Assessing the Use of Geospatial Data for Immunization Program Implementation and Associated Effects on Coverage and Equity in the Democratic Republic of Congo

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Methods The study used a mixed-methods research design including survey tools, in-depth interviews and direct observation to document the uptake, use, and perceived impact of georeferenced immunization microplans in the intervention provinces of Haut-Lomami and Kasai and in the control province of Kasai Central. A total of 113 health facilities in 98 Health Areas in 15 Health Zones in the three provinces were included in the study sample. A gender intervention in select Health Zones and Health Areas in Kasai Province was also evaluated through a targeted qualitative study. A secondary analysis of immunization coverage survey data was conducted to assess the associated effects on immunization coverage, especially for rates of zero-dose children. Results This research study shows that georeferenced microplans are well received, utilized, and led to changes in routine immunization service planning and delivery with perceived improvements in identification and reaching zero-dose children. In addition, the gender intervention is perceived to have led to a significant change in the approaches taken to overcome sociocultural gender norms and engage communities to reach as many children as possible, leveraging the ability of women to engage more effectively to support vaccination services. The quantitative analyses showed that georeferenced microplans may have contributed to a dramatic and sustained trend towards high immunization coverage in the intervention site of Haut Lomami, which rose dramatically from 8.9% in 2020 to 76.8% in 2021 and to 92% in 2022 for Pentavalent 3 antigen, while the DPT1-DPT3 drop-out rate changed little from 1% in 2020 to 1.7% in 2021 and 1.6% in 2022 after three years of implementation. Conclusion The overall study identified positive contributions of the georeferenced data in the planning and delivery of routine immunization services. It is recommended to conduct further analyses in Kasai in 2024 and 2025 to evaluate the effects of the gender intervention on immunization coverage and equity outcomes. Geospatial data mapping for health immunization coverage vaccine coverage survey immunization equity DRC Expanded Program for Immunization routine immunization zero dose microplanning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND The World Health Organization (WHO) Global Vaccine Action Plan was developed to help all individuals and communities enjoy lives free from vaccine-preventable diseases, demonstrating that the benefits of immunization are equitably extended to all people, wherever they are located [ 1 ]. GAVI, the Vaccine Alliance’s 5.0 Strategy 2021–2025 and the Global Immunization Agenda 2030 [ 2 , 3 ] promote the use of geospatial data and technology applications for immunization programs to reach all children, especially those who have never received any vaccine and are designated as “zero-dose”. A considerable proportion of zero-dose children live in remote areas with poor accessibility to health facilities, mainly in low- and middle-income countries (LMICs). In the Democratic Republic of the Congo (DRC), the Mashako Plan is a unique, high-level national health strategy that aims to drastically increase complete childhood immunization coverage at a national level [ 4 ]. The integration of geospatial tools, technologies and data for planning and delivery of immunization services supports the Mashako Plan through a participatory process to create geospatially accurate maps of settlements, define health area boundaries and generate improved population estimates. The integration of these geospatial tools and technologies into immunization programs has demonstrated potential to enhance immunization coverage and equity [ 5 ] and address persistent challenges of data quality, inflated reports of coverage rates and inaccurate denominators [ 6 , 7 ]. The use of geospatial data, geospatial tools and technologies for immunization programming in the DRC was intended to address some of these challenges and to support the immunization program to accurately monitor immunization coverage and plan upcoming vaccination activities. Description of the Intervention The Mapping for Health (M4H) project aimed to strengthen the equity and effectiveness of vaccination interventions in the DRC, increase national geospatial capacities and promote gender-intentional programming through the provision of geo-enabled microplans within the National Expanded Program on Immunization (EPI). The project is implemented by the Ministry of Health with support from the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) Consortium. To improve the effectiveness of microplans, the GRID3 Consortium supported the National Immunization Program activities for gender-responsive planning, generating geospatial data and population modeling to determine the target population (denominator) and produce core geospatial data layers: settlements, health boundaries, and health facilities. These data were then used to optimize vaccination strategies in Health Zones in the form of geo-enabled microplans. The GRID3 project facilitated gender analyses and collaborated with civil society partners to apply gender responsive vaccination strategies to immunization program planning and service delivery alongside the use of geo-enabled microplans in targeted Health Zones and Health Areas. To evaluate the use of geo-enabled microplans and the complementary gender intervention in a sub-set of sites, Gavi engaged HealthEnabled through the “Effective Design, Implementation, Integration, and Evaluation of Digital Health Systems to Enhance the Strategic Use of Data for Immunization Programming” to assess acceptance and use of geospatial data for microplanning and routine immunization implementation and associated effects on the vaccine coverage in DRC. The generation of core geospatial layers is intended to provide key and timely insights for Health Zone and Provincial decision-makers to identify hard-to-reach settlements or settlements likely to fall in between two health catchment areas; estimate the population of the health areas and health zones; estimate a healthcare facility's catchment population; estimate the number of vaccines needed for a health area based on its population; assess the population coverage of current fixed vaccination strategies; optimize outreach vaccination strategies based on population distribution; and optimize the cold chain and new fridge allocations based on population distribution. The theory of change for M4H describes how the systematic generation and use of geospatial data and associated population distribution, including the identification of previously missed settlements, contributes to more effective immunization program planning and service delivery, which contributes to improved immunization coverage and equity. This theory of change was used to inform the development of the qualitative instruments (observation and interview guides), the intervention strength survey instruments, and the secondary analyses of immunisation coverage survey data. The gender interventions were evaluated separately using a rapid ethnographic approach and relevant Health Zones and Health Areas were purposefully included as sites for the intervention strength survey. METHODS This is a mixed-methods study with a quasi-experimental design. Impact was assessed using a pre/post study design which draws upon the National Expanded Program on Immunization (EPI) Vaccine Coverage Surveys (VCS) conducted with support from the Kinshasa School of Public Health (KSPH) in 2021, which was repeated in 2023 [ 8 ]. Efforts to assess impact were both informed and complemented by qualitative research (direct observations and in-depth interviews) and intervention strength surveys in prioritized Health Areas in intervention and control sites to assess adherence to microplans with and without M4H data. A targeted rapid ethnographic study was conducted in Health Zones and Health Areas in Kasai which were exposed to gender-specific program activities. These sites were purposefully included in the intervention strength survey sample. The qualitative approach focused on interviews with various participants, including the Provincial Head of Division, EPI branch Medical Chief, EPI branch Data Managers, and the Analysts in charge of the health information to the Provincial Division, EPI Monitoring and Evaluation Service Chief, and the person in charge of mapping in the NHIS Office at the central level. Interview guides were designed to facilitate the interviews with key informants. Three key equity-related variables were used in this study: household wealth, telephone use by the head of household, and urban or rural residence of the household. The relative household wealth variable is described in five modalities: poorest, second poorest, middle poorest, fourth poorest and richest. The variable "household cell phone access" is measured by two modalities: without cell phone and with cell phone. The "place of residence" variable has two modalities: urban and rural. In addition, a comparative approach of a descriptive study was performed determining whether the implementation of M4H in Haut-Lomami is associated with significant differences in the percentage of zero-dose children aged 12–23 months post-intervention in the poorest and poorest economic strata between 2020 and 2021. Setting The study took place in 3 Provinces, namely Kasai (M4H intervention with gender component), Haut Lomami (M4H intervention without gender component), and Kasai Central (control site). Prioritized survey sites included Health Zones that represent urban and remote areas with the inclusion of conflict settings. Our sample size was 113 health facilities in 98 Health Areas in 15 Health Zones in the three provinces as illustrated in Fig. 1 . For the sampling, we considered 30% of the total number of Health Zones for each stratum. To do this, we carried out simple random sampling using the Android application "randomizer". The same approach was used to select Health Areas. We considered 30% of the total number of Health Areas for each Health Zone. In the control Province, we used the same sampling method and considered 15% of the total number of Health Areas for each stratum and 15% of the total number of Health Areas for each zone. Data Collection Data collection at Provincial and Health Zone levels involved in-depth interviews in French, recorded for qualitative analysis. The focus of these interviews was on the acceptance and use of geospatial data for immunization planning. A total of 19 in-depth interviews were conducted. Before each interview, the interviewers presented the objectives of the evaluation to the participants. Individual written consent was required and obtained from each respondent to participate in the study and record the interview. Interviewers made appointments with each respondent, according to availability. At Health Area and health facility levels, data collection techniques included: (1) a structured survey; (2) direct observation of maps and georeferenced microplans; (3) document review; and (4) semi-structured interviews with key informants. Quality control was carried out on an ongoing basis, at various stages of the study: Prior to the data collection Interviewers with previous research experience were recruited and underwent a two-day training on the objectives of the study and data collection using the CAPI (Computer Assisted Personal Interview) system. Interviewers were then selected to guarantee data quality. The questionnaire was digitized using SurveyCTO with automatic filters, constraints, and relevance criteria for certain questions to control data entry. During and after data collection The supervisor developed a follow-up plan for the field teams to ensure that the interviewers were in the various assignment zones. A supervision form was completed to report on field progress, including the number of interviews completed as well as any problems encountered in the field. All teams were linked by a WhatsApp group for rapid sharing of information in the field. Automatic checks of completed and sent questionnaires were carried out by the coordinator in charge of data processing and analysis. When necessary, the provincial supervisor was alerted to take corrective action. Data editing was carried out during data collection to ensure data quality, notably by searching for "I don't know" or "refusal" responses and by cleaning the database prior to the analysis. Data Analysis The content of the in-depth interviews and open-ended survey questions was analyzed using ATLAS TI software. Through an inductive and iterative process, we used content analysis methods based on thematic codes and sub-codes. The initial list of codes was derived from the themes and questions contained in the interview guides. All transcripts were coded using the coding list. We looked for subgroups to highlight specific experiences and the reasons for those experiences. The intervention strength survey data collected by the interviewers was transferred to the server after verification by the field supervisor. Secondary data cleaning was carried out using Survey CTO software. Data analysis was performed using SPSS Version 25 software. The data were analyzed to produce expected frequencies for categorical variables, and for continuous variables, the measure of central tendency (mean or median) and dispersion (standard deviation or interquartile range) according to the normality of the distribution. The Chi-square test was used to test for association, with an alpha of 0.05. To achieve the objectives of the coverage and equity study objectives, secondary analyses of immunization coverage and equity survey data were conducted. The data extracted covered Haut-Lomami, Kasaï, and Kasaï Central. The extraction of the data was done manually. Once extracted from the VCS database, the data was subjected to a descriptive statistical analysis. The variable measured was zero-dose prevalence, the percentage of children who have not received any dose of vaccine against diphtheria, tetanus and pertussis. To measure the extent of deviation, the standard deviation of each prevalence was associated with each variable. The analysis was then carried out in two stages. First, the general evolution of the overall percentage of zero-dose children was described. Then, from a socio-economic equity perspective, the percentage of zero-dose children in Haut-Lomami, where geospatial data had been in use by the EPI program for more than 12 months was compared over time with its evolution in the two other provinces, using data from the Vaccine Coverage Survey (VCS) from 2020 to 2022, across different household wealth quintiles, telephone use by the head of household, and the urban or rural residence of the household [ 8 ]. RESULTS Our findings are presented by study aims and objectives beginning with the socio-demographic characteristic of the interviewees (Table 1 ). Table 1 Socio-demographic characteristics of participants in the 3 provinces Haut-Lomami Kasai Kasai Central Together n = 46 (%) n = 49 (%) n = 16 (%) n = 111 (%) Sex Male 39 (85) 43 (88) 16 (100) 98 (88) Female 7 (15) 6 (12) 0 (0) 13 (12) Total 46 (100) 49 (100) 16 (100) 111 (100) Age range < 25 years 1 (1) 0 (0) 0 (0) 1 (1) 25–34 15 (33) 5 (10) 5 (31) 25 (23) 35–49 18 (39) 26 (53) 9 (56) 53 (48) ≥ 50 12 (26) 18 37) 2 (13) 32 (29) Total 46 (100) 49 (100) 16 (100) 111 (100) Level of Study Secondary 13 (28) 15 (31) 4 (25) 32 (29) Higher or University 33 (72) 34 (69) 12 (75) 79 (71) Total 46(100) 46(100) 16 (100) 111(100) Marital status Married 44 (96) 45 (92) 16 (100) 105 (95) Single 1 (2) 3 (6) 0 (0) 4 (4) Widower widow 1(2) 1 (2) 0 (0) 2 (2) Total 46 (100) 49 (100) 16 (100) 111 (100) Function IT (Head Nurse of Health center 17 (37) 44 (90) 8 (50) 69 (62) IS (Health Zone Nurse supervisor in charge of immunization) 3 (7) 3 (6) 3 (19) 9 (8) MCZ 0 (0) 1 (2) 0 (0) 1 (1) Other 26 (57) 1 (2) 5 (31) 32 (29) Total 46 (100) 49 (100) 16 (100) 111 (100) Most respondents were male (88%). The modal class in all three provinces is 35–49 years age old with a total of 48% of all respondents interviewed. More than three out of four respondents had a higher or university degree and almost all were married (95%). Three-fifths of respondents assumed the role of Head Nurse of Health center (62%), except in the Province of Haut-Lomami where slightly more than half were in other roles (57%). Study Aim 1: Program implementation context and mechanisms 1.1. Process through which geospatial data was created