Geographically weighted regression analysis of incomplete basic childhood vaccination in Sub-Saharan Africa: Evidence from DHS, 2019–2024

Financial barriers can affect timely access to maternal health services. Health insurance can influence the use and quality of these services and potentially improve maternal and neonatal health outcomes. We conducted a systematic review of the evidence on health insurance and its effects on the use and provision of maternal health services and on maternal and neonatal health outcomes in middle- and low-income countries. Studies were identified through a literature search in key databases and consultation with experts in healthcare financing and maternal health. Twenty-nine articles met the review criteria of focusing on health insurance and its effect on the use or quality of maternal health services, or maternal and neonatal health outcomes. Sixteen studies assessed demand-side effects of insurance, eight focused on supply-side effects, and the remainder addressed both. Geographically, the studies provided evidence from sub-Saharan Africa (n = 11), Asia (n = 9), Latin America (n = 8), and Turkey. The studies included examples from national or social insurance schemes (n = 7), government-run public health insurance schemes (n = 4), community-based health insurance schemes (n = 11), and private insurance (n = 3). Half of the studies used econometric analyses while the remaining provided descriptive statistics or qualitative results. There is relatively consistent evidence that health insurance is positively correlated with the use of maternal health services. Only four studies used methods that can establish this causal relationship. Six studies presented suggestive evidence of over-provision of caesarean sections in response to providers' payment incentives through health insurance. Few studies focused on the relationship between health insurance and the quality of maternal health services or maternal and neonatal health outcomes. The available evidence on the quality and health outcomes is inconclusive, given the differences in measurement, contradictory findings, and statistical limitations. Consistent with economic theories, the studies identified a positive relationship between health insurance and the use of maternal health services. However, more rigorous causal methods are needed to identify the extent to which the use of these services increases among the insured. Better measurement of quality and the use of cross-country analyses would solidify the evidence on the impact of insurance on the quality of maternal health services and maternal and neonatal health outcomes.

The Normalized Difference Vegetation Index (NDVI) and rainfall data were used to model the spatial relationship between vegetation and rainfall. Their correlation in previous studies was typically based on a global regression model, which assumed that the correlation was constant across space. The NDVI–rainfall association, on the other hand, is spatially non-stationary, non-linear, scale-dependent, and influenced by local factors (e.g., soil background). In this study, two statistical methods are used in the modeling, i.e., traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR), to evaluate the NDVI–rainfall relationship. The GWR was implemented annually in the growing seasons of 2000 and 2016, using climate data (Normalized Vegetation Difference Index and rainfall). The NDVI–rainfall relationship in the studied Bisha watershed (an eco-sensitive zone with a complex landscape) was found to have a stable operating scale of around 12 km. The findings support the hypothesis that the OLS model’s average impression could not accurately represent local conditions. By addressing spatial non-stationarity, the GWR approach greatly improves the model’s accuracy and predictive ability. In analyzing the relationship between NDVI patterns and rainfall, our research has shown that GWR outperforms a global OLS model. This superiority stems primarily from the consideration of the relationship’s spatial variance across the study area. Global regression techniques such as OLS can overlook local details, implying that a large portion of the variance in NDVI is unexplained. It appears that rainfall is the most significant factor in deciding the distribution of vegetation in these regions. Furthermore, rainfall had weak relationships with areas predominantly located around wetlands, suggesting the need for additional factors to describe NDVI variations. The GWR method performed better in terms of accuracy, predictive power, and reduced residual autocorrelation. Thus, GWR is recommended as an explanatory and exploratory technique when relations between variables are subject to spatial variability. Since the GWR is a local form of spatial analysis that aligned to local conditions, it has the potential for more accurate prediction; however, a larger amount of data is needed to allow a reliable local fitting.

