Household Global Positioning System (GPS) coordinates duplication in health surveys – a case study of the Vaccination Coverage Surveys in the Democratic Republic of Congo (DRC)

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Increasingly, household GPS coordinates are collected in surveys to improve spatial resolution for targeted intervention. Geolocation data quality impacts coverage estimates, program targeting, and policy decisions. However, little attention has been paid to the accuracy and uniqueness of these data. This study investigated the extent of GPS coordinate duplication in vaccination coverage survey (VCS) in the Democratic Republic of the Congo (DRC), and explored potential causes. Methods Three rounds of VCS were conducted in January 2022 (VCS1), February 2023 (VCS2), and March 2024 (VCS3). In each round, duplicated household GPS coordinates were identified. Duplication patterns were analysed by health region, interviewer ID, tablet device ID, and interview date. Data quality in other fields was compared between interviews recorded by devices with > 90% duplicated GPS coordinates and those recorded by devices with only unique coordinates. Results In VCS1, 17% of GPS points had substantial duplications, with 5 provinces (Mongala, Nord-Ubangi, Sud-Kivu, Kwilu, and Haut-Lomami) showing over 40% duplications. Of all 501 devices IDs retrieved, 116 recorded exclusively unique household GPS coordinates, while the interviews recorded on 10 devices all showed household GPS duplication (7 were deployed to the 5 provinces with the highest duplication rates). In Kwilu Province and Haut Lomami, in particular, tablets deployed to the same health zones and days showed similar duplication patterns. The quality of other data elements in interviews with duplicated GPS coordinates was comparable to those with unique coordinates. Notably, duplication rates in VCS2 and VCS3 were substantially reduced to within 1%. Conclusions Significant GPS coordinate duplication was observed in VCS1 in the DRC, but this issue was largely mitigated in subsequent survey rounds. The duplication identified highlights the need for improved field protocols, including enhanced interviewers training, robust validation steps, and clear hardware and software guidelines. GPS errors in household survey threaten the integrity of spatial data, downstream analyses, and policy decisions based on geospatial metrics. Our findings underscore the need for standardized quality assurance in the geolocation data collection practices of global health survey implementations. Immunization vaccination coverage surveys population surveys health surveys geolocation geocoding GPS Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Vaccines are critical to the prevention and control of many communicable diseases. The Immunization Agenda 2030 (IA2030) aspires to maximize the lifesaving impact of vaccines, including achieving 90% coverage for essential vaccines given in childhood and adolescence 1 , 2 . At the country and subnational level, vaccination coverage - defined as the proportion of a target population that has been vaccinated in a time given period - is a widely used indicator of programme performance. Monitoring childhood vaccination allows national and local governments to be guided by data when identifying areas and subpopulations with low vaccination rates, monitor the effectiveness of immunization programs, and guide policy decisions to improve overall equity and public health impact by closing vaccination gaps where needed. Vaccination coverage estimates are also an indicator to monitor Goal 3 “Good Health and Well-being of the Sustainable Development Goals” 3 . Vaccination coverage may be measured using data from aggregated routine administrative reports, or immunization registers where available, and estimates of target population at various levels. Yet, in resource-constrained settings, where vaccination coverage is typically low and inequitable, population denominator and the reported number of vaccinations used in administrative records are prone to inaccuracies, and national vaccination coverage surveys are often considered more reliable and are recommended to complement administrative coverage data 4 , 5 . In a vaccination coverage survey (VCS), similar to other population-based health surveys, data is collected from a representative sample of the target population. WHO provides detailed guideline on VCS methodology and technical support to countries 6 . This ensures that countries will have high quality VCS data that can be used to monitor vaccination coverage and plan for immunization delivery services 6 . Vaccination data in these surveys is primarily based on vaccination records or history as recalled by the children’s caretakers in household interviews conducted by field interviewers in paper, or now more frequently using computer assisted personal interviewing (CAPI) on mobile devices. Typically, data captured in these VCS also include household demographic information and socioeconomic factors to enable estimating vaccination coverage in population subgroups, providing information on varying vaccination coverages across subpopulations in the country. The collection of geolocation data in VCS plays a pivotal role in revealing geographic inequalities in vaccination coverage. For financial and confidentiality considerations, georeferencing population-based surveys by employing precise location collection methods such as Global Positioning System (GPS) coordinates have mostly been confined to clusters of households and random displacement of the recorded GPS coordinates (also referred to as geo-masking) 7 . The trade-offs are spatial granularities, hidden spatial disparities, and the spatial scale of analysis, impacting its utility to high-resolution targeting at immunization delivery services. From the utility perspective, georeferencing the individual households in fieldwork are therefore more desirable. The approach is increasingly being adopted under stringent ethical guidelines, informed consent, and data sharing protocol for surveys and census 8 , 9 . This step-change in geospatial scale provides unprecedented details that enable more granular vaccination coverage estimates and visual outputs that better guide targeted immunization delivery services. As with making other measurements, the accuracy of the associated estimates and outputs is in part dependent on data quality. In this context, key factors include the quality of GPS signal, atmospheric conditions, and receiver design features/quality 10 . Common challenges in GPS data collection include hardware and software issues, poor network connection, noisy data (i.e., deviations from the true location of the device often caused by a low number of satellites or multipath errors, where signals are reflected off nearby buildings or objects) 11 , and human error. Inaccurate geolocation of households in surveys can introduce errors in estimates of service access, exposure to environmental risks, spatial epidemiological models, health equity assessment, leading to misinforming program planning, resource allocation, national immunization strategies, and funding priorities 12 . These issues are particularly relevant in spatial health and exposure research, where precision is critical. Despite efforts to increase childhood vaccination coverage, the Democratic Republic of the Congo (DRC) persists as one of the most vulnerable countries to vaccine-preventable diseases and has experienced several recent outbreaks, including several measles and vaccine-derived poliovirus epidemics 13 – 15 . Approximately 20% of Congolese infants have not started their routine immunization schedule and are considered “zero-dose”; and over 40% do not get any measles dose 16 . To provide spatially-granular coverage estimates needed to effectively identify and reach zero-dose and under-immunized children, three consecutive rounds of nationwide VCS were fielded in DRC between 2022 and 2024 17 . In each round, vaccination history of eligible children, barriers and enablers to vaccination, household sociodemographic characteristics, and household GPS coordinates were collected 17 . The survey data has been instrumental in an array of immunization strategies and research activities, including monitoring DRC’s Mashako Plan 18 , Presidential Forums on immunization, policy-briefs, and studies looking at factors related to vaccination uptake and timeliness in various subgroups 19 – 22 . The objective of this study was to assess the quality of the recorded household GPS coordinates in the three rounds of VCS in DRC. Specifically, we evaluated interval data validation through examining the extent to which unique household GPS coordinates were duplicated across different households. This analysis was motivated by noticing duplicates when preparing VCS data for secondary analyses. To thoroughly understand the implications of duplicated households GPS coordinates, we assessed the quality of the other data elements for records showing duplicated household GPS coordinates. The results will help call attention to strengthen understanding of data quality within local contexts, assessing quality of survey data, and improve guidelines provided to survey implementers to prevent similar issues 12 . Methods Setting The Democratic Republic of Congo (DRC) is a large country located in the central part of Africa, with a population of approximately 115.7 million in 2021. Administratively, DRC is divided in 26 Provinces and 519 Health Zones (HZs). HZ is the unit of operation of the DRC health systems in resource allocation, healthcare planning and implementation. Each HZ is subdivided into multiple health areas (HAs). Each HA is served by a health centre catering to 5000–10000 people. This structure reflects the DRC's decentralized health management, where primary health care services should, in theory, reach even the most remote areas. Routine vaccination is an essential preventive intervention implemented at health facilities through fixed posts and outreach activities 23 Vaccination Coverage Surveys (VCS) in DRC This is an analysis of data from three consecutive rounds of nationwide vaccination coverage survey (VCS), conducted in January 2022, February 2023 and March 2024, referred to as VCS1, VCS2 and VCS3, respectively. These surveys were implemented by the Kinshasa School of Public Health, a recognized academic institution based in the capital city of Kinshasa. Additional information on sampling protocols for these surveys have been described elsewhere 17 , 22 . Briefly, VCS are nationally representative multi-stage cluster cross-sectional randomized surveys, following a modified version of the recommended WHO sampling method for vaccine coverage, targeting 6–23-month-olds infants. VCS sampled clusters from all HZs, the operation level of the DRC health system in resource allocation, health planning and implementation. GPS coordinates, captured as latitude and longitude reading, were recorded for each surveyed household using the same tablets used for data collection from interviews and transcription of vaccination cards. In addition, data on province, HZ, HA, village name, and household identifier were also recorded. VCS interviewers training All interviewers, comprising both males and females, received standardized training prior to the fieldwork of each VCS round. Training was conducted at two levels. First, training of provincial supervisors recruited from individuals who had already worked with the Kinshasa School of Public Health. Provincial supervisors were trained during a seven-day workshop including a pretest and a debriefing by the coordination team in Kinshasa. Then, the provincial supervisors were deployed to provinces and provided training to the investigators in the province. All investigators were trained with the same tools that had been used during the training of provincial supervisors in Kinshasa. Investigators formed teams to survey 3–4 HZs. Both the supervisors and investigators received training on instructions to use tablet devices equipped with the “SurveyCTO” application for data collection. The application was configured to capture GPS coordinates with accuracy of 6 meters. Capturing household GPS coordinates during fieldwork Preparatory actions regarding the process of recording GPS coordinates were taken prior to field visits. All device systems were updated and connected to the internet during training in Kinshasa, and an initial set of GPS coordinates were recorded to confirm functionality. During fieldwork, interviewers were instructed to, before recording GPS coordinates, keep away from potential obstructions, such as buildings, trees, bridges or underground, and beside metals such as cars to prevent signal degradation. They were also trained to ensure the GPS location functionality was activated on the device, and to wait until the accuracy reached the 6-meters precision threshold or less. At the end of training, devices were assigned to the interviewers, who took the devices back to the provinces for fieldwork. During household visits, data were collected for all eligible children from consenting heads of household and guardians, usually mothers, using the pre-loaded application hosted in SurveyCTO 17 . In addition, household GPS coordinates were acquired with the devices. At this point, sufficient satellite reception would be required. Upon completion of household interviews, data checks were performed by either the interviewer or the supervisor and data would be submitted to a central the server in Kinshasa. At this point, internet connection would be required. This was done typically at the end of the interview, or at the end of the day, depending on factors such as quality of internet connection and interviewer/supervisor availability. All procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the Kinshasa School of Public Health IRB 17 . Personal identifiers are kept confidential, and de-identified during analysis. Data are stored on password protected cloud-based servers. All surveyors and data users signed a non-disclosure data agreement 17 . Data analysis In this study, we examined the extent to which household GPS coordinates were duplicated across different households in each of the three VCS rounds. Duplicates were defined as the same latitude and longitude recorded across two or more households in different health zones (HZs), health areas (HAs), villages, or as records with different household identifiers having the same GPS coordinates. The proportion of potentially duplicated household GPS coordinates were reported by province for all three VCS rounds. To assess the potential contribution of device- and circumstantial- related factors, the provinces with the highest proportion of interviews showing duplicated household GPS coordinates In VCS1 were identified. For these provinces, we assessed the level of GPS reading duplication by the devices, tracking the HZs that they were used in each day. Groups of devices used in the same HZs on the same days showing similar GPS reading duplication were identified. Data fabrication without visiting the sampled households in challenging field conditions, performance pressures, and limited oversight has been documented potentially lead to duplicated household GPS coordinates 24 , 25 . A well-known case, referred to “data from under a tree”, describes a scenario where a fieldworker sits under a tree, completing a questionnaire but without having collected information from the selected research participant. We hypothesized that records with duplicated household GPS coordinates due to data fabrication would also show signs of inaccuracies in the other data elements. To test this, we compared data collected from devices that recorded more than 90% duplicated GPS coordinates with data from devices that recorded exclusively unique household coordinates. We examined the survey responses from these two groups using two indicators of potential fabrication: (1) straight-lining—where respondents select the same option across a series of Likert scale questions or yes/no response questions, and (2) inconsistent skip logic patterns, particularly a high frequency of negative responses that lead to skipping subsequent sections. The sets of Likert scale questions and skip logic questions considered are available in Supplementary Material I . All analyses and visualizations were performed in R version 4.3.2. Results At the national level, in VCS1, GPS coordinates were recorded for 87,431 consenting households with eligible children, 72,247 (82.6%) of which were unique (Table 1 ). In the remaining 17.4% of interviews presenting with duplicated household GPS coordinates, most were shared by children in the same village (44.0%) and children in different villages (36.6%). The 5 provinces presenting with the highest percentage of duplicated household GPS coordinates were Mongala (66.4%), Nord-Ubangi (59.5%), Sud-Kivu (53.3%), Kwilu (50.1%), and Haut-Lomami (39.6%). Sud-Kivu Province presented with the highest percentage of duplicated household GPS coordinates found in different HAs (15.9%) and HZs (5.6%) (Table 1 ). Table 1. Duplicated and unique household GPS coordinates in VCS1 by Province, Democratic Republic of Congo, 2022 Province No. of Health Zones included No. of Health Areas included No. of villages included No. of households included Number and Percentage of Duplicated Household GPS Coordinates Recorded for Potentially Different Households + Number and Percentage of Unique Household GPS Coordinates (%) Total In Different Health Zones In Different Health Areas In Different Villages In the Same Village All Total 646 4.3% 2288 15.1% 5562 36.6% 6688 44.0% 15184 (17.4%) 72247 (82.6%) 87431 Mongala 12 60 441 2028 32 2.2% 170 11.8% 657 45.8% 577 40.2% 1436 66.4% 711 33.6% 2115 Nord Ubangi 11 55 195 1833 12 1.0% 108 9.4% 401 35.0% 626 54.6% 1147 59.3% 780 40.7% 1915 Sud-Kivu 34 160 781 5720 323 10.5% 922 29.9% 939 30.4% 903 29.3% 3087 50.5% 2709 49.5% 5473 Kwilu 24 120 931 3728 59 3.1% 147 7.6% 786 40.8% 934 48.5% 1926 49.4% 1916 50.6% 3783 Haut Lomami 16 79 320 2567 9 0.9% 192 18.1% 449 42.4% 408 38.6% 1058 39.3% 1618 60.7% 2667 Tshuapa 12 59 257 1703 0 0.0% 84 16.0% 249 47.4% 192 36.6% 525 24.8% 1588 75.2% 2113 Kasai Oriental 19 95 577 3236 16 2.1% 88 11.4% 231 30.0% 436 56.5% 771 22.5% 2601 77.5% 3356 Maindombe 14 70 190 2324 0 0.0% 0 0.0% 82 18.5% 361 81.5% 443 18.3% 1979 81.7% 2422 Nord Kivu 33 161 875 5084 169 17.0% 357 35.8% 276 27.7% 194 19.5% 996 15.7% 4453 84.3% 5280 Maniema 18 89 976 2988 0 0.0% 88 18.2% 200 41.3% 196 40.5% 484 15.6% 2616 84.4% 3100 Kongo Central 31 154 1489 5231 16 2.2% 38 5.3% 376 52.7% 283 39.7% 713 13.0% 4646 87.0% 5343 Kwango 14 70 412 2351 0 0.0% 2 0.6% 81 26.0% 229 73.4% 312 12.7% 2137 87.3% 2449 Equateur 18 90 500 2958 0 0.0% 35 12.3% 79 27.8% 170 59.9% 284 9.3% 2782 90.7% 3066 Kasai 30 139 612 4723 0 0.0% 13 3.4% 153 39.8% 218 56.8% 384 8.0% 4438 92.0% 4822 Ituri 18 90 592 3070 6 2.4% 23 9.1% 91 35.8% 134 52.8% 254 7.8% 2923 92.2% 3171 Sankuru 16 80 357 2660 0 0.0% 0 0.0% 47 26.0% 134 74.0% 181 6.4% 2628 93.6% 2809 Tanganyika 11 55 347 1990 0 0.0% 0 0.0% 54 42.2% 74 57.8% 128 6.3% 1913 93.7% 2041 Haut Katanga 27 134 783 4487 0 0.0% 0 0.0% 91 34.9% 170 65.1% 261 5.6% 4364 94.4% 4625 Tshopo 23 115 680 3810 0 0.0% 0 0.0% 83 44.1% 105 55.9% 188 4.8% 3721 95.2% 3909 Kinshasa 35 175 1891 5733 4 1.7% 3 1.3% 105 44.3% 125 52.7% 237 4.0% 5665 96.0% 5898 Sud-Ubangi 16 80 337 2580 0 0.0% 4 4.0% 4 4.0% 91 91.9% 99 3.5% 2708 96.5% 2807 Lomami 16 80 309 2581 0 0.0% 0 0.0% 60 70.6% 25 29.4% 85 3.2% 2555 96.8% 2640 Haut Uele 12 60 290 2010 0 0.0% 0 0.0% 22 36.7% 38 63.3% 60 2.8% 2051 97.2% 2111 Bas Uele 11 55 314 1888 0 0.0% 0 0.0% 9 26.5% 25 73.5% 34 1.8% 1891 98.2% 1925 Kasai Central 26 130 733 4369 0 0.0% 12 17.1% 35 50.0% 23 32.9% 70 1.5% 4474 98.5% 4544 Lualaba 14 70 284 2338 0 0.0% 2 9.5% 2 9.5% 17 81.0% 21 0.9% 2380 99.1% 2401 Urban (82.8%) Rural (82.6%) There were 2725 unique GPS readings used across 15,184 affected records in VCS1. Of which, 38% (or 1043) of the readings were duplicated 2 times each, while 88% (or 2387) coordinates were duplicated ≤ 10 times. In the more extreme cases, 3 GPS coordinates were duplicated for 100, 84, and 81 times each, respectively, all of them in Sud-Kivu Province (Fig. 1 ). In VCS2, the percentage of households with duplicated GPS coordinates was 0.8% (Fig. 2 ), with 3 provinces presenting with > 2% of duplicated household GPS coordinates – Kwilu (4.5%), Kasai (3.7%), and Kinshasa (2.5%). In VCS3, the percentage of households with duplicated GPS coordinates was 0.2%. The three provinces with the highest percentage of duplicated GPS household coordinates were Kwilu (1.6%), Sankuru (0.51%), and Tshuapa (0.5%). Devices’ performance on recording household GPS coordinates by day Out of all 87,431 interviews in VCS1, 2.4% did not have a device ID number. The remaining 85,324 interviews were recorded by 501 devices. Of which, 116 recorded interviews exclusively with unique household GPS coordinates, whilst the interviews recorded on 10 devices all showed household GPS coordinates duplicates (7 were deployed in the 5 aforementioned provinces). The mean and median proportion of interviews presenting with duplicated household GPS coordinates among these devices were 24.4% and 8%, respectively. In Kwilu Provinces, 21 devices were used to capture the VCS interviews across 24 HZs (Fig. 3 ). Apart from Kikwit-Nord HZ and Kikwit-Sud HZ, the devices were deployed in groups of 4. For instance, 4 devices (K1-K4) were deployed in Bagata, Bandundu, Djuma, and Kikongpo between 3–28 January 2022, and 4 devices (K13-K16) were deployed in Idiofa, Ipamu, Kimputu, and Koshibanda HZs between 4–22 January 2022. For these two groups of devices, on any given day, the household GPS coordinates recorded were either largely all duplicated, or that they were all unique. A very similar “synchronization” was found among groups (e.g., HL4-HL9) of devices deployed to Haut Lomami Province. In Nord Ubangi Province and Mongala Province, performance of devices deployed to the same HZs appeared less synchronized (Fig. 4 ). There were 4 devices (NB13, M13, M7, and M4) deployed to these two provinces for which all household GPS coordinates were duplicated at least once. In Nord Ubangi Province, for instance, NB10-NB14 were deployed to Businga, Karawa, and Loko HZs, and all devices but NB13 recorded almost all unique GPS coordinates, whilst NB13 recorded duplicated coordinates exclusively. Similarly, NB1-NB4 were deployed to the same HZs and, and whilst NB4 showed good performance, NB1-NB3 performed sub-optimally. The same was also found among M1-M4 in Mongala Province, and S1-S4 in Sud Kivu Province, for instance. Survey data characteristics and quality by devices’ duplication rate In the five provinces presenting with the highest percentage of duplicated household GPS coordinates in ECV1, we assessed 8 series of skip-pattern questions, each beginning with a leading question. Subsequent questions in the series were skipped on responding “No” to these leading questions. For 3 out of 8 series, the percentages of negative responses given to these leading questions were higher among poor-performing devices (Table 2 ). In addition, we assessed 5 series of questions, each with the same response options (see Supplementary Material II for example), for straight-line responses. The percentage of interviews selecting the same response pattern to all sub-question in a series was not consistently higher among poor-performing devices. Table 2 Percentages of responses given to skip-pattern questions and Likert scale questions in VCS1 Percentage of negative responses given to the lead question in each of 8 series of skip-pattern questions Good-performing devices + Poor-performing devices + Difference 95% CI p-value QA706 series 13.80% 26.30% 12.50% (10.6, 14.3) < 0.001 QA701 series 62.30% 67.40% 4.80% (2.6, 7.1) < 0.001 VS85A series 18.80% 12.40% -6.50% (-8.3, -4.8) < 0.001 VS136 series 11.10% 12.90% 1.80% (0.2, 3.3) 0.028 VS25B series 2.90% 2.20% -0.70% (-1.4, 0.1) 0.085 VS25D series 15.20% 16.20% 1.10% (-0.6, 2.8) 0.208 QA218 series 56.00% 56.40% 0.20% (-2.1, 2.6) 0.863 QA220 series 40.10% 40.00% -0.10% (-2.4, 2.2) 0.943 Percentage of interviews with the same response given to all sub-questions in each of 5 series Good-performing devices + Poor-performing devices + Difference 95% CI p-value VS1 series 31.80% 29.10% -2.70% (-4.8, -0.5) 0.017 QA503 series 2.40% 3.30% 0.90% (0.1, 1.7) 0.031 QA501 series 8.10% 9.40% 1.30% (-0.1, 2.6) 0.063 QA6 series 24.10% 25.50% 1.40% (-0.6, 3.5) 0.181 QA502 series 1.70% 1.80% 0.10% (-0.5, 0.8) 0.659 + Poor-performing devices refer to those devices that recorded > 90% duplicated household GPS coordinates, Good-performing devices refer to those devices that recorded exclusively recorded unique coordinates. Discussion This study assessed duplicates in household GPS coordinates captured in large-scale, nationally representative vaccination coverage surveys, focusing specifically on the extent to which duplication of coordinates were recorded across different households. Over 80% of VCS1 GPS coordinates were unique. In VCS2 and VCS3, duplicate GPS coordinates reduced to below 1%. Duplicated household GPS coordinates in VCS1 were concentrated in certain provinces, the top five being Mongala (66.9%), Nord-Ubangi (59.5%), Sud-Kivu (53.3%), Kwilu (50.1%), and Haut-Lomami (39.6%). In some instances, devices that were deployed to the same health zones on the same days showed similar pattern in household GPS coordinates duplication. When testing for correlated patterns that we assume would occur in other data elements between devices that recorded unique household GPS coordinates and those that recorded a high proportion of duplicates if data fabrication was happening, no consistent pattern was found. High quality population-based vaccination coverage surveys are very useful but resource-intensive. It is important to ensure that the locational information generated can accurately guide vaccination needs assessments, pro-equity interventions, intervention planning, visualizing/monitoring progress, and disease surveillance 8 , 26 – 28 . The programmatic and policy implications of inaccurate and unreliable geolocation data from VCSs and the like are far-reaching. Geolocation errors, in the first instance, can lead to misestimating and misidentifying low-coverage areas, resulting in poor outcome of targeted immunization activities. Wigley and colleagues identified a sizable proportion of the global zero-dose under-one population within an hour to the nearest town or city, challenging the consensual understanding that these children are generally found in rural remote and urban informal areas 29 . In another simulation study, imprecise household locations reduced the proportion of children correctly linked to their source of health services 30 .​ The shifting of perceived clusters of non-vaccination or disease susceptibility can also hamper outbreak preparedness, efficient vaccine stockpiling or deploying. Recurring data inconsistencies can undermine confidence in health data, undermining political will to invest in survey-based monitoring and geospatial analytics. Our findings offer some lessons learned to improve geolocation data collection in health surveys. There has been survey data inaccuracy documented in various contexts, partly driven by data fabrication without visiting the sampled households in challenging field conditions, performance pressures, and limited oversight 12 , 24 , 25 . In this study, however, the localization of groups of devices operating in the same areas on the same days showing a similar extent of household GPS duplications, while quality of the other data elements remained consistent, suggests that environmental and technical constraints were at play. DRC is characterised by rainforests, mountains, and river systems that can obstruct or degrade satellite signal quality. Physical obstructions (e.g., trees, buildings, tunnels, or bridges) can also reduce signal strength and quality. Poor signal reception limits the number of satellites that devices can lock onto, reducing location precision or delaying coordinate acquisition altogether. Moreover, weather conditions, such as cloud cover, rain and fog can reduce signal quality 6 , 10 . Many GPS apps are designed to improve responsiveness by caching recent or the last known 31 . Although this can serve as a fallback when GPS signal is weak or unavailable, if the device fails to acquire a geolocation within a certain time window, cached (or stale) geolocation can be reused across households, introducing spatial data inaccuracies. During VCS1 fieldwork, interviewers reported that under conditions of weak satellite signal, GPS coordinates did not change when attempts to acquire new ones were made. In some cases, interviewers working in areas with insufficient reception carried on with fieldwork as they were unable to obtain support from the coordination team due to insufficient connection. Our coauthors (FB and JBNK) confirmed that two tablet models from different brands were used in VCS1, whereas for VCS2 and VCS3, only one model – also used in VCS1 – was retained, fully replacing the other. Additionally, in VCS2 and VCS3, GPS booster application was installed to improve the process of acquiring accurate GPS coordinates. GPS booster applications are designed and optimized to improve the performance of a device's built-in GPS. They can work to clear outdated A-GPS data and force a fresh download, whilst also enhancing the speed, accuracy, and reliability of GPS fixes. Insights from the three VCSs in DRC speaks to the importance of supporting interviewers working in challenging terrain and suboptimal atmospheric conditions. Several technical and operational measures can mitigate the effects of weak or intermittent GPS signals during health surveys. Training remains critical in GPS data quality. The current WHO Manual on VCS provides ample instructions on certain best practices, such as providing training to interviewers on positioning the receiver in a clear, unobstructed location, testing the signal before and during fieldwork, and ensuring the correct settings 6 . Training can also be expanded to cover the mechanism of GPS acquisition, identifying potentially inaccurate coordinates, when to wait or retry before logging a location, and the programmatic consequences of poor spatial data — particularly when surveys inform microplanning or targeted service delivery. During fieldwork, using high-sensitivity GPS devices can substantially improve location precision in environments with poor satellite visibility GPS diagnostic and booster apps can aid fieldworkers by visualizing satellite availability and clearing outdated GPS cache data. Providing interviewers with preloaded offline maps (offline tiles of OpenStreetMap or satellite imagery) of the target areas can support visual confirmation of household or village locations if GPS is unavailable. When GPS acquisition fails altogether, interviewers may be allowed to pin the household location on a digital map, a less precise method but one that preserves the spatial intent of the data and avoids data gaps. Additionally, implementing a data field to capture the geolocation method used (e.g., live GPS vs. manual pinning) can support quality control during post-field data verification. Taken together, these strategies provide a foundation for improving the accuracy and reliability of geospatial data collection in future survey implementations. Our study revealed circumstantial constrains to capturing accurate geolocations at refined scale in resource-limited settings. The findings should be considered with several limitations in mind. First, we used exact duplications as an indication of inaccurate household geolocation data. Identification of close readings (detected by calculating the distance apart, for instance) and ground-truthing of the recorded coordinates, whether or not duplicated, in future can more comprehensively assess data quality. Second, individual households were identified using data fields on province, health zones, health areas, village, and household identifier, the accuracy of which was assumed. However, especially for coordinates that were highly duplicated, the error was presumably in the coordinates rather than the other data fields. Third, our assessment of overall data quality attempted to identify some level of data fabrication, and although no evidence of such practices was observed, “genuine fakes” may still have subverted data quality 24 . However, the low level of duplication observed in VCS2 and VCS3, conducted after hardware and software upgrade, further supports genuine survey interviews. Lastly, future cross-referencing with interviewers can strengthen understanding of the challenges faced in the field, and in turn help tailor support and shape revised guidelines for future survey implementation. Conclusion This study highlights the importance of ensuring high-quality geolocation data in large-scale health surveys, particularly in settings with complex terrain and infrastructure challenges. By assessing GPS coordinate duplication across three rounds of nationally representative vaccination coverage surveys in the DRC, we identified substantial improvements in data quality following targeted hardware and software changes—including standardizing devices and introducing GPS booster applications. Accurate geolocation is essential for effectively identifying underserved populations, guiding immunization strategies, and supporting equitable health service delivery. As survey data increasingly inform programmatic planning, investments in interviewer training, technical tools, and quality assurance protocols are critical. Our findings underscore the value of adapting operational approaches to field conditions and incorporating flexible fallback methods—such as offline maps and manual location pinning—to safeguard spatial data integrity. Strengthening these elements in future surveys will enhance the reliability of geospatial analytics and the impact of data-driven decision-making in global health. Declarations Author Contributions Conceptualization, KLMW and MCDH.; statistical analysis, KLMW; data collection: FB, JBK, DI, EM; interpretation of data, KLMW and MCDH; writing—original draft preparation, KLMW, MCDH, RB, DI, FB, JBK; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript. Disclaimer The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated. Funding The survey that generated the data used in this study was funded by the United Nations Children Emergency Funds (UNICEF) after mobilizing a significant funding from GAVI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Institutional Review Board Statement Ethical approvals were sought for each round of the vaccination coverage survey from the Kinshasa School of Public Health Ethical Committee before data collection (approvals: ESP/CE/175/2021 and ESP/CE/025/2023). Health and politico-administrative authorities at the local level also provided their authorization. Informed Consent Statement Oral informed consent was obtained from potential study participants before starting the interview. Potential survey participants were informed about the nature of the survey, including objectives, risks and benefits, the confidentiality of their replies, the contact details of study personnel, and that they were free to participate or not without any negative consequences. Confidentiality was maintained and the dataset was anonymized. Data availability statement The data underlying this study from the Vaccination Coverage Surveys (VCS) contain sensitive geographic and health information. To ensure participant privacy and confidentiality, these datasets are not publicly available. The Kinshasa School of Public Health Ethical Committee (Approval Numbers: ESP/CE/175/2021 and ESP/CE/025/2023) reviewed and approved the study, including the data management procedures, and imposed restrictions on public data sharing to protect participant confidentiality. Researchers interested in accessing the data may submit a formal request to Dr. Eric Mafuta at [email protected] . Applications should include a brief research proposal outlining the intended use of the data, data protection measures, and any relevant institutional approvals. Access may be granted on a case-by-case basis, subject to ethical and data-sharing agreements ensuring that privacy and confidentiality standards are maintained. References O’Brien KL, Lemango E, Nandy R, Lindstrand A. The immunization Agenda 2030: A vision of global impact, reaching all, grounded in the realities of a changing world. Vaccine. 2024;42:S1–4. Lindstrand A, Cherian T, Chang-Blanc D, Feikin D, O’brien KL. The World of Immunization: Achievements, Challenges, and Strategic Vision for the Next Decade. J Infect Dis. 2021;224:S452–67. UNSTATS. SDG Indicators — SDG Indicators. https://unstats.un.org/sdgs/metadata/?text=mar (2015). Cutts FT, Claquin P, Danovaro-Holliday MC, Rhoda DA. Monitoring vaccination coverage: Defining the role of surveys. Vaccine vol. 34 4103–4109 Preprint at https://doi.org/10.1016/j.vaccine.2016.06.053 (2016). Cutts FT, Izurieta HS, Rhoda DA. Measuring Coverage in MNCH: Design, Implementation, and Interpretation Challenges Associated with Tracking Vaccination Coverage Using Household Surveys. PLoS Med 10, (2013). World Health Organization. World Health Organization vaccination coverage cluster surveys: reference manual. https://iris.who.int/handle/10665/272820 (2018). Burgert C, Zachary B, Colston J. Incorporating Geographic Information Into Demographic and Health Surveys: A Field Guide to GPS Data Collection (English, French, Spanish) . https://dhsprogram.com/publications/publication-dhsm9-dhs-questionnaires-and-manuals.cfm (2013). United Nations Statistics Division. Guidelines on the Use of Electronic Data Collection Technologies in Population and Housing Censuses . https : //www.caricomstats.org/E-CISTAR/mdocs-posts/guidelines-on-the-use-of-electronic-data-collection-technologies-in-population-and-housing-censuses/ (New York, 2019). Ngo-Bebe D, et al. Assessing the use of geospatial data for immunization program implementation and associated effects on coverage and equity in the Democratic Republic of Congo. BMC Public Health. 2025;25:311. Knippers R, Tempfli K. Spatial referencing and satellite based positioning. The core of GIScience: a systems-based approach. Twente: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC); 2012. Pearson AL et al. Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research. BMJ Open 14, (2024). Seidler V et al. Subnational variations in the quality of household survey data in sub-Saharan Africa. Nat Commun 16, (2025). Ministere De La Sante Publique Hygiene Et Prevention & World Health Organization. RD Congo: Situation de La Lutte Contre La Rougeole En RDC (Mise à Jour Du 24/12/2024) - Democratic Republic of the Congo . https://reliefweb.int/report/democratic-republic-congo/rd-congo-situation-de-la-lutte-contre-la-rougeole-en-rdc-mise-jour-du-24122024 (Kinshasa, 2025). Musa KM, Oduoye MO, Qazi MS, Zulfiqar K. Measles in the Democratic Republic of the Congo needs urgent attention. Ther Adv Infect Dis. 2024;11:20499361241252536. Oduoye MO et al. Re-emergence of vaccine-derived polio in the demographic Republic of Congo: Causes and consequences. New Microbes and New Infections vol. 62 Preprint at https://doi.org/10.1016/j.nmni.2024.101489 (2024). World Health Organization. Democratic Republic of the Congo: WHO and UNICEF Estimates of Immunization Coverage: 2023 Revision . https : //cdn.who.int/media/docs/default-source/country-profiles/immunization/2024-country-profiles/immunization-2024-cod.pdf?sfvrsn=f565c7 c6_3&download=true (2024). Mafuta EM et al. Designing a National Rapid Vaccine Coverage Survey in low-resource settings: Experiences from the Democratic Republic of the Congo 2018–2023. Vaccine 53, (2025). Lame P et al. A Successful National and Multipartner Approach to Increase Immunization Coverage: The Democratic Republic of Congo Mashako Plan 2018–2020. Glob Health Sci Pract 11, (2023). Ishoso DK et al. ‘Zero Dose’ Children in the Democratic Republic of the Congo: How Many and Who Are They? Vaccines (Basel) 11, (2023). Ishoso DK et al. Reasons for Being Zero-Dose and Under-Vaccinated among Children Aged 12–23 Months in the Democratic Republic of the Congo. Vaccines (Basel) 11, (2023). Banza Mpiongo P, et al. Strengthening immunization programs through innovative sub-national public-private partnerships in selected provinces in the Democratic Republic of the Congo. Vaccine. 2023;41:7598–607. Mbunga BK et al. Zero-Dose Childhood Vaccination Status in Rural Democratic Republic of Congo: Quantifying the Relative Impact of Geographic Accessibility and Attitudes toward Vaccination. Vaccines (Basel) 12, (2024). Ministère de la Santé Publique de la RDC Plan D’urgence Pour La relance De La Vaccination De Routine Dit. Plan Mashako Pour La Relance de La Vaccination de Routine . https://www.engagementvaccination.com/monitoring/fr (Kinshasa, 2018). Finn A, Ranchhod V. Genuine fakes: The prevalence and implications of data fabrication in a large South African survey. World Bank Economic Rev. 2017;31:129–57. Kingori P, Gerrets R. Morals, morale and motivations in data fabrication: Medical research fieldworkers views and practices in two Sub-Saharan African contexts. Soc Sci Med. 2016;166:150–9. FHI 360. GIS Mapping: Evidence on pro-Equity Interventions to Improve Immunization Coverage for Zero-Dose Children and Missed Communities . https://zdlh.gavi.org/sites/default/files/2023-09/6._evidence_brief_gis.pdf (2023). Ducharme J et al. Mapping of Pro-Equity Interventions Proposed by Immunisation Programs in Gavi Health Systems Strengthening Grants. Vaccines (Basel) 11, (2023). WHO. Best Practices in Microplanning for Polio Eradication. (Licence: CC BY-NC-SA 3.0 IGO, Geneva. 2018). Wigley A, et al. Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries. PLOS global public health. 2022;2:e0001126. Carter ED, Munos MK. Impact of imprecise household location on effective coverage estimates generated through linking household and health provider data by geographic proximity: a simulation study. Int J Health Geogr 20, (2021). Crudu A, MoldStud. The Role and Impact of Location-Based Services in Mobile App Development. https://moldstud.com/articles/p-the-impact-of-location-based-services-in-mobile-app-development?utm_source=chatgpt.com (2024). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 03 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 06 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor assigned by journal 04 Aug, 2025 Submission checks completed at journal 04 Aug, 2025 First submitted to journal 26 Jul, 2025 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. 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13:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7221573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7221573/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88491818,"identity":"b66e5e08-e513-4d25-adfe-cd2c2675ebb9","added_by":"auto","created_at":"2025-08-07 04:25:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1270624,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of duplicates among VCS1 interviews with duplicated household GPS coordinates, Democratic Republic of Congo, 2022\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/e863b984d82176715cf0f5a3.png"},{"id":88491039,"identity":"8879cc7c-f613-4537-8cc4-b8e6e1d4e3a5","added_by":"auto","created_at":"2025-08-07 04:17:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61669,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of duplicated household GPS coordinates by provinces in VCS1, VCS2, and VCS3, Democratic Republic of Congo\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/8c68fc02f0e13eb8b8e494a0.png"},{"id":88492476,"identity":"2b891739-6464-418e-b43d-d31c94094d0e","added_by":"auto","created_at":"2025-08-07 04:33:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":344136,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/f7725263accbd17337c0d611.png"},{"id":88492475,"identity":"730b9342-7108-4135-8e0a-a7c68045e1ff","added_by":"auto","created_at":"2025-08-07 04:33:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":800882,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/a988bd7aef4d3ed663282f4b.png"},{"id":88493071,"identity":"93c8a973-01b1-4341-9383-79be2b3b54dd","added_by":"auto","created_at":"2025-08-07 04:41:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3366690,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/3fe89d29-aa57-4eda-87b0-3f70bcf0f144.pdf"},{"id":88491038,"identity":"1fa9a133-10ab-43c3-a284-b1289ebd8d30","added_by":"auto","created_at":"2025-08-07 04:17:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":34439,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7221573/v1/06265c984901f625939884f9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Household Global Positioning System (GPS) coordinates duplication in health surveys – a case study of the Vaccination Coverage Surveys in the Democratic Republic of Congo (DRC)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVaccines are critical to the prevention and control of many communicable diseases. The Immunization Agenda 2030 (IA2030) aspires to maximize the lifesaving impact of vaccines, including achieving 90% coverage for essential vaccines given in childhood and adolescence\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. At the country and subnational level, vaccination coverage - defined as the proportion of a target population that has been vaccinated in a time given period - is a widely used indicator of programme performance. Monitoring childhood vaccination allows national and local governments to be guided by data when identifying areas and subpopulations with low vaccination rates, monitor the effectiveness of immunization programs, and guide policy decisions to improve overall equity and public health impact by closing vaccination gaps where needed. Vaccination coverage estimates are also an indicator to monitor Goal 3 “Good Health and Well-being of the Sustainable Development Goals”\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eVaccination coverage may be measured using data from aggregated routine administrative reports, or immunization registers where available, and estimates of target population at various levels. Yet, in resource-constrained settings, where vaccination coverage is typically low and inequitable, population denominator and the reported number of vaccinations used in administrative records are prone to inaccuracies, and national vaccination coverage surveys are often considered more reliable and are recommended to complement administrative coverage data\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In a vaccination coverage survey (VCS), similar to other population-based health surveys, data is collected from a representative sample of the target population. WHO provides detailed guideline on VCS methodology and technical support to countries\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This ensures that countries will have high quality VCS data that can be used to monitor vaccination coverage and plan for immunization delivery services\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Vaccination data in these surveys is primarily based on vaccination records or history as recalled by the children’s caretakers in household interviews conducted by field interviewers in paper, or now more frequently using computer assisted personal interviewing (CAPI) on mobile devices. Typically, data captured in these VCS also include household demographic information and socioeconomic factors to enable estimating vaccination coverage in population subgroups, providing information on varying vaccination coverages across subpopulations in the country.\u003c/p\u003e\u003cp\u003eThe collection of geolocation data in VCS plays a pivotal role in revealing geographic inequalities in vaccination coverage. For financial and confidentiality considerations, georeferencing population-based surveys by employing precise location collection methods such as Global Positioning System (GPS) coordinates have mostly been confined to clusters of households and random displacement of the recorded GPS coordinates (also referred to as geo-masking)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The trade-offs are spatial granularities, hidden spatial disparities, and the spatial scale of analysis, impacting its utility to high-resolution targeting at immunization delivery services. From the utility perspective, georeferencing the individual households in fieldwork are therefore more desirable. The approach is increasingly being adopted under stringent ethical guidelines, informed consent, and data sharing protocol for surveys and census\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This step-change in geospatial scale provides unprecedented details that enable more granular vaccination coverage estimates and visual outputs that better guide targeted immunization delivery services.\u003c/p\u003e\u003cp\u003eAs with making other measurements, the accuracy of the associated estimates and outputs is in part dependent on data quality. In this context, key factors include the quality of GPS signal, atmospheric conditions, and receiver design features/quality\u003csup\u003e10\u003c/sup\u003e. Common challenges in GPS data collection include hardware and software issues, poor network connection, noisy data (i.e., deviations from the true location of the device often caused by a low number of satellites or multipath errors, where signals are reflected off nearby buildings or objects)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and human error. Inaccurate geolocation of households in surveys can introduce errors in estimates of service access, exposure to environmental risks, spatial epidemiological models, health equity assessment, leading to misinforming program planning, resource allocation, national immunization strategies, and funding priorities\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. These issues are particularly relevant in spatial health and exposure research, where precision is critical.\u003c/p\u003e\u003cp\u003eDespite efforts to increase childhood vaccination coverage, the Democratic Republic of the Congo (DRC) persists as one of the most vulnerable countries to vaccine-preventable diseases and has experienced several recent outbreaks, including several measles and vaccine-derived poliovirus epidemics\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Approximately 20% of Congolese infants have not started their routine immunization schedule and are considered “zero-dose”; and over 40% do not get any measles dose\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. To provide spatially-granular coverage estimates needed to effectively identify and reach zero-dose and under-immunized children, three consecutive rounds of nationwide VCS were fielded in DRC between 2022 and 2024\u003csup\u003e17\u003c/sup\u003e. In each round, vaccination history of eligible children, barriers and enablers to vaccination, household sociodemographic characteristics, and household GPS coordinates were collected\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The survey data has been instrumental in an array of immunization strategies and research activities, including monitoring DRC’s Mashako Plan\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, Presidential Forums on immunization, policy-briefs, and studies looking at factors related to vaccination uptake and timeliness in various subgroups\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe objective of this study was to assess the quality of the recorded household GPS coordinates in the three rounds of VCS in DRC. Specifically, we evaluated interval data validation through examining the extent to which unique household GPS coordinates were duplicated across different households. This analysis was motivated by noticing duplicates when preparing VCS data for secondary analyses. To thoroughly understand the implications of duplicated households GPS coordinates, we assessed the quality of the other data elements for records showing duplicated household GPS coordinates. The results will help call attention to strengthen understanding of data quality within local contexts, assessing quality of survey data, and improve guidelines provided to survey implementers to prevent similar issues\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSetting\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Democratic Republic of Congo (DRC) is a large country located in the central part of Africa, with a population of approximately 115.7\u0026nbsp;million in 2021. Administratively, DRC is divided in 26 Provinces and 519 Health Zones (HZs). HZ is the unit of operation of the DRC health systems in resource allocation, healthcare planning and implementation. Each HZ is subdivided into multiple health areas (HAs). Each HA is served by a health centre catering to 5000–10000 people. This structure reflects the DRC's decentralized health management, where primary health care services should, in theory, reach even the most remote areas. Routine vaccination is an essential preventive intervention implemented at health facilities through fixed posts and outreach activities\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVaccination Coverage Surveys (VCS) in DRC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis is an analysis of data from three consecutive rounds of nationwide vaccination coverage survey (VCS), conducted in January 2022, February 2023 and March 2024, referred to as VCS1, VCS2 and VCS3, respectively. These surveys were implemented by the Kinshasa School of Public Health, a recognized academic institution based in the capital city of Kinshasa. Additional information on sampling protocols for these surveys have been described elsewhere\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Briefly, VCS are nationally representative multi-stage cluster cross-sectional randomized surveys, following a modified version of the recommended WHO sampling method for vaccine coverage, targeting 6–23-month-olds infants. VCS sampled clusters from all HZs, the operation level of the DRC health system in resource allocation, health planning and implementation. GPS coordinates, captured as latitude and longitude reading, were recorded for each surveyed household using the same tablets used for data collection from interviews and transcription of vaccination cards. In addition, data on province, HZ, HA, village name, and household identifier were also recorded.\u003c/p\u003e\u003cp\u003e\u003cb\u003eVCS interviewers training\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAll interviewers, comprising both males and females, received standardized training prior to the fieldwork of each VCS round. Training was conducted at two levels. First, training of provincial supervisors recruited from individuals who had already worked with the Kinshasa School of Public Health. Provincial supervisors were trained during a seven-day workshop including a pretest and a debriefing by the coordination team in Kinshasa. Then, the provincial supervisors were deployed to provinces and provided training to the investigators in the province. All investigators were trained with the same tools that had been used during the training of provincial supervisors in Kinshasa. Investigators formed teams to survey 3–4 HZs. Both the supervisors and investigators received training on instructions to use tablet devices equipped with the “SurveyCTO” application for data collection. The application was configured to capture GPS coordinates with accuracy of 6 meters.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCapturing household GPS coordinates during fieldwork\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePreparatory actions regarding the process of recording GPS coordinates were taken prior to field visits. All device systems were updated and connected to the internet during training in Kinshasa, and an initial set of GPS coordinates were recorded to confirm functionality. During fieldwork, interviewers were instructed to, before recording GPS coordinates, keep away from potential obstructions, such as buildings, trees, bridges or underground, and beside metals such as cars to prevent signal degradation. They were also trained to ensure the GPS location functionality was activated on the device, and to wait until the accuracy reached the 6-meters precision threshold or less. At the end of training, devices were assigned to the interviewers, who took the devices back to the provinces for fieldwork.\u003c/p\u003e\u003cp\u003eDuring household visits, data were collected for all eligible children from consenting heads of household and guardians, usually mothers, using the pre-loaded application hosted in SurveyCTO\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In addition, household GPS coordinates were acquired with the devices. At this point, sufficient satellite reception would be required. Upon completion of household interviews, data checks were performed by either the interviewer or the supervisor and data would be submitted to a central the server in Kinshasa. At this point, internet connection would be required. This was done typically at the end of the interview, or at the end of the day, depending on factors such as quality of internet connection and interviewer/supervisor availability.\u003c/p\u003e\u003cp\u003eAll procedures were performed in compliance with relevant laws and institutional guidelines and have been approved by the Kinshasa School of Public Health IRB\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Personal identifiers are kept confidential, and de-identified during analysis. Data are stored on password protected cloud-based servers. All surveyors and data users signed a non-disclosure data agreement\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eIn this study, we examined the extent to which household GPS coordinates were duplicated across different households in each of the three VCS rounds. Duplicates were defined as the same latitude and longitude recorded across two or more households in different health zones (HZs), health areas (HAs), villages, or as records with different household identifiers having the same GPS coordinates. The proportion of potentially duplicated household GPS coordinates were reported by province for all three VCS rounds.\u003c/p\u003e\u003cp\u003eTo assess the potential contribution of device- and circumstantial- related factors, the provinces with the highest proportion of interviews showing duplicated household GPS coordinates In VCS1 were identified. For these provinces, we assessed the level of GPS reading duplication by the devices, tracking the HZs that they were used in each day. Groups of devices used in the same HZs on the same days showing similar GPS reading duplication were identified.\u003c/p\u003e\u003cp\u003eData fabrication without visiting the sampled households in challenging field conditions, performance pressures, and limited oversight has been documented potentially lead to duplicated household GPS coordinates \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A well-known case, referred to “data from under a tree”, describes a scenario where a fieldworker sits under a tree, completing a questionnaire but without having collected information from the selected research participant. We hypothesized that records with duplicated household GPS coordinates due to data fabrication would also show signs of inaccuracies in the other data elements. To test this, we compared data collected from devices that recorded more than 90% duplicated GPS coordinates with data from devices that recorded exclusively unique household coordinates. We examined the survey responses from these two groups using two indicators of potential fabrication: (1) straight-lining—where respondents select the same option across a series of Likert scale questions or yes/no response questions, and (2) inconsistent skip logic patterns, particularly a high frequency of negative responses that lead to skipping subsequent sections. The sets of Likert scale questions and skip logic questions considered are available in \u003cb\u003eSupplementary Material I\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eAll analyses and visualizations were performed in R version 4.3.2.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAt the national level, in VCS1, GPS coordinates were recorded for 87,431 consenting households with eligible children, 72,247 (82.6%) of which were unique (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the remaining 17.4% of interviews presenting with duplicated household GPS coordinates, most were shared by children in the same village (44.0%) and children in different villages (36.6%). The 5 provinces presenting with the highest percentage of duplicated household GPS coordinates were Mongala (66.4%), Nord-Ubangi (59.5%), Sud-Kivu (53.3%), Kwilu (50.1%), and Haut-Lomami (39.6%). Sud-Kivu Province presented with the highest percentage of duplicated household GPS coordinates found in different HAs (15.9%) and HZs (5.6%) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable 1. Duplicated and unique household GPS coordinates in VCS1 by Province, Democratic Republic of Congo, 2022\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse;border: none;width: 96px;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.24%;border-top: 1pt solid windowtext;border-right: 1pt solid windowtext;border-bottom: 1pt solid windowtext;border-image: initial;border-left: none;padding: 0in 0.3pt;height: 26.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eProvince\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:5.6%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eNo. of Health Zones included\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:5.6%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eNo. of Health Areas included\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:5.6%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eNo. of villages included\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:5.6%;border:solid windowtext 1.0pt;border-left:none;padding: 0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eNo. of households included\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"10\" style=\"width:52.72%;border:none;border-top: solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cu\u003e\u003cspan style=\"font-size:12px;\"\u003eNumber and Percentage of Duplicated Household GPS Coordinates\u003c/span\u003e\u003c/u\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cu\u003e\u003cspan style=\"font-size:12px;\"\u003eRecorded for Potentially Different Households\u003csup\u003e+\u003c/sup\u003e\u003c/span\u003e\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 10.52%;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 0.3pt;height: 26.35pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eNumber and\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003ePercentage\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eof Unique\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eHousehold GPS\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eCoordinates (%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:5.1%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width:10.54%;border:none;border-bottom: solid windowtext 1.0pt;padding: 0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eIn Different\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eHealth Zones\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:10.54%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eIn Different\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eHealth Areas\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:10.52%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eIn Different\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eVillages\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:10.52%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eIn the\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eSame Village\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:10.62%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:26.35pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003eAll\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9.24%;border-top: none;border-bottom: none;border-left: none;border-image: initial;border-right: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top: 0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family: \"Times New Roman\";color:black;'\u003eTotal\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border: none;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border: none;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border: none;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border-top: none;border-bottom: none;border-left: none;border-image: initial;border-right: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border: none;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e646\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.28%;border:none;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e4.3%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.26%;border:none;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e2288\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.28%;border:none;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e15.1%\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.24%;border:none;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e5562\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.28%;border:none;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp 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windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.6%;border-top: none;border-left: none;border-bottom: 1pt solid windowtext;border-right: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: top;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.28%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.28%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.24%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.28%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.36%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.26%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:5.26%;border:none;border-bottom:solid windowtext 1.0pt;padding:0in .3pt 0in .3pt;height:1.0pt;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style=\"font-size:12px;\"\u003e(82.6%)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.1%;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 0.3pt;height: 1pt;vertical-align: bottom;\"\u003e\n \u003cp style='margin-top:0in;margin-right:0in;margin-bottom:0in;margin-left:0in;font-size:11.0pt;font-family:\"Segoe UI\",sans-serif;text-align:center;line-height:normal;'\u003e\u003cspan style='font-size:12px;font-family:\"Times New Roman\";color:black;'\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThere were 2725 unique GPS readings used across 15,184 affected records in VCS1. Of which, 38% (or 1043) of the readings were duplicated 2 times each, while 88% (or 2387) coordinates were duplicated\u0026thinsp;\u0026le;\u0026thinsp;10 times. In the more extreme cases, 3 GPS coordinates were duplicated for 100, 84, and 81 times each, respectively, all of them in Sud-Kivu Province (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn VCS2, the percentage of households with duplicated GPS coordinates was 0.8% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), with 3 provinces presenting with \u0026gt;\u0026thinsp;2% of duplicated household GPS coordinates \u0026ndash; Kwilu (4.5%), Kasai (3.7%), and Kinshasa (2.5%). In VCS3, the percentage of households with duplicated GPS coordinates was 0.2%. The three provinces with the highest percentage of duplicated GPS household coordinates were Kwilu (1.6%), Sankuru (0.51%), and Tshuapa (0.5%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevices\u0026rsquo; performance on recording household GPS coordinates by day\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of all 87,431 interviews in VCS1, 2.4% did not have a device ID number. The remaining 85,324 interviews were recorded by 501 devices. Of which, 116 recorded interviews exclusively with unique household GPS coordinates, whilst the interviews recorded on 10 devices all showed household GPS coordinates duplicates (7 were deployed in the 5 aforementioned provinces). The mean and median proportion of interviews presenting with duplicated household GPS coordinates among these devices were 24.4% and 8%, respectively.\u003c/p\u003e\n\u003cp\u003eIn Kwilu Provinces, 21 devices were used to capture the VCS interviews across 24 HZs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Apart from Kikwit-Nord HZ and Kikwit-Sud HZ, the devices were deployed in groups of 4. For instance, 4 devices (K1-K4) were deployed in Bagata, Bandundu, Djuma, and Kikongpo between 3\u0026ndash;28 January 2022, and 4 devices (K13-K16) were deployed in Idiofa, Ipamu, Kimputu, and Koshibanda HZs between 4\u0026ndash;22 January 2022. For these two groups of devices, on any given day, the household GPS coordinates recorded were either largely all duplicated, or that they were all unique. A very similar \u0026ldquo;synchronization\u0026rdquo; was found among groups (e.g., HL4-HL9) of devices deployed to Haut Lomami Province.\u003c/p\u003e\n\u003cp\u003eIn Nord Ubangi Province and Mongala Province, performance of devices deployed to the same HZs appeared less synchronized (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). There were 4 devices (NB13, M13, M7, and M4) deployed to these two provinces for which all household GPS coordinates were duplicated at least once. In Nord Ubangi Province, for instance, NB10-NB14 were deployed to Businga, Karawa, and Loko HZs, and all devices but NB13 recorded almost all unique GPS coordinates, whilst NB13 recorded duplicated coordinates exclusively. Similarly, NB1-NB4 were deployed to the same HZs and, and whilst NB4 showed good performance, NB1-NB3 performed sub-optimally. The same was also found among M1-M4 in Mongala Province, and S1-S4 in Sud Kivu Province, for instance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey data characteristics and quality by devices\u0026rsquo; duplication rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the five provinces presenting with the highest percentage of duplicated household GPS coordinates in ECV1, we assessed 8 series of skip-pattern questions, each beginning with a leading question. Subsequent questions in the series were skipped on responding \u0026ldquo;No\u0026rdquo; to these leading questions. For 3 out of 8 series, the percentages of negative responses given to these leading questions were higher among poor-performing devices (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, we assessed 5 series of questions, each with the same response options (see\u0026nbsp;\u003cstrong\u003eSupplementary Material II\u003c/strong\u003e for example), for straight-line responses. The percentage of interviews selecting the same response pattern to all sub-question in a series was not consistently higher among poor-performing devices.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePercentages of responses given to skip-pattern questions and Likert scale questions in VCS1\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003ePercentage of negative responses given to the lead question in each of 8 series of skip-pattern questions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood-performing devices\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor-performing devices\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA706 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.6, 14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA701 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.6, 7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVS85A series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-8.3, -4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVS136 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.2, 3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVS25B series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-1.4, 0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVS25D series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.6, 2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA218 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.1, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA220 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-2.4, 2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage of interviews with the same response given to all sub-questions in each of 5 series\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood-performing devices\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor-performing devices\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVS1 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-4.8, -0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA503 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.1, 1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA501 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.1, 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA6 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.6, 3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQA502 series\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.5, 0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003e Poor-performing devices refer to those devices that recorded\u0026thinsp;\u0026gt;\u0026thinsp;90% duplicated household GPS coordinates, Good-performing devices refer to those devices that recorded exclusively recorded unique coordinates.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed duplicates in household GPS coordinates captured in large-scale, nationally representative vaccination coverage surveys, focusing specifically on the extent to which duplication of coordinates were recorded across different households. Over 80% of VCS1 GPS coordinates were unique. In VCS2 and VCS3, duplicate GPS coordinates reduced to below 1%. Duplicated household GPS coordinates in VCS1 were concentrated in certain provinces, the top five being Mongala (66.9%), Nord-Ubangi (59.5%), Sud-Kivu (53.3%), Kwilu (50.1%), and Haut-Lomami (39.6%). In some instances, devices that were deployed to the same health zones on the same days showed similar pattern in household GPS coordinates duplication. When testing for correlated patterns that we assume would occur in other data elements between devices that recorded unique household GPS coordinates and those that recorded a high proportion of duplicates if data fabrication was happening, no consistent pattern was found.\u003c/p\u003e\u003cp\u003eHigh quality population-based vaccination coverage surveys are very useful but resource-intensive. It is important to ensure that the locational information generated can accurately guide vaccination needs assessments, pro-equity interventions, intervention planning, visualizing/monitoring progress, and disease surveillance\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The programmatic and policy implications of inaccurate and unreliable geolocation data from VCSs and the like are far-reaching. Geolocation errors, in the first instance, can lead to misestimating and misidentifying low-coverage areas, resulting in poor outcome of targeted immunization activities. Wigley and colleagues identified a sizable proportion of the global zero-dose under-one population within an hour to the nearest town or city, challenging the consensual understanding that these children are generally found in rural remote and urban informal areas\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In another simulation study, imprecise household locations reduced the proportion of children correctly linked to their source of health services\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.​ The shifting of perceived clusters of non-vaccination or disease susceptibility can also hamper outbreak preparedness, efficient vaccine stockpiling or deploying. Recurring data inconsistencies can undermine confidence in health data, undermining political will to invest in survey-based monitoring and geospatial analytics.\u003c/p\u003e\u003cp\u003eOur findings offer some lessons learned to improve geolocation data collection in health surveys. There has been survey data inaccuracy documented in various contexts, partly driven by data fabrication without visiting the sampled households in challenging field conditions, performance pressures, and limited oversight\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In this study, however, the localization of groups of devices operating in the same areas on the same days showing a similar extent of household GPS duplications, while quality of the other data elements remained consistent, suggests that environmental and technical constraints were at play. DRC is characterised by rainforests, mountains, and river systems that can obstruct or degrade satellite signal quality. Physical obstructions (e.g., trees, buildings, tunnels, or bridges) can also reduce signal strength and quality. Poor signal reception limits the number of satellites that devices can lock onto, reducing location precision or delaying coordinate acquisition altogether. Moreover, weather conditions, such as cloud cover, rain and fog can reduce signal quality\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Many GPS apps are designed to improve responsiveness by caching recent or the last known\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Although this can serve as a fallback when GPS signal is weak or unavailable, if the device fails to acquire a geolocation within a certain time window, cached (or stale) geolocation can be reused across households, introducing spatial data inaccuracies.\u003c/p\u003e\u003cp\u003eDuring VCS1 fieldwork, interviewers reported that under conditions of weak satellite signal, GPS coordinates did not change when attempts to acquire new ones were made. In some cases, interviewers working in areas with insufficient reception carried on with fieldwork as they were unable to obtain support from the coordination team due to insufficient connection. Our coauthors (FB and JBNK) confirmed that two tablet models from different brands were used in VCS1, whereas for VCS2 and VCS3, only one model \u0026ndash; also used in VCS1 \u0026ndash; was retained, fully replacing the other. Additionally, in VCS2 and VCS3, GPS booster application was installed to improve the process of acquiring accurate GPS coordinates. GPS booster applications are designed and optimized to improve the performance of a device's built-in GPS. They can work to clear outdated A-GPS data and force a fresh download, whilst also enhancing the speed, accuracy, and reliability of GPS fixes.\u003c/p\u003e\u003cp\u003eInsights from the three VCSs in DRC speaks to the importance of supporting interviewers working in challenging terrain and suboptimal atmospheric conditions. Several technical and operational measures can mitigate the effects of weak or intermittent GPS signals during health surveys. Training remains critical in GPS data quality. The current WHO Manual on VCS provides ample instructions on certain best practices, such as providing training to interviewers on positioning the receiver in a clear, unobstructed location, testing the signal before and during fieldwork, and ensuring the correct settings\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Training can also be expanded to cover the mechanism of GPS acquisition, identifying potentially inaccurate coordinates, when to wait or retry before logging a location, and the programmatic consequences of poor spatial data \u0026mdash; particularly when surveys inform microplanning or targeted service delivery.\u003c/p\u003e\u003cp\u003eDuring fieldwork, using high-sensitivity GPS devices can substantially improve location precision in environments with poor satellite visibility GPS diagnostic and booster apps can aid fieldworkers by visualizing satellite availability and clearing outdated GPS cache data. Providing interviewers with preloaded offline maps (offline tiles of OpenStreetMap or satellite imagery) of the target areas can support visual confirmation of household or village locations if GPS is unavailable. When GPS acquisition fails altogether, interviewers may be allowed to pin the household location on a digital map, a less precise method but one that preserves the spatial intent of the data and avoids data gaps. Additionally, implementing a data field to capture the geolocation method used (e.g., live GPS vs. manual pinning) can support quality control during post-field data verification. Taken together, these strategies provide a foundation for improving the accuracy and reliability of geospatial data collection in future survey implementations.\u003c/p\u003e\u003cp\u003eOur study revealed circumstantial constrains to capturing accurate geolocations at refined scale in resource-limited settings. The findings should be considered with several limitations in mind. First, we used exact duplications as an indication of inaccurate household geolocation data. Identification of close readings (detected by calculating the distance apart, for instance) and ground-truthing of the recorded coordinates, whether or not duplicated, in future can more comprehensively assess data quality. Second, individual households were identified using data fields on province, health zones, health areas, village, and household identifier, the accuracy of which was assumed. However, especially for coordinates that were highly duplicated, the error was presumably in the coordinates rather than the other data fields. Third, our assessment of overall data quality attempted to identify some level of data fabrication, and although no evidence of such practices was observed, \u0026ldquo;genuine fakes\u0026rdquo; may still have subverted data quality\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, the low level of duplication observed in VCS2 and VCS3, conducted after hardware and software upgrade, further supports genuine survey interviews. Lastly, future cross-referencing with interviewers can strengthen understanding of the challenges faced in the field, and in turn help tailor support and shape revised guidelines for future survey implementation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the importance of ensuring high-quality geolocation data in large-scale health surveys, particularly in settings with complex terrain and infrastructure challenges. By assessing GPS coordinate duplication across three rounds of nationally representative vaccination coverage surveys in the DRC, we identified substantial improvements in data quality following targeted hardware and software changes\u0026mdash;including standardizing devices and introducing GPS booster applications.\u003c/p\u003e\u003cp\u003eAccurate geolocation is essential for effectively identifying underserved populations, guiding immunization strategies, and supporting equitable health service delivery. As survey data increasingly inform programmatic planning, investments in interviewer training, technical tools, and quality assurance protocols are critical. Our findings underscore the value of adapting operational approaches to field conditions and incorporating flexible fallback methods\u0026mdash;such as offline maps and manual location pinning\u0026mdash;to safeguard spatial data integrity. Strengthening these elements in future surveys will enhance the reliability of geospatial analytics and the impact of data-driven decision-making in global health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eConceptualization, KLMW and MCDH.; statistical analysis, KLMW; data collection: FB, JBK, DI, EM; interpretation of data, KLMW and MCDH; writing—original draft preparation, KLMW, MCDH, RB, DI, FB, JBK; writing—review and editing, all authors. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eDisclaimer\u003c/h2\u003e\n\u003cp\u003eThe authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe survey that generated the data used in this study was funded by the United Nations Children Emergency Funds (UNICEF) after mobilizing a significant funding from GAVI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e\n\u003cp\u003eEthical approvals were sought for each round of the vaccination coverage survey from the Kinshasa School of Public Health Ethical Committee before data collection (approvals: ESP/CE/175/2021 and ESP/CE/025/2023). Health and politico-administrative authorities at the local level also provided their authorization.\u003c/p\u003e\n\u003ch2\u003eInformed Consent Statement\u003c/h2\u003e\n\u003cp\u003eOral informed consent was obtained from potential study participants before starting the interview. Potential survey participants were informed about the nature of the survey, including objectives, risks and benefits, the confidentiality of their replies, the contact details of study personnel, and that they were free to participate or not without any negative consequences. Confidentiality was maintained and the dataset was anonymized.\u003c/p\u003e\n\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe data underlying this study from the Vaccination Coverage Surveys (VCS) contain sensitive geographic and health information. To ensure participant privacy and confidentiality, these datasets are not publicly available. The Kinshasa School of Public Health Ethical Committee (Approval Numbers: ESP/CE/175/2021 and ESP/CE/025/2023) reviewed and approved the study, including the data management procedures, and imposed restrictions on public data sharing to protect participant confidentiality.\u003c/p\u003e\n\u003cp\u003eResearchers interested in accessing the data may submit a formal request to Dr. Eric Mafuta at [email protected]. Applications should include a brief research proposal outlining the intended use of the data, data protection measures, and any relevant institutional approvals. Access may be granted on a case-by-case basis, subject to ethical and data-sharing agreements ensuring that privacy and confidentiality standards are maintained.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eO\u0026rsquo;Brien KL, Lemango E, Nandy R, Lindstrand A. The immunization Agenda 2030: A vision of global impact, reaching all, grounded in the realities of a changing world. Vaccine. 2024;42:S1\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLindstrand A, Cherian T, Chang-Blanc D, Feikin D, O\u0026rsquo;brien KL. The World of Immunization: Achievements, Challenges, and Strategic Vision for the Next Decade. J Infect Dis. 2021;224:S452\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUNSTATS. 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The Role and Impact of Location-Based Services in Mobile App Development. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://moldstud.com/articles/p-the-impact-of-location-based-services-in-mobile-app-development?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://moldstud.com/articles/p-the-impact-of-location-based-services-in-mobile-app-development?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Immunization, vaccination coverage surveys, population surveys, health surveys, geolocation, geocoding, GPS","lastPublishedDoi":"10.21203/rs.3.rs-7221573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7221573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHousehold-based surveys are fundamental for estimating immunization coverage in low- and middle-income countries. Increasingly, household GPS coordinates are collected in surveys to improve spatial resolution for targeted intervention. Geolocation data quality impacts coverage estimates, program targeting, and policy decisions. However, little attention has been paid to the accuracy and uniqueness of these data. This study investigated the extent of GPS coordinate duplication in vaccination coverage survey (VCS) in the Democratic Republic of the Congo (DRC), and explored potential causes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThree rounds of VCS were conducted in January 2022 (VCS1), February 2023 (VCS2), and March 2024 (VCS3). In each round, duplicated household GPS coordinates were identified. Duplication patterns were analysed by health region, interviewer ID, tablet device ID, and interview date. Data quality in other fields was compared between interviews recorded by devices with \u0026gt; 90% duplicated GPS coordinates and those recorded by devices with only unique coordinates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn VCS1, 17% of GPS points had substantial duplications, with 5 provinces (Mongala, Nord-Ubangi, Sud-Kivu, Kwilu, and Haut-Lomami) showing over 40% duplications. Of all 501 devices IDs retrieved, 116 recorded exclusively unique household GPS coordinates, while the interviews recorded on 10 devices all showed household GPS duplication (7 were deployed to the 5 provinces with the highest duplication rates). In Kwilu Province and Haut Lomami, in particular, tablets deployed to the same health zones and days showed similar duplication patterns. The quality of other data elements in interviews with duplicated GPS coordinates was comparable to those with unique coordinates. Notably, duplication rates in VCS2 and VCS3 were substantially reduced to within 1%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant GPS coordinate duplication was observed in VCS1 in the DRC, but this issue was largely mitigated in subsequent survey rounds. The duplication identified highlights the need for improved field protocols, including enhanced interviewers training, robust validation steps, and clear hardware and software guidelines. GPS errors in household survey threaten the integrity of spatial data, downstream analyses, and policy decisions based on geospatial metrics. Our findings underscore the need for standardized quality assurance in the geolocation data collection practices of global health survey implementations.\u003c/p\u003e","manuscriptTitle":"Household Global Positioning System (GPS) coordinates duplication in health surveys – a case study of the Vaccination Coverage Surveys in the Democratic Republic of Congo (DRC)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 04:17:01","doi":"10.21203/rs.3.rs-7221573/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"328463106334319215764617837868454566363","date":"2026-05-04T13:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292459495776661204767056017546901351625","date":"2026-05-03T23:57:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150419546172016921132273245690513885334","date":"2026-05-02T07:54:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235943458988348360880764642936887737680","date":"2026-05-01T18:48:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T15:04:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196198601332392047096879741699269027822","date":"2026-02-02T13:25:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72427912601448834791919744781392047879","date":"2025-08-06T09:51:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290675279631265532284675956346732298512","date":"2025-08-04T23:38:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T09:35:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-04T09:01:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-04T08:58:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2025-07-26T13:37:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b679f42e-6bbb-49f1-b78f-7afc11528844","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-07T04:17:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 04:17:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7221573","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7221573","identity":"rs-7221573","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}