The geography of measles vaccination in the African Great Lakes region

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.

High-resolution mapping of essential maternal and child health service coverage in Nigeria: a machine learning approach

BACKGROUND

National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Nigeria using machine learning techniques.

METHODS

Essential MCH services in this study included antenatal care, facility-based delivery, childhood vaccinations and treatments of childhood illnesses. We estimated generalised additive models (GAMs) and gradient boosting regressions (GB) for each essential MCH service using data from five national representative cross-sectional surveys in Nigeria from 2003 to 2018 and geospatial socioeconomic, environmental and physical characteristics. Using the best-performed model for each service, we map predicted coverage at 1 km2 and 5 km2 spatial resolutions in urban and rural areas, respectively.

RESULTS

GAMs consistently outperformed GB models across a range of essential MCH services, demonstrating low systematic prediction errors. High-resolution maps revealed stark geographic disparities in MCH service coverage, especially between rural and urban areas and among different states and service types. Temporal trends indicated an overall increase in MCH service coverage from 2003 to 2018, although with variations by service type and location. Priority areas with lower coverage of both maternal and vaccination services were identified, mostly located in the northern parts of Nigeria.

CONCLUSION

High-resolution spatial estimates can guide geographic prioritisation and help develop better strategies for implementation plans, allowing limited resources to be targeted to areas with lower coverage of essential MCH services.

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.

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.

Assessing Geographic Overlap between Zero-Dose Diphtheria–Tetanus–Pertussis Vaccination Prevalence and Other Health Indicators

The integration of immunization with other essential health services is among the strategic priorities of the Immunization Agenda 2030 and has the potential to improve the effectiveness, efficiency, and equity of health service delivery. This study aims to evaluate the degree of spatial overlap between the prevalence of children who have never received a dose of the diphtheria–tetanus–pertussis-containing vaccine (no-DTP) and other health-related indicators, to provide insight into the potential for joint geographic targeting of integrated service delivery efforts. Using geospatially modeled estimates of vaccine coverage and comparator indicators, we develop a framework to delineate and compare areas of high overlap across indicators, both within and between countries, and based upon both counts and prevalence. We derive summary metrics of spatial overlap to facilitate comparison between countries and indicators and over time. As an example, we apply this suite of analyses to five countries—Nigeria, Democratic Republic of the Congo (DRC), Indonesia, Ethiopia, and Angola—and five comparator indicators—children with stunting, under-5 mortality, children missing doses of oral rehydration therapy, prevalence of lymphatic filariasis, and insecticide-treated bed net coverage. Our results demonstrate substantial heterogeneity in the geographic overlap both within and between countries. These results provide a framework to assess the potential for joint geographic targeting of interventions, supporting efforts to ensure that all people, regardless of location, can benefit from vaccines and other essential health services.

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.

Factors consistently associated with utilisation of essential maternal and child health services in Nigeria: analysis of the five Nigerian national household surveys (2003-2018)

OBJECTIVE

This study aims to identify the individual and contextual factors consistently associated with utilisation of essential maternal and child health services in Nigeria across time and household geolocation.

DESIGN, SETTING AND PARTICIPANTS

Secondary data from five nationally representative household surveys conducted in Nigeria from 2003 to 2018 were used in this study. The study participants are women and children depending on essential maternal and child health (MCH) services.

OUTCOME MEASURES

The outcome measures were indicators of whether participants used each of the following essential MCH services: antenatal care, facility-based delivery, modern contraceptive use, childhood immunisations (BCG, diphtheria, tetanus, pertussis/Pentavalent and measles) and treatments of childhood illnesses (fever, cough and diarrhoea).

METHODS

We estimated generalised additive models with logit links and smoothing terms for households' geolocation and survey years.

RESULTS

Higher maternal education and households' wealth were significantly associated with utilisation of all types of essential MCH services (p<0.05). On the other hand, households with more children under 5 years of age and in poor communities were significantly less likely to use essential MCH services (p<0.05). Except for childhood immunisations, greater access to transport was positively associated with utilisation (p<0.05). Households with longer travel times to the most accessible health facility were less likely to use all types of essential MCH services (p<0.05), except modern contraceptive use and treatment of childhood fever and/or cough.

CONCLUSION

This study adds to the evidence that maternal education and household wealth status are consistently associated with utilisation of essential MCH services across time and space. To increase utilisation of essential MCH services across different geolocations, interventions targeting poor communities and households with more children under 5 years of age should be appropriately designed. Moreover, additional interventions should prioritise to reduce inequities of essential MCH service utilisation between the wealth quantiles and between education status.

Gaps in measles vaccination coverage in Kasese district, Western Uganda: results of a qualitative evaluation

Background

Despite the availability of a highly effective vaccine, measles remains a substantial public health problem in many countries including Uganda. In this study, conducted between June–August 2020 following a local outbreak, we sought to explore the factors that could affect measles vaccination coverage in rural western Uganda.

Methods

We conducted a descriptive study using qualitative data collection approaches in the Kasese district. The research team utilized purposive sampling to identify and select participants from the public health sector and district government. We conducted key informant interviews (KII) and one focus group discussion (FGD). Responses were recorded using portable electronic devices with the FGD and KII guide installed. Interviews were conducted at the health centre and district headquarters. Data was coded and analysed using ATLAS.ti version 8 software through deductive thematic analysis to identify key themes.

Results

Barriers to measles vaccination identified in this study were premised around six themes including: (i) availability of supplies and stock management, (ii) health worker attitudes and workload, (iii) financing of vaccination outreach activities, (iv) effectiveness of duty rosters (i.e., health workers’ working schedules), (v) community beliefs, and (vi) accessibility of healthcare facilities. Respondents reported frequent vaccine supply disruptions, lack of resources to facilitate transportation of health workers to communities for outreach events, and health centre staffing that did not adequately support supplemental vaccination activities. Furthermore, community dependence on traditional medicine as a substitute for vaccines and long distances traveled by caregivers to reach a health facility were mentioned as barriers to vaccination uptake.

