Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys

Exploring landscape of measles vaccination coverage: A step towards measles elimination goal in India

INTRODUCTION

Measles remains a critical public health concern causing significant morbidity and mortality globally. Despite the success of measles vaccination programs, challenges persist, particularly in India. This study investigates dose-wise measles vaccination coverage and explores gaps in immunization focusing on zero-dose, one-dose, and two-dose coverage among children aged 24-35 months.

DATA SOURCES AND METHODOLOGY

The National Family Health Survey 2019-21 (NFHS-5) served as the data source and the study analyzed information from 43,864 children aged 24-35 months. Sociodemographic variables such as birth order, wealth quintile, gender, social group, religion, residence, mother education, delivery-related factors, and media exposure were considered. Statistical analysis involved weighted estimates, chi-square tests, and multivariate multinomial logistic regression.

RESULTS

The study revealed that challenges persist in achieving optimal measles vaccination coverage. Analysis by sociodemographic factors highlighted disparities in coverage, with variations in zero dose prevalence across states and districts. The percentage of zero-dose children was significantly higher, with 11.5% of children in India remaining to receive any measles vaccination. Factors influencing vaccine coverage include birth order, age, wealth quintile, social group, religion, residence, maternal education, place of delivery, media exposure, and mode of delivery. The findings from the spatial analysis show the clustering of zero-dose children is high in the northeastern states of India.

DISCUSSION

Measles zero-dose children pose a significant obstacle to achieving elimination goals. Spatial analysis identifies clusters of unvaccinated populations guiding targeted interventions. The study aligns with global initiatives such as the Immunization Agenda 2030 emphasizing equitable vaccine access and discusses how India can tailor its strategies to achieve the goal. Lessons from polio eradication efforts inform strategies for measles elimination, stressing the importance of high-quality data and surveillance. The study underscores the urgency of addressing last-mile measles vaccination gaps in India. Spatially targeted interventions informed by sociodemographic factors can enhance immunization coverage. Achieving measles elimination requires sustained efforts and leveraging lessons from successful vaccination campaigns. The study findings have the potential to contribute to informed decision-making, supporting India's roadmap for the measles and rubella elimination goal.

A measles and rubella vaccine microneedle patch in The Gambia: a phase 1/2, double-blind, double-dummy, randomised, active-controlled, age de-escalation trial

BACKGROUND

Microneedle patches (MNPs) have been ranked as the highest global priority innovation for overcoming immunisation barriers in low-income and middle-income countries. This trial aimed to provide the first data on the tolerability, safety, and immunogenicity of a measles and rubella vaccine (MRV)-MNP in children.

METHODS

This single-centre, phase 1/2, double-blind, double-dummy, randomised, active-controlled, age de-escalation trial was conducted in The Gambia. To be eligible, all participants had to be healthy according to prespecified criteria, aged 18-40 years for the adult cohort, 15-18 months for toddlers, or 9-10 months for infants, and to be available for visits throughout the follow-up period. The three age cohorts were randomly assigned in a 2:1 ratio (adults) or 1:1 ratio (toddlers and infants) to receive either an MRV-MNP (Micron Biomedical, Atlanta, GA, USA) and a placebo (0·9% sodium chloride) subcutaneous injection, or a placebo-MNP and an MRV subcutaneous injection (MRV-SC; Serum Institute of India, Pune, India). Unmasked staff ransomly assigned the participants using an online application, and they prepared visually identical preparations of the MRV-MNP or placebo-MNP and MRV-SC or placebo-SC, but were not involved in collecting endpoint data. Staff administering the study interventions, participants, parents, and study staff assessing trial endpoints were masked to treatment allocation. The safety population consists of all vaccinated participants, and analysis was conducted according to route of MRV administration, irrespective of subsequent protocol deviations. The immunogenicity population consisted of all vaccinated participants who had a baseline and day 42 visit result available, and who had no protocol deviations considered to substantially affect the immunogenicity endpoints. Solicited local and systemic adverse events were collected for 14 days following vaccination. Unsolicited adverse events were collected to day 180. Age de-escalation between cohorts was based on the review of the safety data to day 14 by an independent data monitoring committee. Serum neutralising antibodies to measles and rubella were measured at baseline, day 42, and day 180. Analysis was descriptive and included safety events, seroprotection and seroconversion rates, and geometric mean antibody concentrations. The trial was registered with the Pan African Clinical Trials Registry PACTR202008836432905, and is complete.

