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Dana R Thomson
Department of Social Statistics & Demography, University of Southampton, Southampton SO17 1BJ, UK

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Journal article
Published: 20 June 2021 in Urban Science
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Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data (e.g., ~100 × 100 m) derived from demographic and spatial data are a promising source of population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. In this study, we compared field-referenced boundaries and population counts from Slum Dwellers International in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) with nine gridded population datasets to assess their statistical accuracy in slums. We found that all gridded population estimates vastly underestimated population in slums (RMSE: 4958 to 14,422, Bias: −2853 to −7638), with the most accurate dataset (HRSL) estimating just 39 per cent of slum residents. Using a modelled map of all slums in Lagos to compare gridded population datasets in terms of SDG 11.1.1 (percent of population living in deprived areas), all gridded population datasets estimated this indicator at just 1–3 per cent compared to 56 per cent using UN-Habitat’s approach. We outline steps that might improve that accuracy of each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG 11.1.1, we are optimistic that some could be used in the future following updates to their modelling approaches.

ACS Style

Dana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science 2021, 5, 48 .

AMA Style

Dana Thomson, Andrea Gaughan, Forrest Stevens, Gregory Yetman, Peter Elias, Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. Urban Science. 2021; 5 (2):48.

Chicago/Turabian Style

Dana Thomson; Andrea Gaughan; Forrest Stevens; Gregory Yetman; Peter Elias; Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya." Urban Science 5, no. 2: 48.

Preprint
Published: 17 May 2021
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Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners. These datasets are evaluated for accuracy at the spatial scale of the input data which is often much courser (e.g. administrative units) than the neighbourhood or cell-level scale of many applications. We simulate a realistic "true" 2016 population in Khomas, Namibia, a majority urban region, and introduce realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate these simulated realistic populations by census and administrative boundaries (to mimic census data), and generate 32 gridded population datasets that are typical of a LMIC setting using WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these simulated datasets using the original "true" population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells, driven by the use of average population densities in large areal units to determine cell-level population densities. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy. It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales with relevance to small dense deprived areas within larger administrative units.

ACS Style

Dana R. Thomson; Douglas R. Leasure; Tomas Bird; Nikos Tzavidis; Andrew J. Tatem. How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia. 2021, 1 .

AMA Style

Dana R. Thomson, Douglas R. Leasure, Tomas Bird, Nikos Tzavidis, Andrew J. Tatem. How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia. . 2021; ():1.

Chicago/Turabian Style

Dana R. Thomson; Douglas R. Leasure; Tomas Bird; Nikos Tzavidis; Andrew J. Tatem. 2021. "How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia." , no. : 1.

Preprint
Published: 03 March 2021
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The majority of urban inhabitants in low- and middle-income country (LMIC) cities live in deprived urban areas. However, statistics and data (e.g., local monitoring of Sustainable Development Goals - SDGs) are hindered by the unavailability of spatial data at metropolitan, city and sub-city scales. Deprivation is a complex and multidimensional concept, which has been captured in existing literature with a strong focus on household-level deprivation while giving limited attention to area-level deprivation. Within this scoping review, we build on existing literature on household- as well as area-level deprivation frameworks to arrive at a combined understanding of how urban deprivation is defined with a focus on LMIC cities. The scoping review was enriched with local stakeholder workshops in LMIC cities to arrive at our framework of Domains of Deprivations, splitting deprivation into three different scales and nine domains. (1) Socio-Economic Status and (2) Housing Domains (Household scale); (3) Social Hazards & Assets, (4) Physical Hazards & Assets, (5) Unplanned Urbanization and (6) Contamination (Within Area scale); and (7) Infrastructure, (8) Facilities & Services and (9) city Governance (Area Connect scale). The Domains of Deprivation framework provides a clear guidance for collecting data on various aspects of deprivation, while providing the flexibility to decide at city level which indicators are most relevant to explain individual domains. The framework provides a conceptual and operational base for the Integrated Deprived Area Mapping System (IDEAMAPS) Project for the creation of a data ecosystem, which facilitates the production of routine, accurate maps of deprived “slum” areas at scale across cities in LMICs. The Domains of Deprivation Framework is designed to support diverse health, poverty, and development initiatives globally to characterize and address deprivation in LMIC cities.

ACS Style

Ángela Abascal; Natalie Rothwell; Adenike Shonowo; Dana R. Thomson; Peter Elias; Helen Elsey; Godwin Yeboah; Monika Kuffer. "Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review. 2021, 1 .