Conclusions

Health system barriers limiting vaccination uptake were primarily logistical in nature and reflect inadequate resourcing of immunization efforts. At the same time, local beliefs favouring traditional medicine remain a persistent cultural barrier. These findings suggest an urgent need for more efficient supply management practices and resourcing of immunization outreaches in order to achieve the Uganda Ministry of Health’s targets for childhood immunization and the prevention of disease outbreaks.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07579-w.

Herd immunity and epidemic size in networks with vaccination homophily

We study how the herd immunity threshold and the expected epidemic size depend on homophily with respect to vaccine adoption. We find that the presence of homophily considerably increases the critical vaccine coverage needed for herd immunity and that strong homophily can push the threshold entirely out of reach. The epidemic size monotonically increases as a function of homophily strength for a perfect vaccine, while it is maximized at a nontrivial level of homophily when the vaccine efficacy is limited. Our results highlight the importance of vaccination homophily in epidemic modeling.

Does mothers' and caregivers' access to information on their child's vaccination card impact the timing of their child's measles vaccination in Uganda?

Introduction

On-time measles vaccination is essential for preventing measles infection among children as early in life as possible, especially in areas where measles outbreaks occur frequently. Characterizing the timing of routine measles vaccination (MCV1) among children and identifying risk factors for delayed measles vaccination is important for addressing barriers to recommended childhood vaccination and increasing on-time MCV1 coverage. We aim to assess the timing of children's MCV1 vaccination and to investigate the association between demographic and healthcare factors, mothers'/caregivers' ability to identify information on their child’s vaccination card, and achieving on-time (vs. delayed) MCV1 vaccination.

Methods

We conducted a population-based, door-to-door survey in Kampala, Uganda, from June–August of 2019. We surveyed mothers/caregivers of children aged one to five years to determine how familiar they were with their child’s vaccination card and to determine their child’s MCV1 vaccination status and timing. We assessed the proportion of children vaccinated for MCV1 on-time and delayed, and we evaluated the association between mothers'/caregivers' ability to identify key pieces of information (child’s birth date, sex, and MCV1 date) on their child’s vaccination card and achieving on-time MCV1 vaccination.

Results

Of the 999 mothers/caregivers enrolled, the median age was 27 years (17–50), and median child age was 29 months (12–72). Information on vaccination status was available for 66.0% (n = 659) of children. Of those who had documentation of MCV1 vaccination (n = 475), less than half (46.5%; n = 221) achieved on-time MCV1 vaccination and 53.5% (n = 254) were delayed. We found that only 47.9% (n = 264) of the 551 mothers/caregivers who were asked to identify key pieces of information on their child's vaccination card were able to identify the information, but ability to identify the key pieces of information on the card was not independently associated with achieving on-time MCV1 vaccination.

Conclusion

Mothers'/caregivers' ability to identify key pieces of information on their child’s vaccination card was not associated with achieving on-time MCV1 vaccination. Further research can shed light on interventions that may prompt or remind mothers/caregivers of the time and age when their child is due for measles vaccine to increase the chance of the child receiving it at the recommended time.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13113-z.

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.

A decision support system for prioritised COVID-19 two-dosage vaccination allocation and distribution

This study proposes a decision support system (DSS) that integrates GIS, analytics, and simulation methods to help develop a priority-based distribution of COVID-19 vaccines in a large urban setting. The methodology applies novel hierarchical heuristic-simulation procedures to create a holistic algorithm for prioritising the process of demand allocation and optimising vaccine distribution. The Melbourne metropolitan area in Australia with a population of over five million is used as a case study. Three vaccine supply scenarios, namely limited, excessive, and disruption, were formulated to operationalise a two-dose vaccination program. Vaccine distribution with hard constraints were simulated and then further validated with sensitivity analyses. The results show that vaccines can be prioritised to society's most vulnerable segments and distributed using the current logistics network with 10 vehicles. Compared with other vaccine distribution plans with no prioritisation, such as equal allocation of vaccines to local government areas based on population size or one on a first-come-first-serve basis, the plans generated by the proposed DSS ensure prioritised vaccination of the most needed and vulnerable population. The aim is to curb the spread of the infection and reduce mortality rate more effectively. They also achieve vaccination of the entire population with less logistical resources required. As such, this study contributes to knowledge and practice in pandemic vaccine distribution and enables governments to make real-time decisions and adjustments in daily distribution plans. In this way any unforeseen disruptions in the vaccine supply chain can be coped with.

Innovations in vaccine delivery: increasing access, coverage, and equity and lessons learnt from measles and rubella elimination

Disease eradication and elimination programs drive innovations based on progress toward measurable objectives, evaluations of new strategies and methods, programmatic experiences, and lessons learned from the field. Following progress toward global measles elimination, reducing measles mortality, and increasing introductions of measles and rubella vaccines to national programs, the measles and rubella immunization program has faced setbacks in recent years. Currently available vaccine delivery methods have complicated logistics and drawbacks that create barriers to vaccination; innovations for easier, more efficient, and safer vaccine delivery are needed. Progress can be accelerated by new technologies like microarray patches (MAPs) that are now widely recognized as a potential new tool for enhancing global immunizations efforts. Clinical trials of measles-rubella vaccine MAPs have begun, and several other vaccine MAPs are in the pre-clinical development pathway. MAPs could significantly contribute to Immunization Agenda 2030 priorities, including reaching zero-dose children; increasing vaccine access, demand, coverage, and equity; and achieving measles and rubella elimination. With strong partnerships between public health agencies and biotechnology companies, translational novel vaccine delivery systems can be developed to help solve public health problems and achieve global health priorities.