FINDINGS

Recruitment took place between May 18, 2021, and May 27, 2022. 45 adults, 120 toddlers, and 120 infants were randomly allocated and vaccinated. There were no safety concerns in the first 14 days following vaccination in either adults or toddlers, and age de-escalation proceeded accordingly. In infants, 93% (52/56; 95% CI 83·0-97·2) seroconverted to measles and 100% (58/58; 93·8-100) seroconverted to rubella following MRV-MNP administration, while 90% (52/58; 79·2-95·2) and 100% (59/59; 93·9-100) seroconverted to measles and rubella respectively, following MRV-SC. Induration at the MRV-MNP application site was the most frequent local reaction occurring in 46 (77%) of 60 toddlers and 39 (65%) of 60 infants. Related unsolicited adverse events, most commonly discolouration at the application site, were reported in 35 (58%) of 60 toddlers and 57 (95%) of 60 infants that had received the MRV-MNP. All local reactions were mild. There were no related severe or serious adverse events.

INTERPRETATION

The safety and immunogenicity data support the accelerated development of the MRV-MNP.

FUNDING

Bill & Melinda Gates Foundation.

Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria

The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.

Estimating spatially disaggregated probability of severe COVID-19 and the impact of handwashing interventions: The case of Zimbabwe

INTRODUCTION

The severity of COVID-19 disease varies substantially between individuals, with some infections being asymptomatic while others are fatal. Several risk factors have been identified that affect the progression of SARS-CoV-2 to severe COVID-19. They include age, smoking and presence of underlying comorbidities such as respiratory illness, HIV, anemia and obesity. Given that respiratory illness is one such comorbidity and is affected by hand hygiene, it is plausible that improving access to handwashing could lower the risk of severe COVID-19 among a population. In this paper, we estimate the potential impact of improved access to handwashing on the risk of respiratory illness and its knock-on impact on the risk of developing severe COVID-19 disease across Zimbabwe.

METHODS

Spatial generalized additive models were applied to cluster level data from the 2015 Demographic and Health Survey. These models were used to generate continuous (1km resolution) estimates of risk factors for severe COVID-19, including prevalence of major comorbidities (respiratory illness, HIV without viral load suppression, anemia and obesity) and prevalence of smoking, which were aggregated to district level alongside estimates of the proportion of the population under 50 from Worldpop data. The risk of severe COVID-19 was then calculated for each district using published estimates of the relationship between comorbidities, smoking and age (under 50) and severe COVID-19. Two scenarios were then simulated to see how changing access to handwashing facilities could have knock on implications for the prevalence of severe COVID-19 in the population.

RESULTS

This modeling conducted in this study shows that (1) current risk of severe disease is heterogeneous across the country, due to differences in individual characteristics and household conditions and (2) that if the quantifiable estimates on the importance of handwashing for transmission are sound, then improvements in handwashing access could lead to reductions in the risk of severe COVID-19 of up to 16% from the estimated current levels across all districts.

CONCLUSIONS

Taken alongside the likely impact on transmission of SARS-CoV-2 itself, as well as countless other pathogens, this result adds further support for the expansion of access to handwashing across the country. It also highlights the spatial differences in risk of severe COVID-19, and thus the opportunity for better planning to focus limited resources in high-risk areas in order to potentially reduce the number of severe cases.

COVID-19 outbreak control strategies and their impact on the provision of essential health services in Ghana: An exploratory-sequential study

BACKGROUND

The COVID-19 pandemic has led to substantial interruptions in critical health services, with 90% of countries reporting interruptions in routine vaccinations, maternal health care and chronic disease management. The use of non-pharmaceutical interventions (NPIs) such as lockdowns and self-isolation had implications on the provision of essential health services (EHS). We investigated exemplary COVID-19 outbreak control strategies and explored the extent to which the adoption of these NPIs affected the provision of EHS including immunization coverage and facility-based deliveries. Finally, we document core health system strategies and practices adopted to maintain EHS during the early phase of the pandemic.

METHODS

This study used an explanatory sequential study design. First, we utilized data from routine health management information systems to quantify the impact of the pandemic on the provision of EHS using interrupted time series models. Second, we explored exemplary strategies and health system initiatives that were adopted to prevent the spread of COVID-19 infections while maintaining the provision of EHS using in-depth interviews with key informants including policymakers and healthcare providers.

RESULTS

The COVID-19 pandemic and the interventions that were implemented disrupted the provision of EHS. In the first month of the COVID-19 pandemic, Oral Polio and pentavalent vaccination coverage reduced by 15.2% [95% CI = -22.61, -7.87, p<0.001] and 12.4% [95% CI = 17.68, -7.13; p<0.001] respectively. The exemplary strategies adopted in maintaining the provision of EHS while also responding to the spread of infections include the development of new policy guidelines that were disseminated with modified service delivery models, new treatment and prevention guidelines, the use of telemedicine and medical drones to provide EHS and facilitate rapid testing of suspected cases.

