this, primary data collection including social mapping, a comprehensive household census, and a public health facility assessment will be conducted as a one-time, strategic investment to generate a detailed baseline for integration with routine data sources. These activities will be co-designed with local government users to ensure that the tools, indicators, and outputs directly address their information needs and strengthen institutional capacity for data use. While such data collection may entail higher initial costs and logistical demands, it will be essential to fill critical gaps left by routine systems, validate existing data, and reinforce local data ecosystems. The integration of these primary datasets with routine municipal information within a unified urban health data portal will ensure both immediate utility and long-term sustainability. As routine data systems are strengthened, the portal will increasingly rely on continuous data flows from municipal departments, transitioning from intensive primary data generation toward a sustainable, routine-driven data maintenance model that supports equity-focused decision-making. Therefore, we will conduct three interlinked primary data collection activities in two wards of municipality: social mapping, a household census, and a public health facility assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Social Mapping\u003c/h2\u003e \u003cp\u003eWe will use GIS-based social mapping to create detailed maps of two wards of the Budhanilkantha municipality which were selected by the municipal authorities as they represent a mix of low- and middle-income areas, including both planned neighborhoods and informal settlements. Based on discussions with the municipality team, agreed health risk indicators guided the mapping process. Local community members including youth volunteers, ward officials, will work with the research team to sketch ward boundaries and key landmarks on large printouts or digital map platforms. Locations of both public and private health providers and infrastructure (such as hospitals, health posts, basic health care center, NGO clinics, pharmacies), water sources (wells, taps), education facilities (schools, colleges), markets, transportation routes, and high-risk areas (garbage dumps, flood zones) will be marked. Informal settlements, densely populated clusters, open spaces, green spaces, and blue spaces will also be delineated, along with shops selling meat, tobacco sales and liquor as these locations are relevant to understanding environmental health risks, food safety, and behavioral health determinants within the wards. Social mapping will feed into the urban health data portal by visually pinpointing underserved areas, clustering of health risks, and environmental hazards at the ward level. Using the community knowledge with geotagged data, it will create interactive GIS layers that allow users to compare wards, track changes, and overlay health, infrastructure, and environmental indicators making gaps clear and guiding evidence-based local planning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Household Census\u003c/h2\u003e \u003cp\u003eHousehold census data is the backbone of an effective Urban Health Data Portal, providing a detailed and dynamic picture of the community\u0026rsquo;s health landscape. It reveals who lives where, their health status, behaviors, and the environmental conditions shaping their well-being. This granular insight enables targeted, evidence-based interventions by highlighting vulnerable groups, service gaps, and environmental risks. When geotagged and integrated, household data transforms into powerful maps that pinpoint hotspots and track changes over time, driving smarter resource allocation and timely responses. Beyond data, it empowers communities and health workers by making health information transparent and actionable, ultimately fueling healthier, more equitable urban futures. With this importance, the household census will be conducted among household heads, mothers of children under two, and all family members in two wards of the municipality. We will also adopt a citizen science approach by involving local residents in data collection and fieldwork, which improved data accuracy through their local knowledge and enhanced community understanding of the research process. Household heads or the most knowledgeable members will provide data on household composition, socio-demographics, assets, mortality, healthcare access, environmental conditions, and waste management. Adults aged 18 and above will reported on risk behaviors, including tobacco and alcohol use, diet, physical activity, and the prevalence of communicable and non-communicable diseases. Mothers of young children will provide information on maternal health, antenatal and postnatal care, child immunization, and HPV vaccination coverage among adolescent girls aged 10\u0026ndash;19 years, who also self-reported on HPV vaccination status. Household members involved in agriculture or livestock will provide details on land use and livestock ownership. Anthropometric measurements\u0026mdash;height, weight, BMI, and blood pressure\u0026mdash;will be recorded for all adults present, while mid-upper arm circumference assessed nutritional status in children under five years. The questionnaire will be developed in Nepali (and local languages as needed), pretested in a similar setting, and refined accordingly. Field enumerators will use tablets with electronic data entry forms (e.g., Open Data Kit) to minimize errors and enable daily uploads to a secure database. A physical \u0026ldquo;family health folder\u0026rdquo; with a unique ID, summarizing key health indicators, will be printed for each household to engage families and support local health staff in monitoring. Data quality will ensure through daily reviews, GPS tracking of enumerator routes, and random spot-checks by supervisors. The household census provides ward-level population data and identifies community-level