AMA Style

Ángela Abascal, Natalie Rothwell, Adenike Shonowo, Dana R. Thomson, Peter Elias, Helen Elsey, Godwin Yeboah, Monika Kuffer. "Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review. . 2021; ():1.

Chicago/Turabian Style

Ángela Abascal; Natalie Rothwell; Adenike Shonowo; Dana R. Thomson; Peter Elias; Helen Elsey; Godwin Yeboah; Monika Kuffer. 2021. ""Domains of Deprivation Framework" for Mapping Slums, Informal Settlements, and Other Deprived Areas in LMICs to Improve Urban Planning and Policy: A Scoping Review." , no. : 1.

Preprint
Published: 23 February 2021
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Low- and middle-income country cities face unprecedented urbanization and growth in slums. Gridded population data in small grid squares (e.g., 100x100m) derived from demographic and spatial data are a promising source of current population estimates, but face limitations in slums due to the dynamic nature of this population as well as modelling assumptions. The efficacy of using gridded population data in slum areas remains a question mark especially in the context of UN SDG indicator development. In this study, we use field-referenced boundaries and population counts from Slum Dwellers International (SDI) in Lagos (Nigeria), Port Harcourt (Nigeria), and Nairobi (Kenya) to assess the accuracy of nine gridded population datasets in slums. We also use a modelled map of all slums in Lagos to assess use of gridded population dataset for SDG11.1.1 (percent of population living in deprived areas). We found that all gridded population estimates vastly under-estimated population counts in populous slums, and the calculation of SDG11.1.1 in Lagos was impossibly low; gridded population datasets estimated that just 1-3% of the Lagos population lived in slums, compared to 56% using the UN-Habitat approach. We outline specific steps that might be taken to improve each gridded population dataset in deprived urban areas. While gridded population estimates are not yet sufficiently accurate to estimate SDG11.1.1, we are optimistic that some datasets could be following updates to their modelling approaches.

ACS Style

Dana R. Thomson; Andrea E. Gaughan; Forrest R. Stevens; Gregory Yetman; Peter Elias; Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. 2021, 1 .

AMA Style

Dana R. Thomson, Andrea E. Gaughan, Forrest R. Stevens, Gregory Yetman, Peter Elias, Robert Chen. Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya. . 2021; ():1.

Chicago/Turabian Style

Dana R. Thomson; Andrea E. Gaughan; Forrest R. Stevens; Gregory Yetman; Peter Elias; Robert Chen. 2021. "Evaluating the Accuracy of Gridded Population Estimates in Slums: A Case Study in Nigeria and Kenya." , no. : 1.

Article
Published: 27 October 2020 in Journal of Urban Health
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The methods used in low- and middle-income countries’ (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially and now face unprecedented rates of urbanization and urbanization of poverty. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. We found that a common household definition excluded single adults (46.9%) and migrant-headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying adults (14.3%). Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative area-microcensus design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money, and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. This evidence of exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning and underscores the need to modernize survey methods and practices.

ACS Style

Dana R. Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph Paul Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. Addressing Unintentional Exclusion of Vulnerable and Mobile Households in Traditional Surveys in Kathmandu, Dhaka, and Hanoi: a Mixed-Methods Feasibility Study. Journal of Urban Health 2020, 98, 111 -129.

AMA Style

Dana R. Thomson, Radheshyam Bhattarai, Sudeepa Khanal, Shraddha Manandhar, Rajeev Dhungel, Subash Gajurel, Joseph Paul Hicks, Duong Minh Duc, Junnatul Ferdoush, Tarana Ferdous, Nushrat Jahan Urmy, Riffat Ara Shawon, Khuong Quynh Long, Ak Narayan Poudel, Chris Cartwright, Hilary Wallace, Tim Ensor, Sushil Baral, Saidur Mashreky, Rumana Huque, Hoang Van Minh, Helen Elsey. Addressing Unintentional Exclusion of Vulnerable and Mobile Households in Traditional Surveys in Kathmandu, Dhaka, and Hanoi: a Mixed-Methods Feasibility Study. Journal of Urban Health. 2020; 98 (1):111-129.