Every Child on the Map: A Theory of Change Framework for Improving Childhood Immunization Coverage and Equity Using Geospatial Data and Technologies

The effective use of geospatial data and technologies to collect, manage, analyze, model, and visualize geographic data has great potential to improve data-driven decision-making for immunization programs. This article presents a theory of change for the use of geospatial technologies for immunization programming-a framework to illustrate the ways in which geospatial data and technologies can contribute to improved immunization outcomes and have a positive impact on childhood immunization coverage rates in low- and middle-income countries. The theory of change is the result of a review of the state of the evidence and literature; consultation with implementers, donors, and immunization and geospatial technology experts; and a review of country-level implementation experiences. The framework illustrates how the effective use of geospatial data and technologies can help immunization programs realize improvements in the number of children immunized by producing reliable estimates of target populations, identifying chronically missed settlements and locations with the highest number of zero-dose and under-immunized children, and guiding immunization managers with solutions to optimize resource distribution and location of health services. Through these direct effects on service delivery, geospatial data and technologies can contribute to the strengthening of the overall health system with equity in immunization coverage. Recent implementation of integrated geospatial data and technologies for the immunization program in Myanmar demonstrate the process that countries may experience on the path to achieving lasting systematic improvements. The theory of change presented here may serve as a guide for country program managers, implementers, donors, and other stakeholders to better understand how geospatial tools can support immunization programs and facilitate integrated service planning and equitable delivery through the unifying role of geography and geospatial data.

Lives saved with vaccination for 10 pathogens across 112 countries in a pre-COVID-19 world

Background

Vaccination is one of the most effective public health interventions. We investigate the impact of vaccination activities for Haemophilus influenzae type b, hepatitis B, human papillomavirus, Japanese encephalitis, measles, Neisseria meningitidis serogroup A, rotavirus, rubella, Streptococcus pneumoniae, and yellow fever over the years 2000-2030 across 112 countries.

Methods

Twenty-one mathematical models estimated disease burden using standardised demographic and immunisation data. Impact was attributed to the year of vaccination through vaccine-activity-stratified impact ratios.

Results

We estimate 97 (95%CrI[80, 120]) million deaths would be averted due to vaccination activities over 2000-2030, with 50 (95%CrI[41, 62]) million deaths averted by activities between 2000 and 2019. For children under-5 born between 2000 and 2030, we estimate 52 (95%CrI[41, 69]) million more deaths would occur over their lifetimes without vaccination against these diseases.

Conclusions

This study represents the largest assessment of vaccine impact before COVID-19-related disruptions and provides motivation for sustaining and improving global vaccination coverage in the future.

Funding

VIMC is jointly funded by Gavi, the Vaccine Alliance, and the Bill and Melinda Gates Foundation (BMGF) (BMGF grant number: OPP1157270 / INV-009125). Funding from Gavi is channelled via VIMC to the Consortium's modelling groups (VIMC-funded institutions represented in this paper: Imperial College London, London School of Hygiene and Tropical Medicine, Oxford University Clinical Research Unit, Public Health England, Johns Hopkins University, The Pennsylvania State University, Center for Disease Analysis Foundation, Kaiser Permanente Washington, University of Cambridge, University of Notre Dame, Harvard University, Conservatoire National des Arts et Métiers, Emory University, National University of Singapore). Funding from BMGF was used for salaries of the Consortium secretariat (authors represented here: TBH, MJ, XL, SE-L, JT, KW, NMF, KAMG); and channelled via VIMC for travel and subsistence costs of all Consortium members (all authors). We also acknowledge funding from the UK Medical Research Council and Department for International Development, which supported aspects of VIMC's work (MRC grant number: MR/R015600/1).JHH acknowledges funding from National Science Foundation Graduate Research Fellowship; Richard and Peggy Notebaert Premier Fellowship from the University of Notre Dame. BAL acknowledges funding from NIH/NIGMS (grant number R01 GM124280) and NIH/NIAID (grant number R01 AI112970). The Lives Saved Tool (LiST) receives funding support from the Bill and Melinda Gates Foundation.This paper was compiled by all coauthors, including two coauthors from Gavi. Other funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.

Measles outbreak in Western Uganda: a case-control study

BACKGROUND

Measles outbreaks are prevalent throughout sub-Saharan Africa despite the preventive measures like vaccination that target under five-year-old children and health systems strengthening efforts like prioritizing the supply chain for supplies. Measles immunization coverage for Kasese district and Bugoye HC III in 2018 was 72 and 69%, respectively. This coverage has been very low and always marked red in the Red categorization (below the national target/poor performing) on the national league table indicators. The aim of this study was to assess the scope of the 2018-2019 measles outbreak and the associated risk factors among children aged 0-60 months in Bugoye sub-county, Kasese district, western Uganda.

METHODS

We conducted a retrospective unmatched case-control study among children aged 0-60 months with measles (cases) who had either a clinical presentation or a laboratory confirmation (IgM positivity) presenting at Bugoye Health Centre III (BHC) or in the surrounding communities between December 2018 and October 2019.. Caregivers of the controls (whose children did not have measles) were selected at the time of data collection in July 2020. A modified CDC case investigation form was used in data collection. Quantitative data was collected and analyzed using Microsoft excel and STATA version 13. The children's immunization cards and health registers at BHC were reviewed to ascertain the immunization status of the children before the outbreak.

RESULTS

An extended measles outbreak occurred in Bugoye, Uganda occured between December 2018 and October 2019. All 34 facility-based measles cases were documented to have had maculopapular rash, conjunctivitis, and cough. Also, the majority had fever (97%), coryza (94.1%), lymphadenopathy (76.5%), arthralgias (73.5%) and Koplik Spots (91.2%) as documented in the clinical registers. Similar symptoms were reported among 36 community-based cases. Getting infected even after immunized, low measles vaccination coverage were identified as the principal risk factors for this outbreak.

CONCLUSION

Measles is still a significant problem. This study showed that this outbreak was associated with under-vaccination. Implementing a second routine dose of measles-rubella vaccine would not only increase the number of children with at least one dose but also boost the immunity of those who had the first dose.

[Geographic analyses as a foundation for evidence-based public health interventions: the example identification and typology of risk clusters for mumps, measles, and rubella]

BACKGROUND

Ideally, health services and interventions to improve immunization rates should be tailored to local target populations, such as spatial clusters. However, to date, little attention has been paid to spatial clusters of underimmunization and have instead been typified based on small-scale data.