Participation or contribution in the process The majority of study participants were not involved in the development of the design of the Mapping for Health intervention. Differences in the engagement of the various stakeholders emerged, i.e., at the central level, not all the Ministry stakeholders had the same degree of participation or contribution to the project. At the provincial level, participation was in the planning and implementation phase. The interviewees noted that the project's objectives took account of the gender aspect from the point of view of the service providers and concrete implementation, as in the identification of vaccinated children by sex and age. For the equity aspects, they considered all social strata. The design of the georeferenced and gender data set-up contributed to effective identification of where the targets are and informed the mechanisms to reach and vaccinate them according to National EPI guidelines. The project has also resolved the problem of imprecise Health Area and Health Zone boundaries, as well as the location of populations overlooked during vaccination activities. A content analysis by respondent category according to health system levels revealed a difference in perception of the gender and inclusion aspect. At the central level, the gender intervention was clearly known, and the various stakeholders recognized this dimension in the intervention and also contributed to it in the training aspects of the field teams. At the provincial level, the gender and social inclusion dimension is perceived differently by the various stakeholders. Respondents confirmed that the community had taken part in the process through the community animation cells (CACs) with the agreement of the local authority, applying the principle that "whatever you do without me, you do against me". The Head nurse of Health Areas organized briefing meetings to enlighten community members on the merits of mapping data. However, the community was both a barrier and an enabler. The result was mistrust on the part of the population in some communities, which are not accustomed to seeing sophisticated materials or technological devices. In some Health Zones, the local population believed that they were being expropriated from their land, requiring explanations at all times despite the authorization of the village chief. In some cases, the population forbade the activity or even bought the equipment outright. As described by one of the respondents, " In terms of ease of use, it's the community that knows the boundaries …. From a social point of view, you had to see the chief, because when you say, for example, "Where does your village end?” he's the one who should say, "My land goes as far as here". In terms of barriers, when we see an activity where we have to use fairly technological equipment, we wonder what the purpose is and that's the barrier or reticence that we could feel .” (Head Nurse, Kasai). The contribution of the community extended to the feedback it provided for the validation of mapping data collected, even if community leaders (CAC) are still expecting to receive updated maps with corrected information, where needed. With regard to gender, the main reflection of key informants was that Health Zone management teams take into account the gender dimension in the current immunization register, where vaccinated children are well identified by age and sex. Mapping acceptance, challenges, and prospects The mapping was well accepted by various stakeholders. Positive aspects include the production of better-quality maps, enabling more accurate location of sites compared with the old handwritten maps, and the production of more accurate population estimates and population densities, enabling better planning of vaccination activities. Negative aspects related to the imperfection of the maps, which had some omissions or inaccuracies of certain customary landmarks. For certain Health zones, some health areas had almost disappeared, for which the respondents would like maps to be updated. On the optimal future for the mapping, one respondent commented: " It's a promising future, but it's only the first step. I think that these will be dynamic maps that can be updated as we go along…. So, the project will have to see how to establish a certain periodicity for updating these maps”. (EPI branch office, Haut Lomami). 1.2. Process through which project georeferenced data is shared for use in microplanning Many of the respondents have worked for more than 10 years, directly or indirectly, in the microplanning of routine immunization activities using a paper-based microplanning process. They are therefore experienced resources in this field, from the Head Nurses and the community (community relays, community animation cells and health development committees) who took an active part in the microplanning of their respective Health Areas and transmission to Health Zone central office level. The following considerations were perceived as facilitators for the use of geospatial data for microplanning and routine immunization: the existence of a legend that makes it easy to read a map; attraction to technology; transition from analogue to digital; the desire to do things differently and better; users were involved in the process; user support; buy-in and use of the tool by the service provider. For some Health Zones, the following were perceived as obstacles: the problem of connecting to the Internet; lack of knowledge of the tool; lack of training; unavailability of logistical and financial resources; most of the tools used in vaccination are analogical; most of the tools are intended for people who are not too literate in terms of technology; technological tools require a substantial investment; old habits; other logistical, financial and economic constraints in implementation. 1.3. Process through which geospatial data is used as part of microplanning processes All Health Zones as well as all Health Areas surveyed in the Kasai province received the georeferenced data. Almost the entire Haut-Lomami Province, 98%, received the georeferenced data; no structure in the Kasaï Central Province, which is a control Province, received the georeferenced tools. At the time of the study, different maps generated by the project were observed by the research team to be taped to the office walls of almost all (98%) of the Health Zones and Areas investigated in the Kasaï province and 70% of the walls of the Haut-Lomami Province. Unanimously, respondents mentioned their satisfaction and affirmed that the maps were of capital use in general and that their use in vaccination activities made it possible to improve their knowledge and acquire more information on the respective entities. This also made it possible to resolve conflicts over the delimitation of the geographical boundaries of the Health Areas, since in some cases, the limits defined on these tools did not reflect the reality on the ground. Overall, microplanning tools are displayed by the Health Area Head nurses in 83% of the healthcare establishments visited: respectively 89% and 86% in the two provinces of intervention of Haut-Lomami and Kasaï, and 63% in the control province of Kasai Central. Almost all microplanning tools (98%) were in paper format. The microplanning tool is accessible in most cases to full-time nurses in 88% and to other nurses in 39% of the establishments visited. In the two intervention Provinces, the main users of the microplanning tool are the health center nurses in 92% of cases compared to 75% in the control Province. Before the introduction of georeferenced data, half of the healthcare institutions in Haut-Lomami used data from the National EPI Program (51%) when developing their microplan, while more than four-fifths of Kasai Province (86%) and half of Kasai Central establishments (50%) respondents reported using routine data. In the two intervention Provinces, the georeferenced data actually used are the estimate of the target population (84%), the distribution of the target population by site or location (78%), the identification of the sites of vaccination (76%), the identification of vaccination sites for optimizing vaccination strategies, i.e., advanced strategy (63%) and the identification of new villages (62%), as presented in Table 2 . Table 2 Distribution of Microplan Users and Reported Georeferenced Data and Uses DPS Haut-Lomami DPS Kasai DPS Kasai Central Together Num (%) Num (%) Num (%) Num (%) Microplan Users IT (Head nurse of Health center) 41(91.1) 45(91.8) 12(75.0) 98(89.1) Male nurse 23(51.1) 15(30.6) 1(6.3) 39(35.5) RECO (community relay) 14(31.1) 11(22.4) 1(6.3) 26(23.6) Others 2(4,4) 17(34.7) 2(12.50) 21(19.1) IS (Supervisor Nurse in the Health zone in charge of immunization) 5(11.1) 5(10.2) 6(37.5) 16(14.5) MCZ (Health Zone Chief medical doctor) 4(8.9) 3(6.1) 3(18.8) 10(9.1) What was the source of information before Georeferenced data? Routine data 18(40.0) 42(85.7) 8(50.0) 68(61.8) National EPI 23(51.1) 2(4.1) 6(37.5) 31(28.2) Other (s) to be specified 4(8.9) 5(10.2) 2(12.5) 11(10.0) Type of georeferenced data included in the tools Target population (new denominator) 38(84.4) 43(87.8) N / A 81(84.5) Distribution of the target population by site or location (number) 34(75.6) 43(87.8) N / A 77(77.3) Identification of vaccination sites 34(75.6) 34(69.4) N / A 68(72.7) New villages, neighborhoods, hamlets and/or camps identified (on the map) 34(75.6) 32(65.3) N / A 66(69.1) Identification of advanced strategy vaccination sites 30(66.7) 32(65.3) N / A 62(64.5) Other (s) to be specified 8(17.8) 13(26.5) N / A 21(20.9) Seasonal movement of the target population 11(24.4) 6(12.2) N / A 17(16.4) What planning need is solved with Georeferenced Tools? Location of the target population 39(86.7) 45(91.8) N / A 84(89.4) Number of doses to plan 26(57.8) 37(75.5) N / A 63(67.0) Reliable denominator 20(44.4) 18(36.7) N / A 38(40.0) Others 7(15.6) 11(22.4) N / A 18(19.1) Georeferenced data actually used as reported by microplan users Target population 37(82.2) 42(85.7) N / A 79(84.0) Distribution of the target population by site or location 32(71.1) 41(83.7) N / A 73(77.7) Identification of vaccination sites 35(77.8) 36(73.5) N / A 71(75.5) Identification of advanced strategy vaccination sites 32(71.1) 27(55.1) N / A 59(62.8) Identification of villages, neighbourhoods, hamlets, camps (mapping) 31(68.9) 27(55.1) N / A 58(61.7) Seasonal movement of the target population 2(4.4) 10(20.4) N / A 12(12.8) Others 4(8.9) 3(6.1) N / A 7(7.4) According to the qualitative analyses, respondents, particularly at the Health Zone and provincial levels, indicated that support for vaccination activities has significantly improved with the introduction of geospatial data. Apart from the numbers of the target populations which experienced variation in the direction of increase (Kasaï Province) or decrease (Haut Lomami Province), other data from the health areas in terms of the number of settlements, fixed or advanced sites, neighborhoods remained almost the same before and after of the introduction of geospatial data. 1.4. Process through which geospatial data is used as part of routine immunization programme implementation Almost all (94%) of health facilities in the intervention provinces use geospatial data for routine immunization programme implementation in their Health Areas. This use is more pronounced in the Haut-Lomami Province (96%) compared to that of Kasaï (92%). The interviews unanimously emphasized that georeferenced data was of capital importance in the planning process. They made it possible to improve information relating to the different vaccination strategies (e.g. fixed, outreach, mobile), the number of vaccines to order, the availability and location of refrigerators, and the size of the population to be covered in the context of vaccination activities. A small group of respondents reported that the use of geospatial data made it possible to improve distribution in terms of the number of vaccines to be requisitioned according to consumption. Most respondents (69%) declared that the geospatial enabled tools are very easy to use. More than three quarters are at least satisfied with the information contained in the tool and its use in activity planning. Most respondents agreed that the geospatial tool has reduced their working time and improved data quality, as shown in Table 3 . Table 3 Distribution of participants according to satisfaction with informational content and use of georeferenced tool Haut-Lomami Kasai Kasai Central Together n= (%) n= (%) n= (%) n= (%) Is the tool easy to use Easy to use 34 (75.6) 31(63.3) N / A 65 (69.1) Very easy 5 (11.1) 13 (26.5) N / A 18 (19.1) Not easy to use 4 (8.9) 5(10.2) N / A 9 (9.6) Easy enough 2 (4.4) 0(0,0) N / A 2 (2.1) Are you satisfied with the information contained in the georeferenced microplanning tool? Satisfied 26 (57.8) 33(67.3) N / A 59 (62.8) Very satisfied 13(28.9) 11(22.4) N / A 24 (25.5) Somewhat satisfied 3 (6.7) 5(10.2) N / A 8 (8.5) Unsatisfied 3 (6.7) 0(0,0) N / A 3 (3.2) Are you satisfied with using this tool? Satisfied 26 (57.8) 33(67.3) N / A 59 (62.8) Very satisfied 13(28.9) 12 (24.5) N / A 25(26.6) Somewhat satisfied 5 (11.1) 3 (6.1) N / A 8 (8.5) Unsatisfied 1 (2,2) 1 (2.0) N / A 2 (2.1) The reason for not being satisfied with the information contained in the microplan Too long 3 (100) 0(0,0) N / A 3 (100) Difficult to use 3 (100) 0(0,0) N / A 3 (100) Contribution of microplanning tool in reducing working time All right 22 (48.9) 26(53.1) N / A 48 (51.1) Totally agree 10 (22.2) 17(34.70) N / A 27(28.7) Disagree 7 (15.6) 2(4,1) N / A 9 (9.6) Fairly agree 6 (13.3) 3 (6.1) N / A 9 (9.6) not agree at all 0(0,0) 1 (2.0) N / A 1 (1,1) Will the microplanning tool improve the quality of your data All right 30 (66.7) 31(63.3) N / A 61 (64.9) Totally agree 10 (22.2) 13(26.5) N / A 23 (24.5) Fairly agree 3 (6.7) 2(4,1) N / A 5 (5.3) Disagree 1 (2,2) 3(6,1) N / A 4 (4.3) not agree at all 1 (2,2) 0(0,0) N / A 1 (1,1) According to the qualitative results, respondents unanimously stated that the tool had more advantages than disadvantages. One of the most significant benefits mentioned by respondents is reaching zero-dose children in each health area. At the provincial level, the tool helped improve the planning, implementation, and supervision of vaccination activities. 