patterns such as clusters of vulnerability. We will identify the key indicators from the census will be integrated into the Urban Health Data Portal (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of key indicators from household census that can be integrated into urban health data portal\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocio-Demographic Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold composition (age, gender, education, employment, family structure)\u003c/p\u003e \u003cp\u003eSocio-economic status and assets\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Status and Disease Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrevalence of communicable and non-communicable diseases (NCDs)\u003c/p\u003e \u003cp\u003eMaternal health indicators (antenatal, postnatal care)\u003c/p\u003e \u003cp\u003eChild health indicators (immunization status, including HPV vaccination)\u003c/p\u003e \u003cp\u003eHealth-seeking behaviors and access to healthcare facilities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Behaviors and Lifestyle Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTobacco and alcohol use\u003c/p\u003e \u003cp\u003eDietary habits and physical activity levels\u003c/p\u003e \u003cp\u003eSedentary behavior\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Health Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSanitation and waste management practices\u003c/p\u003e \u003cp\u003eExposure to air pollution\u003c/p\u003e \u003cp\u003eHousing quality and access to safe drinking water\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnthropometric and Clinical Measurements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeight, weight, BMI of household members\u003c/p\u003e \u003cp\u003eBlood pressure readings for adults\u003c/p\u003e \u003cp\u003eMid-upper arm circumference (MUAC) for children under five\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeospatial Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold locations linked to ward boundaries\u003c/p\u003e \u003cp\u003eClusters of informal settlements and densely populated areas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgriculture and Livestock Data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use for agriculture\u003c/p\u003e \u003cp\u003eLivestock and poultry ownership and business activities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here\u003c/p\u003e \u003cp\u003eQuantitative data from the household census and facility survey will be analyzed to compute descriptive statistics (demographic profiles, prevalence of key health and socio-economic indicators). We will use GIS analysis to identify spatial patterns (e.g. clustering of poverty, distance to health services). The integrated portal itself will serve as a living analysis tool, allowing users to generate tables and charts in real time. We will conduct a preliminary evaluation of the portal\u0026rsquo;s usability (e.g. through user acceptance testing with municipal staff) and document any implementation challenges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Health Facility Assessment\u003c/h2\u003e \u003cp\u003eHealth facilities are the heartbeat of an Urban Health Data Portal, delivering real-time, actionable data that brings the city\u0026rsquo;s health landscape into sharp focus. This integration will inform targeted interventions to improve health service coverage and equity in the municipality. They provide crucial insights into service availability, staffing, equipment, and medicine stocks highlighting where resources meet needs and where gaps persist. Understanding patient flow and utilization patterns further reveals emerging health trends and demand spikes, supporting timely, data-driven interventions.\u003c/p\u003e \u003cp\u003eIn response to the municipality\u0026rsquo;s growing demand for evidence-based planning and equitable health service delivery, we will conduct a comprehensive assessment of all public health facilities serving Wards 4 and 7 of Budhanilkantha Municipality. Public facilities are prioritized because they represent the primary access point for essential health services for the majority of residents particularly low-income and marginalized groups and their performance directly reflects municipal accountability for health outcomes. The assessment will cover 11 public facilities, including one public hospital, four primary health care centers, and six health posts.\u003c/p\u003e \u003cp\u003eData will be collected using the structured assessment tools developed for this study to capture detailed information on facility infrastructure, service readiness, equipment, and logistics. The survey will also assess the availability of 18 tracer drugs and collect data on health care providers, including their education, years of experience, and training received. Additionally, secondary data from Nepal\u0026rsquo;s Minimum Service Standards (MSS), a nationally endorsed framework of the Ministry of Health and Population will be used to benchmark performance across domains such as human resources, infrastructure, and service delivery capacity.\u003c/p\u003e \u003cp\u003eGPS coordinates of each facility will be captured to support spatial analysis. Facility data will be linked with household census data using GIS and patient flow information to identify service gaps relative to community health needs and geographic distribution.