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Article ; Online: National variation in patterns of bone disease treatment-seeking behaviors

Yanchao Tang / Yongze Song / Yongqiang Wang / Shengjie Lai / Victor A. Alegana / Xiaoguang Liu

International Journal of Applied Earth Observations and Geoinformation, Vol 117, Iss , Pp 103219- (2023)

A study of more than 50,000 hospital admissions between 2008 and 2021

2023

Abstract: Understanding disease treatment-seeking behaviors is a fundamental issue for national and regional healthcare management. However, treatment-seeking behaviors are complex and affected by various factors, including disease incidence, healthcare resources, ...

Abstract	Understanding disease treatment-seeking behaviors is a fundamental issue for national and regional healthcare management. However, treatment-seeking behaviors are complex and affected by various factors, including disease incidence, healthcare resources, and population accessibility to hospitals. Geospatial analysis is a practical approach to investigating treatment-seeking behaviors. Still, methods and cases are limited due to the lack of long-term data, interdisciplinary knowledge, and data analytic techniques. We develop a new paradigm for investigating spatial patterns and factors affecting bone disease treatment-seeking behaviors. We leverage consecutive long-term records of over 50,000 nationwide bone disease patients outside Beijing who had surgeries in a prestigious hospital in Beijing, China. Five categories of patient individual-level geographical and environmental variables are derived from multi-source remote sensing and geospatial data to explain treatment-seeking behaviors. First, we develop a scaling approach to assess the relationships between bone patients and population migration. Next, we develop a treatment-seeking index to measure treatment-seeking behaviors and develop spatial models to identify their regional disparities, i.e., hotspots and coldspots. Finally, we develop spatial heterogeneity models to explore the complex factors affecting treatment-seeking behaviors. Results show that the developed paradigm is effective in examining national variations of the patterns of disease treatment-seeking behaviors. We find that (i) population migration is an effective predictor of the treatment-seeking behaviors of bone patients, (ii) significant hotspots and coldspots are identified for informing regional disparities, and (iii), multiple types of factors affecting the treatment-seeking behaviors through a geospatially overlapped approach. This study pioneers the development of geospatial models and implementation of patient individual-level data derived from satellite remote sensing for ...
Keywords	Treatment-seeking behavior ; Bone diseases ; Remote sensing ; Geospatial big data modeling ; Public health management ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Understanding factors associated with attending secondary school in Tanzania using household survey data.

Carla Pezzulo / Victor A Alegana / Andrew Christensen / Omar Bakari / Andrew J Tatem

PLoS ONE, Vol 17, Iss 2, p e

2022 Volume 0263734

Abstract: Background Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3 ... ...

Abstract	Background Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example. Methods Nationally representative household survey data (2015-16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household's levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework. Results Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance. Conclusions Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
Keywords	Medicine ; R ; Science ; Q
Subject code	370
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Understanding factors associated with attending secondary school in Tanzania using household survey data

Carla Pezzulo / Victor A. Alegana / Andrew Christensen / Omar Bakari / Andrew J. Tatem

PLoS ONE, Vol 17, Iss

2022 Volume 2

Abstract	Background Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example. Methods Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework. Results Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance. Conclusions Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
Keywords	Medicine ; R ; Science ; Q
Subject code	370
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Geographic accessibility and hospital competition for emergency blood transfusion services in Bungoma, Western Kenya

Eda Mumo / Nathan O. Agutu / Angela K. Moturi / Anitah Cherono / Samuel K. Muchiri / Robert W. Snow / Victor A. Alegana

International Journal of Health Geographics, Vol 22, Iss 1, Pp 1-

2023 Volume 13

Abstract: Abstract Background Estimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models ... ...

Abstract	Abstract Background Estimating accessibility gaps to essential health interventions helps to allocate and prioritize health resources. Access to blood transfusion represents an important emergency health requirement. Here, we develop geo-spatial models of accessibility and competition to blood transfusion services in Bungoma County, Western Kenya. Methods Hospitals providing blood transfusion services in Bungoma were identified from an up-dated geo-coded facility database. AccessMod was used to define care-seeker’s travel times to the nearest blood transfusion service. A spatial accessibility index for each enumeration area (EA) was defined using modelled travel time, population demand, and supply available at the hospital, assuming a uniform risk of emergency occurrence in the county. To identify populations marginalized from transfusion services, the number of people outside 1-h travel time and those residing in EAs with low accessibility indexes were computed at the sub-county level. Competition between the transfusing hospitals was estimated using a spatial competition index which provided a measure of the level of attractiveness of each hospital. To understand whether highly competitive facilities had better capacity for blood transfusion services, a correlation test between the computed competition metric and the blood units received and transfused at the hospital was done. Results 15 hospitals in Bungoma county provide transfusion services, however these are unevenly distributed across the sub-counties. Average travel time to a blood transfusion centre in the county was 33 min and 5% of the population resided outside 1-h travel time. Based on the accessibility index, 38% of the EAs were classified to have low accessibility, representing 34% of the population, with one sub-county having the highest marginalized population. The computed competition index showed that hospitals in the urban areas had a spatial competitive advantage over those in rural areas. Conclusion The modelled spatial accessibility has ...
Keywords	Accessibility ; Spatial competition ; Blood transfusion ; Travel time ; Emergency ; Bungoma ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	360
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Routine data for malaria morbidity estimation in Africa