Soil erosion is one of the most common types of land degradation. To provide useful information for proper management, quantitative soil erosion evaluation and identifying influential factors are needed. However, rare studies have been reported on spatial modeling of soil erosion in connection with affective factors to prioritize the locality and the type of erosion control measures. Hence, the aim of this study was to (1) assess erosion-prone areas in the Talar watershed, Iran, using the revised universal soil loss equation (RUSLE) model and (2) investigate the relationship between soil erosion variability and land-use changes. Toward that, the ordinary least squares (OLS), geographically weighted regression (GWR) models, and principal component analysis (PCA) were used to analyze spatial relationships between soil erosion, land-use, and the RUSLE factors. The results of the OLS and GWR models indicated that these relationships are spatially non-stationary. GWR models had a good predictive performance than OLS with lower Akaike’s Information Criterion (from 254.31 to 276.81 in OLS and from 247.87 to 269.42 in GWR) and higher adjusted R2 values (from 0.12 to 0.54 in OLS, and from 0.36 to 0.66 in GWR). Among the variables mentioned above, LS factor, P factor, forest, and irrigated land were the most influential variables in GWR models. The results of PCA showed that PC1 and PC2 explained 66.2% of the variation in soil erosion concerning land-use and the RUSLE factors. These results provided appropriate references for managers and experts properly planning the study watershed.Graphical abstract

Newtown Business of Seoul had been performed to reduce deterioration of Gangbuk and economic inequality between Gangnam and Gangbuk. According to this, Eunpyeong-gu was set as test-bed for Newtown business and Newtown business had been completed until 2013. This study aims to analyze the influence of social and economical factors which affect land price using GWR (Geographically Weighted Regression) considered spatial effect. As a result of analysis, GWR model demonstrated a better goodness-of-fit than OLS (Ordinary least square) model typically used in most study. Furthermore, AIC value and Moran’s I of residual prove that GWR model is more suitable than OLS model. GWR model enable to explain more detailed than global regression model as coefficient and sign show different value locally. In future, this research will be helpful to develop Eunpyeong-gu considering spatial characters and strength effectiveness of development.Keywords : Eunpyeong Newtown, Newtown Projects, Land Value, Geographically Weighted Regression

BackgroundAngiostrongyliasis is a food-borne parasitic zoonosis. Human infection is caused by infection with the third-stage larvae of Angiostrongylus cantonensis. The life cycle of A. cantonensis involves rodents as definitive hosts and molluscs as intermediate hosts. This study aims to investigate on the infection status and characteristics of spatial distribution of these hosts, which are key components in the strategy for the prevention and control of angiostrongyliasis.MethodsThree villages from Nanao Island, Guangdong Province, China, were chosen as study area by stratified random sampling. The density and natural infection of Pomacea canaliculata and various rat species were surveyed every three months from December 2015 to September 2016, with spatial correlations of the positive P. canaliculata and the infection rates analysed by ArcGIS, scan statistics, ordinary least squares (OLS) and geographically weighted regression (GWR) models.ResultsA total of 2192 P. canaliculata specimens were collected from the field, of which 1190 were randomly chosen to be examined for third-stage larvae of A. cantonensis. Seventy-two Angiostrongylus-infected snails were found, which represents a larval infection rate of 6.1% (72/1190). In total, 110 rats including 85 Rattus norvegicus, 10 R. flavipectus, one R. losea and 14 Suncus murinus were captured, and 32 individuals were positive (for adult worms), representing an infection rate of 29.1% of the definitive hosts (32/110). Worms were only found in R. norvegicus and R. flavipectus, representing a prevalence of 36.5% (31/85) and 10% (1/10), respectively in these species, but none in R. losea and S. murinus, despite testing as many as 32 of the latter species. Statistically, spatial correlation and spatial clusters in the spatial distribution of positive P. canaliculata and positive rats existed. Most of the spatial variability of the host infection rates came from spatial autocorrelation. Nine spatial clusters with respect to positive P. canaliculata were identified, but only two correlated to infection rates. The results show that corrected Akaike information criterion, R2, R2 adjusted and σ2 in the GWR model were superior to those in the OLS model.ConclusionsP. canaliculata and rats were widely distributed in Nanao Island and positive infection has also been found in the hosts, demonstrating that there was a risk of angiostrongyliasis in this region of China. The distribution of positive P. canaliculata and rats exhibited spatial correlation, and the GWR model had advantage over the OLS model in the spatial analysis of hosts of A. cantonensis.