Chicago/Turabian Style

Dana R. Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph Paul Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. 2020. "Addressing Unintentional Exclusion of Vulnerable and Mobile Households in Traditional Surveys in Kathmandu, Dhaka, and Hanoi: a Mixed-Methods Feasibility Study." Journal of Urban Health 98, no. 1: 111-129.

Review
Published: 09 September 2020 in International Journal of Health Geographics
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Introduction In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods We performed a systematic scoping review in Scopus of specific gridded population datasets and "population" or "household" "survey" reports, and solicited additional published and unpublished sources from colleagues. Results We identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, some survey teams interviewed all households in each cluster or segment, and others sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

ACS Style

Dana R. Thomson; Dale A. Rhoda; Andrew J. Tatem; Marcia C. Castro. Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda. International Journal of Health Geographics 2020, 19, 1 -16.

AMA Style

Dana R. Thomson, Dale A. Rhoda, Andrew J. Tatem, Marcia C. Castro. Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda. International Journal of Health Geographics. 2020; 19 (1):1-16.

Chicago/Turabian Style

Dana R. Thomson; Dale A. Rhoda; Andrew J. Tatem; Marcia C. Castro. 2020. "Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda." International Journal of Health Geographics 19, no. 1: 1-16.

Article
Published: 13 May 2020 in Social Sciences
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Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely siloed, and each fall short of producing accurate, timely, and comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human (visual) interpretation and machine classification of air or spaceborne imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of public services. We summarize common areas of understanding, and present a set of requirements and a framework to produce routine, accurate maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making across Low- and Middle-Income Countries (LMICs). We suggest that machine learning models be extended to incorporate social area-level covariates and regular contributions of up-to-date and context-relevant field-based classification of deprived urban areas.

ACS Style

Dana R. Thomson; Monika Kuffer; Gianluca Boo; Beatrice Hati; Tais Grippa; Helen Elsey; Catherine Linard; Ron Mahabir; Catherine Kyobutungi; Joshua Maviti; Dennis Mwaniki; Robert Ndugwa; Jack Makau; Richard Sliuzas; Salome Cheruiyot; Kilion Nyambuga; Nicholus Mboga; Nicera Wanjiru Kimani; Joao Porto de Albuquerque; Caroline Kabaria. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs). Social Sciences 2020, 9, 80 .

AMA Style

Dana R. Thomson, Monika Kuffer, Gianluca Boo, Beatrice Hati, Tais Grippa, Helen Elsey, Catherine Linard, Ron Mahabir, Catherine Kyobutungi, Joshua Maviti, Dennis Mwaniki, Robert Ndugwa, Jack Makau, Richard Sliuzas, Salome Cheruiyot, Kilion Nyambuga, Nicholus Mboga, Nicera Wanjiru Kimani, Joao Porto de Albuquerque, Caroline Kabaria. Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs). Social Sciences. 2020; 9 (5):80.

Chicago/Turabian Style

Dana R. Thomson; Monika Kuffer; Gianluca Boo; Beatrice Hati; Tais Grippa; Helen Elsey; Catherine Linard; Ron Mahabir; Catherine Kyobutungi; Joshua Maviti; Dennis Mwaniki; Robert Ndugwa; Jack Makau; Richard Sliuzas; Salome Cheruiyot; Kilion Nyambuga; Nicholus Mboga; Nicera Wanjiru Kimani; Joao Porto de Albuquerque; Caroline Kabaria. 2020. "Need for an Integrated Deprived Area “Slum” Mapping System (IDEAMAPS) in Low- and Middle-Income Countries (LMICs)." Social Sciences 9, no. 5: 80.

Review
Published: 19 April 2020
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Objective: In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than ten years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs. Methods: We performed a systematic review and identified 43 national and sub-national gridded population-based household surveys implemented across 29 LMICs. Findings: Gridded population surveys used automated and manual approaches to derive clusters from WorldPop and LandScan gridded population estimates. After sampling, many surveys interviewed all households in each cluster or segment, though some sampled households from larger clusters. Tools to select gridded population survey clusters include the GridSample R package, Geo-sampling tool, and GridSample.org. In the field, gridded population surveys generally relied on geographically accurate maps based on satellite imagery or OpenStreetMap, and a tablet or GPS technology for navigation. Conclusions: For gridded population survey sampling to be adopted more widely, several strategic questions need answering regarding cell-level accuracy and uncertainty of gridded population estimates, the methods used to group/split cells into sample frame units, design effects of new sample designs, and feasibility of tools and methods to implement surveys across diverse settings.