Victor A. Alegana / Emelda A. Okiro / Robert W. Snow

BMC Medicine, Vol 18, Iss 1, Pp 1-

challenges and prospects

2020 Volume 13

Abstract: Abstract Background The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response ... ...

Abstract	Abstract Background The burden of malaria in sub-Saharan Africa remains challenging to measure relying on epidemiological modelling to evaluate the impact of investments and providing an in-depth analysis of progress and trends in malaria response globally. In malaria-endemic countries of Africa, there is increasing use of routine surveillance data to define national strategic targets, estimate malaria case burdens and measure control progress to identify financing priorities. Existing research focuses mainly on the strengths of these data with less emphasis on existing challenges and opportunities presented. Conclusion Here we define the current imperfections common to routine malaria morbidity data at national levels and offer prospects into their future use to reflect changing disease burdens.
Keywords	Malaria burden ; Morbidity ; Routine surveillance ; Medicine ; R
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Malaria micro-stratification using routine surveillance data in Western Kenya

Victor A. Alegana / Laurissa Suiyanka / Peter M. Macharia / Grace Ikahu-Muchangi / Robert W. Snow

Malaria Journal, Vol 20, Iss 1, Pp 1-

2021 Volume 9

Abstract: Abstract Background There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub- ... ...

Abstract	Abstract Background There is an increasing need for finer spatial resolution data on malaria risk to provide micro-stratification to guide sub-national strategic plans. Here, spatial-statistical techniques are used to exploit routine data to depict sub-national heterogeneities in test positivity rate (TPR) for malaria among patients attending health facilities in Kenya. Methods Routine data from health facilities (n = 1804) representing all ages over 24 months (2018–2019) were assembled across 8 counties (62 sub-counties) in Western Kenya. Statistical model-based approaches were used to quantify heterogeneities in TPR and uncertainty at fine spatial resolution adjusting for missingness, population distribution, spatial data structure, month, and type of health facility. Results The overall monthly reporting rate was 78.7% (IQR 75.0–100.0) and public-based health facilities were more likely than private facilities to report ≥ 12 months (OR 5.7, 95% CI 4.3–7.5). There was marked heterogeneity in population-weighted TPR with sub-counties in the north of the lake-endemic region exhibiting the highest rates (exceedance probability > 70% with 90% certainty) where approximately 2.7 million (28.5%) people reside. At micro-level the lowest rates were in 14 sub-counties (exceedance probability < 30% with 90% certainty) where approximately 2.2 million (23.1%) people lived and indoor residual spraying had been conducted since 2017. Conclusion The value of routine health data on TPR can be enhanced when adjusting for underlying population and spatial structures of the data, highlighting small-scale heterogeneities in malaria risk often masked in broad national stratifications. Future research should aim at relating these heterogeneities in TPR with traditional community-level prevalence to improve tailoring malaria control activities at sub-national levels.
Keywords	Malaria ; Routine data ; Test positivity rate ; Arctic medicine. Tropical medicine ; RC955-962 ; Infectious and parasitic diseases ; RC109-216
Subject code	333
Language	English
Publishing date	2021-01-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Estimating hospital catchments from in-patient admission records

Victor A. Alegana / Cynthia Khazenzi / Samuel O. Akech / Robert W. Snow

Scientific Reports, Vol 10, Iss 1, Pp 1-

a spatial statistical approach applied to malaria

2020 Volume 11

Abstract: Abstract Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and ... ...

Abstract	Abstract Admission records are seldom used in sub-Saharan Africa to delineate hospital catchments for the spatial description of hospitalised disease events. We set out to investigate spatial hospital accessibility for severe malarial anaemia (SMA) and cerebral malaria (CM). Malaria admissions for children between 1 month and 14 years old were identified from prospective clinical surveillance data recorded routinely at four referral hospitals covering two complete years between December 2015 to November 2016 and November 2017 to October 2018. These were linked to census enumeration areas (EAs) with an age-structured population. A novel mathematical-statistical framework that included EAs with zero observations was used to predict hospital catchment for malaria admissions adjusting for spatial distance. From 5766 malaria admissions, 5486 (95.14%) were linked to specific EA address, of which 272 (5%) were classified as cerebral malaria while 1001 (10%) were severe malaria anaemia. Further, results suggest a marked geographic catchment of malaria admission around the four sentinel hospitals although the extent varied. The relative rate-ratio of hospitalisation was highest at <1-hour travel time for SMA and CM although this was lower outside the predicted hospital catchments. Delineation of catchments is important for planning emergency care delivery and in the use of hospital data to define epidemiological disease burdens. Further hospital and community-based studies on treatment-seeking pathways to hospitals for severe disease would improve our understanding of catchments.
Keywords	Medicine ; R ; Science ; Q
Subject code	360
Language	English
Publishing date	2020-01-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Spatio-temporal modelling of routine health facility data for malaria risk micro-stratification in mainland Tanzania

Sumaiyya G. Thawer / Monica Golumbeanu / Samwel Lazaro / Frank Chacky / Khalifa Munisi / Sijenunu Aaron / Fabrizio Molteni / Christian Lengeler / Emilie Pothin / Robert W. Snow / Victor A. Alegana

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological ... ...

Abstract	Abstract As malaria transmission declines, the need to monitor the heterogeneity of malaria risk at finer scales becomes critical to guide community-based targeted interventions. Although routine health facility (HF) data can provide epidemiological evidence at high spatial and temporal resolution, its incomplete nature of information can result in lower administrative units without empirical data. To overcome geographic sparsity of data and its representativeness, geo-spatial models can leverage routine information to predict risk in un-represented areas as well as estimate uncertainty of predictions. Here, a Bayesian spatio-temporal model was applied on malaria test positivity rate (TPR) data for the period 2017–2019 to predict risks at the ward level, the lowest decision-making unit in mainland Tanzania. To quantify the associated uncertainty, the probability of malaria TPR exceeding programmatic threshold was estimated. Results showed a marked spatial heterogeneity in malaria TPR across wards. 17.7 million people resided in areas where malaria TPR was high (≥ 30; 90% certainty) in the North-West and South-East parts of Tanzania. Approximately 11.7 million people lived in areas where malaria TPR was very low (< 5%; 90% certainty). HF data can be used to identify different epidemiological strata and guide malaria interventions at micro-planning units in Tanzania. These data, however, are imperfect in many settings in Africa and often require application of geo-spatial modelling techniques for estimation.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World

Sarchil Hama Qader / Jadu Dash / Victor A. Alegana / Nabaz R. Khwarahm / Andrew J. Tatem / Peter M. Atkinson

Remote Sensing, Vol 13, Iss 3382, p

2021 Volume 3382

Abstract: Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food ... ...

Abstract	Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.
Keywords	agriculture ; arid and semi-arid regions ; crop monitoring ; remote sensing ; crop yield ; Science ; Q
Subject code	333
Language	English
Publishing date	2021-08-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model

Victor A. Alegana / Jim Wright / Carla Pezzulo / Andrew J. Tatem / Peter M. Atkinson

BMC Medical Research Methodology, Vol 17, Iss 1, Pp 1-

2017 Volume 12

Abstract: Abstract Background Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is ... ...

Abstract	Abstract Background Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA). Methods Using nationally representative household survey data from the 2013 Demographic and Health Survey (DHS) in Namibia, individual-level responses (n = 1138) were linked to theoretical estimates of travel time to the nearest public or private health facility. Bayesian Item Response Theory (IRT) models were fitted via Markov Chain Monte Carlo (MCMC) simulation to estimate parameters related to fever treatment and estimate probability of treatment for children under five years. Different models were implemented to evaluate computational needs and the effect of including predictor variables such as rurality. The mean treatment rates were then estimated at regional level. Results Modelling results suggested probability of fever treatment was highest in regions with relatively high incidence of malaria historically. The minimum predicted threshold probability of seeking treatment was 0.3 (model 1: 0.340; 95% CI 0.155–0.597), suggesting that even in populations at large distances from facilities, there was still a 30% chance of an individual seeking treatment for fever. The agreement between correctly predicted probability of treatment at individual level based on a subset of data (n = 247) was high (AUC = 0.978), with a sensitivity of 96.7% and a specificity of 75.3%. Conclusion We have shown how individual responses in national surveys can be transformed to ...
Keywords	Bayesian hierarchical model ; Treatment-seeking behaviour ; Item response theory ; Markov Chain Monte Carlo ; Medicine (General) ; R5-920
Subject code	310
Language	English
Publishing date	2017-04-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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