CONCLUSION

The implementation of different NPIs during the peak phase of the pandemic disrupted the provision of EHS. However, the Ministry of Health leveraged the resilient health system and deployed efficient, all-inclusive, and integrated infectious disease management and infection prevention control strategies to maintain the provision of EHS while responding to the spread of infections.

Examining spatial variation for immunisation coverage in pregnant women: A nationwide and geospatial retrospective cohort study in Aotearoa New Zealand

BACKGROUND

Maternal influenza and pertussis immunisation is crucial for protecting mothers during pregnancy and their babies in the first weeks of life against severe disease. We examined geospatial variation in maternal immunisation coverage among pregnant women in Aotearoa New Zealand and its health equity implications.

METHOD

We constructed a retrospective cohort including all pregnant women who delivered between 01 January 2013 and 31 December 2020 using administrative health datasets. Our outcomes were receipt of influenza or pertussis vaccine in any one of three relevant national databases (e.g. National Immunisation Register, Proclaims, or Pharmaceutical collection) during the eligible pregnancy.

RESULTS

Data from our retrospective cohort study show significant regional variation in maternal immunisation coverage for both influenza and pertussis from 2013 to 2020. Maximal coverage was around 50% in the best performing regions, which means that half of the women who were pregnant (183,737 women) were not protected. In addition, we found significant spatio-temporal variation and clustering of immunisation coverage. Our findings are interactively available to explore here: https://geohealthlab.shinyapps.io/hapumama/ CONCLUSION: Our study is one of the first to examine spatial variation in maternal vaccination coverage in pregnant women at a national level over space and time. This provides powerful tools to measure the impact of interventions to improve coverage at national and regional levels, with specific reference to inequities between ethnic groups, likely applicable to similar settings internationally.

Malaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

BACKGROUND

The World Health Organization approved the RTS,S/AS01 malaria vaccine for wider rollout, and Kenya participated in a phased pilot implementation from 2019 to understand its impact under routine conditions. Vaccine delivery requires coverage measures at national and sub-national levels to evaluate progress over time. This study aimed to estimate the coverage of the RTS,S/AS01 vaccine during the first 36 months of the Kenyan pilot implementation.

METHODS

Monthly dose-specific immunization data for 23 sub-counties were obtained from routine health information systems at the facility level for 2019-2022. Coverage of each RTS,S/AS01 dose was determined using reported doses as a numerator and service-based (Penta 1 and Measles) or population (projected infant populations from WorldPop) as denominators. Descriptive statistics of vaccine delivery, dropout rates and coverage estimates were computed across the 36-month implementation period.

RESULTS

Over 36 months, 818,648 RTSS/AS01 doses were administered. Facilities managed by the Ministry of Health and faith-based organizations accounted for over 88% of all vaccines delivered. Overall, service-based malaria vaccine coverage was 96%, 87%, 78%, and 39% for doses 1-4 respectively. Using a population-derived denominator for age-eligible children, vaccine coverage was 78%, 68%, 57%, and 24% for doses 1-4, respectively. Of the children that received measles dose 1 vaccines delivered at 9 months (coverage: 95%), 82% received RTSS/AS01 dose 3, only 66% of children who received measles dose 2 at 18 months (coverage: 59%) also received dose 4.

CONCLUSION

The implementation programme successfully maintained high levels of coverage for the first three doses of RTSS/AS01 among children defined as EPI service users up to 9 months of age but had much lower coverage within the community with up to 1 in 5 children not receiving the vaccine. Consistent with vaccines delivered over the age of 1 year, coverage of the fourth malaria dose was low. Vaccine uptake, service access and dropout rates for malaria vaccines require constant monitoring and intervention to ensure maximum protection is conferred.

Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study

INTRODUCTION

Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and makeitdifficultto identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants.

METHODS

We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019-20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation-stochastic partial differential equation (INLA-SPDE) approach.

RESULTS

We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specificdistricts in the central and eastern regions of The Gambia consistentlyexhibited the highest prevalence of delayed vaccination, while the coastal districts showed alower prevalence forallthree vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants.

CONCLUSIONS

Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.

Using geographic information system to track children and optimize immunization coverage and equity in Karachi, Pakistan

BACKGROUND

Despite the potential of geospatial technologies to track and monitor coverage, they are underutilized for guiding immunization program strategy and implementation, especially in low-and-middle-income countries. We conducted geospatial analysis to explore the geographic and temporal trends of immunization coverage, and examined the pattern of immunization service access (outreach and facility based) by children.

METHODOLOGY

We extracted data to analyze coverage rates across different dimensions (by enrolment year, birth year and vaccination year) from 2018 till 2020 in Karachi, Pakistan using the Sindh Electronic Immunization Registry (SEIR). We conducted geospatial analysis to assess variation in coverage rates of BCG, Pentavalent (Penta)-1, Penta-3, and Measles-1 vaccines using Government targets. We also analyzed the proportion of children receiving their routine vaccinations at fixed centers and outreach and examined whether children received vaccinations at the same or multiple immunization centers.