AIM

Using the example of vaccination against measles, mumps, and rubella (MMR) in children, the present study aims to (1) identify the spatial distribution of insufficient MMR vaccination in Westphalia-Lippe on a small scale, (2) identify specific, spatial risk clusters with insufficient vaccination protection, and (3) describe spatial-neighborhood influencing factors of the different risk clusters as starting points for public health interventions.

MATERIAL AND METHODS

Account data from the Kassenärztliche Vereinigung Westfalen-Lippe (KVWL) were used as a basis. Birth cohorts 2013-2016 of children with statutory health insurance were formed and aggregated at postcode level (n = 410). Statistically significant, spatially compact clusters and relative risks (RRs) of underimmunization were identified. Local risk models were estimated in binary logistic regressions based on spatial-neighborhood variables.

RESULTS AND DISCUSSION

Two significant clusters of underimmunization were identified for each of the vaccination rates "at least one MMR vaccination" and "both MMR vaccinations." Significant risk factors for low immunization rates included age structure, socioeconomic variables, population density, medical coverage, and value attitude. The proposed methodology is suitable for describing spatial variations in vaccination behavior based on identified typologies for targeted evidence-based interventions.

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.

Characterization of immunization secondary analyses using demographic and health surveys (DHS) and multiple indicator cluster surveys (MICS), 2006-2018

BACKGROUND

Infant immunization coverage worldwide has plateaued at about 85%. Using existing survey data to conduct analyses beyond estimating coverage may help immunization programmes better tailor strategies to reach un- and under-immunized children. The Demographic and Health Survey (DHS) and the Multiple Indicators Cluster Survey (MICS), routinely conducted in low and middle-income countries (LMICs), collect immunization data, yet vaccination coverage is often the only indicator reported and used. We conducted a review of published immunization-related analyses to characterize and quantify immunization secondary analyses done using DHS and MICS databases.

METHODS

We conducted a systematic search of the literature, of immunization-related secondary analyses from DHS or MICS published between 2006 and August 2018. We searched 15 electronic databases without language restrictions. For the articles included, relevant information was extracted and analyzed to summarize the characteristics of immunization-related secondary analyses. Results are presented following the PRISMA guidelines.

RESULTS

Among 1411 papers identified, 115 met our eligibility criteria; additionally, one article was supplemented by the Pan American Health Organization. The majority were published since 2012 (77.6%), and most (68.9%) had a first or corresponding author affiliated with institutions in high-income countries (as opposed to LMICs where these surveys are conducted). The median delay between survey implementation and publication of the secondary analysis was 5.4 years, with papers with authors affiliated to institutions in LMIC having a longer median publication delay (p < 0.001). Over 80% of the published analyses looked at factors associated with a specific vaccine or with full immunization. Quality proxies, such as reporting percent of immunization data from cards vs recall; occurrence and handling of missing data; whether survey analyses were weighted; and listing of potential biases or limitations of the original survey or analyses, were infrequently mentioned.

CONCLUSION

Our review suggests that more needs to be done to increase the increase the utilization of existing DHS and MICS datasets and improve the quality of the analyses to inform immunization programmes. This would include increasing the proportion of analyses done in LMICs, reducing the time lag between survey implementation and publication of additional analyses, and including more qualitative information about the survey in the publications to better interpret the results.

The Impact of Rubella Vaccine Introduction on Rubella Infection and Congenital Rubella Syndrome: A Systematic Review of Mathematical Modelling Studies

INTRODUCTION

Rubella vaccines have been used to prevent rubella and congenital rubella syndrome (CRS) in several World Health Organization (WHO) regions. Mathematical modelling studies have simulated introduction of rubella-containing vaccines (RCVs), and their results have been used to inform rubella introduction strategies in several countries. This systematic review aimed to synthesize the evidence from mathematical models regarding the impact of introducing RCVs.

METHODS

We registered the review in the international prospective register of systematic reviews (PROSPERO) with registration number CRD42020192638. Systematic review methods for classical epidemiological studies and reporting guidelines were followed as far as possible. A comprehensive search strategy was used to identify published and unpublished studies with no language restrictions. We included deterministic and stochastic models that simulated RCV introduction into the public sector vaccination schedule, with a time horizon of at least five years. Models focused only on estimating epidemiological parameters were excluded. Outcomes of interest were time to rubella and CRS elimination, trends in incidence of rubella and CRS, number of vaccinated individuals per CRS case averted, and cost-effectiveness of vaccine introduction strategies. The methodological quality of included studies was assessed using a modified risk of bias tool, and a qualitative narrative was provided, given that data synthesis was not feasible.

RESULTS

Seven studies were included from a total of 1393 records retrieved. The methodological quality was scored high for six studies and very high for one study. Quantitative data synthesis was not possible, because only one study reported point estimates and uncertainty intervals for the outcomes. All seven included studies presented trends in rubella incidence, six studies reported trends in CRS incidence, two studies reported the number vaccinated individuals per CRS case averted, and two studies reported an economic evaluation measure. Time to CRS elimination and time to rubella elimination were not reported by any of the included studies. Reported trends in CRS incidence showed elimination within five years of RCV introduction with scenarios involving mass vaccination of older children in addition to routine infant vaccination. CRS incidence was higher with RCV introduction than without RCV when public vaccine coverage was lower than 50% or only private sector vaccination was implemented. Although vaccination of children at a given age achieved slower declines in CRS incidence compared to mass campaigns targeting a wide age range, this approach resulted in the lowest number of vaccinated individuals per CRS case averted.

CONCLUSION AND RECOMMENDATIONS

We were unable to conduct data synthesis of included studies due to discrepancies in outcome reporting. However, qualitative assessment of results of individual studies suggests that vaccination of infants should be combined with vaccination of older children to achieve rapid elimination of CRS. Better outcomes are obtained when rubella vaccination is introduced into public vaccination schedules at coverage figures of 80%, as recommended by WHO, or higher. Guidelines for reporting of outcomes in mathematical modelling studies and the conduct of systematic reviews of mathematical modelling studies are required.

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.