1.5. Acceptance and use of geospatial data through the gender intervention in Kasai The gender intervention involved a systematic analysis of gender within the immunization program. This then led to collaboration with the Ministry of Gender and Social Affairs to engage more women in immunization program roles and the development of microplans using geospatial data. Interviews with all gender training participants (14/14) in the targeted Health Zones in Kasai Province revealed that this was a good training course based on gender considerations. Field teams are now starting to disaggregate data in terms of gender and increase women’s participation as vaccinators. Knowledge of vaccination teams has improved, and gender principles were included in vaccination activities and complimented the geo-enabled microplans for better immunization coverage. Most of the community members who took part in this study recognized that the gender training helped them to solve problems linked to inequality and discrimination between men and women in the community, starting with immunization but also more generally. All the participants in the interviews recognized that now, some women are involved in vaccination activities in the community. As noted by a participant: “ I too find that gender or parity has helped a lot, even at the level of vaccination teams. Back then, it was mainly men who went around vaccinating children in the Health Area. Now, we also see women giving vaccines, this has brought about a change in the community". (Gender intervention respondent). However, the ratio of women to men is still low, and many participants felt that all the authorities should enhance women's capacities and skills, as they are able to contribute to strengthened immunization services in the community. A key informant noted, " In our Health Zone, there is no female managing the CODESA [Comité de Développement de l’Aire de Santé /Health Area Development Committee]. All the twenty-eight are men. So, we've made a plea to our partners to help us revitalize the CODESAs, to see where there are shortcomings so that we can get back on track with competent women”. (Gender intervention respondent). How does gender affect health workers' use of mapping and georeferenced data? Most respondents acknowledged that this focus on gender has enabled them to put into practice this new strategy involving women and men in microplanning, awareness-raising, and mobilizing mothers for immunization. They also noted that complementarity between women and men is essential to reach zero-dose children and children lost to follow-up or incompletely vaccinated children in the community. For the gender distribution in training in the production of spatial maps and estimates of vaccination target populations, interviews revealed that in each Health Zone, there were a total of twenty (20) people, fifteen (15) women, and five (5) men. It was clear that all the women had carried out the process of capturing data by GPS, so that they could have the matrix to demonstrate to the other members of their community. However, on service delivery, deep-rooted social and cultural norms concerning the roles and responsibilities of men and women constitute challenges, obstacles or barriers to immunization, which affect both caregivers and health workers, and influence the provision, demand and use of immunization services. Geospatial data use by Gender Regarding the impact of gender on health workers' immunization planning and conduct of routine immunization activities, most of the respondents revealed that today, immunization campaigns are prepared using telephones, which means that health workers have a very good grasp of the boundaries of their Health Areas, as well as the targets to be immunized in the Health Area. They noted that the representation of men and women facilitated the participation and complementarity of all health workers in all upstream and downstream activities to achieve good results. One participant pointed out that " in terms of vaccination, for example, you'll find that when a woman administers the vaccine, people are so happy. So, there are always positive influences ". (Gender intervention respondent). The gender intervention in the use of georeferenced microplans has contributed to reevaluating the composition of vaccination teams, namely the CODESA and CAC teams, supporting women to achieve good results to reach and vaccinate all the children expected. To this end, most respondents indicated that all providers (women and men) work together to achieve the targets expected. Study Aim 2: Associated effects of the acceptance and use of georeferenced data by Health Zones and Health Areas on Immunization Coverage and Equity For the second study aim, a quasi-experimental design study in the three provinces was used to determine the associated effects of the acceptance and use of geospatial data on immunization coverage and equity. We based our analyses on secondary data from the immunization coverage and equity surveys of children 12–23 months of age. It is important to note that the intervention had not been implemented for a sufficient time in Kasai to contribute to significant improvements in immunization coverage or equity. Georeferenced tools were distributed in Kasai in 2023 and would need at least 12 months of implementation to contribute to substantial changes in immunization outcomes. Thus, for this part of the study, Kasai was also considered as control with Haut Lomami as the unique intervention site. 2.1 Changes in immunization coverage and timeliness after at least 12 months of implementation in the three provinces Data on initial vaccination coverage from the 2020 vaccine coverage survey (VCS), and from VCSs carried out in 2021 and 2022, for BCG and OPV 0 antigens in the three Provinces show clear progress on one side, and stagnation on the other [ 8 ]. A clear improvement in BCG antigen vaccination coverage was observed in Haut-Lomami Province (intervention Province), with VCS rising from 9.9% (2020) to 78.9% (2021) and then to 94% (2022). Kasaï Central (control Province) saw an improvement in BCG antigen coverage from 25.3% in 2020 to 56.9% in 2021, and stagnation at 56.2% in 2022. On the other hand, Kasaï (intervention province) showed an improvement in BCG antigen coverage, with a "V"-shaped evolution over the three years, i.e. a drop from 52.9% in 2020 to 44.9% and then an improvement to 57.1% in 2022. (Fig. 2). With regard to OPV 0 antigen, the trend remains the same as that observed with BCG antigen, except for the province of Kasaï Central (Control). OPV 0 coverage rates showed an improvement in the three provinces of Haut-Lomami (Intervention), Kasaï (Control) and Kasaï Central (Control). This improvement has been maintained for Haut-Lomami and Kasaï Central provinces, 8.6% in 2020 to 93.9% in 2022 and 45.4% in 2020 to 51.9% respectively. Kasaï province, on the other hand, although having improved its OPV 0 antigen coverage from 45.4% in 2020 to 51.9% in 2022, recorded a drop in 2021 (40.6%). (See Fig. 3). Figure 2: Estimates of BCG antigen vaccination coverage for children aged 12 to 23 months Figure 2 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 12 to 23 months in the provinces of Kasaï, Kasaï Central and Haut-Lomami in the DRC in 2020, 2021 and 2022. Source : Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [ 8 ]. Figure 3: Estimates of OPV0 antigen vaccination coverage for children aged 12 to 23 months Figure 3 legend: Point estimates of OPV0 antigen vaccination coverage indicators according to the vaccination map in children aged 12 to 23 months in the Provinces of Kasaï, Kasaï Central and Haut-Lomami in the DRC from 2020, 2021 and 2022. Source : Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [ 8 ]. For Pentavalent 1 antigen, the VCS rate observed for the three provinces showed an improvement when comparing 2020 to 2022. In Haut-Lomami Province, coverage rose from 9.9% in 2020 to 78.5% in 2021 and 93.6% in 2022. For the other Provinces, the rate rose from 51% in 2020 to 58.3% in 2022 in Kasai and from 26.3% in 2020 to 59.3% in 2022 for the Kasai central. However, the Pentavalent 1 antigen vaccination coverage rate fell in 2021 to 48.1% for Kasaï and was higher at 61.1% for Kasaï Central Province. For Pentavalent 3 antigen, the observed vaccine coverage rate showed a significant improvement for Haut-Lomami. It rose from 8.9% in 2020 to 76.8% in 2021 and to 92% in 2022. In the two Control Provinces of Kasaï (Control) and Kasaï Central (Control), there has been a net increase in the dropout rate between Penta 1 and Penta 3, indicating a decline in the immunization programme i.e., 5.7% in 2020, 13.7% in 2021 and 20.3% in 2022 for the Kasai Province, and 4.5% in 2020, 13.9% in 2021 and 15.5% in 2022 for the Kasai Central Province. On the other hand, the drop-out rate has changed little in Haut Lomami Province, indicating stability within the immunization programme. It rose from 1% in 2020 to 1.7% in 2021 and 1.6% in 2022 (See Table 4 ). Table 4 Estimates of Penta 1 and 3 vaccination coverage ages 12–23 months. Provinces VCS 2020 VCS 2021 VCS 2022 Penta1 (%) Penta 3 (%) Drop-out rate (%) Penta 1 (%) Penta 3 (%) Drop-out rate (%) Penta 1 (%) Penta 3 (%) Drop-out rate (%) Kasaï 51.0 45.3 5.7 48.1 34.4 13.7 58.3 38.0 20.3 Haut-Lomami 9.9 8.9 1.0 78.5 76.8 1.7 93.6 92.0 1.6 Kasaï Central 26.3 21.8 4.5 61.1 47.2 13.9 59.3 43.8 15.5 Legend: Point estimates of Penta 1 and 3 vaccination coverage indicators in children aged 12 to 23 months in the 3 provinces of the study in 2020, 2021 and 2022. Source: Vaccination coverage survey in DRC: 2020, 2021 and 2022 [ 8 ]. 2.2 Impact of georeferenced data use as compared to the status quo on its effectiveness to increase immunisation coverage and timeliness Initial vaccination coverage data from the VCS survey in 2020 and those from the VCS carried out in 2021 and 2022 for BCG and OPV 0 antigens in the three provinces surveyed reveal a certain disparity between provinces. Improvements in BCG antigen vaccination coverage rates were observed in the two provinces of Haut-Lomami (intervention province) and Kasaï Central (control province), which respectively increased from 20.2% (VCS 2020) to 91.8% (VCS 2022) and from 33.2% in 2020 to 63% in 2022. Kasaï province (control province) showed a marked drop in BCG antigen coverage, from 61.4% in 2020 to 51.5% in 2021, and 57.5% in 2022. (Fig. 4). Regarding the OPV 0 antigen, the trend remains the same as that observed with BCG antigen. The OPV 0 coverage rates have improved significantly in the two provinces of Haut-Lomami (Intervention) and Kasaï Central (Control). They have respectively risen from 19.5% in 2020 to 91.8% in 2022, and from 30.3% in 2020 to 53.2% in 2022, starting from a higher rate of 55.6% in 2021. In Kasaï Province (Control), OPV 0 antigen coverage fell from 55.2% in 2020 to 48.4% in 2021 and 52.1% in 2022. (Fig. 5). Figure 4: Estimates of BCG antigen vaccination coverage for children aged 6 to 11 months Figure 4 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasaï, Haut-Lomami and Kasaï Central in the DRC in 2020, 2021 and 2022. Source : Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [ 8 ]. Figure 5: Estimates of OPV 0 antigen vaccination coverage for children aged 6 to 11 months Figure 5 legend: Point estimates of OPV 0 antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasaï, Haut-Lomami and Kasaï Central in the DRC in 2020, 2021 and 2022. Source : Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [ 8 ]. Role of georeferenced data in locating zero-dose children It emerged from interviews that in general, the use of georeferenced data made it possible to improve involvement of health facilities that have not offered immunization services and consequently to reach children who missed vaccination days. In addition, this made it possible to improve the vaccination catch-up which was carried out previously by the community relays to reach zero-dose children. A nurse stated the following: “ You know a health facility which 15 kms far away from a health center and does not vaccinate. If for example the nursing staff of a health center starts moving with vaccines to the health facility that does not vaccinate, when they arrive there, you will see all these children who were zero dose will come to be vaccinated and even this health facility will also be interested in vaccination. So, it does influence positively the reduction of zero doses and even the involvement of other types health facilities in vaccination.” (Respondent; Health Area, Haut-Lomami). However, in Kasai, some respondents reported difficulties linked to recurrent population movements. This makes vaccination activities challenging in locating target children with the consequence of uneven vaccination indicators. For example, one respondent said: “ The obstacles that we often experience is movement, we are in a purely mining Health Zone where the population is moving all the times. ” (Respondent _09, HA, Kasaï). For the same children aged 6 to 11 months, Pentavalent 1 and Pentavalent 3 antigen coverage rates in Haut-Lomami (intervention Province) in 2020 were 20.3% and 17.8% respectively, representing a dropout rate of 2.5%. By 2021, these rates had risen to 69.1% and 63.8% respectively, representing a drop-out rate of 5.3%. In 2022, these rates reached 91.6% and 87.6% respectively, for a drop-out rate of 4%. From 2020 to 2022, vaccine coverage rates for pentavalent 1 and 3 antigens improved, with a wavering dropout rate. In Kasaï (control), Pentavalent 1 and Pentavalent 3 antigen coverage rates from 2020 to 2021 were 60.9% and 51.3% respectively, with a dropout rate of 9.6% in 2020. These rates were 61.7–38.4%, or a drop-out rate of 23.3% in 2021, then 52.0–25.3%, or a drop-out rate of 26.7% in 2022. The dropout rate for Kasaï province has increased in 2022 compared with 2020. In the control province of Kasaï Central, vaccine coverage rates for pentavalent 1 and 3 antigens were 34.6% and 26.3% respectively in 2020, representing a drop-out rate of 8.3%. These rates were 72.4–50.5% in 2021, with a dropout rate of 21.9%, and then 64.6–41.5%, with a dropout rate of 23.1%. As in Haut-Lomami Province, the dropout rate increased slightly from 2020 to 2022. (Table 5 ). Table 5 Estimates of Penta 1 and 3 vaccination coverage ages 6–11 months. Provinces ECV 2020 ECV 2021 ECV 2022 Penta 1 (%) Penta 3 (%) Drop-out rate (%) Penta 1 (%) Penta 3 (%) Drop-out rate (%) Penta 1 (%) Penta 3 (%) Drop-out rate (%) Kasaï 60.9 51.3 9.6 61.7 38.4 23.3 52 25.3 26.7 Haut -Lomami 20.3 17.8 2.5 69.1 63.8 5.3 91.6 87.6 4.0 Kasaï Central 34.6 26.3 8.3 72.4 50.5 21.9 64.6 41.5 23.1 Legend: Point estimates of Penta 1 and 3 vaccination coverage for children aged 6 to 11 months in the 3 provinces of the study over 3 years. Source: Vaccination Coverage Survey in the DRC 2020, 2021 and 2022 [ 8 ]. 2.3. Impact of georeferenced data use as compared to the status quo on equity The impact of geospatial data on equity was seen in terms of reaching the most marginalized children 0–23 months (girls/boys) and the main caregivers - women and adolescent girls in their reproductive years (15–49 years of age). However, because of the unavailability of economic data, the ability to conduct robust equity analyses was limited. The results of the equity analyses were inconclusive, and therefore, not presented here. They have been included in the comprehensive research report provided as Supplementary Material. DISCUSSION The study tested the hypothesis that the effective use of geospatial data can contribute to an increase immunization coverage and equity, through the identification of missed settlements and zero-dose children, the optimization of vaccination strategies, and the supply distribution. It also incorporated a gender-sensitive approach and included a gender sub-study to assess gender-specific interventions in a sub-set of Health Zones and Health Areas in Kasai, as part of Gavi’s intensified strategy to address gender inequity and the global Immunization Agenda 2030 [ 1 – 3 ]. The results indicate that the geospatial microplans in Haut-Lomami and Kasai were well received, used, and led to changes in the delivery of vaccination services. As an innovation, the context and mechanisms through which geospatial data and tools were created and accepted for use confirmed their importance and effective adoption at Health Zones and Health Areas. As found in Haut Lomami, geospatial data enabled the visualization and analysis of health data in spatial contexts, offering insights into the geographical distribution of the population, health area boundaries, healthcare facilities and immunization coverage. In line with our results, it has been largely documented that Geospatial Information Systems (GIS) and other geospatial technologies facilitate targeted interventions, allowing health authorities to optimize and enhance the precision of resource allocation in resource-constrained settings and identified underserved areas to allocate resources efficiently in specific geographic areas [ 9 – 14 ]. The potential of Geographic Information System (GIS) and spatial analysis in enhancing the effectiveness of health providers and monitoring immunization coverage has been emphasized in other Central African countries [ 15 – 18 ]. Other illustrations of the application of geospatial mapping to address health issues such as malnutrition and guiding program planning in resource-limited settings, or reducing measles incidence through frequent supplementary immunization activities were also found in the literature [ 19 , 20 ]. However, to be fully effective, the production and use of geospatial data and maps need more work to build capacity and ensure the quality of data and maps, as persistent challenges in data quality, such as inflated coverage figures and inaccurate denominators, remain significant hurdles [ 7 , 15 , 21 , 22 ]. The associated effects of geospatial data on immunization coverage in Haut Lomami have shown that it may have contributed as a component of a broader set of immunization strategies to significant increases in immunization coverage rates and lower dropouts, including reduced numbers of zero-dose children. These results corroborated with other studies in the context of LMICs [ 5 , 12 , 16 ]. Additional study with a longer observation time, i.e., more than three years, is needed in Kasai to come up with a strong evidence conclusion. The drastic improvements in immunization outcomes may be also explained by other factors, such as the implementation, in parallel, of some specific immunization projects, which worked in synergy. The Province of Haut-Lomami has been a Mashako Plan site with intensified support of the EPI [ 4 , 23 ]. In addition, an intervention aiming to improve the distribution of vaccine products up to the last kilometer and using Information and Communication Technologies (ICTs) in the fields of health has intensively assisted the Province through the EPI branch office of Kamina [ 23 , 24 ]. We are unable to attribute the full increase observed for the Penta3 vaccine between 2020 and 2021 (from 8.9% in 2020 to 76.8% in 2021) to the adoption and use of geospatial data alone and acknowledge that it may be an important part of a larger package of strategic EPI interventions. Taking a gender lens for the overall study, we identified perceived positive contributions to the intervention and the evaluation. In the delivery of immunization services, it is important to include transformative and equitable gender strategies, taking into account the socio-cultural contexts in which health workers and caregivers live and work. Gender mainstreaming must be carried out at all levels of microplanning design and implementation, the use of georeferenced data, the conduct of routine immunization, and monitoring and evaluation. To achieve this, awareness and action is needed at national and sub-national levels to conduct gender analyses and design gender-sensitive interventions to reduce gender-related barriers to immunization and georeferenced data use. Targeted interventions based on spatial analysis effectively reduced disparities, promoting a more equitable distribution of immunization services, that address specific barriers faced by vulnerable populations [ 12 , 25 – 27 ]. Due to the lack of availability of robust data that would enable the assessment of effects related to equity, we were unable to compare results beyond those associated with the reduced rate of zero-dose children detected in Haut-Lomami Province. However, to tackle inequities in immunization, since a decade, countries are focusing on effective immunization microplans at the subdistrict level, using georeferenced data and maps for better planning of immunization activities, such as community-based Reach Every Child (REC) intervention [ 10 , 11 , 26 – 29 ]. Overall, while our study is in line with the recent literature and demonstrates the positive contribution of geospatial data on immunization outcomes, challenges persist. Issues related to data quality, privacy concerns, the need for enhanced infrastructure, and the need for capacity building are common challenges [ 15 , 21 ]. On the other hand, some studies underscored the underutilization of routine immunization data in decision-making, emphasizing the need for more rigorous evaluations of interventions and continued research, aimed at improving data utilization, thus, addressing disparities and increasing vaccine coverage [ 30 – 32 ]. Future research should also focus on overcoming these challenges, optimizing the integration of geospatial data into immunization strategies, and expanding gender-sensitive approaches across the full EPI. CONCLUSION The situation of “zero-dose” children in the Democratic Republic of Congo (DRC) is a major concern. The overall objective of the study was to evaluate the use geo-enabled microplans for the microplanning and implementation of routine immunization programs, and the associated contribution to increased and sustained immunization coverage with a focus on the identification and vaccination of zero-dose children. The results indicate that the georeferenced microplans in Haut-Lomami and Kasai were well received, used, and led to changes in planning for and delivery of vaccination services. In addition, the gender ethnographic study in Kasai indicates that the gender intervention led to the greater inclusion of women in immunization activities. Due to the delayed time of georeferenced microplan adoption and use, it is recommended to conduct a supplemental study to follow the implementation in Kasai in 2024 and 2025 for further immunization coverage and equity analyses. Consistently across analyses, we observed a significant positive trend in Haut-Lomami in immunization outcomes, including an increase in overall coverage, identification and immunization of zero-dose, and reduced dropouts. This aligns with other studies related to the use of geospatial data for immunization. Abbreviations CAC Cellule d’Animation Communautaire (Community Animation Cells) CAPI Computer Assisted Personal Interview DRC The Democratic Republic of Congo CODESA Comité de Développement de l’Aire de Santé (Health Area Development Committee) DTP1 diphtheria-tetanus-pertussis containing vaccine EPI Expanded Program on Immunization Gavi Gavi, the Vaccine Alliance GRID3 Geo-Referenced Infrastructure and Demographic Data for Development KSPH Kinshasa School of Public Health LMIC Low- and middle-income country M4H Mapping for Health NHIS National Health Information System OPV Oral polio vaccine VCS Vaccine Coverage Survey WHO World Health Organization Declarations Ethics approval and consent to participate : The Mapping for Health Study has been reviewed and cleared by the Kinshasa School of Public Health Internal Review Board. The study has also been registered with BMC Central International Standard Randomised Controlled Trial Number ISRCTN65876428 on 3/11/2021. Consent for publication : All human subjects have provided written consent to participate in the study and for results to be published. All co-authors have reviewed the paper and agreed to have it submitted for review and publication. Availability of data and materials : Research data and materials will be made available upon request. Please contact Dosithee Ngo Bebe at [email protected] . Competing interests : There are no known competing interests. Funding : Funding was provided for the implementation and evaluation of Mapping for Health by Gavi, the Vaccine Alliance through the INFUSE Project. Authors' contributions : DNB is the Principal Investigator and led the research on behalf of the KSPH. PM led the overall research design and publication process on behalf of HealthEnabled. FK, TB, GL, KL, FL, and MK supported the research design, field research, report and publication writing with the KSPH. 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Door–to–door immunization strategy for improving access and utilization of immunization Services in Hard-to-Reach Areas: a case of Migori County, Kenya. BMC public health. 2019;19: 1064. https://doi.org/10.1186/s12889-019-7415-8 Pradhan N, Ryman TK, Varkey S, Ranjan A, Gupta SK, Krishna G, Swetanki RP, Young R. Expanding and improving urban outreach immunization in Patna, India. Tropical Medicine & International Health. 2010;17:292-299. https://doi.org/10.1111/j.1365-3156.2011.02916.x Osterman AL, Shearer JC, Salisbury NA. A realist systematic review of evidence from low- and middle-income countries of interventions to improve immunization data use. BMC Health Serv Res. 2021;21:672. https://doi.org/10.1186/s12913-021-06633-8 Sadr-Azodi N, DeRoeck D, Senouci K. Breaking the inertia in coverage: Mainstreaming under-utilized immunization strategies in the Middle East and North Africa region. Vaccine. 2018;36:4425-4432. https://doi.org/10.1016/j.vaccine.2018.05.088 Tilahun B, Teklu A, Mancuso A, Endehabtu BF, Gashu KD, Mekonnen ZA. Using health data for decision-making at each level of the health system to achieve universal health coverage in Ethiopia: the case of an immunization programme in a low-resource setting. Health Res Policy Sys. 2021;19 :48. https://doi.org/10.1186/s12961-021-00694-1. Supplementary Material Supplementary Material is not available with this version. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 22 Mar, 2024 Editor assigned by journal 22 Mar, 2024 Submission checks completed at journal 12 Mar, 2024 First submitted to journal 28 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3997296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278725114,"identity":"4eaa90d2-7359-475d-ac05-3f19aaedda91","order_by":0,"name":"Dosithée Ngo-Bebe","email":"","orcid":"","institution":"University of Kinshasa","correspondingAuthor":false,"prefix":"","firstName":"Dosithée","middleName":"","lastName":"Ngo-Bebe","suffix":""},{"id":278725115,"identity":"33f51294-ebde-4b3c-afa3-c63fad881ae6","order_by":1,"name":"Patricia Mechael","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYNCCigMMDOwMbCBmAh8DgwEB5cyMDQxngFqYmSFa2IjSwthGihZz9vPHH/POuyPH38x/7MHHHXZ5bOzNGxg+7qnFqcWyJ5mxmXfbM2OJw8zshjPPJBez8RwrYJzx7DhOLQYHwFoOJzYcZmaT5m1jTmyTyDFg5jlwDLeW84+BWuYcrp8P0vK3rT6xTf4NAS03QLY0HE4wAGlhbDsMtIUHpKUGt19mPDacOefYYcONh5nNJHvbjgP9klZwcMaBAzi1mPMnPvjwpuawvNzxxmcSP9uq8/jZD2988OFAHW6HATETD7oo0IrDeLUw/sAigduWUTAKRsEoGHEAAL+oWQLqQdfbAAAAAElFTkSuQmCC","orcid":"","institution":"Health Enabled","correspondingAuthor":true,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Mechael","suffix":""},{"id":278725116,"identity":"d070d038-3082-43c9-9610-0816ec7eccd1","order_by":2,"name":"Fulbert Nappa Kwilu","email":"","orcid":"","institution":"University of 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17:00:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3997296/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3997296/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-21578-x","type":"published","date":"2025-01-24T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52786604,"identity":"5792b3c9-78cb-4546-a67c-d904311a5a01","added_by":"auto","created_at":"2024-03-15 18:54:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eRepresentation of the Sampling of Heath Areas\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/8a58c1ec427f5a76963d042c.png"},{"id":52786800,"identity":"96f0f55a-b87a-48c5-a8b9-7b626dac4b1b","added_by":"auto","created_at":"2024-03-15 18:54:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46516,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of BCG antigen vaccination coverage for children aged 12 to 23 months\u003c/p\u003e\n\u003cp\u003eFigure 2 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 12 to 23 months in the provinces of Kasaï, Kasaï Central and Haut-Lomami in the DRC in 2020, 2021 and 2022. \u003cstrong\u003eSource:\u003c/strong\u003e Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [8].\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/7aea1d1e3565ae4efd1a6d40.png"},{"id":52786676,"identity":"3b912919-8798-4770-aa09-549c0658ab1f","added_by":"auto","created_at":"2024-03-15 18:54:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52298,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of OPV0 antigen vaccination coverage for children aged 12 to 23 months\u003c/p\u003e\n\u003cp\u003eFigure 3 legend: Point estimates of OPV0 antigen vaccination coverage indicators according to the vaccination map in children aged 12 to 23 months in the Provinces of Kasaï, Kasaï Central and Haut-Lomami in the DRC from 2020, 2021 and 2022. \u003cstrong\u003eSource:\u003c/strong\u003e Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [8].\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/8706f2849878482fe601ffac.png"},{"id":52786616,"identity":"ba7c290b-7202-4c98-beb7-140e4e106eed","added_by":"auto","created_at":"2024-03-15 18:54:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56948,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of BCG antigen vaccination coverage for children aged 6 to 11 months\u003c/p\u003e\n\u003cp\u003eFigure 4 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasaï, Haut-Lomami and Kasaï Central in the DRC in 2020, 2021 and 2022. \u003cstrong\u003eSource:\u003c/strong\u003e Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [8].\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/e6b398de8d8940316262ccd6.png"},{"id":52786601,"identity":"e3691bef-42ee-44bb-9f83-d0ff724d7472","added_by":"auto","created_at":"2024-03-15 18:54:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58198,"visible":true,"origin":"","legend":"\u003cp\u003eEstimates of OPV 0 antigen vaccination coverage for children aged 6 to 11 months\u003c/p\u003e\n\u003cp\u003eFigure 5 legend: Point estimates of OPV 0 antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasaï, Haut-Lomami and Kasaï Central in the DRC in 2020, 2021 and 2022. \u003cstrong\u003eSource:\u003c/strong\u003e Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [8].\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/3303d756d118f49a69a8958f.png"},{"id":74858663,"identity":"3ab83068-2c5e-4d35-859e-1cbe755ef371","added_by":"auto","created_at":"2025-01-27 16:12:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2226941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3997296/v1/79452e0c-b1d2-4bfa-a873-1ca55fde15c3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Use of Geospatial Data for Immunization Program Implementation and Associated Effects on Coverage and Equity in the Democratic Republic of Congo","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe World Health Organization (WHO) Global Vaccine Action Plan was developed to help all individuals and communities enjoy lives free from vaccine-preventable diseases, demonstrating that the benefits of immunization are equitably extended to all people, wherever they are located [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. GAVI, the Vaccine Alliance\u0026rsquo;s 5.0 Strategy 2021\u0026ndash;2025 and the Global Immunization Agenda 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] promote the use of geospatial data and technology applications for immunization programs to reach all children, especially those who have never received any vaccine and are designated as \u0026ldquo;zero-dose\u0026rdquo;. A considerable proportion of zero-dose children live in remote areas with poor accessibility to health facilities, mainly in low- and middle-income countries (LMICs).\u003c/p\u003e \u003cp\u003eIn the Democratic Republic of the Congo (DRC), the Mashako Plan is a unique, high-level national health strategy that aims to drastically increase complete childhood immunization coverage at a national level [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The integration of geospatial tools, technologies and data for planning and delivery of immunization services supports the Mashako Plan through a participatory process to create geospatially accurate maps of settlements, define health area boundaries and generate improved population estimates. The integration of these geospatial tools and technologies into immunization programs has demonstrated potential to enhance immunization coverage and equity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and address persistent challenges of data quality, inflated reports of coverage rates and inaccurate denominators [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The use of geospatial data, geospatial tools and technologies for immunization programming in the DRC was intended to address some of these challenges and to support the immunization program to accurately monitor immunization coverage and plan upcoming vaccination activities.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the Intervention\u003c/h2\u003e \u003cp\u003eThe Mapping for Health (M4H) project aimed to strengthen the equity and effectiveness of vaccination interventions in the DRC, increase national geospatial capacities and promote gender-intentional programming through the provision of geo-enabled microplans within the National Expanded Program on Immunization (EPI). The project is implemented by the Ministry of Health with support from the Geo-Referenced Infrastructure and Demographic Data for Development (GRID3) Consortium.\u003c/p\u003e \u003cp\u003eTo improve the effectiveness of microplans, the GRID3 Consortium supported the National Immunization Program activities for gender-responsive planning, generating geospatial data and population modeling to determine the target population (denominator) and produce core geospatial data layers: settlements, health boundaries, and health facilities. These data were then used to optimize vaccination strategies in Health Zones in the form of geo-enabled microplans. The GRID3 project facilitated gender analyses and collaborated with civil society partners to apply gender responsive vaccination strategies to immunization program planning and service delivery alongside the use of geo-enabled microplans in targeted Health Zones and Health Areas.\u003c/p\u003e \u003cp\u003eTo evaluate the use of geo-enabled microplans and the complementary gender intervention in a sub-set of sites, Gavi engaged HealthEnabled through the \u0026ldquo;Effective Design, Implementation, Integration, and Evaluation of Digital Health Systems to Enhance the Strategic Use of Data for Immunization Programming\u0026rdquo; to assess acceptance and use of geospatial data for microplanning and routine immunization implementation and associated effects on the vaccine coverage in DRC.