RESULTS

A total of 1,298,555 children were born, enrolled or vaccinated from 2018 till 2020. At the district level, analysis by enrollment and birth year showed coverage increased between 2018 and 2019 and declined in 2020, while analysis by vaccination year showed consistent increase in coverage. However, micro-geographic analysis revealed pockets where coverage persistently declined. Notably 27/168, 39/168 and 3/156 Union councils showed consistently declining coverage when analyzing by enrollment, birth and vaccination year respectively. More than half (52.2%, 678,280/1,298,555) of the children received all their vaccinations exclusively through fixed centers and, 71.7% (499,391/696,701) received all vaccinations from the same centers.

CONCLUSION

Despite overall improving vaccination coverage between 2018 and 2020, certain geographic areas have consistently declining coverage rates, which is detrimental for equity. Making immunization inequities visible through geospatial analysis is the first step to ensure resources are allocated optimally. Our study provides impetus for immunization programs to develop and invest in geospatial technologies, harnessing its potential for improved coverage and equity.

Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria

Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country's RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.

Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines

Many vaccines are often administered in multiple doses to boost their effectiveness. In the case of childhood vaccines, the coverage maps of the doses and the differences between these often constitute an evidence base to guide investments in improving access to vaccination services and health system performance in low and middle-income countries. A major problem often encountered when mapping the coverage of multi-dose vaccines is the need to ensure that the coverage maps decrease monotonically with successive doses. That is, for doses i $$ i $$ and j $$ j $$ , i < j ⇒ p i ( s ) ≥ p j ( s ) $$ i<j\Rightarrow {p}_i\left(\boldsymbol{s}\right)\ge {p}_j\left(\boldsymbol{s}\right) $$ , where p i ( s ) $$ {p}_i\left(\boldsymbol{s}\right) $$ is the coverage of dose i $$ i $$ at spatial location s $$ \boldsymbol{s} $$ . Here, we explore conditional probability (CP) and ratio-based (RB) approaches for mapping p i ( s ) $$ {p}_i\left(\boldsymbol{s}\right) $$ , embedded within a binomial geostatistical modeling framework, to address this problem. The fully Bayesian model is implemented using the INLA and SPDE approaches. Using a simulation study, we find that both approaches perform comparably for out-of-sample estimation under varying point-level sample size distributions. We apply the methodology to map the coverage of the three doses of diphtheria-tetanus-pertussis vaccine using data from the 2018 Nigeria Demographic and Health Survey. The coverage maps produced using both approaches are almost indistinguishable, although the CP approach yielded more precise estimates on average in this application. We also provide estimates of zero-dose children and the dropout rates between the doses. The methodology is straightforward to implement and can be applied to other vaccines and geographical contexts.

Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling

Background

The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.

Methods

We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.

Results

CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.

Conclusions

Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-14371-7.

Spatial distribution, determinants and trends of full vaccination coverage in children aged 12-59 months in Peru: A subanalysis of the Peruvian Demographic and Health Survey

OBJECTIVE

To assess the spatial distribution, trends and determinants of crude full vaccination coverage (FVC) in children aged 12-59 months between 2010 and 2019 in Peru.

DESIGN, SETTING AND ANALYSIS

A cross-sectional study based on the secondary data analysis of the 2010 and 2019 Peruvian Demographic and Health Surveys (DHSs) was conducted. Logit based multivariate decomposition analysis was employed to identify factors contributing to differences in FVC between 2010 and 2019. The spatial distribution of FVC in 2019 was evaluated through spatial autocorrelation (Global Moran's I), ordinary kriging interpolation (Gaussian process regression) and Bernoulli-based purely spatial scan statistic.

OUTCOME MEASURE

FVC, as crude coverage, was defined as having completely received BCG; three doses of diphtheria, pertussis, and tetanus, and polio vaccines; and measles vaccine by 12 months of age.

PARTICIPANTS

A total of 5 751 and 14 144 children aged 12-59 months from 2010 and 2019 DHSs, respectively, were included.

RESULTS

FVC increased from 53.62% (95% CI 51.75% to 55.49%) in 2010 to 75.86% (95% CI 74.84% to 76.85%) in 2019. Most of the increase (70.39%) was attributable to differences in coefficients effects. Family size, visit of health workers in the last 12 months, age of the mother at first delivery, place of delivery and antenatal care follow-up were all significantly associated with the increase. The trend of FVC was non-linear and increased by 2.22% annually between 2010 and 2019. FVC distribution was heterogeneous at intradepartmental and interdepartmental level. Seven high-risk clusters of incomplete coverage were identified.