Mapping routine measles vaccination in low- and middle-income countries

The safe, highly effective measles vaccine has been recommended globally since 1974, yet in 2017 there were more than 17 million cases of measles and 83,400 deaths in children under 5 years old, and more than 99% of both occurred in low- and middle-income countries (LMICs)1-4. Globally comparable, annual, local estimates of routine first-dose measles-containing vaccine (MCV1) coverage are critical for understanding geographically precise immunity patterns, progress towards the targets of the Global Vaccine Action Plan (GVAP), and high-risk areas amid disruptions to vaccination programmes caused by coronavirus disease 2019 (COVID-19)5-8. Here we generated annual estimates of routine childhood MCV1 coverage at 5 × 5-km2 pixel and second administrative levels from 2000 to 2019 in 101 LMICs, quantified geographical inequality and assessed vaccination status by geographical remoteness. After widespread MCV1 gains from 2000 to 2010, coverage regressed in more than half of the districts between 2010 and 2019, leaving many LMICs far from the GVAP goal of 80% coverage in all districts by 2019. MCV1 coverage was lower in rural than in urban locations, although a larger proportion of unvaccinated children overall lived in urban locations; strategies to provide essential vaccination services should address both geographical contexts. These results provide a tool for decision-makers to strengthen routine MCV1 immunization programmes and provide equitable disease protection for all children.

Heterogeneity in social and epidemiological factors determines the risk of measles outbreaks

Political and environmental factors-e.g., regional conflicts and global warming-increase large-scale migrations, posing extraordinary societal challenges to policymakers of destination countries. A common concern is that such a massive arrival of people-often from a country with a disrupted healthcare system-can increase the risk of vaccine-preventable disease outbreaks like measles. We analyze human flows of 3.5 million (M) Syrian refugees in Turkey inferred from massive mobile-phone data to verify this concern. We use multilayer modeling of interdependent social and epidemic dynamics to demonstrate that the risk of disease reemergence in Turkey, the main host country, can be dramatically reduced by 75 to 90% when the mixing of Turkish and Syrian populations is high. Our results suggest that maximizing the dispersal of refugees in the recipient population contributes to impede the spread of sustained measles epidemics, rather than favoring it. Targeted vaccination campaigns and policies enhancing social integration of refugees are the most effective strategies to reduce epidemic risks for all citizens.

Clustering of susceptible individuals within households can drive measles outbreaks: an individual-based model exploration

When estimating important measures such as the herd immunity threshold, and the corresponding efforts required to eliminate measles, it is often assumed that susceptible individuals are uniformly distributed throughout populations. However, unvaccinated individuals may be clustered in a variety of ways, including by geographic location, by age, in schools, or in households. Here, we investigate to which extent different levels of within-household clustering of susceptible individuals may impact the risk and persistence of measles outbreaks. To this end, we apply an individual-based model, Stride, to a population of 600,000 individuals, using data from Flanders, Belgium. We construct a metric to estimate the level of within-household susceptibility clustering in the population. Furthermore, we compare realistic scenarios regarding the distribution of susceptible individuals within households in terms of their impact on epidemiological measures for outbreak risk and persistence. We find that higher levels of within-household clustering of susceptible individuals increase the risk, size and persistence of measles outbreaks. Ignoring within-household clustering thus leads to underestimations of required measles elimination and outbreak mitigation efforts.

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.

Socio-demographic correlates of first dose of measles (MCV1) vaccination coverage in India

Background

Between 2010 and 2018, measles-related mortality had halved in India mainly with effective measles vaccination campaigns and widespread coverage across the states and population subgroups. Despite the commendable vaccination coverage, 2.9 million children in India missed the first dose of measles vaccine (MCV1) in 2017, and many of those vaccinated were not vaccinated at the recommended age (i.e. between 9 and 12 months). This study analyzed pattern and correlates of MCV1 coverage and MCV1 administration at recommended age among children aged 12–23 months in India.

Methods

We used the official data from the recent round of National Family Health Survey (NFHS-4), a nationally representative cross-sectional household survey in India conducted in 2015–16. Descriptive statistics and logistic regression analysis were applied to ascertain the influence of specified socio-demographic variables affecting measles vaccination coverage in India.

Results

The study revealed the distinct variations in coverage of MCV1 between the districts of India. There were also major challenges with age recommended vaccination, with about 15% of eligible children not vaccinated within the recommended age range, attributable to several socio-demographic factors. Significantly, antenatal care utilization of mothers strongly influenced MCV1 coverage and age recommended MCV1 coverage in India. The study also identified that children who missed MCV1 had one or more adverse health risks such as malnutrition, anemia and diarrhea disease.

Conclusions

A socio-economic gradient exists in India’s MCV1 coverage, mediated by antenatal visits, education of mothers, and highlighted socio-demographic factors. Infection with measles was significantly correlated with greater anthropometric deficits among the study cohort, indicating a wider range of benefits from preventing measles infection. Eliminating morbidity and mortality from measles in India is feasible, although it will require efficient expanded program on immunization management, enhanced health literacy among mothers, continuing commitment from central state and district political authorities.

Using Serology to Anticipate Measles Post-honeymoon Period Outbreaks

Measles vaccination is a public health 'best buy', with the highest cost of illness averted of any vaccine-preventable disease (Ozawa et al., Bull. WHO 2017;95:629). In recent decades, substantial reductions have been made in the number of measles cases, with an estimated 20 million deaths averted from 2000 to 2017 (Dabbagh et al., MMWR 2018;67:1323). Yet, an important feature of epidemic dynamics is that large outbreaks can occur following years of apparently successful control (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Such 'post-honeymoon period' outbreaks are a result of the nonlinear dynamics of epidemics (Mclean et al., Epidemiol. Infect. 1988;100:419-442). Anticipating post-honeymoon outbreaks could lead to substantial gains in public health, helping to guide the timing, age-range, and location of catch-up vaccination campaigns (Grais et al., J. Roy. Soc. Interface 2008003B6:67-74). Theoretical conditions for such outbreaks are well understood for measles, yet the information required to make these calculations policy-relevant is largely lacking. We propose that a major extension of serological studies to directly characterize measles susceptibility is a high priority.