With the fast urbanization process, how does the vegetation environment change in one of the most economically developed metropolis, Shanghai in East China? To answer this question, there is a pressing demand to explore the non-stationary relationship between socio-economic conditions and vegetation across Shanghai. In this study, environmental data on vegetation cover, the Normalized Difference Vegetation Index (NDVI) derived from MODIS imagery in 2003 were integrated with socio-economic data to reflect the city’s vegetative conditions at the census block group level. To explore regional variations in the relationship of vegetation and socio-economic conditions, Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models were applied to characterize mean NDVI against three independent socio-economic variables, an urban land use ratio, Gross Domestic Product (GDP) and population density. The study results show that a considerable distinctive spatial variation exists in the relationship for each model. The GWR model has superior effects and higher precision than the OLS model at the census block group scale. So, it is more suitable to account for local effects and geographical variations. This study also indicates that unreasonable excessive urbanization, together with non-sustainable economic development, has a negative influence of vegetation vigor for some neighborhoods in Shanghai.

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987-2000, 2000-2010, and 1987-2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran's I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R (2) and corrected Akaike's Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R (2) values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.

Detailed information about spatial distribution of soil properties is important in ecological modelling, environmental prediction, precision agriculture, and natural resources management, as well as land-use planning. In the present study, a recently developed method called geographically weighted regression (GWR) is applied to predict spatial distribution of soil properties (pH, soil organic matter, available nitrogen, available potassium) based on topographical indicators, climate factors, and geological stratum at a regional scale. In total, 1914 soil samples collected from a depth of 0–20 cm were used to calibrate and validate the models. Performances of the GWR models were compared with the traditional, ordinary least-squares (OLS) regression. The results indicated that the GWR models made significant improvements to model performances over OLS regression, based on F-test, coefficient of determination, and corrected Akaike information criterion. GWR models also improved the reliability of the soil–environment relationships by reducing the spatial autocorrelations in model residuals. Meanwhile, the use of GWR models disclosed that the relationships between soil properties and environmental variables were not invariant over space but exhibited significant spatial non-stationarity. Accordingly, the GWR models remarkably improved the prediction accuracies over the corresponding OLS models. The results demonstrated that GWR could serve as a useful tool for digital soil mapping in areas with complex terrain.

BackgroundLimited information exists on the impact of mobile health (mHealth) use by community health workers (CHWs) on improving the use of maternal health services in sub-Saharan Africa (SSA).ObjectiveThis systematic review addresses 2 objectives: evaluating the impact of mHealth use by CHWs on antenatal care (ANC) use, facility-based births, and postnatal care (PNC) use in SSA; and identifying facilitators and barriers to mHealth use by CHWs in programs designed to increase ANC use, facility-based births, and PNC use in SSA using a sociotechnical system approach.MethodsWe searched for articles in 6 databases (MEDLINE, CINAHL, Web of Science, Embase, Scopus, and Africa Index Medicus) from inception up to September 2022, with additional articles identified from Google Scholar. After article selection, 2 independent reviewers performed title and abstract screening, full-text screening, and data extraction using Covidence software (Veritas Health Innovation Ltd). In addition, we manually screened the references lists of the included articles. Finally, we performed a narrative synthesis of the outcomes.ResultsAmong the 2594 records retrieved, 10 (0.39%) studies (n=22, 0.85% articles) met the inclusion criteria and underwent data extraction. The studies were published between 2012 and 2022 in 6 countries. Of the studies reporting on ANC outcomes, 43% (3/7) reported that mHealth use by CHWs increased ANC use. Similarly, of the studies reporting on facility-based births, 89% (8/9) demonstrated an increase due to mHealth use by CHWs. In addition, in the PNC studies, 75% (3/4) showed increased PNC use associated with mHealth use by CHWs. Many of the studies reported on the importance of addressing factors related to the social environment of mHealth-enabled CHWs, including the perception of CHWs by the community, trust, relationships, digital literacy, training, mentorship and supervision, skills, CHW program ownership, and the provision of incentives. Very few studies reported on how program goals and culture influenced mHealth use by CHWs. Providing free equipment, accessories, and internet connectivity while addressing ongoing challenges with connectivity, power, the ease of using mHealth software, and equipment maintenance support allowed mHealth-enabled CHW programs to thrive.ConclusionsmHealth use by CHWs was associated with an increase in ANC use, facility-based births, and PNC use in SSA. Identifying and addressing social and technical barriers to the use of mHealth is essential to ensure the success of mHealth programs.Trial RegistrationPROSPERO CRD42022346364; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346364