ACS Style

Dana R. Thomson; Dale A. Rhoda; Andrew J. Tatem; Marcia C. Castro. Gridded Population Survey Sampling: A Review of the Field and Strategic Research Agenda. 2020, 1 .

AMA Style

Dana R. Thomson, Dale A. Rhoda, Andrew J. Tatem, Marcia C. Castro. Gridded Population Survey Sampling: A Review of the Field and Strategic Research Agenda. . 2020; ():1.

Chicago/Turabian Style

Dana R. Thomson; Dale A. Rhoda; Andrew J. Tatem; Marcia C. Castro. 2020. "Gridded Population Survey Sampling: A Review of the Field and Strategic Research Agenda." , no. : 1.

Preprint
Published: 15 April 2020
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Ninety percent of the people added to the planet over the next 30 years will live in African and Asian cities, and a large portion of these populations will reside in deprived neighborhoods defined by slum conditions, informal settlement, or inadequate housing. The four current approaches to neighborhood deprivation mapping are largely silo-ed, and each fall short of producing accurate, timely, comparable maps that reflect local contexts. The first approach, classifying “slum households” in census and survey data and aggregating to administrative areas, reflects household-level rather than neighborhood-level deprivation. The second approach, field-based mapping, can produce the most accurate and context-relevant maps for a given neighborhood, however it requires substantial resources, preventing up-scaling. The third and fourth approaches, human interpretation and machine classification of satellite, aerial, or drone imagery, both overemphasize informal settlements, and fail to represent key social characteristics of deprived areas such as lack of tenure, exposure to pollution, and lack of basic public services. The latter, machine classification of imagery, can be automated and extended to incorporate new and multiple sources of data. This diverse collection of authors represent experts from these four approaches to neighborhood deprivation mapping. We summarize common areas of understanding, and present a set of requirements to produce maps of deprived urban areas that can be used by local-to-international stakeholders for advocacy, planning, and decision-making.

ACS Style

Dana Thomson; Monika Kuffer; Gianluca Boo; Beatrice Hati; Tais Grippa; Helen Elsey; Catherine Linard; Ron Mahabir; Catherine Kyobutungi; Joshua Maviti; Dennis Mwaniki; Robert Ndugwa; Jack Makau; Richard Sliuzas; Salome Cheruiyot; Kilion Nyambuga; Nicholus Mboga; Nicera Wanjiru; Joao Porto De Albuquerque; Caroline Kabaria. Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs. 2020, 1 .

AMA Style

Dana Thomson, Monika Kuffer, Gianluca Boo, Beatrice Hati, Tais Grippa, Helen Elsey, Catherine Linard, Ron Mahabir, Catherine Kyobutungi, Joshua Maviti, Dennis Mwaniki, Robert Ndugwa, Jack Makau, Richard Sliuzas, Salome Cheruiyot, Kilion Nyambuga, Nicholus Mboga, Nicera Wanjiru, Joao Porto De Albuquerque, Caroline Kabaria. Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs. . 2020; ():1.

Chicago/Turabian Style

Dana Thomson; Monika Kuffer; Gianluca Boo; Beatrice Hati; Tais Grippa; Helen Elsey; Catherine Linard; Ron Mahabir; Catherine Kyobutungi; Joshua Maviti; Dennis Mwaniki; Robert Ndugwa; Jack Makau; Richard Sliuzas; Salome Cheruiyot; Kilion Nyambuga; Nicholus Mboga; Nicera Wanjiru; Joao Porto De Albuquerque; Caroline Kabaria. 2020. "Need for an Integrated Deprived Area “Slum” Mapping System (IDeAMapS) in LMICs." , no. : 1.

Preprint
Published: 10 April 2020
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Background: The methods used in low- and middle-income countries (LMICs) household surveys have not changed in four decades; however, LMIC societies have changed substantially. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi, and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences. Methods: We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets. Results: We found that a common household definition excluded single adult (46.9%) and migrant headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying (14.3%) adults. Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative one-stage design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork. Conclusions: This evidence of unintentional exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning, and underscores the need to modernize survey methods and practices.

ACS Style

Dana Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph P Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka and Hanoi. 2020, 1 .