CONCLUSIONS

Although FVC has increased in Peru, it still remains below the recommended threshold. The increase of FVC was mainly attributed to the change in the effects of the characteristics of the population. There was high heterogeneity across Peruvian regions with the presence of high-risk clusters. Interventions must be redirected to reduce these geographical disparities.

Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries

While there has been great success in increasing the coverage of new childhood vaccines globally, expanding routine immunization to reliably reach all children and communities has proven more challenging in many low- and middle-income countries. Achieving this requires vaccination strategies and interventions that identify and target those unvaccinated, guided by the most current and detailed data regarding their size and spatial distribution. Through the integration and harmonisation of a range of geospatial data sets, including population, vaccination coverage, travel-time, settlement type, and conflict locations. We estimated the numbers of children un- or under-vaccinated for measles and diphtheria-tetanus-pertussis, within remote-rural, urban, and conflict-affected locations. We explored how these numbers vary both nationally and sub-nationally, and assessed what proportions of children these categories captured, for 99 lower- and middle-income countries, for which data was available. We found that substantial heterogeneities exist both between and within countries. Of the total 14,030,486 children unvaccinated for DTP1, over 11% (1,656,757) of un- or under-vaccinated children were in remote-rural areas, more than 28% (2,849,671 and 1,129,915) in urban and peri-urban areas, and up to 60% in other settings, with nearly 40% found to be within 1-hour of the nearest town or city (though outside of urban/peri-urban areas). Of the total number of those unvaccinated, we estimated between 6% and 15% (826,976 to 2,068,785) to be in conflict-affected locations, based on either broad or narrow definitions of conflict. Our estimates provide insights into the inequalities in vaccination coverage, with the distributions of those unvaccinated varying significantly by country, region, and district. We demonstrate the need for further inquiry and characterisation of those unvaccinated, the thresholds used to define these, and for more country-specific and targeted approaches to defining such populations in the strategies and interventions used to reach them.

The COVID-19 pandemic and vaccination abandonment in children: spatial heterogeneity maps

OBJECTIVE

to identify spatial clusters corresponding to abandonment of routine vaccines in children.

METHOD

an ecological study, according to data from the 853 municipalities of a Brazilian state. The records analyzed were those of the multidose pentavalent, pneumococcal 10-valent, inactivated poliomyelitis and oral human rotavirus vaccines of 781,489 children aged less than one year old. The spatial scan statistics was used to identify spatial clusters and assess the relative risk based on the vaccination abandonment indicator.

RESULTS

the spatial scan statistics detected the presence of statistically significant clusters for abandonment regarding the four vaccines in all the years analyzed. However, the highest number of clusters with high relative risk estimates was identified in 2020. The Vale do Aço and West, North and West, and Southwest regions stand out for the pentavalent, poliomyelitis and rotavirus vaccines, respectively.

CONCLUSION

in an attempt to mitigate the devastating impact of the COVID-19 pandemic, the immunization program experienced setbacks. The presence of clusters points to the need to implement integrated strategies that may involve different sectors for an active search for children and prevent outbreaks of vaccine-preventable diseases in the near future.

Zero- or missed-dose children in Nigeria: Contributing factors and interventions to overcome immunization service delivery challenges

•

Comprehensive review of recent literature on zero- or missed-dose children in Nigeria.

•

Risk factors are well-known and widely studied.

•

Literature on interventions was scattered, and focussed on campaigns and polio.

•

Gaps exist in investigating how to deliver sustainable immunization programs.

•

Further work is needed to operationalise findings of this review.

'Zero-dose' refers to a person who does not receive a single dose of any vaccine in the routine national immunization schedule, while ‘missed dose’ refers to a person who does not complete the schedule. These people remain vulnerable to vaccine-preventable diseases, and are often already disadvantaged due to poverty, conflict, and lack of access to basic health services. Globally, more 22.7 million children are estimated to be zero- or missed-dose, of which an estimated 3.1 million (∼14 %) reside in Nigeria. We conducted a scoping review to synthesize recent literature on risk factors and interventions for zero- and missed-dose children in Nigeria. Our search identified 127 papers, including research into risk factors only (n = 66); interventions only (n = 34); both risk factors and interventions (n = 18); and publications that made recommendations only (n = 9). The most frequently reported factors influencing childhood vaccine uptake were maternal factors (n = 77), particularly maternal education (n = 22) and access to ante- and perinatal care (n = 19); heterogeneity between different types of communities – including location, region, wealth, religion, population composition, and other challenges (n = 50); access to vaccination, i.e., proximity of facilities with vaccines and vaccinators (n = 37); and awareness about immunization – including safety, efficacy, importance, and schedules (n = 18). Literature assessing implementation of interventions was more scattered, and heavily skewed towards vaccination campaigns and polio eradication efforts. Major evidence gaps exist in how to deliver effective and sustainable routine childhood immunization. Overall, further work is needed to operationalise the learnings from these studies, e.g. through applying findings to Nigeria’s next review of vaccination plans, and using this summary as a basis for further investigation and specific recommendations on effective interventions.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