Mapping the Geographic Distribution of Tungiasis in Sub-Saharan Africa

The geographic distribution of tungiasis is poorly understood, despite the frequent occurrence of the disease in marginalized populations of low socioeconomic status. To date, little work is available to define the geography of this neglected tropical disease (NTD). This exploratory study incorporated geostatistical modeling to map the suitability for tungiasis transmission in sub-Saharan Africa (SSA). In SSA, environmental suitability is predicted in 44 countries, including Angola, Nigeria, Ghana, Cameroon, Cote de Ivoire, Mali, Ethiopia, the Democratic Republic of the Congo, Kenya, Gabon, Uganda, Rwanda, Tanzania, Zambia, Zimbabwe, Madagascar, and South Africa. In total, an estimated 668 million people live in suitable areas, 46% (304 million) of which reside in East Africa. These evidence-based maps provide vital evidence of the potential geographic extent of SSA. They will help to guide disease control programs, inform policymakers, and raise awareness at the global level. Likewise, these results will hopefully provide decisionmakers with the pertinent information necessary to lessen morbidity and mortality in communities located in environmentally suitable areas.

Finding hotspots: development of an adaptive spatial sampling approach

The identification of disease hotspots is an increasingly important public health problem. While geospatial modeling offers an opportunity to predict the locations of hotspots using suitable environmental and climatological data, little attention has been paid to optimizing the design of surveys used to inform such models. Here we introduce an adaptive sampling scheme optimized to identify hotspot locations where prevalence exceeds a relevant threshold. Our approach incorporates ideas from Bayesian optimization theory to adaptively select sample batches. We present an experimental simulation study based on survey data of schistosomiasis and lymphatic filariasis across four countries. Results across all scenarios explored show that adaptive sampling produces superior results and suggest that similar performance to random sampling can be achieved with a fraction of the sample size.

A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa

Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels.

Inequality in measles vaccination coverage in the "big six" countries of the WHO South-East Asia region

The "big six" countries (Bangladesh, India, Indonesia, Myanmar, Nepal, and Thailand) in the World Health Organization South-East Asia Region (WHO SEAR) are currently facing severe challenges in measles elimination and consequent childhood mortality reduction, with inadequacies and inequalities in the coverage of the measles-containing-vaccine first-dose (MCV1) being major obstacles. However, these issues of inequality in MCV1 coverage have not yet been systematically examined. We used data from the latest Demographic and Health Surveys and Multiple Indicator Cluster Surveys. To provide a comprehensive picture of existing MCV1 coverage gaps, data were disaggregated by geographic location, as well as by socioeconomic and nutritional dimensions. National MCV1 coverage ranged from 77% in Myanmar to 92% in Thailand. Only nine of the 104 sub-national districts had achieved the 95% MCV1 coverage goal as set by the WHO. Geographic inequalities were more pronounced in countries with lower coverage levels. Areas in clusters with poor MCV1 coverage performances as well as disadvantaged socioeconomic profiles require increased attention. Inequalities were evident in all countries, except Thailand, and were more pronounced in the sectors of wealth, education, antenatal care (ANC) status, and vitamin A supplementation (VAS) when compared against the areas of gender and urban/rural residence. Wealth-related inequality in Bangladesh, education-related inequality in Indonesia, ANC-related inequalities in Myanmar and Nepal, and VAS-related inequalities in Indonesia and Myanmar were all noteworthy. Equity-oriented changes in policies focusing on health promotion and integrated interventions among disadvantaged populations need to be implemented in order to increase MCV1 coverage and reduce childhood mortality.

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

Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017-18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9-59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016-17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign's reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.

Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications

After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Accelerating measles and rubella elimination through research and innovation - Findings from the Measles & Rubella Initiative research prioritization process, 2016

The Measles & Rubella Initiative (M&RI) identified five key strategies to achieve measles and rubella elimination, including research and innovation to support cost-effective operations and improve vaccination and diagnostic tools. In 2016, the M&RI Research and Innovation Working Group (R&IWG) completed a research prioritization process to identify key research questions and update the global research agenda. The R&IWG reviewed meeting reports and strategic planning documents and solicited programmatic inputs from vaccination experts at the program operational level through a web survey, to identify previous research priorities and new research questions. The R&IWG then convened a meeting of experts to prioritize the identified research questions in four strategic areas: (1) epidemiology and economics, (2) surveillance and laboratory, (3) immunization strategies, and (4) demand creation and communications. The experts identified 19 priority research questions in the four strategic areas to address key areas of work necessary to further progress toward elimination. Future commitments from partners will be needed to develop a platform for improved coordination with adequate and predictable resources for research implementation and innovation to address these identified priorities.

Phylogeography of rubella virus in Asia: Vaccination and demography shape synchronous outbreaks

Rubella virus causes mild disease in children but for women in the early stages of pregnancy, it can cause spontaneous abortion, congenital rubella syndrome (CRS) and associated birth defects. Despite the availability of an effective vaccine, rubella virus continues to circulate endemically in several regions of the world. This is particularly true in East and Southeast (E/SE) Asia, where control efforts vary widely among countries that are well connected through travel and immigration. It is therefore important to understand how the regional persistence of rubella is affected both by dynamics occurring across countries and susceptibility within countries. Here, we use genetic and epidemiological data from countries in E/SE Asia to explore the phylogeography of rubella virus in this region. Our results underline that metapopulation dynamics are key for rubella persistence and highlight the source-sink population structure of the region. We identify countries that contribute to the regional metapopulation network and link epidemic dynamics to susceptibility profiles within each country. Our results indicate that human movement plays an important role in driving epidemic dynamics in E/SE Asia.

Estimating under-five mortality in space and time in a developing world context

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

Geographical variation and associated factors of childhood measles vaccination in Ethiopia: a spatial and multilevel analysis

BACKGROUND

In Ethiopia, despite considerable improvement of measles vaccination, measles outbreaks is occurring in most parts of the country. Understanding the neighborhood variation in childhood measles vaccination is crucial for evidence-based decision-making. However, the spatial pattern of measles-containing vaccine (MCV1) and its predictors are poorly understood. Hence, this study aimed to explore the spatial pattern and associated factors of childhood MCV1 coverage.