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the diverse data sources that will be used to develop the Integrated Urban Health Data System. The table highlights how each dataset contributes unique and complementary information needed to generate a comprehensive understanding of urban health, service delivery, and population characteristics in Nepal. The Household Census provides the most detailed demographic, socioeconomic, health and housing information, forming the foundational dataset for constructing population and health indicators. Social mapping adds a spatial dimension by visualizing settlements, services, and environmental risks at the ward level, enabling geographic targeting and resource allocation. The Health Facility Assessment offers systematic information on service availability, infrastructure, and readiness, which is essential for evaluating health system capacity. Routine health information from HMIS/DHIS2 complements this by supplying monthly data on maternal and child health, disease burden, and service utilization trends. Civil Registration/Vital Events, which track births, deaths, and marriages, and the Women, Children, and Social Inclusion system, which records data on disability, single mothers, and vulnerable groups strengthen the system\u0026rsquo;s ability to capture population dynamics and equity-related dimensions of urban health. Administrative insights from municipal records help contextualize local governance, planning, and service delivery functions.\u003c/p\u003e \u003cp\u003eTogether, these datasets ensure that the Urban Health Data System integrates demographic, health, administrative, and social inclusion perspectives, enabling municipalities to plan, monitor, and improve urban health services in a more informed and evidence-based manner.\u003c/p\u003e \u003cp\u003eInsert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Sources Included in the Development of the Urban Health Data System\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of Data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose in the Study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpected Output\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Census\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary household-level demographic, socioeconomic, and housing data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriodic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo obtain baseline information on population, housing, and SES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBase dataset for indicator construction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSocial Mapping\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpatial data on settlements, services, and environmental risks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriodic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo map community structure, service distribution, and hazards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWard-level spatial layers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth Facility Assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFacility readiness, service delivery, infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriodic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo assess availability, readiness, and quality of health services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFacility readiness index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHMIS / DHIS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoutine health service data (MCH, NCDs, diseases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo extract key health indicators and service utilization trends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHealth service indicator set\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMunicipal Records\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative and program data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo understand municipal service delivery, planning, and governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMunicipal administrative profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCivil Registration (Vital Events)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth, marriage, death registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo incorporate vital statistics into municipal-level demographic and health profiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBirth rate, death rate, marriage statistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen, Children, and Social Inclusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData on disability, single mothers, vulnerable households, inclusion indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriodic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo integrate social inclusion and vulnerability characteristics into urban health profiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDisability indicators, vulnerable household profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMunicipal Records\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdministrative and program data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTo understand municipal service delivery, planning, and governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMunicipal administrative profiles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data Integration and Portal Development","content":"\u003cp\u003eThe final phase focuses on integrating all collected data and developing the urban health data portal framework. We will geocode all mapped locations households, health facilities, and infrastructure assigning each household a unique ID to enable linkage with facility records (with consent) and standardize administrative codes across datasets. This allows seamless cross-referencing, such as connecting household health indicators to nearby facility readiness. Data cleaning and standardization will harmonize variables and ensure quality through checks for inconsistencies and duplicates. Routine government data will be incorporated and aligned with ward boundaries using common identifiers, supported by a comprehensive data dictionary. In collaboration with municipal stakeholders, we will co-create a user-friendly, web-based, multilingual portal accessible via computers and mobile devices. In collaboration with municipal stakeholders, we will co-create a user-friendly, web-based, multilingual portal accessible via computers and mobile devices. We will adopt a user-centered design (UCD) approach, which emphasizes iterative engagement with end users throughout all stages of design, testing, and refinement. Municipal health officials, and community representatives will be actively involved through participatory workshops, prototype testing sessions, and feedback loops to ensure that the portal\u0026rsquo;s structure, visualizations, and functionalities align with their practical needs and digital capacities. This approach reflects established principles of user-centered design, which highlight the importance of engaging users to enhance system usability, acceptability, and long-term adoption [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKey features will include interactive dashboards displaying health indicators by ward, GIS mapping layers for household density, infrastructure, WASH coverage, and health outcomes, as well as basic analytics tools to identify hotspots and generate custom reports. Role-based access will ensure appropriate data security and privacy. To promote sustainability, the portal will be built on the open-source platforms DHIS2 enhanced with GIS modules, with thorough documentation and training provided to municipal IT staff. Evidence shows that integrated, geo-referenced urban health data enables cities to identify inequities and allocate resources effectively. Without such systems, informal settlements and migrant populations often remain invisible, limiting targeted interventions. Interconnecting population dynamics, environmental risks, and service coverage, this integrated data approach can empower LMIC cities to reduce slum health burdens and create more equitable, resilient urban health systems.\u003c/p\u003e"},{"header":"4. Stakeholder Engagement and Cocreation","content":"\u003cp\u003eThroughout the project, we will maintain active stakeholder engagement, guided by principles of user-centered design to ensure that end users such as municipal officials, and community representatives are meaningfully involved in shaping the system\u0026rsquo;s functionality, usability, and relevance. This participatory approach will promote ownership, enhance user experience, and support the long-term sustainability of the portal. At key milestones including planning, social mapping, household census, and portal development we will hold co-creation workshops with municipal officials, the mayor, deputy mayor, department focal persons, and ward representatives, fostering collaborative decision-making and strong support for each activity. During the household census, we will discuss and identify innovative approaches tailored to the community\u0026rsquo;s needs such as developing and distributing pamphlets to households and broadcasting jingles through local taxis to inform residents about the purpose and benefits of the census and to encourage active participation. These workshops will play a vital role in supporting the Urban Health Data Portal by bringing together diverse stakeholders to collaboratively shape its design, functionality, and implementation. Through open dialogue and shared decision-making, the portal will be tailored to local needs and priorities, fostering ownership among municipal officials, health workers, and community members. Co-creation will help identify relevant data sources, prioritize key health indicators, and develop user-friendly features suited to different users. Moreover, these workshops will be designed to facilitate capacity building by involving stakeholders directly in the process, increasing their ability to effectively use and maintain the portal. This collaborative approach aims to promote transparency, trust, and sustained engagement ultimately leading to a more responsive, inclusive, and impactful urban health data system.\u003c/p\u003e"},{"header":"5. How Integrated Urban Health Data Can Help","content":"\u003cp\u003eAn integrated urban health data system \u0026ndash; linking demographic, environmental, and service data at neighborhood scales can help municipalities tackle these challenges. Such systems combine maps of populations (even in slums) with layers on health facilities, water networks, and socioeconomic indicators. For instance, new geospatial methods (satellite imagery, mobile data, surveys) can now estimate key SDG indicators at high resolution. Recent projects have \u0026ldquo;modeled dozens of household survey indicators\u0026rdquo; (e.g. poverty, stunting, literacy) at 1\u0026times;1 km or finer across entire countries [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In practice, these data allow city planners to see precisely which informal neighborhoods lack clinics, sanitation or parks, and to monitor changes as slums expand. Area-level \u0026ldquo;health determinants\u0026rdquo; can include access to quality health facilities, traffic density, level of informality, pollution and social exclusion [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] all measurable by integrating diverse datasets. \u0026ldquo;Vital registration data such as records of births, deaths, marriages, and migration are examples of routine data collected continuously. Municipalities can utilize this routine data to strengthen their health information systems, improve population monitoring, and enhance planning and service delivery. Aggregating this information helps cities anticipate health service demand as people relocate. During outbreaks or seasonal epidemics, integrated urban data enable quicker, localized responses. For example, in western Kenya a digital HDSS used tablets and a central database (OpenHDS) to collect malaria data in real time [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This system ensured \u0026ldquo;quickly and effectively\u0026rdquo; gathered data with built-in quality checks, saving time and cost and demonstrating feasibility of electronic urban health surveillance [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, GIS-based dashboards (overlaying clinic cases, environmental risks and mobility patterns) have proven powerful in targeting vector control or immunization in Latin American slums and elsewhere. However, maintaining such a hub and keeping the data up to date presents significant challenges, including ensuring timely data collection, managing data quality, and integrating multiple data sources effectively.\u003c/p\u003e"},{"header":"6. Ethical Considerations","content":"\u003cp\u003eEthical approval has been obtained from the Nepal Health Research Council (NHRC). All participants in interviews and the household census will provide informed consent. The purpose of data collection w explained, and participation is voluntary. Personal identifiers will be removed from analysis datasets; household survey data will be de-identified and stored securely. We will comply with NHRC guidelines and the principles of the Helsinki Declaration. Community consent will also be obtained from ward leaders, and we will share findings in an understandable format with the communities involved.\u003c/p\u003e \u003c/p\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eUrbanization in low- and middle-income countries (LMICs) like Nepal presents complex health challenges characterized by rapid population growth, migration, and the expansion of informal settlements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Addressing these challenges requires data systems that transcend traditional siloed approaches, integrating diverse data sources to capture the multifaceted determinants of urban health [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study describes an ambitious effort to develop an integrated urban health data system in a Nepalese municipality by combining routine administrative data with participatory mapping and household surveys. Our goal is to fill critical data gaps and facilitate equity-oriented urban health planning aligned with both local and global commitments such as the Sustainable Development Goals (SDGs), particularly SDG 11 on sustainable cities and communities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Importance of Integrated Urban Health Data Systems\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIntegrated data systems are increasingly recognized as foundational for \u0026ldquo;healthy cities\u0026rdquo; initiatives, enabling timely, evidence-informed decisions that improve health equity and service delivery [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Unlike traditional top-down health surveillance systems, which often rely on aggregated or incomplete data, our approach adopts a bottom-up, cross-sectoral methodology. This reflects the complex social, environmental, and infrastructural determinants that shape urban health outcomes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. An integrated health data portal enhances municipal capacity to identify underserved populations and allocate resources more fairly. For example, our preliminary data reveal that Ward 7 in Budhanilkantha municipality hosts a high density of informal settlers but is served by only one under-resourced clinic. Such insights prompt targeted investment and resource mobilization, crucial for addressing inequities [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Real-time dashboards enable continuous monitoring of health indicators, such as spikes in diarrheal disease following floods, facilitating rapid response and containment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This \u0026ldquo;making visible\u0026rdquo; of the urban poor addresses equity concerns by ensuring that marginalized groups are not overlooked in data-driven planning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAddressing Equity and Service Gaps\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEquity remains central to our project, as urban health disparities are often compounded by factors like migration status, informal settlement residence, and lack of insurance coverage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By capturing detailed data on these populations, we quantify service gaps, such as the proportion of children in Ward 4 who are not enrolled in school or remain unvaccinated. Furthermore, linking household-level data to health facility readiness exposes spatial mismatches areas with high need but inadequate services highlighting inefficiencies that would otherwise remain hidden [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Such granular, disaggregated data are indispensable for aligning local interventions with national and global policy goals. The project\u0026rsquo;s focus on equity supports Nepal\u0026rsquo;s commitment to universal health coverage and the SDGs, emphasizing inclusive urban growth and access to quality health services for all [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolicy and Planning Implications\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe integrated data portal directly informs municipal decision-making processes. Ward officials can utilize it to draft health budgets, plan the location of new clinics, and monitor progress toward local health goals. Additionally, the portal provides robust evidence to support proposals to regional and national authorities, strengthening advocacy for equitable urban health investments [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By demonstrating the feasibility and utility of such a system in a pilot municipality, this project lays the groundwork for scaling integrated health data systems nationally. Dissemination of policy briefs and targeted forums will engage policymakers, facilitating institutional adoption and alignment with broader urban development agendas [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eScalability and Adaptability\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAlthough this study focuses on two wards within Budhanilkantha municipality, the data architecture built on common unique identifiers and GIS layers is designed to be scalable and adaptable. The framework can be extended to additional wards and municipalities, with