Sustainable Development Goals and childhood measles vaccination in Ekiti State, Nigeria: Results from spatial and interrupted time series analyses

Measles remains an important cause of childhood mortality in many resource-limited countries. With Sustainable Development Goals (SDG), there has been increasing emphasis on measles vaccination as a key strategy to remarkably improve child survival. While progress has been made towards measles vaccination coverage due to SDG in some settings, there has been no prior study evaluating its impact in Nigeria. To assess the effectiveness of SDG policy implementation on measles vaccination coverage, we examined the changes in first dose of measles vaccination coverage rates among children aged 9-15 months following the implementation of SDG, and changes in spatial patterns of measles vaccination from 2014 to 2019 in Ekiti State, Southwest Nigeria. Using state and local government area-level District Health Information data from January 2014 to December 2019, we conducted interrupted time series (ITS) and spatiotemporal analyses. The ITS evaluated the immediate and continuous effects of SDG policy implementation on the monthly childhood measles vaccination coverage by comparing longitudinal changes in rates between pre-intervention period (January 2014-December 2015) and during-intervention period (January 2016-December 2019). The low and high coverage clusters across the years were detected with spatial cluster analysis. The average state-level measles vaccination coverage rates from 2014 to 2019 was 70.67%. In 2019, coverage rate was 49%-a 35.53% decline from 76% in 2014 and a state-level gap of 46%. The geographical distribution of measles vaccination varied considerably across the local government areas from 2014 to 2019. There was an initial acceleration of vaccination rates (β^ = 24.07, p-value = 0.012), but a significant decrease in coverage rates after implementation of SDG policy in Ekiti State (β^ = -1.08, p-value < 0.001). No local government area had accelerated monthly coverage rates following SDG-implementation. Evidence suggests immediate acceleration in coverage rates which could not be sustained during SDG-era and calls for a rethink measles immunization coverage strategy in the state and other resource-limited jurisdictions to ensure vaccination scale-up.

Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria

Background

Substantial inequalities exist in childhood vaccination coverage levels. To increase vaccine uptake, factors that predict vaccination coverage in children should be identified and addressed.

Methods

Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, we fitted Bayesian multilevel binomial and multinomial logistic regression models to analyse independent predictors of three vaccination outcomes: receipt of the first dose of Pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines) (PENTA1) (n = 6059) and receipt of the third dose having received the first (PENTA3/1) (n = 3937) in children aged 12–23 months, and receipt of measles vaccine (MV) (n = 11839) among children aged 12–35 months.

Results

Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination. Based on any evidence of vaccination, we found that health card/document ownership, receipt of vitamin A and maternal educational level were significantly associated with each outcome. Although the coverage of each vaccine dose was higher in urban than rural areas, urban residence was not significant in multivariable analyses that included travel time. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to the nearest health facility and problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1 and PENTA3/1 and maternal age related to MV and PENTA3/1; other significant variables were associated with one outcome each. Substantial residual community level variances in different strata were observed in the fitted models for each outcome.

Conclusion

Our analysis has highlighted socio-demographic and health care access factors that affect not only beginning but completing the vaccination series in Nigeria. Other factors not measured by the DHS such as health service quality and community attitudes should also be investigated and addressed to tackle inequities in coverage.

Unmet need for COVID-19 vaccination coverage in Kenya

COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 - 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 - 16.74) - 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 - 36.96) - 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.

Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study

Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12-23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables-remoteness, conflict and urban slum-were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.

Pandemia de COVID-19 y abandono de la vacunación en niños: mapas de heterogeneidad espacial

Resumen Objetivo: identificar grupos espaciales que abandonaron la vacunación de rutina de los niños. Método: estudio ecológico, basado en los datos de 853 municipios de un Estado brasileño. Se analizaron los registros de vacunas multidosis pentavalente, antineumocócica 10-valente y antipoliomielítica inactivada y vacuna oral contra el rotavirus humano de 781.489 niños menores de un año de edad. Se utilizó la estadística scan espacial para identificar agrupaciones espaciales y medir el riesgo relativo del indicador abandono de la vacunación. Resultados: la estadística scan espacial detectó la presencia de grupos estadísticamente significativos para el abandono de las cuatro vacunas en todos los años analizados. Sin embargo, el mayor número de grupos con estimaciones altas de riesgos relativos se identificó en 2020. Se destacan las macrorregiones del Vale do Aço y Oeste; Norte y Oeste; y Sudeste para las vacunas pentavalente, antipoliomielítica y contra el rotavirus, respectivamente. Conclusión: mientras se intentaba disminuir el impacto devastador de la pandemia de COVID-19, retrocedió el programa de inmunización. La presencia de grupos indica que es necesario implementar estrategias integradas que puedan involucrar a diferentes sectores para la búsqueda activa de niños y evitar brotes de enfermedades inmunoprevenibles en el futuro próximo.