METHODS

An in-depth analysis of the 2016 Ethiopia demographic and health survey data was conducted, and a total of 3722 children nested in 611 enumeration areas were included in the analysis. Global Moran's I statistic and Poisson-based purely spatial scan statistics were employed to explore spatial patterns and detect spatial clusters of childhood MCV1, respectively. Multilevel logistic regression models were fitted to identify factors associated with childhood MCV1.

RESULTS

Spatial hetrogeniety of childhood MCV1 was observed (Global Moran's I = 0.13, p-value < 0.0001), and seven significant SaTScan clusters of areas with low MCV1 coverage were detected. The most likely primary SaTScan cluster was detected in the Afar Region, secondary cluster in Somali Region, and tertiary cluster in Gambella Region. In the final model of the multilevel analysis, individual and community level factors accounted for 82% of the variance in the odds of MCV1 vaccination. Child age (AOR = 1.53; 95%CI: 1.25-1.88), pentavalent vaccination first dose (AOR = 9.09; 95%CI: 6.86-12.03) and third dose (AOR = 7.12; 95%CI: 5.51-9.18, secondary and above maternal education (AOR = 1.62; 95%CI: 1.03-2.55) and media exposure were the factors that increased the odds of MCV1 vaccination at the individual level. Children with older maternal age had lower odds of receiving MCV1. Living in Afar, Oromia, Somali, Gambella and Harari regions were factors associated with lower odds of MCV1 from the community-level factors. Children far from health facilities had higher odds of receiving MCV1 (AOR = 1.31, 95%CI = 1.12-1.61).

CONCLUSION

A clustered pattern of areas with low childhood MCV1 coverage was observed in Ethiopia. Both individual and community level factors were significant predictors of childhood MCV1. Hence, it is good to give priority for the areas with low childhood MCV1 coverage, and to consider the identified factors for vaccination interventions.

Estimation and prediction for a mechanistic model of measles transmission using particle filtering and maximum likelihood estimation

Disease incidence reported directly within health systems frequently reflects a partial observation relative to the true incidence in the population. State‐space models present a general framework for inferring both the dynamics of infectious disease processes and the unobserved burden of disease in the population. Here, we present a state‐space model of measles transmission and vaccine‐based interventions at the country‐level and a particle filter‐based estimation procedure. Our dynamic transmission model builds on previous work by incorporating population age‐structure to allow explicit representation of age‐targeted vaccine interventions. We illustrate the performance of estimators of model parameters and predictions of unobserved states on simulated data from two dynamic models: one on the annual time‐scale of observations and one on the biweekly time‐scale of the epidemiological dynamics. We show that our model results in approximately unbiased estimates of unobserved burden and the underreporting rate. We further illustrate the performance of the fitted model for prediction of future disease burden in the next one to 15 years.

Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies

The success of vaccination programs depends largely on the mechanisms used in vaccine delivery. National immunization programs offer childhood vaccines through fixed and outreach services within the health system and often, additional supplementary immunization activities (SIAs) are undertaken to fill gaps and boost coverage. Here, we map predicted coverage at 1 × 1 km spatial resolution in five low- and middle-income countries to identify areas that are under-vaccinated via each delivery method using Demographic and Health Surveys data. We compare estimates of the coverage of the third dose of diphtheria-tetanus-pertussis-containing vaccine (DTP3), which is typically delivered through routine immunization (RI), with those of measles-containing vaccine (MCV) for which SIAs are also undertaken. We find that SIAs have boosted MCV coverage in some places, but not in others, particularly where RI had been deficient, as depicted by DTP coverage. The modelling approaches outlined here can help to guide geographical prioritization and strategy design.

Integrated Serologic Surveillance of Population Immunity and Disease Transmission

Antibodies are unique among biomarkers in their ability to identify persons with protective immunity to vaccine-preventable diseases and to measure past exposure to diverse pathogens. Most infectious disease surveillance maintains a single-disease focus, but broader testing of existing serologic surveys with multiplex antibody assays would create new opportunities for integrated surveillance. In this perspective, we highlight multiple areas for potential synergy where integrated surveillance could add more value to public health efforts than the current trend of independent disease monitoring through vertical programs. We describe innovations in laboratory and data science that should accelerate integration and identify remaining challenges with respect to specimen collection, testing, and analysis. Throughout, we illustrate how information generated through integrated surveillance platforms can create new opportunities to more quickly and precisely identify global health program gaps that range from undervaccination to emerging pathogens to multilayered health disparities that span diverse communicable diseases.

Characterizing the impact of spatial clustering of susceptibility for measles elimination

Measles elimination efforts are primarily focused on achieving and maintaining national vaccination coverage goals, based on estimates of the critical vaccination threshold (Vc): the proportion of the population that must be immune to prevent sustained epidemics. Traditionally, Vc estimates assume evenly mixing populations, an invalid assumption. If susceptible individuals preferentially contact one another, communities may remain vulnerable to epidemics even when vaccination coverage targets are met at the national level. Here we present a simple method to estimate Vc and the effective reproductive number, R, while accounting for spatial clustering of susceptibility. For measles, assuming R0 = 15 and 95% population immunity, adjustment for high clustering of susceptibility increases R from 0.75 to 1.29, Vc from 93% to 96%, and outbreak probability after a single introduction from <1% to 23%. The impact of clustering remains minimal until vaccination coverage nears elimination levels. We illustrate our approach using Demographic and Health Survey data from Tanzania and show how non-vaccination clustering potentially contributed to continued endemic transmission of measles virus during the last two decades. Our approach demonstrates why high national vaccination coverage sometimes fails to achieve measles elimination, and that a shift from national to subnational focus is needed as countries approach elimination.

Innovation to impact in spatial epidemiology

Spatial epidemiology is a rapidly advancing field, pushing our abilities to measure, monitor and map pathogens at increasingly finer spatiotemporal scales. However, these scales often do not align with the abilities of control programmes to act at them, building a disconnect between academia and implementation. Efforts are being made to feed innovations into government, build spatial data skills, and strengthen links between disease control programmes and universities, yet work remains to be done if goals for disease control, elimination and 'leaving no one behind' are to be met.