Forest modelers have attempted to account for the spatial autocorrelations among trees in growth and yield models by applying alternative regression techniques such as linear mixed models (LMM), generalized additive models (GAM), and geographically weighted regression (GWR). However, the model errors are commonly assessed using average errors across the entire study area and across tree size classes. Little attention has been paid to the spatial heterogeneity of model performance. In this study, we used local Moran coefficients to investigate the spatial distributions of the model errors from the four regression models. The results indicated that GAM improved model-fitting to the data and provided better predictions for the response variable. However, it is nonspatial in nature and, consequently, generated spatial distributions for the model errors similar to the ones from ordinary least-squares (OLS). Furthermore, OLS and GAM yielded more clusters of similar (either positive or negative) model errors, indicating that trees in some subareas were either all underestimated or all overestimated for the response variable. In contrast, LMM was able to model the spatial covariance structures in the data and obtain more accurate predictions by accounting for the effects of spatial autocorrelations through the empirical best linear unbiased predictors. GWR is a varying-coefficient modeling technique. It estimated the model coefficients locally at each tree in the example plot and resulted in more accurate predictions for the response variable. Moreover, the spatial distributions of the model errors from LMM and GWR were more desirable, with fewer clusters of dissimilar model errors than the ones derived from OLS and GAM. FOR. SCI. 51(4):334–346.

BackgroundHealthcare accessibility, a key public health issue, includes potential (spatial accessibility) and realized access (healthcare utilization) dimensions. Moreover, the assessment of healthcare service potential access and utilization should take into account the care provided by primary and secondary services. Previous studies on the relationship between healthcare spatial accessibility and utilization often used conventional statistical methods without addressing the scale effect and spatial processes. This study investigated the impact of spatial accessibility to primary and secondary healthcare services on length of hospital stay (LOS), and the efficiency of using a geospatial approach to model this relationship.MethodsThis study focused on the ≥ 75-year-old population of the Nord administrative region of France. Inpatient hospital spatial accessibility was computed with the E2SFCA method, and then the LOS was calculated from the French national hospital activity and patient discharge database. Ordinary least squares (OLS), spatial autoregressive (SAR), and geographically weighted regression (GWR) were used to analyse the relationship between LOS and spatial accessibility to inpatient hospital care and to three primary care service types (general practitioners, physiotherapists, and home-visiting nurses). Each model performance was assessed with measures of goodness of fit. Spatial statistical methods to reduce or eliminate spatial autocorrelation in the residuals were also explored.ResultsGWR performed best (highest R2 and lowest Akaike information criterion). Depending on global model (OLS and SAR), LOS was negatively associated with spatial accessibility to general practitioners and physiotherapists. GWR highlighted local patterns of spatial variation in LOS estimates. The distribution of areas in which LOS was positively or negatively associated with spatial accessibility varied when considering accessibility to general practitioners and physiotherapists.ConclusionsOur findings suggest that spatial regressions could be useful for analysing the relationship between healthcare spatial accessibility and utilization. In our case study, hospitalization of elderly people was shorter in areas with better accessibility to general practitioners and physiotherapists. This may be related to the presence of effective community healthcare services. GWR performed better than LOS and SAR. The identification by GWR of how these relationships vary spatially could bring important information for public healthcare policies, hospital decision-making, and healthcare resource allocation.

BackgroundLow use of maternal health services, as well as poor quality care, contribute to the high maternal mortality in sub-Saharan Africa (SSA). In particular, poor person-centered maternity care (PCMC), which captures user experience, contributes both directly to pregnancy outcomes and indirectly through decreased demand for services. While many studies have examined disparities in use of maternal health services, few have examined disparities in quality of care, and none to our knowledge has empirically examined disparities in PCMC in SSA. The aim of this study is to examine factors associated with PCMC, particularly the role of household wealth, personal empowerment, and type of facility.MethodsData are from a survey conducted in western Kenya in 2016, with women aged 15 to 49 years who delivered in the 9 weeks preceding the survey (N = 877). PCMC is operationalized as a summative score based on responses to 30 items in the PCMC scale capturing dignity and respect, communication and autonomy, and supportive care.ResultsWe find that net of other factors; wealthier, employed, literate, and married women report higher PCMC than poorer, unemployed, illiterate, and unmarried women respectively. Also, women who have experienced domestic violence report lower PCMC than those who have never experienced domestic violence. In addition, women who delivered in health centers and private facilities reported higher PCMC than those who delivered in public hospitals. The effect of employment and facility type is conditional on wealth, and is strongest for the poorest women. Poor women who are unemployed and poor women who deliver in higher-level facilities receive the lowest quality PCMC.ConclusionsThe findings imply the most disadvantaged women receive the lowest quality PCMC, especially when they seek care in higher-level facilities. Interventions to reduce disparities in PCMC are essential to improve maternal outcomes among disadvantaged groups.