AMA Style

Dana Thomson, Radheshyam Bhattarai, Sudeepa Khanal, Shraddha Manandhar, Rajeev Dhungel, Subash Gajurel, Joseph P Hicks, Duong Minh Duc, Junnatul Ferdoush, Tarana Ferdous, Nushrat Jahan Urmy, Riffat Ara Shawon, Khuong Quynh Long, Ak Narayan Poudel, Chris Cartwright, Hilary Wallace, Tim Ensor, Sushil Baral, Saidur Mashreky, Rumana Huque, Hoang Van Minh, Helen Elsey. Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka and Hanoi. . 2020; ():1.

Chicago/Turabian Style

Dana Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph P Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. 2020. "Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka and Hanoi." , no. : 1.

Review
Published: 18 March 2020 in Remote Sensing
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Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

ACS Style

Monika Kuffer; Dana R. Thomson; Gianluca Boo; Ron Mahabir; Taïs Grippa; Sabine Vanhuysse; Ryan Engstrom; Robert Ndugwa; Jack Makau; Edith Darin; João Porto De Albuquerque; Caroline Kabaria. The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries. Remote Sensing 2020, 12, 982 .

AMA Style

Monika Kuffer, Dana R. Thomson, Gianluca Boo, Ron Mahabir, Taïs Grippa, Sabine Vanhuysse, Ryan Engstrom, Robert Ndugwa, Jack Makau, Edith Darin, João Porto De Albuquerque, Caroline Kabaria. The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries. Remote Sensing. 2020; 12 (6):982.

Chicago/Turabian Style

Monika Kuffer; Dana R. Thomson; Gianluca Boo; Ron Mahabir; Taïs Grippa; Sabine Vanhuysse; Ryan Engstrom; Robert Ndugwa; Jack Makau; Edith Darin; João Porto De Albuquerque; Caroline Kabaria. 2020. "The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries." Remote Sensing 12, no. 6: 982.

Research article
Published: 05 February 2020 in PLOS ONE
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Urbanisation brings with it rapid socio-economic change with volatile livelihoods and unstable ownership of assets. Yet, current measures of wealth are based predominantly on static livelihoods found in rural areas. We sought to assess the extent to which seven common measures of wealth appropriately capture vulnerability to poverty in urban areas. We then sought to develop a measure that captures the characteristics of one urban area in Nepal. We collected and analysed data from 1,180 households collected during a survey conducted between November 2017 and January 2018 and designed to be representative of the Kathmandu valley. A separate survey of a sub set of households was conducted using participatory qualitative methods in slum and non-slum neighbourhoods. A series of currently used indices of deprivation were calculated from questionnaire data. We used bivariate statistical methods to examine the association between each index and identify characteristics of poor and non-poor. Qualitative data was used to identify characteristics of poverty from the perspective of urban poor communities which were used to construct an Urban Poverty Index that combined asset and consumption focused context specific measures of poverty that could be proxied by easily measured indicators as assessed through multivariate modelling. We found a strong but not perfect association between each measure of poverty. There was disagreement when comparing the consumption and deprivation index on the classification of 19% of the sample. Choice of short-term monetary and longer-term capital approaches accounted for much of the difference. Those who reported migrating due to economic necessity were most likely to be categorised as poor. A combined index was developed to capture these dimension of poverty and understand urban vulnerability. A second version of the index was constructed that can be computed using a smaller range of variables to identify those in poverty. Current measures may hide important aspects of urban poverty. Those who migrate out of economic necessity are particularly vulnerable. A composite index of socioeconomic status helps to capture the complex nature of economic vulnerability.

ACS Style

Tim Ensor; Radheshyam Bhattarai; Shraddha Manandhar; Ak Narayan Poudel; Rajeev Dhungel; Sushil Baral; Joseph P. Hicks; Dana Thomson; Helen Elsey. From Rags to Riches: Assessing poverty and vulnerability in urban Nepal. PLOS ONE 2020, 15, e0226646 .

AMA Style

Tim Ensor, Radheshyam Bhattarai, Shraddha Manandhar, Ak Narayan Poudel, Rajeev Dhungel, Sushil Baral, Joseph P. Hicks, Dana Thomson, Helen Elsey. From Rags to Riches: Assessing poverty and vulnerability in urban Nepal. PLOS ONE. 2020; 15 (2):e0226646.

Chicago/Turabian Style

Tim Ensor; Radheshyam Bhattarai; Shraddha Manandhar; Ak Narayan Poudel; Rajeev Dhungel; Sushil Baral; Joseph P. Hicks; Dana Thomson; Helen Elsey. 2020. "From Rags to Riches: Assessing poverty and vulnerability in urban Nepal." PLOS ONE 15, no. 2: e0226646.