The hidden burden of measles in Ethiopia: how distance to hospital shapes the disease mortality rate

BACKGROUND

A sequence of annual measles epidemics has been observed from January 2013 to April 2017 in the South West Shoa Zone of the Oromia Region, Ethiopia. We aimed at estimating the burden of disease in the affected area, taking into account inequalities in access to health care due to travel distances from the nearest hospital.

METHODS

We developed a dynamic transmission model calibrated on the time series of hospitalized measles cases. The model provided estimates of disease transmissibility and incidence at a population level. Model estimates were combined with a spatial analysis to quantify the hidden burden of disease and to identify spatial heterogeneities characterizing the effectiveness of the public health system in detecting severe measles infections and preventing deaths.

RESULTS

A total of 1819 case patients and 36 deaths were recorded at the hospital. The mean age was 6.0 years (range, 0-65). The estimated reproduction number was 16.5 (95% credible interval (CI) 14.5-18.3) with a cumulative disease incidence of 2.34% (95% CI 2.06-2.66). Three thousand eight hundred twenty-one (95% CI 1969-5671) severe cases, including 2337 (95% CI 716-4009) measles-related deaths, were estimated in the Woliso hospital's catchment area (521,771 inhabitants). The case fatality rate was found to remarkably increase with travel distance from the nearest hospital: ranging from 0.6% to more than 19% at 20 km. Accordingly, hospital treatment prevented 1049 (95% CI 757-1342) deaths in the area.

CONCLUSIONS

Spatial heterogeneity in the access to health care can dramatically affect the burden of measles disease in low-income settings. In sub-Saharan Africa, passive surveillance based on hospital admitted cases might miss up to 60% of severe cases and 98% of related deaths.

A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km² in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Use of the revised World Health Organization cluster survey methodology to classify measles-rubella vaccination campaign coverage in 47 counties in Kenya, 2016

INTRODUCTION

To achieve measles elimination, two doses of measles-containing vaccine (MCV) are provided through routine immunization services or vaccination campaigns. In May 2016, Kenya conducted a measles-rubella (MR) vaccination campaign targeting 19 million children aged 9 months-14 years, with a goal of achieving ≥95% coverage. We conducted a post-campaign cluster survey to estimate national coverage and classify coverage in Kenya's 47 counties.

METHODS

The stratified multi-stage cluster survey included data from 20,011 children in 8,253 households sampled using the recently revised World Health Organization coverage survey methodology (2015). Point estimates and 95% confidence intervals (95% CI) of national campaign coverage were calculated, accounting for study design. County vaccination coverage was classified as 'pass,' 'fail,' or 'intermediate,' using one-sided hypothesis tests against a 95% threshold.

RESULTS

Estimated national MR campaign coverage was 95% (95% CI: 94%-96%). Coverage differed significantly (p < 0.05) by child's school attendance, mother's education, household wealth, and other factors. In classifying coverage, 20 counties passed (≥95%), two failed (<95%), and 25 were intermediate (unable to classify either way). Reported campaign awareness among caretakers was 92%. After the 2016 MR campaign, an estimated 93% (95% CI: 92%-94%) of children aged 9 months to 14 years had received ≥2 MCV doses; 6% (95% CI: 6%-7%) had 1 MCV dose; and 0.7% (95% CI: 0.6%-0.9%) remained unvaccinated.

CONCLUSIONS

Kenya reached the MR campaign target of 95% vaccination coverage, representing a substantial achievement towards increasing population immunity. High campaign awareness reflected the comprehensive social mobilization strategy implemented in Kenya and supports the importance of including strong communications platforms in future vaccination campaigns. In counties with sub-optimal MR campaign coverage, further efforts are needed to increase MCV coverage to achieve the national goal of measles elimination by 2020.

Benefits and Challenges in Using Seroprevalence Data to Inform Models for Measles and Rubella Elimination

Background

Control efforts for measles and rubella are intensifying globally. It becomes increasingly important to identify and reach remaining susceptible populations as elimination is approached.

Methods

Serological surveys for measles and rubella can potentially measure susceptibility directly, but their use remains rare. In this study, using simulations, we outline key subtleties in interpretation associated with the dynamic context of age-specific immunity, highlighting how the patterns of immunity predicted from disease surveillance and vaccination coverage data may be misleading.

Results

High-quality representative serosurveys could provide a more accurate assessment of immunity if challenges of conducting, analyzing, and interpreting them are overcome. We frame the core disease control and elimination questions that could be addressed by improved serological tools, discussing challenges and suggesting approaches to increase the feasibility and sustainability of the tool.

Conclusions

Accounting for the dynamical context, serosurveys could play a key role in efforts to achieve and sustain elimination.

Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control

Although measles incidence has reached historic lows in many parts of the world, the disease still causes substantial morbidity globally. Even where control programs have succeeded in driving measles locally extinct, unless vaccination coverage is maintained at extremely high levels, susceptible numbers may increase sufficiently to spark large outbreaks. Human mobility will drive potentially infectious contacts and interact with the landscape of susceptibility to determine the pattern of measles outbreaks. These interactions have proved difficult to characterise empirically. We explore the degree to which new sources of data combined with existing public health data can be used to evaluate the landscape of immunity and the role of spatial movement for measles introductions by retrospectively evaluating our ability to predict measles outbreaks in vaccinated populations. Using inferred spatial patterns of accumulation of susceptible individuals and travel data, we predicted the timing of epidemics in each district of Pakistan during a large measles outbreak in 2012–2013 with over 30 000 reported cases. We combined these data with mobility data extracted from over 40 million mobile phone subscribers during the same time frame in the country to quantify the role of connectivity in the spread of measles. We investigate how different approaches could contribute to targeting vaccination efforts to reach districts before outbreaks started. While some prediction was possible, accuracy was low and we discuss key uncertainties linked to existing data streams that impede such inference and detail what data might be necessary to robustly infer timing of epidemics.