Authors:Mujiyanto Mujiyanto; Basuki Rachmat, Aris Yulianto, Made Agus Nurjana, Wawan Ridwan, Endang Puji Astuti, Doni Lasut, Pandji Wibawa Dhewantara Abstract: Typhoid fever is one of the common enteric fevers in developing countries, especially in emerging metropolitan areas in Indonesia. Yet, studies on spatial and temporal distribution of tyhoid fever are lacking. This study was conducted to analyze retrospective hospital-based data at the village level over the period 2017-2023 to understand the spatial and temporal variation of typhoid fever in Jakarta. Spatial analyses were performed by Moran’s I and Local Indicators of Spatial Association (LISA) to examine spatial clustering of typhoid incidence and to identify high-risk villages for typhoid fever, respectively. Seasonal decomposition analysis was performed to investigate the seasonality of this infection. A total of 57,468 typhoid cases, resulting in a cumulative incidence of 533.99 per 100,000 people, were reported during the study period. The incidence was significantly clustered (I=0.548; p=0.001) at the village level across Jakarta. Statistically significant high-risk clusters were detected in the South and East of Jakarta that were heterogeneous over time. We identified seven persistent high-risk clusters in the eastern part of the city and two in the southern part. Moreover, the typhoid incidence showed a strong seasonality trend, significantly associated with monthly total rainfall (p=0.018). The study revealed a significant spatial variation with strong seasonality in typhoid incidence across the city suggesting a variation in transmission intensity and needs for effective public health interventions, especially in the high-risk areas. Improvement in water and sanitation facilities, hygiene awareness and surveillance are essential to help reduce typhoid transmission in Jakarta. PubDate: Thu, 15 May 2025 00:00:00 +000
Authors:Sarayu Muntaphan; Kittipong Sornlorm Abstract: During the COVID-19 pandemic in 2021–2022, vaccination against this infection was crucial for Thailand’s recovery. This research aimed to identify spatial patterns of association between the distribution and spread of the COVID-19 pandemic on the one hand and vaccine coverage, health service and socio-economic factors on the other. Univariate analysis using Getis-Ord GI* found strong clustering of the vaccine coverage, mostly in Eastern, Central, and Southern regions (Andaman coast), while bivariate analysis using Moran’s I revealed significant positive spatial correlation vaccine coverage with the presence of COVID-19 patients (2021 = 0.273; 2022 = 0.273), Night Time Light (NTL) (2021 = 0.159; 2022 = 0.118) and medical personnel (2021 = 0.174; 2022 = 0.123). In addition, Local Indicators of Spatial Association (LISA) analysis found High-High clusters predominantly in the Eastern and Central regions. Areas with high economic growth (as reflected by high NTL) had greater COVID-19 vaccine coverage, likely due to better access to information and efficient transport systems in areas with stronger financial resources than elsewhere. These factors facilitated access to healthcare ensured presence of adequate personnel and enabled rapid distribution of the vaccine. Additionally, high rates of COVID-19 infections increased public awareness of infection risk leading to better vaccination uptake. Policymakers should prioritise vaccine distribution in high-risk and underserved areas to ensure equitable access. Additionally, increasing health workforce capacity is essential to improving service efficiency and readiness for future outbreaks. PubDate: Tue, 13 May 2025 00:00:00 +000
Authors:Kabeya Clement Mulamba Abstract: The main objective of this paper was to model the relationship between married women’s contraceptive use and the influence of their male partners. The study took place in Angola and Zambia, which stems from the fact that these countries ratified the Maputo Protocol that emphasises promotion of reproductive health among women. Most previous studies investigating women’s progress towards the realisation of what is advocated in this protocol have overlooked the role of the male partners. Hence, it has become imperative to reduce this gap in the literature. This paper discusses the application of spatial multilevel modelling, which incorporates two levels of information based on the nature of the data available. This approach acknowledges the hypothesis that contraceptive use is a social phenomenon occurring within the geographical space and is therefore susceptible to autocorrelation. Findings confirm that the level of influence of male partners’ exertion on women’s contraceptive use is dependent on the situation in the country where it takes place as shown by various study variables analysed. The results indicate that socioeconomic and education factors play a major role, a phenomenon that calls for tailor-made reproductive health policies considering these aspects. PubDate: Mon, 12 May 2025 00:00:00 +000
Authors:Jihong Zhang; Guohua Yin, Qiuhua Zhang, Juan Fang, Duo Jiang, Chao Yang, Na Sun Abstract: The geo-inequality of COVID-19 risk has attracted a great deal of research attention. In this study, the spatial correlation between community environment and the incidence of COVID-19 cases in 30 Chinese cities is discussed. The spread of the disease is analyzed based on timing and spatial monitoring at the km2-grid level, with the use of publicly available data relating to housing prices, Gross Deomestic Product (GDP), medical facilities, consumer sites, public green spaces, and industrial sites. The results indicate substantial geographical variations in the distribution of COVID-19 communities in all 30 cities. Significant global bivariate spatial dependence was observed between the disease and housing prices (Moran’s I =0.099, p<0.01, z=488.6), medical facilities (Moran’s I = 0.349, p<0.01, z=1675.0), consumer sites (Moran’s I =0.369, p<0.01, z=1843.4), green space (Moran’s I =0.205, p<0.01, z=1037.8), and industrial sites (Moran’s I =0.234, p<0.01, z=1178.6). The risk of COVID-19 under the influence of GDP is further examined for cities with per capita GDPs from high to low ranging from 1.69 to 4.62 (1.69~3.74~4.62, 95% CI). These findings provide greater detail on the interplay between the infectious disease and community environments. PubDate: Mon, 28 Apr 2025 00:00:00 +000
Authors:Iuria Betco; Ana Isabel Ribeiro, David S. Vale, Luis Encalada-Abarca, Cláudia M. Viana, Jorge Rocha Abstract: Advances in digital sensors and Information flow have created an abundance of data generated by users under various emotional states in different situations. Although this opens up a new facet in spatial research, the large amount of data makes it difficult to analyze and obtain complete and comprehensive information leading to an increase in the demand for sentiment analysis. In this study, the Canadian National Research Council (NRC) of Sentiment and Emotion Lexicon (EmoLex) was used, based on data from the social network Twitter (now X), thus enabling the identification of the places in Lisbon where both positive and negative sentiment prevails. From the results obtained, the Portuguese are happy in spaces associated with leisure and consumption, such as museums, event venues, gardens, shopping centres, stores, and restaurants. The high score of words associated with negative sentiment have more bias, since the lexicon sometimes has difficulties to identify the context in which the word appears, ending up giving it a negative score (e.g., war, terminal). PubDate: Thu, 24 Apr 2025 00:00:00 +000
Authors:Sarah Isnan; Ahmad Fikri bin Abdullah, Abdul Rashid Shariff, Iskandar Ishak, Sharifah Norkhadijah Syed Ismail, Maheshwara Rao Appanan Abstract: The COVID-19 outbreak has precipitated severe occurrences on a global scale. Hence, spatial analysis is crucial in determining the relationships and patterns of geospatial data. Moran’s I and Geary’s C are prominent methodologies used to measure the spatial autocorrelation of geographical data. Both measure the degree of similarity or dissimilarity between nearby locations based on attribute values in such a way that the selection of distance techniques and weight matrices significantly impact the spatial autocorrelation results. This paper aimed at carrying out the spatial epidemiological characteristics analysis of the pandemic comparing the results of Moran’s I and Geary’s C with different parameters to gain a comprehensive understanding of the spatial relationship of COVID-19 cases. We employed distance-based techniques, K-nearest neighbour, and Queen contiguity techniques to assess the sensitivity of the different parameter configurations for both Moran’s I and Geary’s C. The findings revealed that former provided more reliable and robust results compared to the latter, with consistent results of spatial autocorrelation (positive spatial autocorrelation). The distance weight of 0.05 using the Manhattan method of Moran’s I is the recommended distance weight, as it outperformed other weight matrices (Moran’s I = 0.0152, Z-value= 110.8844 and p-value=0.001). PubDate: Mon, 07 Apr 2025 00:00:00 +000
Authors:Alireza Mohammadi; Bardia Mashhoodi , Ali Shamsoddini , Elahe Pishgar, Robert Bergquist Abstract: Introduction: Chronic Obstructive Pulmonary Disease (COPD) mortality rates and global warming have been in the focus of scientists and policymakers in the past decade. The long-term shifts in temperature and weather patterns, commonly referred to as climate change, is an important public health issue, especially with regard to COPD. Method: Using the most recent county-level age-adjusted COPD mortality rates among adults older than 25 years, this study aimed to investigate the spatial trajectory of COPD in the United States between 2001 and 2020. Global Moran's I was used to investigate spatial relationships utilising data from Terra satellite for night-time land surface temperatures (LSTnt), which served as an indicator of warming within the same time period across the United States. The forest-based classification and regression model (FCR) was applied to predict mortality rates. Results: It was found that COPD mortality over the 20-year period was spatially clustered in certain counties. Moran's I statistic (I=0.18) showed that the COPD mortality rates increased with LSTnt, with the strongest spatial association in the eastern and south-eastern counties. The FCR model was able to predict mortality rates based on LSTnt values in the study area with a R2 value of 0.68. Conclusion: Policymakers in the United States could use the findings of this study to develop long-term spatial and health-related strategies to reduce the vulnerability to global warming of patients with acute respiratory symptoms. PubDate: Wed, 26 Mar 2025 00:00:00 +000
Authors:Wentao Yang; Fengjie Wang, Yihan You, Zhixiong Fang, Xing Wang, Xiaoming Mei Abstract: Equitable spatial accessibility to vaccination sites is essential for enhancing the effectiveness of infectious disease prevention and control. While traffic modes significantly influence the evaluation of spatial accessibility to vaccination sites, most existing studies measure it separately using homogeneous or single travel modes making it challenging to comprehensively understand the overall accessibility and support spatial optimization for vaccination sites. This study proposes to optimize the spatial distribution of vaccination sites based on heterogeneous travel modes in multiple scenarios by a hybrid travel time approach. This was done by first considering heterogeneous travel modes to measure spatial accessibility to vaccination sites followed by spatial optimization using hybrid travel time to determine the optimal configuration of vaccination sites across multiple scenarios. In the study area of Xiangtan, a prefecture-level city in east-central Hunan Province, China, spatial inequality in accessibility to COVID-19 vaccination sites were identified. The public in the Yuhu and Yuetang districts benefit from easy access to vaccination sites, and spatial accessibility within these areas is also equitable. By utilizing spatial optimization under the condition that the addition of a new site would not result in a comprehensive hybrid travel time increase exceeding 0.1%, up to 21 redundant sites were detected among the original ones and when newly added sites were considered, the optimal number of the optimized sites amounted to 124. These findings provide crucial spatial information to support for enhancing the efficiency of infectious disease prevention and control. PubDate: Mon, 24 Mar 2025 00:00:00 +000
Authors:Peter Nezval; Takeshi Shirabe Abstract: Accessibility is an essential consideration in the design of public spaces, and commonly referred to as ‘pedestrian accessibility’ when walking is the primary mode of transportation. Computational methods, frequently coupled with Geographic Information systems (GIS), are increasingly available for assessing pedestrian accessibility using digital cartographic data such as road networks and digital terrain models. However, they often implicitly assume a level of mobility that may not be achievable by individuals with mobility impairments, e.g., wheelchair users. Therefore, it remains uncertain whether conventional pedestrian accessibility adequately approximates ‘wheelchair accessibility,’ and, if not, what computational resources would be required to evaluate it more accurately. We therefore designed a spatial database aimed at customizing mobility networks according to mobility limitations and compared the accessibility of a university campus for people with and without wheelchairs under various assumptions. The results showed there are clusters of locations either completely inaccessible or substantially less accessible for wheelchair users, indicating the presence of particular ‘wheelchair coldspots’, not only due to steep slopes and stairways but also arising from unforeseen consequences of aesthetic and safety enhancements, such as pebble pavements and raised sidewalks. It was found that a combination of simple spatial queries would help identifying potential locations for mobility aids such as ramps. These findings suggest that accessibility is not an invariant of a public space but experienced differently by different groups. Therefore, more comprehensive needs analysis and spatial database design are necessary to support inclusive design of healthier public spaces. PubDate: Mon, 24 Mar 2025 00:00:00 +000
Authors:Sang Min Lee; Dong Woo Huh, Young Gyu Kwon Abstract: Despite national initiatives to enhance healthcare accessibility, unmet healthcare needs in South Korea remain notably high, particularly in specific regions. This study investigated the factors contributing to geographical disparities in unmet healthcare needs by employing spatial regression models to examine the spatial interactions between healthcare resources and unmet needs. Utilizing data from the 2020 Community Health Survey and Statistics Korea for 216 local government entities, excluding remote areas to ensure data consistency, we identified significant spatial clusters of unmet healthcare needs. These clusters are primarily located in non-metropolitan regions facing transportation barriers and limited healthcare infrastructure. Spatial regression analysis revealed that general hospitals and clinics are significantly associated with reduced unmet healthcare needs underscoring their critical role in mitigating regional disparities. In contrast, hospitals (≥30 beds) and convalescent hospitals did not exhibit significant effects, likely owing to their focus on specialised inpatient and long-term care services, which do not directly address immediate outpatient needs. These findings advance the understanding of how healthcare resource distribution impacts unmet needs at a regional level in South Korea and highlight the necessity for allocating general hospitals and clinics strategically to promote health equity. Based on these results, we recommend evidence- based policy interventions that optimise existing healthcare resources and strategically deploy new facilities in underserved regions. These insights provide valuable guidance for policymakers to reduce geographical health disparities and enhance overall public health outcomes. PubDate: Tue, 11 Mar 2025 00:00:00 +000
Authors:Adel Al-Huraibi; Sherif Amer, Justine Blanford Abstract: Once a vaccine against COVID-19 had been developed, distribution strategies were needed to vaccinate large numbers of the population as efficiently as possible. In this study we explored the geographical accessibility of vaccination centres and examined their optimal location. To achieve this, we used open-source data. For the analysis we assessed the centre-to-population ratio served to assess inequalities and examined the optimal number and location of centres needed to serve 50%, 70% and 85% of the population, while ensuring physical accessibility using a common mode of transportation, the bicycle. The Location Set Covering Problem (LSCP) model was used to determine the lowest number of vaccination centres needed and assess where these should be located for each Municipal Health Service (GGD) region in The Netherlands. Our analysis identified an unequal distribution of health centres by GGD region, with a primary concentration of vaccination locations in the central region of the Netherlands. GGD Region Noord en Oost Gelderland (N=34), Utrecht (N=29) and Hollands-Midden (N=26) had the highest numbers, while the lowest were found in West-Brabant (N=1), Brabant-Zuidoost (N=2), with Kennemerland, Hollands-Noorden, Groningen and Flevoland (N=3) each. The centre-to-population ratio ranged from 1 centre serving 22,000 people (Noord en Oost Gelderland) to 1 centre serving 672,000 people (West Brabant region). The location-allocation analysis identified several regions that would benefit by adding more centres, most of which would serve densely populated regions previously neglected by the existing vaccination strategy. The number of centres needed ranged from 110 to 322 to achieve 50% and 85% population coverage respectively. In conclusion, location-allocation models coupled with Geographic Information Systems (GIS) can aid decision-making efforts during mass vaccination efforts. To increase effectiveness, a nuanced distribution approach considering accessibility and coverage would be useful. The methodology presented here is valuable for aiding decisionmakers in providing optimized locally adapted crucial health services accessible for the population, such as vaccination centres. PubDate: Mon, 03 Mar 2025 00:00:00 +000
Authors:Mai Liu; Yin Zhang Abstract: Dengue is the most widespread and fastest-growing vectorborne disease worldwide. We employed bibliometric analysis to provide an overview of research on the impact of climate change on dengue fever focusing on both global and Southeast Asian regions. Using the Web of Science Core Collection (WoSCC) database, we reviewed studies on the impact of climate change on dengue fever between 1974 and 2022 taking into account study locations and international collaboration. The VOS viewer software (https://www.vosviewer.com/) and the Bibliometrix R package (https://www.bibliometrix.org/) were used to visualise country networks and keywords. We collected 2,055 relevant articles published globally between 1974 and 2022 on the impact of climate change on dengue fever, 449 of which published in Southeast Asia. Peaking in 2021, the overall number of publications showed a strong increase in the period 2000-2022. The United States had the highest number of publications (n=558) followed by China (261) and Brazil (228). Among the Southeast Asian countries, Thailand had most publications (n=123). Global and Southeast Asian concerns about the impact of climate change on dengue fever are essentially the same. They all emphasise the relationship between temperature and other climatic conditions on the one hand and the transmission of Aedes aegypti on the other. A significant positive correlation exists between the number of national publications and socioeconomic index and between international collaboration and scientific productivity in the field. Our study demonstrates the current state of research on the impact of climate change on dengue and provides a comparative analysis of the Southeast Asian region. Publication output in Southeast Asia lags behind that of major countries worldwide, and various strategies should be implemented to improve international collaboration, such as increasing the number of international collaborative projects and providing academic resources and research platforms for researchers. PubDate: Wed, 19 Feb 2025 00:00:00 +000
Authors:Jonas Schoo; Frank Schüssler Abstract: Ensuring universal and equitable accessibility to healthcare services is crucial for fostering equal living conditions aligned with global and national objectives. This study examines disparities in accessing General Practitioner (GP) care within Lower Saxony and Bremen, Germany, using the two-step floating catchment area method for spatial analysis at street section level, incorporating various transportation modes. Findings are compared with needs-related planning guidelines to uncover spatial disparities and deviations between prescribed guidelines (target state) and empirical findings (actual state). The analysis reveals significant discrepancies, with over 50% of the population inadequately supplied due to accessibility or capacity issues, particularly in rural and some urban areas, challenging assumptions of sufficient urban healthcare provision. This is the first detailed analysis of primary care provision at this granular level in Lower Saxony, exposing substantial gaps between current GP care and planning targets. Fine-grained spatial analysis proves essential for revealing healthcare accessibility inequities and offers a roadmap for targeted policy interventions. Despite limitations, such as not fully capturing real-world dynamics or patient preferences, the study provides valuable insights into enhancing geographically equitable GP care. It contributes to the discourse on achieving equal living conditions through equitable healthcare accessibility, advocating a more refined, localised approach to healthcare planning, emphasizing the importance of detailed spatial analysis for informed decision-making and promoting health equity. PubDate: Wed, 19 Feb 2025 00:00:00 +000
Authors:Néstor DelaPaz-Ruíz; Ellen-Wien Augustijn, Mahdi Farnaghi, Sheheen A. Abdulkareem, Raul Zurita Milla Abstract: Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics. PubDate: Tue, 11 Feb 2025 00:00:00 +000
Authors:Kella Douzouné; Joseph Oloukoi, Ismaila Toko Imorou, Toure Gorgui Ba, Derrick Chefor Ymele Demeveng Abstract: This study aimed to compile an inventory of the main diseases affecting these species in Mayo-Kebbi Ouest Province in Chad. A survey was conducted between 6 May and 7 August 2024 using a cascade data collection method identifying 310 farmers and 19 veterinarians with an average of 10 to 12 years of experience in advising and supporting livestock practices The data collected included socio-professional characteristics of participants, livestock practices, and geospatial information. These data were managed in Excel and analysed with R. The analysis involved descriptive and inferential statistical techniques including binary logistic regression resulting in maps illustrating disease hotspots and livestock systems. Thematic maps, tables and charts with a 5% significance threshold visualised risk areas and associated livestock practices. The results show a predominance of male farmers (91.9%) from 20 different ethnic groups. The livestock systems identified include data on farming divided into extensive (14.8%), mixed (0.3%) and semi-intensive farming (84.8%). On average, farms have 41 cattle and 25 goats. Animal diseases were found to cause 29.5% reduction in herd productivity. Transhumance (p=0.000356) and animal disease incidence (p=0.03) were observed as significant risk factors associated with the abandonment of livestock farming. The main diseases recorded in cattle include contagious bovine pleuropneumonia (11.3%), bovine tuberculosis (2.5%), foot-and-mouth disease (45.0%), bluetongue (1.7%) and disease with symptoms reminiscent of rinderpest (2.5%). For goats, notable diseases include brucellosis (3.8%), lumpy skin disease (19.2%), goat plague (7.9%) and Rift Valley fever (6.3%). These findings confirm the importance of a geospatial epidemiological surveillance tool for monitoring animal diseases in this region. PubDate: Mon, 03 Feb 2025 00:00:00 +000
Authors:Worrayot Darasawang; Wongsa Laohasiriwong, Kittipong Sornlorm , Warangkana Sungsitthisawad, Roshan Kumar Mahato Abstract: Antibiotic Self-Medication (ASM) is a major contributing factor to Antimicrobial Resistance (AMR) that can lead to both mortality and long-term hospitalizations. High provincial ASM proportions associated with mortality due to AMR have been observed in Thailand but there is a lack of studies on geographic factors contributing to ASM. The present study aimed to quantify the distribution of ASM in Thailand and its correlated factors. Socioeconomic and health services factors were included in the spatial analysis. Moran’s I was performed to identify global autocorrelation with the significance level set at p=0.05 and spatial regression were applied to identify the factors associated with ASM, the proportion of which is predominant in the north-eastern, central and eastern regions with Phitsanulok Province reporting the highest proportion of Thailand’s 77 provinces. Autocorrelation between Night-Time Light (NTL) and the proportion of ASM was observed to be statistically significant at p=0.030. The Spatial Lag Model (SLM) and the Spatial Error Model (SEM) were used with the latter providing both the lowest R2 and Akaike Information Criterion (AIC). It was demonstrated that the proportion of alcohol consumption significantly increased the proportion of ASM. The annual number of outpatient department visits and the average NTL decreased the proportion of ASM by 1.5% and 0.4%, respectively. Average monthly household expenditures also decreased the ASM proportion. Policies to control alcohol consumption while promoting healthcare visits are essential strategies to mitigate the burden of AMR in Thailand. PubDate: Mon, 27 Jan 2025 00:00:00 +000
Authors:Enríque Ibarra-Zapata; Darío Gaytán-Hernández, Yolanda Terán-Figueroa, Verónica Gallegos-García, Carmen del Pilar Suárez-Rodríguez, Sergio Zarazúa Guzmán, Omar Parra Rodríguez Abstract: This study aimed to estimate a socio-spatial vulnerability index for type 2 diabetes mellitus (T2DM) at the municipal level in Mexico for 2020. It incorporated factors such as poverty, social backwardness, marginalization index, and human development index. This retrospective ecological study analyzed 317,011 incident cases of T2DM in 2020. Utilizing multi-criteria decision analysis, weighted values were assigned to each vulnerability criterion. A multiple linear regression model was developed, complemented by cluster and outlier analyses using Moran I’s and the high-low clustering method. A clustered spatial autocorrelation of high values was found across 17.65% of Mexico, which was statistically significant (p < 0.001). Conversely, 37.78% of the territory showed a pattern of low values without significant evidence of groupings. The analysis revealed 117 nodes of very high vulnerability forming six focal areas, 172 nodes with high vulnerability across five areas, 168 nodes with medium vulnerability in two areas, 112 nodes with low vulnerability across 16 areas, and 152 nodes with very low vulnerability in 24 focal areas. This method proves to be robust and offers a technical-scientific basis for guiding T2DM prevention strategies and actions using a spatial/epidemiological approach. It is recommended that future strategies take into account factors such as poverty, social backwardness, marginalization index, and human development index to be effective. PubDate: Mon, 27 Jan 2025 00:00:00 +000
Authors:Mintesnot Tenkir Teni; Travis Loux, Ness Sandoval, Anne Sebert Kuhlmann Abstract: Background: Increasing access to and utilization of long-acting reversible contraceptives (LARC) can prevent unintended pregnancies and reduce unmet need for family planning in Ethiopia However, LARC uptake lags behind less effective contraceptive methods. This study aimed to analyze the geographical distribution and demographic factors associated to LARC uptake. Methods: The 2019 Performance Monitoring For Action Ethiopia (PMA Ethiopia) survey data was used. Spatial autocorrelation was examined using Global Moran's I and Local Indicators of Spatial Association (LISA). Bivariate Moran's I and bivariate LISA (BiLISA), Spatial lag, and spatial error regression analyses were performed to assess the spatial correlation and association between LARC uptake and demographic factors. Results: LARC uptake was 8% among the study population, with Afar and Somali regions having the lowest uptake. There was a statistically significant positive spatial autocorrelation for LARC uptake (Moran’s I= 0.308, p<0.001). Additionally, an inverse correlation was observed between LARC uptake and the percentage of Muslims, rural population, no formal education, and low wealth quantile. The spatial lag model indicated that zones with higher Muslim populations and those with higher percentages of population with no formal education had lower LARC uptake. Conclusions: To expand access to LARC, the Ethiopian government, policymakers, and non-governmental organizations might implement programs targeting low-uptake areas (Afar and Somali regions). Muslim religious leaders could play an important role in promoting acceptance of LARC among their members. Tailored health education programs should be developed for Muslim populations and those with no formal education to enhance awareness and acceptance of LARC. PubDate: Thu, 23 Jan 2025 00:00:00 +000
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