Journal article
Published: 27 January 2020 in Gates Open Research
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Traditional sample designs for household surveys are contingent upon the availability of a representative primary sampling frame. This is defined using enumeration units and population counts retrieved from decennial national censuses that can become rapidly inaccurate in highly dynamic demographic settings. To tackle the need for representative sampling frames, we propose an original grid-based sample design framework introducing essential concepts of spatial sampling in household surveys. In this framework, the sampling frame is defined based on gridded population estimates and formalized as a bi-dimensional random field, characterized by spatial trends, spatial autocorrelation, and stratification. The sampling design reflects the characteristics of the random field by combining contextual stratification and proportional to population size sampling. A nonparametric estimator is applied to evaluate the sampling design and inform sample size estimation. We demonstrate an application of the proposed framework through a case study developed in two provinces located in the western part of the Democratic Republic of the Congo. We define a sampling frame consisting of settled cells with associated population estimates. We then perform a contextual stratification by applying a principal component analysis (PCA) and k-means clustering to a set of gridded geospatial covariates, and sample settled cells proportionally to population size. Lastly, we evaluate the sampling design by contrasting the empirical cumulative distribution function for the entire population of interest and its weighted counterpart across different sample sizes and identify an adequate sample size using the Kolmogorov-Smirnov distance between the two functions. The results of the case study underscore the strengths and limitations of the proposed grid-based sample design framework and foster further research into the application of spatial sampling concepts in household surveys.

ACS Style

Gianluca Boo; Edith Darin; Dana Thomson; Andrew J. Tatem. A grid-based sample design framework for household surveys. Gates Open Research 2020, 4, 13 .

AMA Style

Gianluca Boo, Edith Darin, Dana Thomson, Andrew J. Tatem. A grid-based sample design framework for household surveys. Gates Open Research. 2020; 4 ():13.

Chicago/Turabian Style

Gianluca Boo; Edith Darin; Dana Thomson; Andrew J. Tatem. 2020. "A grid-based sample design framework for household surveys." Gates Open Research 4, no. : 13.

Correction
Published: 03 September 2019 in Journal of Urban Health
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Readers should note an additional Acknowledgment for this article: Dana Thomson is funded by the Economic and Social Research Council grant number ES/5500161/1.

ACS Style

Dana R. Thomson; Catherine Linard; Sabine VanHuysse; Jessica E. Steele; Michal Shimoni; Jose Siri; Waleska Teixeira Caiaffa; Megumi Rosenberg; Eléonore Wolff; Taïs Grippa; Stefanos Georganos; Helen Elsey. Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs. Journal of Urban Health 2019, 96, 792 -792.

AMA Style

Dana R. Thomson, Catherine Linard, Sabine VanHuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Teixeira Caiaffa, Megumi Rosenberg, Eléonore Wolff, Taïs Grippa, Stefanos Georganos, Helen Elsey. Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs. Journal of Urban Health. 2019; 96 (5):792-792.

Chicago/Turabian Style

Dana R. Thomson; Catherine Linard; Sabine VanHuysse; Jessica E. Steele; Michal Shimoni; Jose Siri; Waleska Teixeira Caiaffa; Megumi Rosenberg; Eléonore Wolff; Taïs Grippa; Stefanos Georganos; Helen Elsey. 2019. "Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs." Journal of Urban Health 96, no. 5: 792-792.

Article
Published: 18 June 2019 in Journal of Urban Health
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Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data—ideally to be made free and publicly available—and offer lay descriptions of some of the difficulties in generating such data products.

ACS Style

Dana R. Thomson; Catherine Linard; Sabine VanHuysse; Jessica E. Steele; Michal Shimoni; Jose Siri; Waleska Teixeira Caiaffa; Megumi Rosenberg; Eléonore Wolff; Taïs Grippa; Stefanos Georganos; Helen Elsey. Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs. Journal of Urban Health 2019, 96, 514 -536.

AMA Style

Dana R. Thomson, Catherine Linard, Sabine VanHuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Teixeira Caiaffa, Megumi Rosenberg, Eléonore Wolff, Taïs Grippa, Stefanos Georganos, Helen Elsey. Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs. Journal of Urban Health. 2019; 96 (4):514-536.

Chicago/Turabian Style

Dana R. