The Citizens Survey on Healthcare in India

The Citizens Survey on Healthcare in India | 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 Data Note The Citizens Survey on Healthcare in India Anuska Kalita, Siddhesh Zadey, Sudheer Kumar Shukla, Shubhangi Bhadada, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7274969/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 The pursuit of Universal Health Coverage (UHC) in India is particularly challenging given the country's vast population and pronounced socioeconomic disparities. Although extensive research addresses specific healthcare areas, contemporary data on citizens' healthcare access, quality, and preferences to inform UHC design are lacking. To bridge this gap, the Lancet Citizens' Commission on Reimagining India's Health System conducted a Citizens Survey from November 2022 to April 2023, interviewing respondents in person from 50,000 randomly selected households across 125 districts in 29 Indian states and Union Territories in the country. The survey comprised 141 questions covering healthcare utilization, experiences, costs, satisfaction, delivery preferences, insurance coverage, willingness to pay, health information behaviors, technology use, aspirational health norms, and electoral attitudes towards health. The survey had a high participation rate (98%) and a low non-response rate (9.5%), 70% of households were rural, 56% of respondents were male, 79% were Hindu, and 39% identified as Scheduled Caste or Tribes. The data aim to inform citizen-centric reforms, advancing a UHC responsive to India's diverse population needs. Universal Health Coverage India Health Systems Citizens Survey Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background & Summary Universal Health Coverage (UHC) is a cornerstone of the Sustainable Development Goals (SDGs), particularly Goal 3 ("Ensure healthy lives and promote well-being for all at all ages"), reflecting a global consensus on the need to improve population health and reduce health inequalities 1 . A core guiding principle of UHC is access to timely, quality, affordable and efficient healthcare across the full range of health needs. In India, achieving UHC is particularly challenging due to the country's vast population, regional diversity, federal polity and persistent socioeconomic disparities. India has introduced several policy initiatives and flagship health programs to expand coverage, improve service quality, and reduce out-of-pocket expenditures. However, measuring progress toward UHC within a large and varied population often relies on top-down metrics such as insurance enrollment rates, quantifying service use, facility-level performance or aggregated health indicators. While these measures offer valuable insights, they do not directly capture citizens' lived experiences and expectations. In particular, there is limited empirical evidence on citizens’ preferences of various models of health care delivery, such as primary health care; citizen expectations, such as willingness to pay and desired quality of health care; utilization of technology for health care; and healthcare related expectations from elected representatives. Such data is critical for designing person-centered healthcare systems which are equitable, engender trust in health systems, and ensure that policies are aligned with people’s aspirations and expectations. Such data can also serve as a benchmark for assessing the progress towards UHC over time and evaluating the impact of new policy initiatives. The Citizens Survey was undertaken by the Lancet Citizens' Commission on Reimagining India's Health System (the Commission), involving a partnership of investigators from diverse institutions, to address this knowledge gap. The Commission, formed in 2021, aims to lay out a roadmap to achieve UHC for the people of India, and understanding citizens' experiences, expectations and experiences is its central pillar 2 . As such, the Citizens' Survey was undertaken to enable health system researchers, practitioners, and policymakers to design and implement more responsive health policies by incorporating citizens' inputs. The Commission has drawn heavily upon findings of the Survey to design its recommendations. This paper describes the design, methods and respondent characteristics of the Citizens Survey. What sets this dataset apart from several existing national and state-level household surveys is its granular information on (i) experiences of and preferences for providers across different levels of care for public and private sectors; (ii) perceptions of healthcare quality; (iii) perceptions of healthcare affordability and willingness to pay; (iv) political prioritization of health in voting and electoral accountability; (v) sources of health information; and (vi) utilization of digital technologies for health. Post-hoc coding of the dataset has generated three linked geographic variables for each household– administrative names of survey villages and wards, geographical information system mapping, and names of electoral constituencies, which do not always overlap with administrative units. The dataset aims to inform evidence-based decision-making and contribute meaningfully to India's journey towards achieving UHC by providing information on these underexplored dimensions. Methods Study Setting As of 2025, India is home to 1.4 billion people 3 , comprising a fifth of the world's population. The country's estimated gross domestic product (GDP) is $ 3.9 trillion, with a nominal per capita value of $ 2,878 and India is the most populous country in the world 4 . The country is divided into 28 states and 8 Union Territories, each with populations ranging from under a million (as in some smaller Union Territories) to over 200 million (such as Uttar Pradesh) 3 . These states and Union Territories are further divided into 780 districts, which typically serve populations of approximately 1 to 3 million people 5 , although district boundaries have frequently changed in recent decades due to evolving governance structures and socio-political considerations. For example, India had 640 districts according to the 2011 Census compared to 593 districts in the 2001 Census, illustrating dynamic administrative restructuring reflecting population growth. Each district is then subdivided into blocks—often referred to as tehsils or talukas—where populations generally range from around 100,000 to 300,000 5 . Rural areas are governed by local bodies called Panchayats and are characterized by lower population densities, with agriculture or related activities forming the mainstay of regional economies. Urban areas, by contrast, are defined by higher population densities and are administered by Urban Local Bodies – either municipalities or municipal corporations. These areas typically exhibit a broader economic base, including diverse non-agricultural livelihoods. At the upper end of the urban spectrum, India's metropolitan cities, such as Mumbai, Delhi, and Bengaluru, feature tens of millions of residents and more advanced infrastructure. Significant district-level variations exist in socioeconomic and health care infrastructure indicators, which profoundly influence access to and the quality of healthcare services. While national and state-level assessments of UHC provide broad overviews, they often obscure marked heterogeneities observed at the district level. Variability in the performance of health systems at the district scale underscores pronounced disparities in healthcare outcomes, access, utilization, and financial risk protection, influenced by geographic, economic, social, and demographic factors 6 – 8 . These district-level disparities are further amplified by urban-rural differences, with urban districts typically demonstrating better educational, healthcare, and economic opportunities compared to their rural counterparts. Socioeconomic inequalities, driven by factors such as income, caste, tribe, gender, and religion, further shape differences. Capturing this district-level diversity and its impact on health system outcomes has been a central consideration in designing and implementing the Citizens' Survey, allowing nuanced insights into the realities of UHC across India's diverse landscape and aligning with India’s goals of decentralized planning and delivery of healthcare. Sampling The study employs a multi-stage sampling design. In the first stage, districts served as the Primary Sampling Units (PSUs). In the second stage, Secondary Sampling Units (SSUs) comprised villages in rural areas and wards (also known as mohallas) in urban areas. Households were selected within each SSU in the third stage, and finally, one respondent per selected household was chosen in the fourth stage, forming the Ultimate Sampling Units (USUs). The total sample size was 50,000 respondents, distributed across 125 districts in 29 states and union territories. The sampling strategy is illustrated in Fig. 1 and described below. Stage 1 - Selection of Primary Sampling Units First, a district-level sample size was established to determine the total number of districts required for the national sample. Assuming an average district population of 2 million, selecting 383 households per district provides a 95% confidence level with a 5% margin of error for most major indicators of interest for the Citizens Survey. For practical purposes, this required number was rounded up to 400 households per district, yielding 400 respondents per district (one respondent per household). To reach the desired national sample of 50,000 respondents, 125 districts were selected. Among these: One hundred and eight districts were selected through Stratified Random Sampling using the district UHC index (UHCd index), published elsewhere 7 . The UHCd index was developed to provide a detailed assessment of UHC at the district level. It adapts the measurement framework initially created by the World Health Organization and the World Bank 9 , focusing on five key domains: reproductive, maternal, newborn, and child health; infectious diseases; non-communicable diseases; service capacity and access; and financial risk protection. This index encompasses 24 tracer indicators aggregated using geometric means to generate an overall UHC score for each district. Scores range from 0–100%, with higher values indicating better health service coverage and financial protection performance. The UHCd index captures disparities in health coverage, offering insights into geographic and socioeconomic inequalities. It highlights substantial regional variations, with southern districts generally performing better, while those in central, eastern, and northeastern ones performing worse. Additionally, the index addresses inter- district inequalities by analyzing dimensions such as wealth, urban-rural location, religion, and social group, identifying areas where disadvantaged populations face significant barriers to health access 7 . The index was used to classify districts into tertiles (High, Medium, and Low). For the Citizens' Survey, 36 districts were selected from each of the High, Medium, and Low UHCd index categories. Seven large cities, encompassing 11 districts, were selected based on the House Rent Allowance (HRA) classification in the Census of India 10 . These seven cities – Delhi, Mumbai, Chennai, Kolkata, Hyderabad, Bengaluru, and Pune – are classified as Category X (or Tier 1) cities in the Census and represent the largest and most economically significant urban centers. They are characterized by high population densities, advanced infrastructure, and substantial contributions to the national economy. Six districts were selected purposively to align with a parallel qualitative study – the District Case Studies –undertaken by the Commission to understand the perceptions of different health system stakeholders on progress and barriers towards UHC. Figure 2 shows the sampled districts along with their UHCd index categories. Table S1 in the supplementary material lists the names and number of households of the sampled districts. Stage 2 - Selection of Secondary Sampling Units Within each sampled district (PSU), 40 SSUs were selected through stratified random sampling, with 10 respondents targeted per SSU. The allocation of SSUs between rural and urban areas was proportional to the district's rural-urban population ratio, according to estimates from the 2011 Census of India. For each sampled district, two strata were created Stratum 1 (Rural) : A list of all villages with information on total population, number of households, and total vulnerable caste (Scheduled Caste—SC) and indigenous tribe (Scheduled Tribe—ST) populations. Stratum 2 (Urban) : A list of all wards with corresponding population data, number of households, and total SC and ST populations. From both lists, the required number of SSUs was selected using random sampling, weighted by the SC/ST population of each SSU based on Census estimates 10 . In the urban SSUs (wards), one mohalla (an area with a population of at least 1,000) was randomly selected. Since wards often encompass large populations, focusing on one mohalla helped maintain data quality for the 10 targeted respondents. In larger villages (those with more than 300 households), the village was segmented into a minimum of three equal parts of around 125–150 households each, and two segments were randomly selected for data collection. Smaller villages (those with fewer than 300 households) were covered in their entirety to draw the sample. Stage 3 - Selection of Households For each village and mohalla, the survey team obtained an estimate of the total number of households from local authorities or community leaders. This total was divided by 10 to determine the relative risk reduction (rrr) sampling interval. Starting from a randomly chosen number between 1 and rrr, every rrr-th household was selected until the 10th household was reached, yielding 10 households per SSU. This was done by following the random walking method with the right-hand rule. From the entry point of the village, a random household was selected as the random start, and after that, the investigator moved through the village using a right-hand rule, selecting every 'rth' household. Table 1 shows the state-wise proportions of household samples and the number of households sampled across districts. Table 1 Households and population of Indian states as a percentage of the national totals and sampled households by states shown as percentage of the total sample of the Citizens Survey State Total Households Number of Sampled households in state households households in the state* as % of sampled as % of total for the total national Citizens sample of households Survey (n) the (%) Citizens Survey (%) Jammu & Kashmir 21,19,718 0.8 400 0.8 Himachal Pradesh 14,83,280 0.6 400 0.8 Punjab 55,13,071 2.2 1,600 3.2 Uttarakhand 20,56,975 0.8 800 1.6 Haryana 48,57,524 1.9 2,000 4.0 NCT of Delhi 34,35,999 1.4 800 1.6 Rajasthan 1,27,11,146 5.1 2,000 4.0 Uttar Pradesh 3,34,48,035 13.4 5,600 11.2 Bihar 1,89,13,565 7.6 2,800 5.6 Sikkim 1,29,006 0.1 400 0.8 Arunachal Pradesh 2,70,577 0.1 1,600 3.2 Nagaland 3,96,002 0.2 1,200 2.4 Manipur 5,57,859 0.2 800 1.6 Mizoram 2,22,853 0.1 800 1.6 Tripura 8,55,556 0.3 400 0.8 Meghalaya 5,48,059 0.2 400 0.8 Assam 64,06,471 2.6 2,400 4.8 West Bengal 2,03,80,315 8.2 2,000 4.0 Jharkhand 62,54,781 2.5 1,600 3.2 Odisha 96,37,820 3.9 2,000 4.0 Chhattisgarh 56,50,724 2.3 1,600 3.2 Madhya Pradesh 1,50,93,256 6.0 3,600 7.2 Gujarat 1,22,48,428 4.9 2,000 4.0 Maharashtra 2,44,21,519 9.8 3,600 7.2 Andhra Pradesh 2,10,22,588 8.4 800 1.6 Karnataka 1,33,57,027 5.4 2,800 5.6 Kerala 78,53,754 3.1 800 1.6 Tamil Nadu 1,85,24,982 7.4 2,400 4.8 Telangana NA 2,400 4.8 Total 24,95,01,663 99.5 50,000 100.0 Note: * Census 2011. NA: not available. The state of Telangana was formed in 2014, after the last census. Stage 4 - Selection of Ultimate Sampling Units Within each sampled household, one member— aged 15 years or older, permanently residing in the household—was interviewed as the respondent. The interviewer prepared a list of all family members aged 15 years or above in descending order of their ages, then selected one respondent using the Kish Grid 11 . The Kish Grid, introduced by Leslie Kish in 1949, is widely used in large-scale sample surveys. This technique ensures equal- probability sampling by facilitating random selection when multiple individuals are eligible for inclusion at a sampled household during a surveyor's visit 11 . In the event that the selected individual in the sampled household was unavailable, the next eligible individual was invited to participate. If a respondent refused to participate in the survey, the household immediately adjacent to the sampled household that refused was targeted for interview, and a skip was followed as per the next 'rth' household (described in Stage 3 above). Households without any members aged 15 years or older were excluded from the survey. Additionally, within selected households, individuals who did not have sufficient knowledge or information about the family were not included in the interview process. Design and Testing of Survey Instrument The Citizens Survey used a single survey instrument. This was designed by the authors of this paper, led by a Scientific Advisory Committee (SAC), with the co-Principal Investigators (VP and GK) serving as chairs. Extensive literature reviews, including a review of existing survey instruments used in global and Indian studies, were conducted to inform the design of the instruments. Validated questions were adapted from surveys like the Consumer Assessment of Healthcare Providers and Systems (CAHPS) 12 , the National Family Health Survey (NFHS) 13 , the National Sample Survey (NSS) 14 , and the India Health Systems Reform Project 15 . The survey instruments were translated into 18 local Indian languages by professional translators. These were checked by two separate teams and reverse-translated into English to ensure that the meaning and intention of each survey item were maintained. The Citizens Survey has eight main sections focusing on demographics, use and experiences of outpatient and inpatient care on access, utilization, expenditure, and user satisfaction, preferences for future care, health insurance, health information seeking and technology use, aspirational norms, and electoral preferences. Table 2 lists the section themes, examples of constructs covered, and the number of questions in each section. The detailed questionnaire is included with the dataset and associated material. Table 2 Citizens Survey instrument themes, constructs, and questions Themes Examples of constructs Number of questions Demographics* and general self-health perceptions. Respondent gender, education, occupation, household details including size, religion, social group, etc. 12 Outpatient visit experience Visit type, disease category, expenditures, travel and wait times, facility conditions, patient satisfaction, etc. 30 Outpatient care preferences* Reasons for choice of healthcare facility/provider 16 Inpatient hospitalization experience Visit type, disease category, expenditures, travel and wait times, facility conditions, patient satisfaction, etc. 25 Inpatient care preferences* Reasons for choice of healthcare facility/provider 16 Healthcare insurance Enrolment in different schemes, insurance premium, perceived adequacy of coverage, willingness to pay, etc. 11 Health information and technology use (internet and smartphones) use* Sources of health information, different use cases of technology for information seeking, use of identity card (Aadhar) and linking records, etc. 18 Healthcare (aspirational) norms and electoral preferences* Normative preferences for types of providers, access to care, costs, agency in health-seeking, importance of health in electoral decision-making, etc. 13 Notes: * indicates data about the individual respondent of the survey/recent health facility encounter. The rest are data about the household. The survey instruments were tested in two different phases – pre-testing and piloting. The first phase involved a pre-test conducted using paper-based tools on a small sample of 21respondents to assess the comprehensibility and clarity of the survey questions to both the interviewer and the respondent, including errors in translation. The revised instrument was programmed onto electronic tablets using Computer-Assisted Personal Interviewing (CAPI) software. The second phase involved piloting these electronic versions of the instruments with 51 households across four units (two rural and two urban units) in Uttar Pradesh and Tamil Nadu in July 2022. The purpose of the pilot phase was to further test the clarity and comprehensibility of the revised instrument by confirming that respondents understood each question and its accompanying response options, and that they interpreted the questions exactly as the researchers intended, minimizing the risk of ambiguity or misinterpretation. The pilot phase also examined the instrument's logical flow, ensuring it guided participants seamlessly from one topic to the next without confusion. Another key objective was to test the software's user-friendliness and any errors in the programming. Finally, the pilot measured the time it took participants to complete the survey, enabling the researchers to estimate the overall time requirement and make necessary adjustments for efficiency. The survey instruments were finalized after incorporating feedback from the pilot phase and deliberating with the Scientific Advisory Committee. Data Collection and Quality Assurance The Citizens Survey was implemented from November 2022 to April 2023 by an independent research agency, Development and Research Services (DRS), based in India. DRS is among India's largest survey research firms, specializing exclusively in the development and social sectors. Headquartered in New Delhi, DRS operates regional and state-level offices and field teams, enabling comprehensive data gathering across India's districts, including remote and hard-to-reach areas. DRS has served as the primary data collection agency for several prominent household surveys, including multiple rounds of the National Family Health Survey. The Citizens Survey data was collected via CAPI software-enabled devices and uploaded daily to a central server. Answers were recorded in coded form for each survey response. Identifying information (e.g., name, sex, age, address) was stored in the database, but only the codes appeared in the analytical dataset. A master code list was maintained to facilitate data interpretation. Once processing was complete, aggregated results for different parameters were presented at the state or district level, with corresponding state or district codes de-identified using the digital master code list. Respondents were assigned a unique ID for identification and de-identification, ensuring confidentiality and data integrity. Several measures were taken to ensure the integrity and quality of all collected data through layered mechanisms—robust hiring, comprehensive training, field-level monitoring, and ongoing quality checks. First, recruitment and selection of survey teams were conducted