\u003c/p\u003e \u003cp\u003eThe generation of core geospatial layers is intended to provide key and timely insights for Health Zone and Provincial decision-makers to identify hard-to-reach settlements or settlements likely to fall in between two health catchment areas; estimate the population of the health areas and health zones; estimate a healthcare facility's catchment population; estimate the number of vaccines needed for a health area based on its population; assess the population coverage of current fixed vaccination strategies; optimize outreach vaccination strategies based on population distribution; and optimize the cold chain and new fridge allocations based on population distribution. The theory of change for M4H describes how the systematic generation and use of geospatial data and associated population distribution, including the identification of previously missed settlements, contributes to more effective immunization program planning and service delivery, which contributes to improved immunization coverage and equity. This theory of change was used to inform the development of the qualitative instruments (observation and interview guides), the intervention strength survey instruments, and the secondary analyses of immunisation coverage survey data. The gender interventions were evaluated separately using a rapid ethnographic approach and relevant Health Zones and Health Areas were purposefully included as sites for the intervention strength survey.\u003c/p\u003e \u003c/div\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis is a mixed-methods study with a quasi-experimental design. Impact was assessed using a pre/post study design which draws upon the National Expanded Program on Immunization (EPI) Vaccine Coverage Surveys (VCS) conducted with support from the Kinshasa School of Public Health (KSPH) in 2021, which was repeated in 2023 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Efforts to assess impact were both informed and complemented by qualitative research (direct observations and in-depth interviews) and intervention strength surveys in prioritized Health Areas in intervention and control sites to assess adherence to microplans with and without M4H data. A targeted rapid ethnographic study was conducted in Health Zones and Health Areas in Kasai which were exposed to gender-specific program activities. These sites were purposefully included in the intervention strength survey sample.\u003c/p\u003e \u003cp\u003e The qualitative approach focused on interviews with various participants, including the Provincial Head of Division, EPI branch Medical Chief, EPI branch Data Managers, and the Analysts in charge of the health information to the Provincial Division, EPI Monitoring and Evaluation Service Chief, and the person in charge of mapping in the NHIS Office at the central level. Interview guides were designed to facilitate the interviews with key informants.\u003c/p\u003e \u003cp\u003eThree key equity-related variables were used in this study: household wealth, telephone use by the head of household, and urban or rural residence of the household. The relative household wealth variable is described in five modalities: poorest, second poorest, middle poorest, fourth poorest and richest. The variable \"household cell phone access\" is measured by two modalities: without cell phone and with cell phone. The \"place of residence\" variable has two modalities: urban and rural.\u003c/p\u003e \u003cp\u003eIn addition, a comparative approach of a descriptive study was performed determining whether the implementation of M4H in Haut-Lomami is associated with significant differences in the percentage of zero-dose children aged 12\u0026ndash;23 months post-intervention in the poorest and poorest economic strata between 2020 and 2021.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003eThe study took place in 3 Provinces, namely Kasai (M4H intervention with gender component), Haut Lomami (M4H intervention without gender component), and Kasai Central (control site). Prioritized survey sites included Health Zones that represent urban and remote areas with the inclusion of conflict settings. Our sample size was 113 health facilities in 98 Health Areas in 15 Health Zones in the three provinces as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the sampling, we considered 30% of the total number of Health Zones for each stratum. To do this, we carried out simple random sampling using the Android application \"randomizer\". The same approach was used to select Health Areas. We considered 30% of the total number of Health Areas for each Health Zone. In the control Province, we used the same sampling method and considered 15% of the total number of Health Areas for each stratum and 15% of the total number of Health Areas for each zone.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eData collection at Provincial and Health Zone levels involved in-depth interviews in French, recorded for qualitative analysis. The focus of these interviews was on the acceptance and use of geospatial data for immunization planning. A total of 19 in-depth interviews were conducted. Before each interview, the interviewers presented the objectives of the evaluation to the participants. Individual written consent was required and obtained from each respondent to participate in the study and record the interview. Interviewers made appointments with each respondent, according to availability.\u003c/p\u003e \u003cp\u003eAt Health Area and health facility levels, data collection techniques included: (1) a structured survey; (2) direct observation of maps and georeferenced microplans; (3) document review; and (4) semi-structured interviews with key informants. Quality control was carried out on an ongoing basis, at various stages of the study:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrior to the data collection\u003c/strong\u003e \u003cp\u003eInterviewers with previous research experience were recruited and underwent a two-day training on the objectives of the study and data collection using the CAPI (Computer Assisted Personal Interview) system. Interviewers were then selected to guarantee data quality. The questionnaire was digitized using SurveyCTO with automatic filters, constraints, and relevance criteria for certain questions to control data entry.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDuring and after data collection\u003c/strong\u003e \u003cp\u003eThe supervisor developed a follow-up plan for the field teams to ensure that the interviewers were in the various assignment zones. A supervision form was completed to report on field progress, including the number of interviews completed as well as any problems encountered in the field. All teams were linked by a WhatsApp group for rapid sharing of information in the field. Automatic checks of completed and sent questionnaires were carried out by the coordinator in charge of data processing and analysis. When necessary, the provincial supervisor was alerted to take corrective action. Data editing was carried out during data collection to ensure data quality, notably by searching for \"I don't know\" or \"refusal\" responses and by cleaning the database prior to the analysis.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe content of the in-depth interviews and open-ended survey questions was analyzed using ATLAS TI software. Through an inductive and iterative process, we used content analysis methods based on thematic codes and sub-codes. The initial list of codes was derived from the themes and questions contained in the interview guides. All transcripts were coded using the coding list. We looked for subgroups to highlight specific experiences and the reasons for those experiences.\u003c/p\u003e \u003cp\u003eThe intervention strength survey data collected by the interviewers was transferred to the server after verification by the field supervisor. Secondary data cleaning was carried out using Survey CTO software. Data analysis was performed using SPSS Version 25 software. The data were analyzed to produce expected frequencies for categorical variables, and for continuous variables, the measure of central tendency (mean or median) and dispersion (standard deviation or interquartile range) according to the normality of the distribution. The Chi-square test was used to test for association, with an alpha of 0.05.\u003c/p\u003e \u003cp\u003eTo achieve the objectives of the coverage and equity study objectives, secondary analyses of immunization coverage and equity survey data were conducted. The data extracted covered Haut-Lomami, Kasa\u0026iuml;, and Kasa\u0026iuml; Central. The extraction of the data was done manually. Once extracted from the VCS database, the data was subjected to a descriptive statistical analysis. The variable measured was zero-dose prevalence, the percentage of children who have not received any dose of vaccine against diphtheria, tetanus and pertussis.\u003c/p\u003e \u003cp\u003eTo measure the extent of deviation, the standard deviation of each prevalence was associated with each variable. The analysis was then carried out in two stages. First, the general evolution of the overall percentage of zero-dose children was described. Then, from a socio-economic equity perspective, the percentage of zero-dose children in Haut-Lomami, where geospatial data had been in use by the EPI program for more than 12 months was compared over time with its evolution in the two other provinces, using data from the Vaccine Coverage Survey (VCS) from 2020 to 2022, across different household wealth quintiles, telephone use by the head of household, and the urban or rural residence of the household [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOur findings are presented by study aims and objectives beginning with the socio-demographic characteristic of the interviewees (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of participants in the 3 provinces\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaut-Lomami\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKasai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKasai Central\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTogether\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;46 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;49 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;111 (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98 (88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher or University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111(100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105 (95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidower widow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT (Head Nurse of Health center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69 (62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIS (Health Zone Nurse supervisor in charge of immunization)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e46 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e49 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e16 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e111 (100)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost respondents were male (88%). The modal class in all three provinces is 35\u0026ndash;49 years age old with a total of 48% of all respondents interviewed. More than three out of four respondents had a higher or university degree and almost all were married (95%). Three-fifths of respondents assumed the role of Head Nurse of Health center (62%), except in the Province of Haut-Lomami where slightly more than half were in other roles (57%).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Aim 1: Program implementation context and mechanisms\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e1.1. Process through which geospatial data was created\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003eParticipation or contribution in the process\u003c/h2\u003e \u003cp\u003eThe majority of study participants were not involved in the development of the design of the Mapping for Health intervention. Differences in the engagement of the various stakeholders emerged, i.e., at the central level, not all the Ministry stakeholders had the same degree of participation or contribution to the project. At the provincial level, participation was in the planning and implementation phase. The interviewees noted that the project's objectives took account of the gender aspect from the point of view of the service providers and concrete implementation, as in the identification of vaccinated children by sex and age. For the equity aspects, they considered all social strata. The design of the georeferenced and gender data set-up contributed to effective identification of where the targets are and informed the mechanisms to reach and vaccinate them according to National EPI guidelines. The project has also resolved the problem of imprecise Health Area and Health Zone boundaries, as well as the location of populations overlooked during vaccination activities.\u003c/p\u003e \u003cp\u003eA content analysis by respondent category according to health system levels revealed a difference in perception of the gender and inclusion aspect. At the central level, the gender intervention was clearly known, and the various stakeholders recognized this dimension in the intervention and also contributed to it in the training aspects of the field teams. At the provincial level, the gender and social inclusion dimension is perceived differently by the various stakeholders.\u003c/p\u003e \u003cp\u003eRespondents confirmed that the community had taken part in the process through the community animation cells (CACs) with the agreement of the local authority, applying the principle that \"whatever you do without me, you do against me\". The Head nurse of Health Areas organized briefing meetings to enlighten community members on the merits of mapping data. However, the community was both a barrier and an enabler. The result was mistrust on the part of the population in some communities, which are not accustomed to seeing sophisticated materials or technological devices. In some Health Zones, the local population believed that they were being expropriated from their land, requiring explanations at all times despite the authorization of the village chief. In some cases, the population forbade the activity or even bought the equipment outright.\u003c/p\u003e \u003cp\u003eAs described by one of the respondents, \"\u003cem\u003eIn terms of ease of use, it's the community that knows the boundaries \u0026hellip;. From a social point of view, you had to see the chief, because when you say, for example, \"Where does your village end?\u0026rdquo; he's the one who should say, \"My land goes as far as here\". In terms of barriers, when we see an activity where we have to use fairly technological equipment, we wonder what the purpose is and that's the barrier or reticence that we could feel\u003c/em\u003e.\u0026rdquo; \u003cem\u003e(Head Nurse, Kasai).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe contribution of the community extended to the feedback it provided for the validation of mapping data collected, even if community leaders (CAC) are still expecting to receive updated maps with corrected information, where needed. With regard to gender, the main reflection of key informants was that Health Zone management teams take into account the gender dimension in the current immunization register, where vaccinated children are well identified by age and sex.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMapping acceptance, challenges, and prospects\u003c/h2\u003e \u003cp\u003eThe mapping was well accepted by various stakeholders. Positive aspects include the production of better-quality maps, enabling more accurate location of sites compared with the old handwritten maps, and the production of more accurate population estimates and population densities, enabling better planning of vaccination activities. Negative aspects related to the imperfection of the maps, which had some omissions or inaccuracies of certain customary landmarks. For certain Health zones, some health areas had almost disappeared, for which the respondents would like maps to be updated.\u003c/p\u003e \u003cp\u003eOn the optimal future for the mapping, one respondent commented: \"\u003cem\u003eIt's a promising future, but it's only the first step. I think that these will be dynamic maps that can be updated as we go along\u0026hellip;. So, the project will have to see how to establish a certain periodicity for updating these maps\u0026rdquo;. (EPI\u003c/em\u003e branch office, \u003cem\u003eHaut Lomami).\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Process through which project georeferenced data is shared for use in microplanning\u003c/h2\u003e \u003cp\u003eMany of the respondents have worked for more than 10 years, directly or indirectly, in the microplanning of routine immunization activities using a paper-based microplanning process. They are therefore experienced resources in this field, from the Head Nurses and the community (community relays, community animation cells and health development committees) who took an active part in the microplanning of their respective Health Areas and transmission to Health Zone central office level.\u003c/p\u003e \u003cp\u003eThe following considerations were perceived as facilitators for the use of geospatial data for microplanning and routine immunization: the existence of a legend that makes it easy to read a map; attraction to technology; transition from analogue to digital; the desire to do things differently and better; users were involved in the process; user support; buy-in and use of the tool by the service provider.\u003c/p\u003e \u003cp\u003eFor some Health Zones, the following were perceived as obstacles: the problem of connecting to the Internet; lack of knowledge of the tool; lack of training; unavailability of logistical and financial resources; most of the tools used in vaccination are analogical; most of the tools are intended for people who are not too literate in terms of technology; technological tools require a substantial investment; old habits; other logistical, financial and economic constraints in implementation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Process through which geospatial data is used as part of microplanning processes\u003c/h2\u003e \u003cp\u003eAll Health Zones as well as all Health Areas surveyed in the Kasai province received the georeferenced data. Almost the entire Haut-Lomami Province, 98%, received the georeferenced data; no structure in the Kasa\u0026iuml; Central Province, which is a control Province, received the georeferenced tools. At the time of the study, different maps generated by the project were observed by the research team to be taped to the office walls of almost all (98%) of the Health Zones and Areas investigated in the Kasa\u0026iuml; province and 70% of the walls of the Haut-Lomami Province. Unanimously, respondents mentioned their satisfaction and affirmed that the maps were of capital use in general and that their use in vaccination activities made it possible to improve their knowledge and acquire more information on the respective entities. This also made it possible to resolve conflicts over the delimitation of the geographical boundaries of the Health Areas, since in some cases, the limits defined on these tools did not reflect the reality on the ground.\u003c/p\u003e \u003cp\u003eOverall, microplanning tools are displayed by the Health Area Head nurses in 83% of the healthcare establishments visited: respectively 89% and 86% in the two provinces of intervention of Haut-Lomami and Kasa\u0026iuml;, and 63% in the control province of Kasai Central. Almost all microplanning tools (98%) were in paper format. The microplanning tool is accessible in most cases to full-time nurses in 88% and to other nurses in 39% of the establishments visited.\u003c/p\u003e \u003cp\u003eIn the two intervention Provinces, the main users of the microplanning tool are the health center nurses in 92% of cases compared to 75% in the control Province. Before the introduction of georeferenced data, half of the healthcare institutions in Haut-Lomami used data from the National EPI Program (51%) when developing their microplan, while more than four-fifths of Kasai Province (86%) and half of Kasai Central establishments (50%) respondents reported using routine data.\u003c/p\u003e \u003cp\u003eIn the two intervention Provinces, the georeferenced data actually used are the estimate of the target population (84%), the distribution of the target population by site or location (78%), the identification of the sites of vaccination (76%), the identification of vaccination sites for optimizing vaccination strategies, i.e., advanced strategy (63%) and the identification of new villages (62%), as presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Microplan Users and Reported Georeferenced Data and Uses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDPS Haut-Lomami\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDPS Kasai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDPS Kasai Central\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTogether\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNum (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNum (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNum (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNum (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMicroplan Users\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIT (Head nurse of Health center)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98(89.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale nurse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(35.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRECO (community relay)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26(23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(4,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(12.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIS (Supervisor Nurse in the Health zone in charge of immunization)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCZ (Health Zone Chief medical doctor)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(9.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat was the source of information before Georeferenced data?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoutine data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8(50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68(61.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational EPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(28.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (s) to be specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of georeferenced data included in the tools\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget population (new denominator)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(84.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81(84.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of the target population by site or location (number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(87.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77(77.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of vaccination sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68(72.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNew villages, neighborhoods, hamlets and/or camps identified (on the map)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66(69.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of advanced strategy vaccination sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(65.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62(64.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther (s) to be specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasonal movement of the target population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhat planning need is solved with Georeferenced Tools?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of the target population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(91.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84(89.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of doses to plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63(67.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReliable denominator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38(40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeoreferenced data actually used as reported by microplan users\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79(84.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution of the target population by site or location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(83.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(77.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of vaccination sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71(75.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of advanced strategy vaccination sites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59(62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdentification of villages, neighbourhoods, hamlets, camps (mapping)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31(68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27(55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58(61.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasonal movement of the target population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(7.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the qualitative analyses, respondents, particularly at the Health Zone and provincial levels, indicated that support for vaccination activities has significantly improved with the introduction of geospatial data. Apart from the numbers of the target populations which experienced variation in the direction of increase (Kasa\u0026iuml; Province) or decrease (Haut Lomami Province), other data from the health areas in terms of the number of settlements, fixed or advanced sites, neighborhoods remained almost the same before and after of the introduction of geospatial data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e1.4. Process through which geospatial data is used as part of routine immunization programme implementation\u003c/h2\u003e \u003cp\u003eAlmost all (94%) of health facilities in the intervention provinces use geospatial data for routine immunization programme implementation in their Health Areas. This use is more pronounced in the Haut-Lomami Province (96%) compared to that of Kasa\u0026iuml; (92%).\u003c/p\u003e \u003cp\u003eThe interviews unanimously emphasized that georeferenced data was of capital importance in the planning process. They made it possible to improve information relating to the different vaccination strategies (e.g. fixed, outreach, mobile), the number of vaccines to order, the availability and location of refrigerators, and the size of the population to be covered in the context of vaccination activities. A small group of respondents reported that the use of geospatial data made it possible to improve distribution in terms of the number of vaccines to be requisitioned according to consumption.\u003c/p\u003e \u003cp\u003eMost respondents (69%) declared that the geospatial enabled tools are very easy to use. More than three quarters are at least satisfied with the information contained in the tool and its use in activity planning. Most respondents agreed that the geospatial tool has reduced their working time and improved data quality, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of participants according to satisfaction with informational content and use of georeferenced tool\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaut-Lomami\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKasai\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKasai Central\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTogether\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en= (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003en= (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003en= (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003en= (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIs the tool easy to use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEasy to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (69.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery easy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (19.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot easy to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEasy enough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAre you satisfied with the information contained in the georeferenced microplanning tool?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (25.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAre you satisfied with using this tool?\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59 (62.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(26.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomewhat satisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnsatisfied\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe reason for not being satisfied with the information contained in the microplan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToo long\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifficult to use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContribution of microplanning tool in reducing working time\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48 (51.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotally agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(34.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(28.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enot agree at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWill the microplanning tool improve the quality of your data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll right\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(63.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (64.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotally agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (24.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFairly agree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(6,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enot agree at all\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN / A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the qualitative results, respondents unanimously stated that the tool had more advantages than disadvantages. One of the most significant benefits mentioned by respondents is reaching zero-dose children in each health area. At the provincial level, the tool helped improve the planning, implementation, and supervision of vaccination activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e1.5. Acceptance and use of geospatial data through the gender intervention in Kasai\u003c/h2\u003e \u003cp\u003eThe gender intervention involved a systematic analysis of gender within the immunization program. This then led to collaboration with the Ministry of Gender and Social Affairs to engage more women in immunization program roles and the development of microplans using geospatial data. Interviews with all gender training participants (14/14) in the targeted Health Zones in Kasai Province revealed that this was a good training course based on gender considerations. Field teams are now starting to disaggregate data in terms of gender and increase women\u0026rsquo;s participation as vaccinators. Knowledge of vaccination teams has improved, and gender principles were included in vaccination activities and complimented the geo-enabled microplans for better immunization coverage.\u003c/p\u003e \u003cp\u003eMost of the community members who took part in this study recognized that the gender training helped them to solve problems linked to inequality and discrimination between men and women in the community, starting with immunization but also more generally. All the participants in the interviews recognized that now, some women are involved in vaccination activities in the community. As noted by a participant: \u0026ldquo;\u003cem\u003eI too find that gender or parity has helped a lot, even at the level of vaccination teams. Back then, it was mainly men who went around vaccinating children in the Health Area. Now, we also see women giving vaccines, this has brought about a change in the community\". (Gender intervention respondent).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eHowever, the ratio of women to men is still low, and many participants felt that all the authorities should enhance women's capacities and skills, as they are able to contribute to strengthened immunization services in the community. A key informant noted, \"\u003cem\u003eIn our Health Zone, there is no female managing the CODESA [Comit\u0026eacute; de D\u0026eacute;veloppement de l\u0026rsquo;Aire de Sant\u0026eacute; /Health Area Development Committee]. All the twenty-eight are men. So, we've made a plea to our partners to help us revitalize the CODESAs, to see where there are shortcomings so that we can get back on track with competent women\u0026rdquo;.\u003c/em\u003e (Gender intervention respondent).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHow does gender affect health workers' use of mapping and georeferenced data?\u003c/h2\u003e \u003cp\u003eMost respondents acknowledged that this focus on gender has enabled them to put into practice this new strategy involving women and men in microplanning, awareness-raising, and mobilizing mothers for immunization. They also noted that complementarity between women and men is essential to reach zero-dose children and children lost to follow-up or incompletely vaccinated children in the community.\u003c/p\u003e \u003cp\u003eFor the gender distribution in training in the production of spatial maps and estimates of vaccination target populations, interviews revealed that in each Health Zone, there were a total of twenty (20) people, fifteen (15) women, and five (5) men. It was clear that all the women had carried out the process of capturing data by GPS, so that they could have the matrix to demonstrate to the other members of their community.\u003c/p\u003e \u003cp\u003eHowever, on service delivery, deep-rooted social and cultural norms concerning the roles and responsibilities of men and women constitute challenges, obstacles or barriers to immunization, which affect both caregivers and health workers, and influence the provision, demand and use of immunization services.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGeospatial data use by Gender\u003c/h2\u003e \u003cp\u003eRegarding the impact of gender on health workers' immunization planning and conduct of routine immunization activities, most of the respondents revealed that today, immunization campaigns are prepared using telephones, which means that health workers have a very good grasp of the boundaries of their Health Areas, as well as the targets to be immunized in the Health Area. They noted that the representation of men and women facilitated the participation and complementarity of all health workers in all upstream and downstream activities to achieve good results. One participant pointed out that \"\u003cem\u003ein terms of vaccination, for example, you'll find that when a woman administers the vaccine, people are so happy. So, there are always positive influences\u003c/em\u003e\". \u003cem\u003e(Gender intervention respondent).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe gender intervention in the use of georeferenced microplans has contributed to reevaluating the composition of vaccination teams, namely the CODESA and CAC teams, supporting women to achieve good results to reach and vaccinate all the children expected. To this end, most respondents indicated that all providers (women and men) work together to achieve the targets expected.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Aim 2: Associated effects of the acceptance and use of georeferenced data by Health Zones and Health Areas on Immunization Coverage and Equity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor the second study aim, a quasi-experimental design study in the three provinces was used to determine the associated effects of the acceptance and use of geospatial data on immunization coverage and equity. We based our analyses on secondary data from the immunization coverage and equity surveys of children 12\u0026ndash;23 months of age.\u003c/p\u003e \u003cp\u003eIt is important to note that the intervention had not been implemented for a sufficient time in Kasai to contribute to significant improvements in immunization coverage or equity. Georeferenced tools were distributed in Kasai in 2023 and would need at least 12 months of implementation to contribute to substantial changes in immunization outcomes. Thus, for this part of the study, Kasai was also considered as control with Haut Lomami as the unique intervention site.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Changes in immunization coverage and timeliness after at least 12 months of implementation in the three provinces\u003c/b\u003e \u003c/p\u003e \u003cp\u003eData on initial vaccination coverage from the 2020 vaccine coverage survey (VCS), and from VCSs carried out in 2021 and 2022, for BCG and OPV 0 antigens in the three Provinces show clear progress on one side, and stagnation on the other [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA clear improvement in BCG antigen vaccination coverage was observed in Haut-Lomami Province (intervention Province), with VCS rising from 9.9% (2020) to 78.9% (2021) and then to 94% (2022). Kasa\u0026iuml; Central (control Province) saw an improvement in BCG antigen coverage from 25.3% in 2020 to 56.9% in 2021, and stagnation at 56.2% in 2022. On the other hand, Kasa\u0026iuml; (intervention province) showed an improvement in BCG antigen coverage, with a \"V\"-shaped evolution over the three years, i.e. a drop from 52.9% in 2020 to 44.9% and then an improvement to 57.1% in 2022. (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eWith regard to OPV 0 antigen, the trend remains the same as that observed with BCG antigen, except for the province of Kasa\u0026iuml; Central (Control). OPV 0 coverage rates showed an improvement in the three provinces of Haut-Lomami (Intervention), Kasa\u0026iuml; (Control) and Kasa\u0026iuml; Central (Control). This improvement has been maintained for Haut-Lomami and Kasa\u0026iuml; Central provinces, 8.6% in 2020 to 93.9% in 2022 and 45.4% in 2020 to 51.9% respectively. Kasa\u0026iuml; province, on the other hand, although having improved its OPV 0 antigen coverage from 45.4% in 2020 to 51.9% in 2022, recorded a drop in 2021 (40.6%). (See Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eFigure 2: Estimates of BCG antigen vaccination coverage for children aged 12 to 23 months\u003c/p\u003e \u003cp\u003eFigure 2 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 12 to 23 months in the provinces of Kasa\u0026iuml;, Kasa\u0026iuml; Central and Haut-Lomami in the DRC in 2020, 2021 and 2022. \u003cb\u003eSource\u003c/b\u003e: Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure 3: Estimates of OPV0 antigen vaccination coverage for children aged 12 to 23 months\u003c/p\u003e \u003cp\u003eFigure 3 legend: Point estimates of OPV0 antigen vaccination coverage indicators according to the vaccination map in children aged 12 to 23 months in the Provinces of Kasa\u0026iuml;, Kasa\u0026iuml; Central and Haut-Lomami in the DRC from 2020, 2021 and 2022. \u003cb\u003eSource\u003c/b\u003e: Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFor Pentavalent 1 antigen, the VCS rate observed for the three provinces showed an improvement when comparing 2020 to 2022. In Haut-Lomami Province, coverage rose from 9.9% in 2020 to 78.5% in 2021 and 93.6% in 2022. For the other Provinces, the rate rose from 51% in 2020 to 58.3% in 2022 in Kasai and from 26.3% in 2020 to 59.3% in 2022 for the Kasai central. However, the Pentavalent 1 antigen vaccination coverage rate fell in 2021 to 48.1% for Kasa\u0026iuml; and was higher at 61.1% for Kasa\u0026iuml; Central Province.\u003c/p\u003e \u003cp\u003eFor Pentavalent 3 antigen, the observed vaccine coverage rate showed a significant improvement for Haut-Lomami. It rose from 8.9% in 2020 to 76.8% in 2021 and to 92% in 2022.\u003c/p\u003e \u003cp\u003eIn the two Control Provinces of Kasa\u0026iuml; (Control) and Kasa\u0026iuml; Central (Control), there has been a net increase in the dropout rate between Penta 1 and Penta 3, indicating a decline in the immunization programme i.e., 5.7% in 2020, 13.7% in 2021 and 20.3% in 2022 for the Kasai Province, and 4.5% in 2020, 13.9% in 2021 and 15.5% in 2022 for the Kasai Central Province. On the other hand, the drop-out rate has changed little in Haut Lomami Province, indicating stability within the immunization programme. It rose from 1% in 2020 to 1.7% in 2021 and 1.6% in 2022 (See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of Penta 1 and 3 vaccination coverage ages 12\u0026ndash;23 months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eVCS 2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eVCS 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eVCS 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePenta1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePenta 1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePenta 1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKasa\u0026iuml;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaut-Lomami\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKasa\u0026iuml; Central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: Point estimates of Penta 1 and 3 vaccination coverage indicators in children aged 12 to 23 months in the 3 provinces of the study in 2020, 2021 and 2022. Source: Vaccination coverage survey in DRC: 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Impact of georeferenced data use as compared to the status quo on its effectiveness to increase immunisation coverage and timeliness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInitial vaccination coverage data from the VCS survey in 2020 and those from the VCS carried out in 2021 and 2022 for BCG and OPV 0 antigens in the three provinces surveyed reveal a certain disparity between provinces.\u003c/p\u003e \u003cp\u003eImprovements in BCG antigen vaccination coverage rates were observed in the two provinces of Haut-Lomami (intervention province) and Kasa\u0026iuml; Central (control province), which respectively increased from 20.2% (VCS 2020) to 91.8% (VCS 2022) and from 33.2% in 2020 to 63% in 2022. Kasa\u0026iuml; province (control province) showed a marked drop in BCG antigen coverage, from 61.4% in 2020 to 51.5% in 2021, and 57.5% in 2022. (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eRegarding the OPV 0 antigen, the trend remains the same as that observed with BCG antigen. The OPV 0 coverage rates have improved significantly in the two provinces of Haut-Lomami (Intervention) and Kasa\u0026iuml; Central (Control). They have respectively risen from 19.5% in 2020 to 91.8% in 2022, and from 30.3% in 2020 to 53.2% in 2022, starting from a higher rate of 55.6% in 2021. In Kasa\u0026iuml; Province (Control), OPV 0 antigen coverage fell from 55.2% in 2020 to 48.4% in 2021 and 52.1% in 2022. (Fig.\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eFigure 4: Estimates of BCG antigen vaccination coverage for children aged 6 to 11 months\u003c/p\u003e \u003cp\u003eFigure 4 legend: Point estimates of BCG antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasa\u0026iuml;, Haut-Lomami and Kasa\u0026iuml; Central in the DRC in 2020, 2021 and 2022. \u003cb\u003eSource\u003c/b\u003e: Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFigure 5: Estimates of OPV 0 antigen vaccination coverage for children aged 6 to 11 months\u003c/p\u003e \u003cp\u003eFigure 5 legend: Point estimates of OPV 0 antigen vaccination coverage indicators according to the vaccination map for children aged 6 to 11 months in the provinces of Kasa\u0026iuml;, Haut-Lomami and Kasa\u0026iuml; Central in the DRC in 2020, 2021 and 2022. \u003cb\u003eSource\u003c/b\u003e: Vaccination coverage survey (VCS) in DRC: 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRole of georeferenced data in locating zero-dose children\u003c/h2\u003e \u003cp\u003eIt emerged from interviews that in general, the use of georeferenced data made it possible to improve involvement of health facilities that have not offered immunization services and consequently to reach children who missed vaccination days. In addition, this made it possible to improve the vaccination catch-up which was carried out previously by the community relays to reach zero-dose children. A nurse stated the following: \u0026ldquo;\u003cem\u003eYou know a health facility which 15 kms far away from a health center and does not vaccinate. If for example the nursing staff of a health center starts moving with vaccines to the health facility that does not vaccinate, when they arrive there, you will see all these children who were zero dose will come to be vaccinated and even this health facility will also be interested in vaccination. So, it does influence positively the reduction of zero doses and even the involvement of other types health facilities in vaccination.\u0026rdquo;\u003c/em\u003e (Respondent; Health Area, Haut-Lomami).\u003c/p\u003e \u003cp\u003eHowever, in Kasai, some respondents reported difficulties linked to recurrent population movements. This makes vaccination activities challenging in locating target children with the consequence of uneven vaccination indicators. For example, one respondent said: \u0026ldquo;\u003cem\u003eThe obstacles that we often experience is movement, we are in a purely mining Health Zone where the population is moving all the times.\u003c/em\u003e\u0026rdquo; (Respondent _09, HA, Kasa\u0026iuml;).\u003c/p\u003e \u003cp\u003eFor the same children aged 6 to 11 months, Pentavalent 1 and Pentavalent 3 antigen coverage rates in Haut-Lomami (intervention Province) in 2020 were 20.3% and 17.8% respectively, representing a dropout rate of 2.5%. By 2021, these rates had risen to 69.1% and 63.8% respectively, representing a drop-out rate of 5.3%. In 2022, these rates reached 91.6% and 87.6% respectively, for a drop-out rate of 4%. From 2020 to 2022, vaccine coverage rates for pentavalent 1 and 3 antigens improved, with a wavering dropout rate.\u003c/p\u003e \u003cp\u003eIn Kasa\u0026iuml; (control), Pentavalent 1 and Pentavalent 3 antigen coverage rates from 2020 to 2021 were 60.9% and 51.3% respectively, with a dropout rate of 9.6% in 2020. These rates were 61.7\u0026ndash;38.4%, or a drop-out rate of 23.3% in 2021, then 52.0\u0026ndash;25.3%, or a drop-out rate of 26.7% in 2022. The dropout rate for Kasa\u0026iuml; province has increased in 2022 compared with 2020.\u003c/p\u003e \u003cp\u003eIn the control province of Kasa\u0026iuml; Central, vaccine coverage rates for pentavalent 1 and 3 antigens were 34.6% and 26.3% respectively in 2020, representing a drop-out rate of 8.3%. These rates were 72.4\u0026ndash;50.5% in 2021, with a dropout rate of 21.9%, and then 64.6\u0026ndash;41.5%, with a dropout rate of 23.1%. As in Haut-Lomami Province, the dropout rate increased slightly from 2020 to 2022. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of Penta 1 and 3 vaccination coverage ages 6\u0026ndash;11 months.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProvinces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eECV 2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eECV 2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eECV 2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePenta 1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePenta 1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePenta 1 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePenta 3 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDrop-out rate (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKasa\u0026iuml;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHaut -Lomami\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKasa\u0026iuml; Central\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLegend: Point estimates of Penta 1 and 3 vaccination coverage for children aged 6 to 11 months in the 3 provinces of the study over 3 years. Source: Vaccination Coverage Survey in the DRC 2020, 2021 and 2022 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Impact of georeferenced data use as compared to the status quo on equity\u003c/h2\u003e \u003cp\u003eThe impact of geospatial data on equity was seen in terms of reaching the most marginalized children 0\u0026ndash;23 months (girls/boys) and the main caregivers - women and adolescent girls in their reproductive years (15\u0026ndash;49 years of age). However, because of the unavailability of economic data, the ability to conduct robust equity analyses was limited. The results of the equity analyses were inconclusive, and therefore, not presented here. They have been included in the comprehensive research report provided as Supplementary Material.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe study tested the hypothesis that the effective use of geospatial data can contribute to an increase immunization coverage and equity, through the identification of missed settlements and zero-dose children, the optimization of vaccination strategies, and the supply distribution. It also incorporated a gender-sensitive approach and included a gender sub-study to assess gender-specific interventions in a sub-set of Health Zones and Health Areas in Kasai, as part of Gavi\u0026rsquo;s intensified strategy to address gender inequity and the global Immunization Agenda 2030 [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results indicate that the geospatial microplans in Haut-Lomami and Kasai were well received, used, and led to changes in the delivery of vaccination services. As an innovation, the context and mechanisms through which geospatial data and tools were created and accepted for use confirmed their importance and effective adoption at Health Zones and Health Areas. As found in Haut Lomami, geospatial data enabled the visualization and analysis of health data in spatial contexts, offering insights into the geographical distribution of the population, health area boundaries, healthcare facilities and immunization coverage. In line with our results, it has been largely documented that Geospatial Information Systems (GIS) and other geospatial technologies facilitate targeted interventions, allowing health authorities to optimize and enhance the precision of resource allocation in resource-constrained settings and identified underserved areas to allocate resources efficiently in specific geographic areas [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe potential of Geographic Information System (GIS) and spatial analysis in enhancing the effectiveness of health providers and monitoring immunization coverage has been emphasized in other Central African countries [\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Other illustrations of the application of geospatial mapping to address health issues such as malnutrition and guiding program planning in resource-limited settings, or reducing measles incidence through frequent supplementary immunization activities were also found in the literature [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, to be fully effective, the production and use of geospatial data and maps need more work to build capacity and ensure the quality of data and maps, as persistent challenges in data quality, such as inflated coverage figures and inaccurate denominators, remain significant hurdles [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe associated effects of geospatial data on immunization coverage in Haut Lomami have shown that it may have contributed as a component of a broader set of immunization strategies to significant increases in immunization coverage rates and lower dropouts, including reduced numbers of zero-dose children. These results corroborated with other studies in the context of LMICs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additional study with a longer observation time, i.e., more than three years, is needed in Kasai to come up with a strong evidence conclusion.\u003c/p\u003e \u003cp\u003eThe drastic improvements in immunization outcomes may be also explained by other factors, such as the implementation, in parallel, of some specific immunization projects, which worked in synergy. The Province of Haut-Lomami has been a Mashako Plan site with intensified support of the EPI [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, an intervention aiming to improve the distribution of vaccine products up to the last kilometer and using Information and Communication Technologies (ICTs) in the fields of health has intensively assisted the Province through the EPI branch office of Kamina [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We are unable to attribute the full increase observed for the Penta3 vaccine between 2020 and 2021 (from 8.9% in 2020 to 76.8% in 2021) to the adoption and use of geospatial data alone and acknowledge that it may be an important part of a larger package of strategic EPI interventions.\u003c/p\u003e \u003cp\u003eTaking a gender lens for the overall study, we identified perceived positive contributions to the intervention and the evaluation. In the delivery of immunization services, it is important to include transformative and equitable gender strategies, taking into account the socio-cultural contexts in which health workers and caregivers live and work. Gender mainstreaming must be carried out at all levels of microplanning design and implementation, the use of georeferenced data, the conduct of routine immunization, and monitoring and evaluation. To achieve this, awareness and action is needed at national and sub-national levels to conduct gender analyses and design gender-sensitive interventions to reduce gender-related barriers to immunization and georeferenced data use. Targeted interventions based on spatial analysis effectively reduced disparities, promoting a more equitable distribution of immunization services, that address specific barriers faced by vulnerable populations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Due to the lack of availability of robust data that would enable the assessment of effects related to equity, we were unable to compare results beyond those associated with the reduced rate of zero-dose children detected in Haut-Lomami Province. However, to tackle inequities in immunization, since a decade, countries are focusing on effective immunization microplans at the subdistrict level, using georeferenced data and maps for better planning of immunization activities, such as community-based Reach Every Child (REC) intervention [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, while our study is in line with the recent literature and demonstrates the positive contribution of geospatial data on immunization outcomes, challenges persist. Issues related to data quality, privacy concerns, the need for enhanced infrastructure, and the need for capacity building are common challenges [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. On the other hand, some studies underscored the underutilization of routine immunization data in decision-making, emphasizing the need for more rigorous evaluations of interventions and continued research, aimed at improving data utilization, thus, addressing disparities and increasing vaccine coverage [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Future research should also focus on overcoming these challenges, optimizing the integration of geospatial data into immunization strategies, and expanding gender-sensitive approaches across the full EPI.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe situation of \u0026ldquo;zero-dose\u0026rdquo; children in the Democratic Republic of Congo (DRC) is a major concern. The overall objective of the study was to evaluate the use geo-enabled microplans for the microplanning and implementation of routine immunization programs, and the associated contribution to increased and sustained immunization coverage with a focus on the identification and vaccination of zero-dose children. The results indicate that the georeferenced microplans in Haut-Lomami and Kasai were well received, used, and led to changes in planning for and delivery of vaccination services. In addition, the gender ethnographic study in Kasai indicates that the gender intervention led to the greater inclusion of women in immunization activities. Due to the delayed time of georeferenced microplan adoption and use, it is recommended to conduct a supplemental study to follow the implementation in Kasai in 2024 and 2025 for further immunization coverage and equity analyses. Consistently across analyses, we observed a significant positive trend in Haut-Lomami in immunization outcomes, including an increase in overall coverage, identification and immunization of zero-dose, and reduced dropouts. This aligns with other studies related to the use of geospatial data for immunization.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCAC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cellule d\u0026rsquo;Animation Communautaire (Community Animation Cells)\u003cbr\u003eCAPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Computer Assisted Personal Interview\u0026nbsp;\u003cbr\u003eDRC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Democratic Republic of Congo\u003cbr\u003eCODESA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Comit\u0026eacute; de D\u0026eacute;veloppement de l\u0026rsquo;Aire de Sant\u0026eacute; (Health Area Development Committee)\u0026nbsp;\u003cbr\u003e\u0026nbsp;DTP1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cem\u003ediphtheria-tetanus-pertussis containing vaccine\u003cbr\u003e\u0026nbsp;\u003c/em\u003eEPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Expanded Program on Immunization\u003cbr\u003eGavi\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gavi, the Vaccine Alliance\u003cbr\u003eGRID3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Geo-Referenced Infrastructure and Demographic Data for Development\u003cbr\u003eKSPH\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kinshasa School of Public Health\u0026nbsp;\u003cbr\u003eLMIC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Low- and middle-income country\u003cbr\u003eM4H\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Mapping for Health\u003cbr\u003eNHIS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;National Health Information System\u003cbr\u003eOPV\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Oral polio vaccine\u0026nbsp;\u003cbr\u003eVCS\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Vaccine Coverage Survey\u0026nbsp;\u003cbr\u003eWHO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;World Health Organization\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The Mapping for Health Study has been reviewed and cleared by the Kinshasa School of Public Health Internal Review Board. The study has also been registered with BMC Central International Standard Randomised Controlled Trial Number ISRCTN65876428 on 3/11/2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: All human subjects have provided written consent to participate in the study and for results to be published. All co-authors have reviewed the paper and agreed to have it submitted for review and publication. \u0026nbsp;\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e: Research data and materials will be made available upon request. Please contact Dosithee Ngo Bebe at [email protected].\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Competing interests\u003c/strong\u003e: There are no known competing interests.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Funding\u003c/strong\u003e: Funding was provided for the implementation and evaluation of Mapping for Health by Gavi, the Vaccine Alliance through the INFUSE Project.\u0026nbsp;\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Authors\u0026apos; contributions\u003c/strong\u003e: DNB is the Principal Investigator and led the research on behalf of the KSPH. PM led the overall research design and publication process on behalf of HealthEnabled. FK, TB, GL, KL, FL, and MK supported the research design, field research, report and publication writing with the KSPH. BK provided overall support for research activities in DRC on behalf of HealthEnabled. KT provided inputs into the research study and review of findings on behalf of the implementation partner, Columbia University. CL reviewed the findings of the research study on behalf of National Expanded Program on Immunization. CG provided overall guidance for the research study and review of findings on behalf of Gavi, the Vaccine Alliance.\u003cbr\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;Acknowledgements\u003c/strong\u003e: The authors would like to thank the DRC EPI at the national, provincial, facility, and community levels and implementing partners for their support and participation in the evaluation. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. 2013. Global vaccine action plan 2011-2020. https://www.who.int/publications/i/item/global-vaccine-action-plan-2011-2020 . Accessed 16 Dec 2023.\u003c/li\u003e\n\u003cli\u003eGavi. 2021. Gavi Phase V (2021-2025) Strategy. https://www.gavi.org/our-alliance/strategy/phase-5-2021-2025 . Accessed 16 Dec 2023.\u003c/li\u003e\n\u003cli\u003eIA2030. IMPLEMENTING THE IMMUNIZATION AGENDA 2030: A Framework for Action through Coordinated Planning, Monitoring \u0026amp; Evaluation, Ownership \u0026amp; Accountability, and Communications \u0026amp; Advocacy. http://www.immunizationagenda2030.org/framework-for-action . Accessed 16 Dec 2023. \u003c/li\u003e\n\u003cli\u003eLame P, Milabyo A, Tangney S, Mbaka GO, Luhata C, Le Gargasson JB, Mputu C, Hoff NA, Merritt S, Nkamba DM, Sall DS. A Successful National and Multipartner Approach to Increase Immunization Coverage: The Democratic Republic of Congo Mashako Plan 2018–2020. Glob Health Sci Pract. 2023;11:e2200326. doi: 10.9745/GHSP-D-22-00326\u003c/li\u003e\n\u003cli\u003eChaney SC, Mechael P, Thu NM, Diallo MS, Gachen C. 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Health Res Policy Sys. 2021;19 :48. https://doi.org/10.1186/s12961-021-00694-1.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Material","content":"\u003cp\u003eSupplementary Material is not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Geospatial data, mapping for health, immunization coverage, vaccine coverage survey, immunization equity, DRC, Expanded Program for Immunization, routine immunization, zero dose, microplanning","lastPublishedDoi":"10.21203/rs.3.rs-3997296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3997296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe National Expanded Program on Immunization in the Democratic Republic of the Congo started using geospatial data at scale in 8 Provinces to strengthen the planning and implementation of vaccination services with a focus on the identification and immunization of zero-dose children, children who have not received the first dose of \u003cem\u003ediphtheria-tetanus-pertussis containing vaccine (DTP1\u003c/em\u003e).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study used a mixed-methods research design including survey tools, in-depth interviews and direct observation to document the uptake, use, and perceived impact of georeferenced immunization microplans in the intervention provinces of Haut-Lomami and Kasai and in the control province of Kasai Central. A total of 113 health facilities in 98 Health Areas in 15 Health Zones in the three provinces were included in the study sample. A gender intervention in select Health Zones and Health Areas in Kasai Province was also evaluated through a targeted qualitative study. A secondary analysis of immunization coverage survey data was conducted to assess the associated effects on immunization coverage, especially for rates of zero-dose children.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis research study shows that georeferenced microplans are well received, utilized, and led to changes in routine immunization service planning and delivery with perceived improvements in identification and reaching zero-dose children. In addition, the gender intervention is perceived to have led to a significant change in the approaches taken to overcome sociocultural gender norms and engage communities to reach as many children as possible, leveraging the ability of women to engage more effectively to support vaccination services. The quantitative analyses showed that georeferenced microplans may have contributed to a dramatic and sustained trend towards high immunization coverage in the intervention site of Haut Lomami, which rose dramatically from 8.9% in 2020 to 76.8% in 2021 and to 92% in 2022 for Pentavalent 3 antigen, while the DPT1-DPT3 drop-out rate changed little from 1% in 2020 to 1.7% in 2021 and 1.6% in 2022 after three years of implementation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe overall study identified positive contributions of the georeferenced data in the planning and delivery of routine immunization services. It is recommended to conduct further analyses in Kasai in 2024 and 2025 to evaluate the effects of the gender intervention on immunization coverage and equity outcomes.\u003c/p\u003e","manuscriptTitle":"Assessing the Use of Geospatial Data for Immunization Program Implementation and Associated Effects on Coverage and Equity in the Democratic Republic of Congo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-15 18:41:47","doi":"10.21203/rs.3.rs-3997296/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-03-22T06:39:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-22T06:31:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-12T11:12:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-02-28T16:49:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6b384e87-6728-4f65-aa01-d4125a1f68e1","owner":[],"postedDate":"March 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-27T16:07:28+00:00","versionOfRecord":{"articleIdentity":"rs-3997296","link":"https://doi.org/10.1186/s12889-025-21578-x","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-01-24 15:58:12","publishedOnDateReadable":"January 24th, 2025"},"versionCreatedAt":"2024-03-15 18:41:47","video":"","vorDoi":"10.1186/s12889-025-21578-x","vorDoiUrl":"https://doi.org/10.1186/s12889-025-21578-x","workflowStages":[]},"version":"v1","identity":"rs-3997296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3997296","identity":"rs-3997296","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}