Measuring routine childhood vaccination coverage in 204 countries and territories, 1980-2019: a systematic analysis for the Global Burden of Disease Study 2020, Release 1

BACKGROUND

Measuring routine childhood vaccination is crucial to inform global vaccine policies and programme implementation, and to track progress towards targets set by the Global Vaccine Action Plan (GVAP) and Immunization Agenda 2030. Robust estimates of routine vaccine coverage are needed to identify past successes and persistent vulnerabilities. Drawing from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020, Release 1, we did a systematic analysis of global, regional, and national vaccine coverage trends using a statistical framework, by vaccine and over time.

METHODS

For this analysis we collated 55 326 country-specific, cohort-specific, year-specific, vaccine-specific, and dose-specific observations of routine childhood vaccination coverage between 1980 and 2019. Using spatiotemporal Gaussian process regression, we produced location-specific and year-specific estimates of 11 routine childhood vaccine coverage indicators for 204 countries and territories from 1980 to 2019, adjusting for biases in country-reported data and reflecting reported stockouts and supply disruptions. We analysed global and regional trends in coverage and numbers of zero-dose children (defined as those who never received a diphtheria-tetanus-pertussis [DTP] vaccine dose), progress towards GVAP targets, and the relationship between vaccine coverage and sociodemographic development.

FINDINGS

By 2019, global coverage of third-dose DTP (DTP3; 81·6% [95% uncertainty interval 80·4-82·7]) more than doubled from levels estimated in 1980 (39·9% [37·5-42·1]), as did global coverage of the first-dose measles-containing vaccine (MCV1; from 38·5% [35·4-41·3] in 1980 to 83·6% [82·3-84·8] in 2019). Third-dose polio vaccine (Pol3) coverage also increased, from 42·6% (41·4-44·1) in 1980 to 79·8% (78·4-81·1) in 2019, and global coverage of newer vaccines increased rapidly between 2000 and 2019. The global number of zero-dose children fell by nearly 75% between 1980 and 2019, from 56·8 million (52·6-60·9) to 14·5 million (13·4-15·9). However, over the past decade, global vaccine coverage broadly plateaued; 94 countries and territories recorded decreasing DTP3 coverage since 2010. Only 11 countries and territories were estimated to have reached the national GVAP target of at least 90% coverage for all assessed vaccines in 2019.

INTERPRETATION

After achieving large gains in childhood vaccine coverage worldwide, in much of the world this progress was stalled or reversed from 2010 to 2019. These findings underscore the importance of revisiting routine immunisation strategies and programmatic approaches, recentring service delivery around equity and underserved populations. Strengthening vaccine data and monitoring systems is crucial to these pursuits, now and through to 2030, to ensure that all children have access to, and can benefit from, lifesaving vaccines.

FUNDING

Bill & Melinda Gates Foundation.

Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya

Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

Modeling and presentation of vaccination coverage estimates using data from household surveys

It is becoming increasingly popular to produce high-resolution maps of vaccination coverage by fitting Bayesian geostatistical models to data from household surveys. Usually, the surveys adopt a stratified cluster sampling design. We discuss a number of crucial choices with respect to two key aspects of the map production process: the acknowledgement of the survey design in modeling, and the appropriate presentation of estimates and their uncertainties. Specifically, we consider the importance of accounting for urban/rural stratification and cluster-level non-spatial excess variation in survey outcomes, when fitting geostatistical models. We also discuss the trade-off between the geographical scale and precision of model-based estimates, and demonstrate visualization methods for mapping and ranking that emphasize the probabilistic interpretation of results. A novel approach to coverage map presentation is proposed to allow comparison and control of the overall map uncertainty. We use measles vaccination coverage in Nigeria as a motivating example and illustrate the different issues using data from the 2018 Nigeria Demographic and Health Survey.