meticulously to ensure the inclusion of skilled, experienced, and culturally competent individuals. Field investigators were required to have a minimum of a bachelor's degree and were further evaluated on their previous work experience, linguistic proficiency, familiarity with local customs, and prior survey experience relevant to the project's scope. This process was overseen by the National Field Manager, who coordinated recruitment efforts in close collaboration with the Supervisors. Supervisors, each holding at least an undergraduate degree, were proficient in local languages and had at least five years of experience in survey research, often with extensive expertise in using CAPI devices. These Supervisors maintained rosters of pre-vetted field investigators within their regions and recruited teams on a project-by-project basis. After passing initial screenings, selected candidates attended a rigorous training program. During or upon completion of the training, investigators were assessed for attentiveness, understanding of the material, and performance on a final evaluation. Only those who demonstrated the required competence and reliability were officially selected for field deployment. Training played a critical role in preparing field investigators for the Citizens Survey, which spanned multiple languages and involved a large-scale data collection effort. To address the complexity and scale of the survey, a Training of Master Trainers was conducted at two central locations – Delhi and Bengaluru. These sessions comprehensively covered the study's research design, methodology, data collection protocols, and quality control measures. The objective was to ensure Master Trainers were fully equipped to lead training sessions in their respective regions. Upon completing this specialized training, Master Trainers returned to their home states to train Supervisors and Field Research Officers (FRO) tasked with data collection. FRO were provided with printed manuals and resources translated into local languages to eliminate any potential linguistic barriers to learning. The training process progressed in three structured phases. First, classroom-based sessions introduced the study's objectives, scope, methodology, and questionnaire. FRO initially worked with paper versions of the questionnaire before transitioning to multiple practice rounds using CAPI devices. During these sessions, investigators conducted mock interviews under the close supervision of trainers. Next, field practice sessions simulated real-world data collection scenarios. In small groups, participants conducted interviews with volunteers from nearby communities, utilizing paper surveys and CAPI devices. Each participant typically completed at least five practice interviews, gaining confidence and familiarity with the instruments. Finally, a debriefing session was held to allow participants to share their challenges, discoveries, and lessons from the field practice. This phase addressed any remaining ambiguities, ensuring that teams were fully prepared to begin formal data collection. This process was followed for each state team. Beyond training, a tiered monitoring system was implemented to maintain oversight throughout the data collection process. Supervisors and core team members conducted regular back checks, spot checks, and accompanied calls during data collection, ensuring that at least 20% of all interviews were independently verified. Any mistakes detected during this process were immediately corrected, and the details of the errors, in particular those related to the wording of questions or response categories, were shared with all teams to prevent recurrence and consistency across sites. To ensure quality and consistency, the first five interviews conducted by every interviewer were recorded and reviewed. Additionally, debriefing sessions were held regularly throughout the data collection period. These sessions allowed investigators to discuss challenges, share best practices, and clarify procedural doubts. The duration of interviews ranged from a minimum of 15 minutes to a maximum of 45 minutes. On an average, each interview took approximately 35 minutes to complete. When respondents struggled to answer a question, investigators employed several techniques to encourage participation. This included rephrasing questions for better clarity, offering relatable examples, allowing respondents additional time to think, and suggesting different ways to respond. These strategies were part of the comprehensive training provided to all investigators before the survey began. The Survey Sample Overall, survey participation rates were high – over 98% of the households approached agreed to participate, while the mean national non-response rate was low at 9.47%, with rates of 5.7% in rural areas, 10.2% in urban areas, and 16% in metropolitan cities. These are comparable to the non-response rates in the nationally representative survey–National Family and Health Survey-5 (NFHS-5) 2019–2021, ranging from 14.7% among urban men respondents (see Table A.4 in the source report) 13 to 4.6% among rural women respondents (see Table A.3 in the source report) 13 . Across states, the non-response rates in the Citizens Survey varied from 4.3% in Chhattisgarh to 39.1% in Delhi ( Fig. 3 ) . This variation follows the well-known pattern observed in India and globally, where urbanized regions and households with higher socioeconomic status have greater survey refusal rates 16 – 18 . Table 3 summarizes the key characteristics of the sample. There were 217 cases where individuals opted not to participate after hearing the consent information. These were considered non-participating cases, and no data was collected from them. For all other interviews, data collection was completed in full, resulting in a final dataset with no missing entries. Table 3: Key characteristics of the Citizens Survey sample (N=50,000) Background Characteristics Sampled Households Sampled Households (n) (%) Place of Residence of Household Rural 35,175 70.4 Urban 14,825 29.7 Age of Respondent 15-24 6,790 13.6 25-34 14,496 29.0 35-49 18,265 36.5 50-59 6,321 12.6 60+ 4,128 8.3 Gender of Respondent Male 28,154 56.3 Female 21,840 43.7 Other 4 0.0 Do not answer (DNA) 2 0.0 Educational Attainment of Respondent Below-Primary (Not Literate or below Class 5th) 9,067 18.1 Primary (Class 5th) 6,767 13.5 Up to Middle (Class 8th) 9,076 18.2 Up to Secondary (Class 10th) 10,834 21.7 Higher-Secondary and Above (11-12th and above) 13,959 27.9 Do not answer (DNA) 297 0.6 Social Group of Household General 13,039 26.1 SCs 10,328 20.7 STs 9,231 18.5 OBCs 16,741 33.5 Do not answer (DNA) 277 0.6 Others 384 0.8 Religion of Household Hindu 39,296 78.6 Muslim 4,039 8.1 Others 6,665 13.3 Marital Status of Respondent Never married 7,146 14.3 Married / Live in 40,706 81.4 Widow / Widower 1,678 3.4 Divorced/Separated 171 0.3 Not Stated 269 0.5 Do not answer (DNA) 30 0.1 Occupation of Respondent Cultivator 7,666 15.33 Wage-Labourer 12,457 24.91 Self-Employed 10,496 20.99 Regular-Salaried 4,809 9.62 Others 14,572 29.14 Household Size 1-3 9,211 18.42 4-5 23,434 46.87 6+ 17,355 34.71 Total 50,000 100.0 Ethical approval Ethical approval for the Citizens Survey was obtained from the Institutional Review Board of the Christian Medical College, Vellore in India (No. 14934, dated October 22, 2022). Technical Validation We conducted a range of assessments of the concurrent and criterion validity of our data. First, we assessed whether we could replicate the rural-urban differences in internet technology penetration. Figure 4 illustrates the consistently higher uptake of technology in the healthcare context among urban respondents compared to rural respondents. Second, previous assessments using nationally representative samples, for example the National Sample Surveys, have established that household healthcare expenditures are greater for those seeking care in the private sector compared to those seeking care in the public sector 14 . Figure 5 notes the differences in the average health expenditures for outpatient visits and inpatient hospitalizations in the last 12 months, demonstrating consistently high expenditures across all components for the private sector. The UHCd categorization, developed independently based on distinct criteria and utilized for our sampling, provided an external standard against which the Citizens Survey data were assessed for concurrent validation. Specifically, indicators such as utilization of outpatient care, public sector healthcare usage, and out-of-pocket expenditures demonstrated clear gradients aligned with the expected patterns derived from the UHCd categorization. We see that utilization of outpatient care in the public sector was higher in districts in the high UHCd tertile and these were predominantly in states considered as better performing on health and development indicators (such as the southern states of Tamil Nadu, Kerala, and Karnataka), compared to districts in the low tertiles belonging mostly to states with weaker performance (such as Uttar Pradesh, Bihar, and Jharkhand) 19,20 . Additionally, utilization of public sector outpatient care was accompanied by lower OOPE. We considered comparisons of our data with a number of recent national datasets – the Comprehensive Annual Modular Survey with health-related data (NSS 79 th Round July 2022 – June 2023), Survey on AYUSH 2022-2023 (NSS 79 th Round July 2022 – June 2023), and the Survey on Household Consumption Expenditure or SHCE 2022-23 (August 2022 – July 2023). However, several key differences in the sampling strategies between the Citizens Survey and these datasets prevent us from drawing definitive conclusions; for instance, the NSS datasets employ stratification of households within villages or urban blocks based on monthly per capita consumption expenditure prior to household selection, whereas the Citizens Survey uses a uniform interval-based sampling method without such stratification. Declarations Acknowledgments We thank the other members of our Scientific Advisory Committee (Gagandeep Kang, Sapna Desai, and Atul Gupta) for their guidance in formulating the study. We thank the Commissioners of the Lancet Citizens’ Commission for their feedback on the survey and analysis. We thank the researchers who worked on the formulation of the survey, data collection and analysis, including Dipanwita Sengupta, Sandul Yasobant, Vinod Joseph, Hasna Ashraf, Sanghamitra Sengupta, and Alok Vajpayi. We thank the DRS team for the data collection. We express our gratitude to CMC Vellore and Infosys Limited for the support. Most importantly, we are deeply grateful to all the respondents of this survey who generously shared their time and information with us to create this dataset. Funding Funding for this study was provided by a grant from Infosys Limited, Bangalore, India. Author Contributions AK conceptualized the manuscript. AK and SZ drafted the manuscript. VP, SVS, SK, SB, SC conceptualized and designed the survey. VP and SVS guided the development of the manuscript. DR and MKC led the data collection and quality assurance. SZ and SKS led the data cleaning and data preparation, with contributions from JM and PC. All authors critically reviewed the manuscript and guided its finalization. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. All authors had access to the data and a role in writing the manuscript. Competing Interests The authors declare no competing interests. References Patrick Hoang-Vu Eozenou, S. N., Ana Florina Pirlea. Universal Health Coverage as a Sustainable Development Goal. https://datatopics.worldbank.org/world-development- indicators/stories/universal-health-coverage-as-a-sustainable-development-goal.html (2023). Patel, V., Mazumdar-Shaw, K., Kang, G., Das, P. & Khanna, T. Reimagining India’s health system: a Lancet Citizens’ Commission. The Lancet 397 , 1427–1430 (2021). The World Bank. World Development Indicators (online dataset). (2023). World Bank. 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Addressing Non-response Bias in Surveys of Wealthy Households in Low- and Middle-Income Countries: Strategies and Implementation. J. Dev. Stud. 59 , 1427–1442 (2023). Prinja, S. et al. A composite indicator to measure universal health care coverage in India: way forward for post-2015 health system performance monitoring framework. Health Policy Plan. 32 , 43–56 (2017). Sehgal, M., Jatrana, S. & Johnson, L. A comprehensive health index for India: development, validation, and spatial variation. J. Popul. Res. 41 , 21 (2024). Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTables.docx Supplementary Table S1: Names of sampled districts and metropolitan cities with respective number of households across the Indian states included in the Citizens Survey 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7274969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":494534037,"identity":"a5cbfff6-2c46-47ba-9436-aee20c8b55d9","order_by":0,"name":"Anuska Kalita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie3RsWrDMBCA4RMGdwl4VYYmr3DBEFIS8ixnAtbSpB0LCcSTp0DXPoY7td0EBnuR8OoxXjKnW6ZSiVIKJSrt1kH/JIQ+TkIAPt9/rAdMEsAAAUJg2ccmBwh+IiCJIP4jAYKk+DWJduVedqe5eIp0tWfP8xusddfC3TTJHITrHM3FFsuXh5VAphZXhRLxBJRwEmjAkmBZtL0xZ3mA/SwNzaJ0kmFzcTRkK7BRlmyxf38w5M1NUO/slJJQXltSYsTtlMxNRkrdSkrrUdGuUp7ktSGHYEKViF1koMRjd5qth9joir/mawyjlLXHzfTS+fyvzA/R55rcx74Rn8/n853rHXgFXFDBKtudAAAAAElFTkSuQmCC","orcid":"","institution":"Harvard University","correspondingAuthor":true,"prefix":"","firstName":"Anuska","middleName":"","lastName":"Kalita","suffix":""},{"id":494535859,"identity":"64070342-899d-4c45-a383-79d7e30c3e32","order_by":1,"name":"Siddhesh Zadey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYDACZgYDBgY2BgZ+MM8GzDY4QJQWyQYQL40YLQxQLRBlaTARPEC+nXnzhx9lNombjx9/+LkiwSafT7p544EfDDb58g44rDjMVmDYcy4tcduZHGPJMwlplm0yxwoO9jCkWW7E4TwDZh6DBN62w4nbDuQwSDb+OGzAJpFjcICH4bCBYQMOhzXzGBz82/Y/cXP/88c/GxIgWg7+waOF4TCPYTNv24HEDRIJZpIwLYdBtsjj0AH0SzGzzLlk4xk33phZNiSkAbWkFRyWMUgzwBVu8v2HN398U2Yn29+f/vhmQ4KNgfyMZKBIBZCBy2FQ4Igmb0A4Qu2xOICALaNgFIyCUTBiAADZyV1UfP/OvwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-1176-1529","institution":"Association for Socially Applicable Research (ASAR)","correspondingAuthor":true,"prefix":"","firstName":"Siddhesh","middleName":"","lastName":"Zadey","suffix":""},{"id":494535860,"identity":"2132a8fb-1051-43be-bb94-38c9849af8ba","order_by":2,"name":"Sudheer Kumar Shukla","email":"","orcid":"","institution":"Health Systems Transformation Platform","correspondingAuthor":false,"prefix":"","firstName":"Sudheer","middleName":"Kumar","lastName":"Shukla","suffix":""},{"id":494535861,"identity":"7fcab530-47ce-4b35-8e58-9a00502dc362","order_by":3,"name":"Shubhangi Bhadada","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Shubhangi","middleName":"","lastName":"Bhadada","suffix":""},{"id":494535862,"identity":"bb39d8f1-f66d-4532-9466-b32165fe8b0c","order_by":4,"name":"Sumit Kane","email":"","orcid":"","institution":"University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Sumit","middleName":"","lastName":"Kane","suffix":""},{"id":494535863,"identity":"ddae4012-ee80-493e-9808-c02b2a77f748","order_by":5,"name":"Dolon Roy","email":"","orcid":"","institution":"Development and Research Services","correspondingAuthor":false,"prefix":"","firstName":"Dolon","middleName":"","lastName":"Roy","suffix":""},{"id":494535864,"identity":"101da83d-9608-42e3-9686-a645978484c6","order_by":6,"name":"Mukund Kumar Chandan","email":"","orcid":"","institution":"Development and Research Services","correspondingAuthor":false,"prefix":"","firstName":"Mukund","middleName":"Kumar","lastName":"Chandan","suffix":""},{"id":494535865,"identity":"dbf81112-2bc9-43d2-9b52-0ee2395aadd4","order_by":7,"name":"Jashanjot Singh Mangat","email":"","orcid":"","institution":"Association for Socially Applicable Research (ASAR)","correspondingAuthor":false,"prefix":"","firstName":"Jashanjot","middleName":"Singh","lastName":"Mangat","suffix":""},{"id":494535866,"identity":"da088ee1-f9c4-45f1-a61e-93d0bbc94750","order_by":8,"name":"Preeyati Chopra","email":"","orcid":"","institution":"Association for Socially Applicable Research (ASAR)","correspondingAuthor":false,"prefix":"","firstName":"Preeyati","middleName":"","lastName":"Chopra","suffix":""},{"id":494535867,"identity":"1d177d96-7795-4a4c-9ec9-cbe18fd521ba","order_by":9,"name":"Sarika Chaturvedi","email":"","orcid":"","institution":"Dr. D.Y. Patil Medical College Hospital and Research Centre, Dr D Y Patil Vidyapeeth","correspondingAuthor":false,"prefix":"","firstName":"Sarika","middleName":"","lastName":"Chaturvedi","suffix":""},{"id":494535868,"identity":"ba689419-baba-478f-95e1-c8d36abb20c5","order_by":10,"name":"Sonia Bhalotra","email":"","orcid":"","institution":"University of Warick","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Bhalotra","suffix":""},{"id":494535869,"identity":"474faa4d-b498-4c7f-8ee3-8bc92df9e491","order_by":11,"name":"SV Subramanian","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"SV","middleName":"","lastName":"Subramanian","suffix":""},{"id":494535870,"identity":"5f66f729-ff27-4690-971b-14471716d488","order_by":12,"name":"Vikram Patel","email":"","orcid":"","institution":"Harvard University","correspondingAuthor":false,"prefix":"","firstName":"Vikram","middleName":"","lastName":"Patel","suffix":""}],"badges":[],"createdAt":"2025-08-02 02:37:02","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7274969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7274969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88301702,"identity":"2c7704a6-8803-4f73-a0e6-a7b34a54a198","added_by":"auto","created_at":"2025-08-05 04:48:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSampling Methodology used in the Citizens Survey.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/637256008e0232727cc052b4.png"},{"id":88301703,"identity":"fe156207-e481-4259-86c5-406b62a4ae0e","added_by":"auto","created_at":"2025-08-05 04:48:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":406566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistricts classified according to the Universal Health Coverage Index (UHCd) tertiles sampled for the Citizens Survey. \u003c/strong\u003eNote: Map is not drawn to scale.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/09bd307e7154c4934b336a37.png"},{"id":88301454,"identity":"4984981b-f322-4b96-a853-31bcbf1f1e8f","added_by":"auto","created_at":"2025-08-05 04:40:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":419283,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-response rates in the Citizens Survey across Indian states. \u003c/strong\u003eRates were derived using a manually maintained logbook that documented every household approached for the survey. Households were categorized as follows: (1) those who declined immediately (upfront refusals), (2) those who declined after listening to the consent statement, and (3) those who consented and completed the interview. Non-response rates were calculated based on the first two categories— i.e., households that did not proceed beyond the consent stage. Note: Map is not drawn to scale.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/3715fd6ee263ab8ebed88941.png"},{"id":88301704,"identity":"ad7226b0-c08d-48f2-8d06-6ad01611b256","added_by":"auto","created_at":"2025-08-05 04:48:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":328760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth technology uptake variables across rural and urban areas reported by the Citizens Survey.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/3f88387895ba649d0b65ff5c.png"},{"id":88301456,"identity":"d4589c93-ed20-4573-ad49-fd5c662d10bc","added_by":"auto","created_at":"2025-08-05 04:40:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":338052,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHealth expenditures (in Indian Rupees) by service components on the most recent outpatient visit and inpatient hospitalization in the last 12 months across public and private sector providers reported by the Citizens Survey.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/7ca4ddccca12b90c7235b228.png"},{"id":88302495,"identity":"b3acbce4-b436-4c9f-8c90-04cf71c3d0b2","added_by":"auto","created_at":"2025-08-05 04:56:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2747745,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/0f41ef6d-7def-4211-8273-e9df03aa4ae4.pdf"},{"id":88301450,"identity":"c5d7e75e-8105-49b0-a78a-296d1930a1d4","added_by":"auto","created_at":"2025-08-05 04:40:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S1: Names of sampled districts and metropolitan cities with respective number of households across the Indian states included in the Citizens Survey\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7274969/v1/7f0c0289e3346deaa83b1c71.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Citizens Survey on Healthcare in India\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Background \u0026 Summary","content":"\u003cp\u003eUniversal Health Coverage (UHC) is a cornerstone of the Sustainable Development Goals (SDGs), particularly Goal 3 (\"Ensure healthy lives and promote well-being for all at all ages\"), reflecting a global consensus on the need to improve population health and reduce health inequalities\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A core guiding principle of UHC is access to timely, quality, affordable and efficient healthcare across the full range of health needs. In India, achieving UHC is particularly challenging due to the country's vast population, regional diversity, federal polity and persistent socioeconomic disparities. India has introduced several policy initiatives and flagship health programs to expand coverage, improve service quality, and reduce out-of-pocket expenditures. However, measuring progress toward UHC within a large and varied population often relies on top-down metrics such as insurance enrollment rates, quantifying service use, facility-level performance or aggregated health indicators. While these measures offer valuable insights, they do not directly capture citizens' lived experiences and expectations. In particular, there is limited empirical evidence on citizens\u0026rsquo; preferences of various models of health care delivery, such as primary health care; citizen expectations, such as willingness to pay and desired quality of health care; utilization of technology for health care; and healthcare related expectations from elected representatives. Such data is critical for designing person-centered healthcare systems which are equitable, engender trust in health systems, and ensure that policies are aligned with people\u0026rsquo;s aspirations and expectations. Such data can also serve as a benchmark for assessing the progress towards UHC over time and evaluating the impact of new policy initiatives.\u003c/p\u003e\u003cp\u003eThe Citizens Survey was undertaken by the Lancet Citizens' Commission on Reimagining India's Health System (the Commission), involving a partnership of investigators from diverse institutions, to address this knowledge gap. The Commission, formed in 2021, aims to lay out a roadmap to achieve UHC for the people of India, and understanding citizens' experiences, expectations and experiences is its central pillar\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. As such, the Citizens' Survey was undertaken to enable health system researchers, practitioners, and policymakers to design and implement more responsive health policies by incorporating citizens' inputs. The Commission has drawn heavily upon findings of the Survey to design its recommendations. This paper describes the design, methods and respondent characteristics of the Citizens Survey. What sets this dataset apart from several existing national and state-level household surveys is its granular information on (i)\u003c/p\u003e\u003cp\u003eexperiences of and preferences for providers across different levels of care for public and private sectors; (ii) perceptions of healthcare quality; (iii) perceptions of healthcare affordability and willingness to pay; (iv) political prioritization of health in voting and electoral accountability; (v) sources of health information; and (vi) utilization of digital technologies for health. Post-hoc coding of the dataset has generated three linked geographic variables for each household\u0026ndash; administrative names of survey villages and wards, geographical information system mapping, and names of electoral constituencies, which do not always overlap with administrative units. The dataset aims to inform evidence-based decision-making and contribute meaningfully to India's journey towards achieving UHC by providing information on these underexplored dimensions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Setting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs of 2025, India is home to 1.4\u0026nbsp;billion people\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, comprising a fifth of the world's population. The country's estimated gross domestic product (GDP) is \u003cspan\u003e$\u003c/span\u003e3.9 trillion, with a nominal per capita value of \u003cspan\u003e$\u003c/span\u003e2,878 and India is the most populous country in the world\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The country is divided into 28 states and 8 Union Territories, each with populations ranging from under a million (as in some smaller Union Territories) to over 200\u0026nbsp;million (such as Uttar Pradesh)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These states and Union Territories are further divided into 780 districts, which typically serve populations of approximately 1 to 3\u0026nbsp;million people\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, although district boundaries have frequently changed in recent decades due to evolving governance structures and socio-political considerations. For example, India had 640 districts according to the 2011 Census compared to 593 districts in the 2001 Census, illustrating dynamic administrative restructuring reflecting population growth.\u003c/p\u003e\u003cp\u003eEach district is then subdivided into blocks\u0026mdash;often referred to as tehsils or talukas\u0026mdash;where populations generally range from around 100,000 to 300,000\u003csup\u003e5\u003c/sup\u003e. Rural areas are governed by local bodies called Panchayats and are characterized by lower population densities, with agriculture or related activities forming the mainstay of regional economies. Urban areas, by contrast, are defined by higher population densities and are administered by Urban Local Bodies \u0026ndash; either municipalities or municipal corporations. These areas typically exhibit a broader economic base, including diverse non-agricultural livelihoods. At the upper end of the urban spectrum, India's metropolitan cities, such as Mumbai, Delhi, and Bengaluru, feature tens of millions of residents and more advanced infrastructure.\u003c/p\u003e\u003cp\u003eSignificant district-level variations exist in socioeconomic and health care infrastructure indicators, which profoundly influence access to and the quality of healthcare services. While national and state-level assessments of UHC provide broad overviews, they often obscure marked heterogeneities observed at the district level. Variability in the performance of health systems at the district scale underscores pronounced disparities in healthcare outcomes, access, utilization, and financial risk protection, influenced by geographic, economic, social, and demographic factors\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These district-level disparities are further amplified by urban-rural differences, with urban districts typically demonstrating better educational, healthcare, and economic opportunities compared to their rural counterparts. Socioeconomic inequalities, driven by factors such as income, caste, tribe, gender, and religion, further shape differences. Capturing this district-level diversity and its impact on health system outcomes has been a central consideration in designing and implementing the Citizens' Survey, allowing nuanced insights into the realities of UHC across India's diverse landscape and aligning with India\u0026rsquo;s goals of decentralized planning and delivery of healthcare.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSampling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe study employs a multi-stage sampling design. In the first stage, districts served as the Primary Sampling Units (PSUs). In the second stage, Secondary Sampling Units (SSUs) comprised villages in rural areas and wards (also known as mohallas) in urban areas. Households were selected within each SSU in the third stage, and finally, one respondent per selected household was chosen in the fourth stage, forming the Ultimate Sampling Units (USUs). The total sample size was 50,000 respondents, distributed across 125 districts in 29 states and union territories. The sampling strategy is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and described below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStage 1 - Selection of Primary Sampling Units\u003c/strong\u003e\u003cp\u003eFirst, a district-level sample size was established to determine the total number of districts required for the national sample. Assuming an average district population of 2\u0026nbsp;million, selecting 383 households per district provides a 95% confidence level with a 5% margin of error for most major indicators of interest for the Citizens Survey. For practical purposes, this required number was rounded up to 400 households per district, yielding\u003c/p\u003e\u003c/p\u003e\u003cp\u003e400 respondents per district (one respondent per household). To reach the desired national sample of 50,000 respondents, 125 districts were selected. Among these:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eOne hundred and eight districts were selected through Stratified Random Sampling using the district UHC index (UHCd index), published elsewhere\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The UHCd index was developed to provide a detailed assessment of UHC at the district level. It adapts the measurement framework initially created by the World Health Organization and the World Bank\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, focusing on five key domains: reproductive, maternal, newborn, and child health; infectious diseases; non-communicable diseases; service capacity and access; and financial risk protection. This index encompasses 24 tracer indicators aggregated using geometric means to generate an overall UHC score for each district. Scores range from 0\u0026ndash;100%, with higher values indicating better health service coverage and financial protection performance. The UHCd index captures disparities in health coverage, offering insights into geographic and socioeconomic inequalities. It highlights substantial regional variations, with southern districts generally performing better, while those in central, eastern, and northeastern ones performing worse. Additionally, the index addresses inter- district inequalities by analyzing dimensions such as wealth, urban-rural location, religion, and social group, identifying areas where disadvantaged populations face significant barriers to health access\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The index was used to classify districts into tertiles (High, Medium, and Low). For the Citizens' Survey, 36 districts were selected from each of the High, Medium, and Low UHCd index categories.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSeven large cities, encompassing 11 districts, were selected based on the House Rent Allowance (HRA) classification in the Census of India\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These seven cities \u0026ndash; Delhi, Mumbai, Chennai, Kolkata, Hyderabad, Bengaluru, and Pune \u0026ndash; are classified as Category X (or Tier 1) cities in the Census and represent the largest and most economically significant urban centers. They are characterized by high population densities, advanced infrastructure, and substantial contributions to the national economy.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSix districts were selected purposively to align with a parallel qualitative study \u0026ndash; the District Case Studies \u0026ndash;undertaken by the Commission to understand the perceptions of different health system stakeholders on progress and barriers towards UHC.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the sampled districts along with their UHCd index categories. Table S1 in the supplementary material lists the names and number of households of the sampled districts.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStage 2 - Selection of Secondary Sampling Units\u003c/strong\u003e\u003cp\u003eWithin each sampled district (PSU), 40 SSUs were selected through stratified random sampling, with 10 respondents targeted per SSU. The allocation of SSUs between rural and urban areas was proportional to the district's rural-urban population ratio, according to estimates from the 2011 Census of India. For each sampled district, two strata were created\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eStratum 1 (Rural)\u003c/em\u003e: A list of all villages with information on total population, number of households, and total vulnerable caste (Scheduled Caste\u0026mdash;SC) and indigenous tribe (Scheduled Tribe\u0026mdash;ST) populations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eStratum 2 (Urban)\u003c/em\u003e: A list of all wards with corresponding population data, number of households, and total SC and ST populations.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFrom both lists, the required number of SSUs was selected using random sampling, weighted by the SC/ST population of each SSU based on Census estimates\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In the urban SSUs (wards), one mohalla (an area with a population of at least 1,000) was randomly selected. Since wards often encompass large populations, focusing on one mohalla helped maintain data quality for the 10 targeted respondents. In larger villages (those with more than 300 households), the village was segmented into a minimum of three equal parts of around 125\u0026ndash;150 households each, and two segments were randomly selected for data collection. Smaller villages (those with fewer than 300 households) were covered in their entirety to draw the sample.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStage 3 - Selection of Households\u003c/strong\u003e\u003cp\u003eFor each village and mohalla, the survey team obtained an estimate of the total number of households from local authorities or community leaders. This total was divided by 10 to determine the relative risk reduction (rrr) sampling interval. Starting from a randomly chosen number between 1 and rrr, every rrr-th household was selected until the 10th household was reached, yielding 10 households per SSU. This was done by following the random walking method with the right-hand rule. From the entry point of the village, a random household was selected as the random start, and after that, the investigator moved through the village using\u003c/p\u003e\u003c/p\u003e\u003cp\u003ea right-hand rule, selecting every 'rth' household. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the state-wise proportions of household samples and the number of households sampled across districts.\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\u003eHouseholds and population of Indian states as a percentage of the national totals and sampled households by states shown as percentage of the total sample of the Citizens Survey\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\u003eState\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHouseholds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumber of\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSampled\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ehouseholds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ein state\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehouseholds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ehouseholds\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ein the state*\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eas % of\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003esampled\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eas % of\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003etotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003efor the\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003etotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003enational\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eCitizens\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003esample of\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003ehouseholds\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eSurvey (n)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003ethe\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eCitizens\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eSurvey\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJammu \u0026amp; Kashmir\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,19,718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHimachal Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,83,280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePunjab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55,13,071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUttarakhand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20,56,975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHaryana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48,57,524\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNCT of Delhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34,35,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRajasthan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,27,11,146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUttar Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,34,48,035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBihar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,89,13,565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSikkim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,29,006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArunachal Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,70,577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNagaland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,96,002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,57,859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMizoram\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,22,853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTripura\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,55,556\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeghalaya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,48,059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAssam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64,06,471\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest Bengal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,03,80,315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJharkhand\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62,54,781\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOdisha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96,37,820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChhattisgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56,50,724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMadhya Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,50,93,256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGujarat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,22,48,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaharashtra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,44,21,519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAndhra Pradesh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,10,22,588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKarnataka\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,33,57,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKerala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78,53,754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTamil Nadu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,85,24,982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTelangana\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,95,01,663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: * Census 2011. NA: not available. The state of Telangana was formed in 2014, after the last census.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStage 4 - Selection of Ultimate Sampling Units\u003c/strong\u003e\u003cp\u003eWithin each sampled household, one member\u0026mdash; aged 15 years or older, permanently residing in the household\u0026mdash;was interviewed as the respondent. The interviewer prepared a list of all family members aged 15 years or above in descending order of their ages, then selected one respondent using the Kish Grid\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The Kish Grid, introduced by Leslie Kish in 1949, is widely used in large-scale sample surveys. This technique ensures equal- probability sampling by facilitating random selection when multiple individuals are eligible for inclusion at a sampled household during a surveyor's visit \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. In the event that the selected individual in the sampled household was unavailable, the next eligible individual was invited to participate. If a respondent refused to participate in the survey, the household immediately adjacent to the sampled household that refused was targeted for interview, and a skip was followed as per the next 'rth' household (described in Stage 3 above). Households without any members aged 15 years or older were excluded from the survey. Additionally, within selected households, individuals who did not have sufficient knowledge or information about the family were not included in the interview process.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDesign and Testing of Survey Instrument\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Citizens Survey used a single survey instrument. This was designed by the authors of this paper, led by a Scientific Advisory Committee (SAC), with the co-Principal Investigators (VP and GK) serving as chairs. Extensive literature reviews, including a review of existing survey instruments used in global and Indian studies, were conducted to inform the design of the instruments. Validated questions were adapted from surveys like the Consumer Assessment of Healthcare Providers and Systems (CAHPS)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, the National Family Health Survey (NFHS)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, the National Sample Survey (NSS)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and the India Health Systems Reform Project\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The survey instruments were translated into 18 local Indian languages by professional translators. These were checked by two separate teams and reverse-translated into English to ensure that the meaning and intention of each survey item were maintained. The Citizens Survey has eight main sections focusing on demographics, use and experiences of outpatient and inpatient care on access, utilization, expenditure, and user satisfaction, preferences for future care, health insurance, health\u003c/p\u003e\u003cp\u003einformation seeking and technology use, aspirational norms, and electoral preferences. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the section themes, examples of constructs covered, and the number of questions in each section. The detailed questionnaire is included with the dataset and associated material.\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\u003eCitizens Survey instrument themes, constructs, and questions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThemes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExamples of constructs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNumber of questions\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographics* and general self-health perceptions.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRespondent gender, education, occupation, household details including size, religion, social group, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutpatient visit experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit type, disease category, expenditures, travel and wait times, facility conditions, patient satisfaction, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutpatient care preferences*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReasons for choice of healthcare facility/provider\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInpatient hospitalization experience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVisit type, disease category, expenditures, travel and wait times, facility conditions, patient satisfaction, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInpatient care preferences*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eReasons for choice of healthcare facility/provider\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnrolment in different schemes, insurance premium, perceived adequacy of coverage, willingness to pay, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth information and technology use (internet and smartphones) use*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSources of health information, different use cases of technology for information seeking, use of identity card (Aadhar) and linking records, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealthcare (aspirational) norms and electoral preferences*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNormative preferences for types of providers, access to care, costs, agency in health-seeking, importance of health in electoral decision-making, etc.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\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\u003e\u003cstrong\u003eNotes:\u0026nbsp;\u003c/strong\u003e* indicates data about the individual respondent of the survey/recent health facility encounter. The rest are data about the household.\u003c/p\u003e\u003cp\u003eThe survey instruments were tested in two different phases \u0026ndash; pre-testing and piloting. The first phase involved a pre-test conducted using paper-based tools on a small sample of 21respondents to assess the comprehensibility and clarity of the survey questions to both the interviewer and the respondent, including errors in translation. The revised instrument was programmed onto electronic tablets using Computer-Assisted Personal Interviewing (CAPI) software. The second phase involved piloting these electronic versions of the instruments with 51 households across four units (two rural and two urban units) in Uttar Pradesh and Tamil Nadu in July 2022. The purpose of the pilot phase was to further test the clarity and comprehensibility of the revised instrument by confirming that respondents understood each question and its accompanying response options, and that they interpreted the questions exactly as the researchers intended, minimizing the risk of ambiguity or misinterpretation. The pilot phase also examined the instrument's logical flow, ensuring it guided participants seamlessly from one topic to the next without confusion. Another key objective was to test the software's user-friendliness and any errors in the programming. Finally, the pilot measured the time it took participants to complete the survey, enabling the researchers to estimate the overall time requirement and make necessary adjustments for efficiency. The survey instruments were finalized after incorporating feedback from the pilot phase and deliberating with the Scientific Advisory Committee.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Quality Assurance\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Citizens Survey was implemented from November 2022 to April 2023 by an independent research agency, Development and Research Services (DRS), based in India. DRS is among India's largest survey research firms, specializing exclusively in the development and social sectors. Headquartered in New Delhi, DRS operates regional and state-level offices and field teams, enabling comprehensive data gathering across India's districts, including remote and hard-to-reach areas. DRS has served as the primary data collection agency for several prominent household surveys, including multiple rounds of the National Family Health Survey.\u003c/p\u003e\u003cp\u003eThe Citizens Survey data was collected via CAPI software-enabled devices and uploaded daily to a central server. Answers were recorded in coded form for each survey response. Identifying information (e.g., name, sex, age, address) was stored in the database, but only the codes appeared in the analytical dataset. A master code list was maintained to facilitate data interpretation. Once processing was complete, aggregated results for different parameters were presented at the state or district level, with corresponding state or district codes de-identified using the digital master code list. Respondents were assigned a unique ID for identification and de-identification, ensuring confidentiality and data integrity.\u003c/p\u003e\u003cp\u003eSeveral measures were taken to ensure the integrity and quality of all collected data through layered mechanisms\u0026mdash;robust hiring, comprehensive training, field-level monitoring, and ongoing quality checks. First, recruitment and selection of survey teams were conducted meticulously to ensure the inclusion of skilled, experienced, and culturally competent individuals. Field investigators were required to have a minimum of a bachelor's degree and were further evaluated on their previous work experience, linguistic proficiency, familiarity with local customs, and prior survey experience relevant to the project's scope. This process was overseen by the National Field Manager, who coordinated recruitment efforts in close collaboration with the Supervisors. Supervisors, each holding at least an undergraduate degree, were proficient in local languages and had at least five years of experience in survey research, often with extensive expertise in using CAPI devices. These Supervisors maintained rosters of pre-vetted field investigators within their regions and recruited teams on a project-by-project basis. After passing initial screenings, selected candidates attended a rigorous training program. During or upon completion of the training, investigators were assessed for attentiveness, understanding of the material, and performance on a final evaluation. Only those who demonstrated the required competence and reliability were officially selected for field deployment.\u003c/p\u003e\u003cp\u003eTraining played a critical role in preparing field investigators for the Citizens Survey, which spanned multiple languages and involved a large-scale data collection effort. To address the complexity and scale of the survey, a Training of Master Trainers was conducted at two central locations \u0026ndash; Delhi and Bengaluru. These sessions comprehensively covered the study's research design, methodology, data collection protocols, and quality control measures. The objective was to ensure Master Trainers were fully equipped to lead training sessions in their respective regions.\u003c/p\u003e\u003cp\u003eUpon completing this specialized training, Master Trainers returned to their home states to train Supervisors and Field Research Officers (FRO) tasked with data collection. FRO were provided with printed manuals and resources translated into local languages to eliminate any potential linguistic barriers to learning. The training process progressed in three structured phases. First, classroom-based sessions introduced the study's objectives, scope, methodology, and questionnaire. FRO initially worked with paper versions of the questionnaire before transitioning to multiple practice rounds using CAPI devices. During these sessions, investigators conducted mock interviews under the close supervision of trainers. Next, field practice sessions simulated real-world data collection scenarios. In small groups, participants conducted interviews with volunteers from nearby communities, utilizing paper surveys and CAPI devices. Each participant typically completed at least five practice interviews, gaining confidence and familiarity with the instruments. Finally, a debriefing session was held to allow participants to share their challenges, discoveries, and lessons from the field practice. This phase addressed any remaining ambiguities, ensuring that teams were fully prepared to begin formal data collection. This process was followed for each state team.\u003c/p\u003e\u003cp\u003eBeyond training, a tiered monitoring system was implemented to maintain oversight throughout the data collection process. Supervisors and core team members conducted regular back checks, spot checks, and accompanied calls during data collection, ensuring that at least 20% of all interviews were independently verified. Any mistakes detected during this process were immediately corrected, and the details of the errors, in particular those related to the wording of questions or response categories, were shared with all teams to prevent recurrence and consistency across sites. To ensure quality and consistency, the first five interviews conducted by every interviewer were recorded and reviewed. Additionally, debriefing sessions were held regularly throughout the data collection period. These sessions allowed investigators to discuss challenges, share best practices, and clarify procedural doubts.\u003c/p\u003e\u003cp\u003eThe duration of interviews ranged from a minimum of 15 minutes to a maximum of 45 minutes. On an average, each interview took approximately 35 minutes to complete. When respondents struggled to answer a question, investigators employed several techniques to encourage participation. This included rephrasing questions for better clarity, offering relatable examples, allowing respondents additional time to think, and suggesting different ways to respond. These\u003c/p\u003e\u003cp\u003estrategies were part of the comprehensive training provided to all investigators before the survey began.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe Survey Sample\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOverall, survey participation rates were high \u0026ndash; over 98% of the households approached agreed to participate, while the mean national non-response rate was low at 9.47%, with rates of 5.7% in rural areas, 10.2% in urban areas, and 16% in metropolitan cities. These are comparable to the non-response rates in the nationally representative survey\u0026ndash;National Family and Health Survey-5 (NFHS-5) 2019\u0026ndash;2021, ranging from 14.7% among urban men respondents (see Table A.4 in the source report)\u003csup\u003e13\u003c/sup\u003e to 4.6% among rural women respondents (see Table A.3 in the source report)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Across states, the non-response rates in the Citizens Survey varied from 4.3% in Chhattisgarh to 39.1% in Delhi \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This variation follows the well-known pattern observed in India and globally, where urbanized regions and households with higher socioeconomic status have greater survey refusal rates\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the key characteristics of the sample. There were 217 cases where individuals opted not to participate after hearing the consent information. These were considered non-participating cases, and no data was collected from them. For all other interviews, data collection was completed in full, resulting in a final dataset with no missing entries.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;3:\u0026nbsp;Key\u0026nbsp;characteristics\u0026nbsp;of\u0026nbsp;the\u0026nbsp;Citizens\u0026nbsp;Survey\u0026nbsp;sample (N=50,000)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBackground Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampled Households\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSampled Households\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace\u0026nbsp;of\u0026nbsp;Residence\u0026nbsp;of Household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e35,175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e70.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e14,825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u0026nbsp;of Respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e15-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e6,790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e14,496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e35-49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e18,265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e36.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e50-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e6,321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e60+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e4,128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u0026nbsp;of\u0026nbsp;Respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e28,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e21,840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eDo\u0026nbsp;not\u0026nbsp;answer (DNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducational\u0026nbsp;Attainment\u0026nbsp;of Respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eBelow-Primary\u0026nbsp;(Not\u0026nbsp;Literate\u0026nbsp;or\u0026nbsp;below\u0026nbsp;Class 5th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e9,067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003ePrimary\u0026nbsp;(Class 5th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e6,767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eUp\u0026nbsp;to\u0026nbsp;Middle\u0026nbsp;(Class 8th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e9,076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eUp\u0026nbsp;to\u0026nbsp;Secondary\u0026nbsp;(Class 10th)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e10,834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e21.