Increasing urban heat issues induced by climatic changes and growing urban populations exacerbate the need for adaptation. The present study intends to foster the so far quite rarely realized transfer from research to real-world planning. Subject of investigation is the thermal performance and characterization of urban structure types (USTs) in Berlin, Germany. Applying Landsat 8 derived land surface temperatures (LSTs), we first determine differences in the temperature patterns of the regarded USTs. Second, after running correlation analyses with LST and potentially influencing factors (NDVI – normalized difference vegetation index, imperviousness, building ratio, and building height), we fit ordinary least square (OLS) and geographically weighted regression (GWR) models. Finally, we relate the GWR results to the USTs and determine the effect of each variable on the respective LST regime. We find significant differences in the thermal performance of USTs, strong correlations between explaining variables and LST, and a sophisticated picture concerning GWR coefficients at various locations. Quasi-global r2 for the GWR (0.83) improves the OLS model value (0.53) considerably. The spatially explicit GWR method in combination with results aggregated on the planning-relevant UST-level provides crucially important information for climate adaptation and planning while being adaptable and transferable to other urban areas.

Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination (R2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher (R2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.

IntroductionZero-dose vaccine status refers to a child who has not yet received any childhood vaccines. Globally, zero-dose vaccination status is the major public health problem. In sub-Saharan African countries, among five children, one child did not access the vaccines. The efforts to identify the factors contributing to the zero-dose vaccine have not yet been addressed in Ethiopia.ObjectivesTo assess the distribution and determinants of zero dose vaccination status hot spots among children in Ethiopia using Ethiopian demographic health survey 2019.MethodsA secondary analysis of a cross-sectional study was used among a total of 3208 participants. Stata-14, Arc geographic information system-10.3, and Sat Scan software were used to analyze the data. Moran’s I statistic was used to assess the non-random distribution of zero-dose vaccination status. Spatial and geographically weighted regression was used to map the distribution and determinants of zero-dose vaccination status in Ethiopia. The Getis-Ord-statistics and Sat Scan analysis were also used to identify the hot spot area and significant clusters respectively. To identify significant factors, a p-value <0.05 was used.ResultsThe prevalence of zero-dose vaccination status among children aged 12–35 months old was 16.3% (95%CI, 15%–17.6%). The distribution of zero-dose vaccination status in Ethiopia was not randomly distributed across the area (Moran’s I value = 0.4, p<0.001). The hotspot area of zero-dose vaccination status in Ethiopia was located in Afar, Somalia, and the south nations region. The primary cluster was located at 5.479641 N and 42.196835 E within a 405.96 km radius in the Somali region. The highest effect (β, 0.23–0.24) of give birth at home on zero-dose vaccination status was observed in Afar, Somalia, and Tigray, whereas the lowest effect of home delivery (β, 0.21–0.22) was observed in south nations, Gambela, and the western part of Benishangul Gumuz. Additionally, the poor wealth index had the highest effect on zero-dose vaccination status (β, 0.06–0.07) in south nations, Benishangul Gumuz, and the Gambela region, and the lowest effect of the poor wealth index (β, 0.03–0.04) was observed in Somalia and the Afar region.ConclusionThe zero dose child in Ethiopia was low. Geographically, the highest proportion of zero-dose vaccination status among children was in the Somali and Afar regions. Home delivery had the highest and most positive effect on zero-dose vaccination status in Afar, Somalia, and the Tigray region. Additionally, the poor wealth index had the highest and most positive effect in Somalia and the Afar region. Expanding delivery at the health facility and economic empowerment of women are recommended to improve vaccination in Ethiopia.