Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa

Brownwright, Tenley; Dodson, Zan M.; Panhuis, Willem G. van

doi:10.1186/s12889-017-4961-9

Cited by 43 publications

(37 citation statements)

References 32 publications

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“…However, it is also possible to generate weight matrix using the concept of contiguity when DHS data is used. One study about measles vaccination coverage among children in Africa used queen contiguity for generating a spatial weight matrix [56].…”

Section: Spatial Lag Modelmentioning

confidence: 99%

A method for measuring spatial effects on socioeconomic inequalities using the concentration index

Kim

Haghparast‐Bidgoli

Skordis‐Worrall

et al. 2020

Int J Equity Health

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Background: Although spatial effects contribute to inequalities in health care service utilisation and other health outcomes in low and middle income countries, there have been no attempts to incorporate the impact of neighbourhood effects into equity analyses based on concentration indices. This study aimed to decompose and estimate the contribution of spatial effects on inequalities in uptake of HIV tests in Malawi. Methods: We developed a new method of reflecting spatial effects within the concentration index using a spatial weight matrix. Spatial autocorrelation is presented using a spatial lag model. We use data from the Malawi Demographic Health Survey (n = 24,562) to illustrate the new methodology. Need variables such as 'Any STI last 12 month', 'Genital sore/ulcer', 'Genital discharge' and non need variables such as Education, Literacy, Wealth, Marriage, and education were used in the concentration index. Using our modified concentration index that incorporates spatial effects, we estimate inequalities in uptake of HIV testing amongst both women and men living in Malawi in 2015-2016, controlling for need and non-need variables. Results: For women, inequalities due to need variables were estimated at − 0.001 and − 0.0009 (pro-poor) using the probit and new spatial probit estimators, respectively, whereas inequalities due to non-need variables were estimated at 0.01 and 0.0068 (pro-rich) using the probit and new spatial probit estimators. The results suggest that spatial effects increase estimated inequalities in HIV uptake amongst women. Horizontal inequity was almost identical (0.0103 vs 0.0102) after applying the spatial lag model. For men, inequalities due to need variables were estimated at − 0.0002 using both the probit and new spatial probit estimators; however, inequalities due to non-need variables were estimated at − 0.006 and − 0.0074 for the probit and new spatial probit models. Horizontal inequity was the same for both models (− 0.0057). Conclusion: Our findings suggest that men from lower socioeconomic groups are more likely to receive an HIV test after adjustment for spatial effects. This study develops a novel methodological approach that incorporates estimation of spatial effects into a common approach to equity analysis. We find that a significant component of inequalities in HIV uptake in Malawi driven by non-need factors can be explained by spatial effects. When the spatial model was applied, the inequality due to non need in Lilongwe for men and horizontal inequity in Salima for women changed the sign. This approach can be used to explore inequalities in other contexts and settings to better understand the impact of spatial effects on health service use or other health outcomes, impacting on recommendations for service delivery.

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Section: Spatial Lag Modelmentioning

confidence: 99%

A method for measuring spatial effects on socioeconomic inequalities using the concentration index

Kim

Haghparast‐Bidgoli

Skordis‐Worrall

et al. 2020

Int J Equity Health

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“…Also, the heterogeneities in coverage of different districts and provinces is a key factor in non-achievement of desired periodical targets and eventually of herd immunity at the subnational and national levels. Spatial heterogeneity in vaccination coverage impacts the health status of the residents as it can delay disease elimination, not only in the developing but also in the developed countries where nation-wide vaccination coverage rates are usually higher 48,49 .…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal clustering analysis of Expanded Program on Immunization (EPI) vaccination coverage in Pakistan

Umer

Zofeen

et al. 2020

Sci Rep

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Universal vaccination coverage is still far from desired targets in many global regions including Pakistan, despite the success stories and its scientifically proven benefits. EPI Pakistan vaccination coverage data 2012-2016, at district level was collected from Federal EPI Pakistan. District-wise population data were collected from Pakistan Bureau of Statistics. Descriptive statistics and sequence plots were performed in SPSS 13.0. Purely spatial scanning analysis was done in SaTScan 9.4.4 using discrete Poisson model for detection of low vaccination coverage clusters. Geographical information system (GIS) was used to display spatial patterns and clusters of low vaccination coverage districts in

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“…In this study, the percentages of women aged 15–49 years with an educational level more than secondary was found to be inversely associated with measles risk. This could be explained by the fact that educated mothers have positive attitude towards health-seeking behaviour [ 23 , 24 ]. It is established that the attitude towards health seeking is one among other reasons of missing measles vaccination and underreporting of measles cases [ 11 , 12 ].…”

Section: Discussionmentioning

confidence: 99%

Modelling spatio-temporal patterns of disease for spatially misaligned data: An application on measles incidence data in Namibia from 2005-2014

Ntirampeba

Neema²,

Kazembe

2018

PLoS ONE

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BackgroundMaking inferences about measles distribution patterns at small area level is vital for more focal targeted intervention. However, in statistical literature, the analysis of originally collected data on one resolution with the purpose to make inferences on a different level of spatial resolution is referred to as the misalignment problem. In Namibia the measles data were available in aggregated format at regional level for the period 2005 to 2014. This leads to a spatial misalignment problem if the purpose is to make decisions at constituency level. Moreover, although data on risk covariates of measles could be obtained at constituency level, they were not available each year between 2005 and 2014. Thus, assuming that covariates were constant through the study period would induce measurement errors which might have effects on the analysis results. This paper presents a spatio-temporal model through a multi-step approach in order to deal with misalignment and measurement error.MethodsFor the period 2005–2014, measles data from the Ministry of Health and Social Services (MoHSS) were analysed in two steps. First, a multi-step approach was applied to correct spatial misalignment in the data. Second, a classical measurement error model was fitted to account for measurement errors. The time effects were specified using a nonparametric formulation for the linear trend through first order random walk. An interaction between area and time was modelled through type I and type II interaction structures.ResultsThe study showed that there was high variation in measles risk across constituencies and as well as over the study period (2005–2014). Furthermore, the risk of measles was found to be associated with (i) the number of people aged between 0 and 24 years, (ii) the percentages of women aged 15–49 with an educational level more than secondary, (iii) the percentages of children age 12–23 months who received measles vaccine, (iv) the percentages of malnourished children under 5 years, and (vi) the measles cases for each previous year.ConclusionThe study showed some of the determinants of measles risk and revealed areas at high risk through disease mapping. Additionally, the study showed a non-linear temporal distribution of measles risk over the study period. Finally, it was shown that ignoring the measurement errors may yield misleading results. It was recommended that group and geographically targeted intervention, prevention and control strategies can be tailored on the basis these findings.

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Spatial clustering of measles vaccination coverage among children in sub-Saharan Africa

Cited by 43 publications

References 32 publications

A method for measuring spatial effects on socioeconomic inequalities using the concentration index

A method for measuring spatial effects on socioeconomic inequalities using the concentration index

Spatiotemporal clustering analysis of Expanded Program on Immunization (EPI) vaccination coverage in Pakistan

Modelling spatio-temporal patterns of disease for spatially misaligned data: An application on measles incidence data in Namibia from 2005-2014

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