Implementing WHO guidance on conducting and analysing vaccination coverage cluster surveys: Two examples from Nigeria

In 2015, the World Health Organization substantially revised its guidance for vaccination coverage cluster surveys (revisions were finalized in 2018) and has since developed a set of accompanying resources, including definitions for standardized coverage indicators and software (named the Vaccination Coverage Quality Indicators—VCQI) to calculate them.–The current WHO vaccination coverage survey manual was used to design and conduct two nationally representative vaccination coverage surveys in Nigeria–one to assess routine immunization and one to measure post-measles campaign coverage. The primary analysis for both surveys was conducted using VCQI. In this paper, we describe those surveys and highlight some of the analyses that are facilitated by the new resources. In addition to calculating coverage of each vaccine-dose by age group, VCQI analyses provide insight into several indicators of program quality such as crude coverage versus valid doses, vaccination timeliness, missed opportunities for simultaneous vaccination, and, where relevant, vaccination campaign coverage stratified by several parameters, including the number of previous doses received. The VCQI software furnishes several helpful ways to visualize survey results. We show that routine coverage of all vaccines is far below targets in Nigeria and especially low in northeast and northwest zones, which also have highest rates of dropout and missed opportunities for vaccination. Coverage in the 2017 measles campaign was higher and showed less geospatial variation than routine coverage. Nonetheless, substantial improvement in both routine program performance and campaign implementation will be needed to achieve disease control goals.

District-level estimation of vaccination coverage: Discrete vs continuous spatial models

Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low‐ and middle‐income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model‐based approaches for producing subnational estimates of HDIs using survey data, particularly cluster‐level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district‐level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district‐level data with continuous Gaussian process (GP) models that utilize geolocated cluster‐level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015‐16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between‐cluster variation in the continuous GP models did not have any real effect on the district‐level estimates. Our results provide guidance to practitioners on the reliability of these model‐based approaches for producing estimates of vaccination coverage and other HDIs.

Vaccination strategies for measles control and elimination: time to strengthen local initiatives

BACKGROUND

Through a combination of strong routine immunization (RI), strategic supplemental immunization activities (SIA) and robust surveillance, numerous countries have been able to approach or achieve measles elimination. The fragility of these achievements has been shown, however, by the resurgence of measles since 2016. We describe trends in routine measles vaccine coverage at national and district level, SIA performance and demographic changes in the three regions with the highest measles burden.

FINDINGS

WHO-UNICEF estimates of immunization coverage show that global coverage of the first dose of measles vaccine has stabilized at 85% from 2015 to 19. In 2000, 17 countries in the WHO African and Eastern Mediterranean regions had measles vaccine coverage below 50%, and although all increased coverage by 2019, at a median of 60%, it remained far below levels needed for elimination. Geospatial estimates show many low coverage districts across Africa and much of the Eastern Mediterranean and southeast Asian regions. A large proportion of children unvaccinated for MCV live in conflict-affected areas with remote rural areas and some urban areas also at risk. Countries with low RI coverage use SIAs frequently, yet the ideal timing and target age range for SIAs vary within countries, and the impact of SIAs has often been mitigated by delays or disruptions. SIAs have not been sufficient to achieve or sustain measles elimination in the countries with weakest routine systems. Demographic changes also affect measles transmission, and their variation between and within countries should be incorporated into strategic planning.

CONCLUSIONS

Rebuilding services after the COVID-19 pandemic provides a need and an opportunity to increase community engagement in planning and monitoring services. A broader suite of interventions is needed beyond SIAs. Improved methods for tracking coverage at the individual and community level are needed together with enhanced surveillance. Decision-making needs to be decentralized to develop locally-driven, sustainable strategies for measles control and elimination.

Geospatial estimation of reproductive, maternal, newborn and child health indicators: a systematic review of methodological aspects of studies based on household surveys

BACKGROUND

Geospatial approaches are increasingly used to produce fine spatial scale estimates of reproductive, maternal, newborn and child health (RMNCH) indicators in low- and middle-income countries (LMICs). This study aims to describe important methodological aspects and specificities of geospatial approaches applied to RMNCH coverage and impact outcomes and enable non-specialist readers to critically evaluate and interpret these studies.

METHODS

Two independent searches were carried out using Medline, Web of Science, Scopus, SCIELO and LILACS electronic databases. Studies based on survey data using geospatial approaches on RMNCH in LMICs were considered eligible. Studies whose outcomes were not measures of occurrence were excluded.

RESULTS

We identified 82 studies focused on over 30 different RMNCH outcomes. Bayesian hierarchical models were the predominant modeling approach found in 62 studies. 5 × 5 km estimates were the most common resolution and the main source of information was Demographic and Health Surveys. Model validation was under reported, with the out-of-sample method being reported in only 56% of the studies and 13% of the studies did not present a single validation metric. Uncertainty assessment and reporting lacked standardization, and more than a quarter of the studies failed to report any uncertainty measure.

CONCLUSIONS

The field of geospatial estimation focused on RMNCH outcomes is clearly expanding. However, despite the adoption of a standardized conceptual modeling framework for generating finer spatial scale estimates, methodological aspects such as model validation and uncertainty demand further attention as they are both essential in assisting the reader to evaluate the estimates that are being presented.