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eHigher-Secondary\u0026nbsp;and\u0026nbsp;Above\u0026nbsp;(11-12th\u0026nbsp;and above)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e13,959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e27.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eDo\u0026nbsp;not\u0026nbsp;answer (DNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial\u0026nbsp;Group\u0026nbsp;of Household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e13,039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e10,328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eSTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e9,231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eOBCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e16,741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eDo\u0026nbsp;not\u0026nbsp;answer (DNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReligion\u0026nbsp;of Household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eHindu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e39,296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e78.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eMuslim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e4,039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e6,665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital\u0026nbsp;Status\u0026nbsp;of Respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e7,146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eMarried\u0026nbsp;/\u0026nbsp;Live in\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e40,706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eWidow\u0026nbsp;/\u0026nbsp;Widower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e1,678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eNot\u0026nbsp;Stated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eDo\u0026nbsp;not\u0026nbsp;answer (DNA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupation\u0026nbsp;of Respondent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8037%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.757%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59.4393%;\"\u003e\n \u003cp\u003eCultivator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e7,666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e15.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003eWage-Labourer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e12,457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e24.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003eSelf-Employed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e10,496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e20.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003eRegular-Salaried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e4,809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e14,572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e29.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003e1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e9,211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e18.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e23,434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e46.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003e6+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e17,355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e34.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.6168%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.8224%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e50,000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5607%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the Citizens Survey was obtained from the Institutional Review Board of the Christian Medical College, Vellore in India (No. 14934, dated October 22, 2022).\u003c/p\u003e"},{"header":"Technical Validation","content":"\u003cp\u003eWe conducted a range of assessments of the concurrent and criterion validity of our data. First, we assessed whether we could replicate the rural-urban differences in internet technology penetration. \u003cstrong\u003eFigure 4\u0026nbsp;\u003c/strong\u003eillustrates the consistently higher uptake of technology in the healthcare context among urban respondents compared to rural respondents. Second, previous assessments using nationally representative samples, for example the National Sample Surveys, have established that household healthcare expenditures are greater for those seeking care in the private sector compared to those seeking care in the public sector\u003csup\u003e14\u003c/sup\u003e. \u003cstrong\u003eFigure 5\u0026nbsp;\u003c/strong\u003enotes the differences in the average health expenditures for outpatient visits and inpatient hospitalizations in the last 12 months, demonstrating consistently high expenditures across all components for the private sector.\u003c/p\u003e\n\u003cp\u003eThe UHCd categorization, developed independently based on distinct criteria and utilized for our sampling, provided an external standard against which the Citizens Survey data were assessed for concurrent validation. Specifically, indicators such as utilization of outpatient care, public sector healthcare usage, and out-of-pocket expenditures demonstrated clear gradients aligned with the expected patterns derived from the UHCd categorization. We see that utilization of outpatient care in the public sector was higher in districts in the high UHCd tertile and these were predominantly in states considered as better performing on health and development indicators (such as the southern states of Tamil Nadu, Kerala, and Karnataka), compared to districts in the low tertiles belonging mostly to states with weaker performance (such as Uttar Pradesh, Bihar, and Jharkhand) \u003csup\u003e19,20\u003c/sup\u003e. Additionally, utilization of public sector outpatient care was accompanied by lower OOPE.\u003c/p\u003e\n\u003cp\u003eWe considered comparisons of our data with a number of recent national datasets \u0026ndash; the Comprehensive Annual Modular Survey with health-related data (NSS 79\u003csup\u003eth\u003c/sup\u003e Round July 2022 \u0026ndash; June 2023), Survey on AYUSH 2022-2023 (NSS 79\u003csup\u003eth\u003c/sup\u003e Round July 2022 \u0026ndash; June 2023), and the Survey on Household Consumption Expenditure or SHCE 2022-23 (August 2022 \u0026ndash; July 2023). However, several key differences in the sampling strategies between the Citizens Survey and these datasets prevent us from drawing definitive conclusions; for instance, the NSS datasets employ stratification of households within villages or urban blocks based on monthly per capita consumption expenditure prior to household selection, whereas the Citizens Survey uses a uniform interval-based sampling method without such stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the other members of our Scientific Advisory Committee (Gagandeep Kang, Sapna Desai, and Atul Gupta) for their guidance in formulating the study. We thank the Commissioners of the Lancet Citizens\u0026rsquo; Commission for their feedback on the survey and analysis. We thank the researchers who worked on the formulation of the survey, data collection and analysis, including Dipanwita Sengupta, Sandul Yasobant, Vinod Joseph, Hasna Ashraf, Sanghamitra Sengupta, and Alok Vajpayi. We thank the DRS team for the data collection. We express our gratitude to CMC Vellore and Infosys Limited for the support. Most importantly, we are deeply grateful to all the respondents of this survey who generously shared their time and information with us to create this dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding\u0026nbsp;for\u0026nbsp;this\u0026nbsp;study\u0026nbsp;was\u0026nbsp;provided\u0026nbsp;by\u0026nbsp;a\u0026nbsp;grant\u0026nbsp;from\u0026nbsp;Infosys\u0026nbsp;Limited,\u0026nbsp;Bangalore,\u0026nbsp;India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAK conceptualized the manuscript. AK and SZ drafted the manuscript. VP, SVS, SK, SB, SC conceptualized and designed the survey. VP and SVS guided the development of the manuscript. DR and MKC led the data collection and quality assurance. SZ and SKS led the data cleaning and data preparation, with contributions from JM and PC. All authors critically reviewed the manuscript and guided its finalization. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. All authors had access to the data and a role in writing the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePatrick Hoang-Vu Eozenou, S. N., Ana Florina Pirlea. 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A comprehensive health index for India: development, validation, and spatial variation. \u003cem\u003eJ. Popul. Res. \u003c/em\u003e\u003cstrong\u003e41\u003c/strong\u003e, 21 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Harvard University","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":"Universal Health Coverage, India, Health Systems, Citizens, Survey","lastPublishedDoi":"10.21203/rs.3.rs-7274969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7274969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pursuit of Universal Health Coverage (UHC) in India is particularly challenging given the country's vast population and pronounced socioeconomic disparities. Although extensive research addresses specific healthcare areas, contemporary data on citizens' healthcare access, quality, and preferences to inform UHC design are lacking. To bridge this gap, the Lancet Citizens' Commission on Reimagining India's Health System conducted a Citizens Survey from November 2022 to April 2023, interviewing respondents in person from 50,000 randomly selected households across 125 districts in 29 Indian states and Union Territories in the country. The survey comprised 141 questions covering healthcare utilization, experiences, costs, satisfaction, delivery preferences, insurance coverage, willingness to pay, health information behaviors, technology use, aspirational health norms, and electoral attitudes towards health. The survey had a high participation rate (98%) and a low non-response rate (9.5%), 70% of households were rural, 56% of respondents were male, 79% were Hindu, and 39% identified as Scheduled Caste or Tribes. The data aim to inform citizen-centric reforms, advancing a UHC responsive to India's diverse population needs.\u003c/p\u003e","manuscriptTitle":"The Citizens Survey on Healthcare in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 04:40:38","doi":"10.21203/rs.3.rs-7274969/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":"d5b7cedd-07f1-479f-b137-2928037142ea","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T04:40:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 04:40:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7274969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7274969","identity":"rs-7274969","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}