flexible mapping indicators that other cities can tailor to their specific contexts, such as air quality sensors or traffic accident hotspots [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Documenting implementation lessons around staffing, technology requirements, and costs will provide practical guidance for replication and scaling, addressing a common gap in urban health data initiatives [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eFuture Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePost-implementation, the portal aims to integrate additional data layers including environmental sensors monitoring air and water quality, as well as urban mobility data to capture broader determinants of health. Incorporation of advanced analytics, such as predictive modeling for forecasting disease outbreaks or service demand, could further enhance decision-making capabilities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The portal also envisions evolving into a community-facing platform enabling public health alerts and citizen reporting via mobile apps, fostering community engagement and real-time feedback [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Long-term, integration with national health information systems will improve continuity of care for migrants and facilitate policy evaluation at granular administrative levels, critical for assessing program impact and refining health strategies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study will produce a detailed protocol for developing equitable urban health data systems through an innovative mix of routine data integration and primary data collection. Key contributions include: (i) demonstrating the feasibility of combining participatory mapping, household census, and facility surveys with existing municipal data; (ii) outlining the technical framework for an integrated data portal (dashboards, GIS maps, analytics) to support ward-level planning; and (iii) providing evidence on urban health inequities to inform inclusive governance. We will offer the following recommendations: First, national and municipal governments should invest in local health data infrastructure, including GIS capacity and interoperable information systems. Second, multisectoral collaboration is essential; data from health, water, education, and other sectors must be pooled for a complete picture of urban well-being. Third, this model should be scaled up: pilot results will guide expansion to more wards and cities, with adaptations as needed. Fourth, community engagement throughout the process fosters data ownership and ensures that the system addresses real needs. Finally, we call on policymakers and development partners to support integrated, resilient urban health data systems as a priority in rapidly urbanizing LMICs. Effective implementation of such systems will help achieve equitable access to health services and improve outcomes for all city residents.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations, Department of Economic and Social Affairs. Population Division. World Urbanization Prospects: The 2018 Revision. New York: United Nations; 2019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEzeh A, Oyebode O, Satterthwaite D, Chen YF, Ndugwa R, Sartori J, et al. The history, geography, and sociology of slums and the health problems of people who live in slums. 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Geneva: WHO; 2017.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8497897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8497897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban populations in low- and middle-income countries (LMICs) are growing rapidly, bringing diverse socio-economic groups, extensive informal settlements, and frequent migration into cities. Local governments in cities are central to policies and programmes to protect and improve the health of their urban populations. Yet, many struggle to access, integrate and utilise data across the multiple sectors that influence health, plurality of health providers and to do this at a granular spatial level that supports local decision making. These trends have led to persistent health challenges and stark disparities in access to care and health outcomes, as resource allocation and planning often lag behind dynamic urban need. This protocol describes a mixed-methods implementation study in two wards of Budhanilkantha municipality of Nepal to assess existing municipal data systems, collect geospatial data and household data, and build an integrated urban health data portal. We will conduct stakeholder interviews, community focus groups, and policy reviews to understand data use and gaps. We will then carry out social mapping, a door-to-door household census, and health facility assessments to generate detailed ward-level data on population, infrastructure, and health care services. These data will be geocoded and linked via common identifiers to existing routine health and administrative databases. We will co-create an interactive portal with local government users, featuring real-time dashboards, GIS maps, and analytic tools to support local planning and equity-focused decision-making. The expected outcome is a proof-of-concept framework showing how combined routine and primary data can inform resilient, multisectoral urban health governance.\u003c/p\u003e","manuscriptTitle":"Integrated Urban Health Data: Processes and Prospects for Strengthening Urban Health Systems in Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 13:59:08","doi":"10.21203/rs.3.rs-8497897/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cab4a3e4-f053-48b5-9797-870019c18859","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T20:24:43+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 13:59:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8497897","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8497897","identity":"rs-8497897","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}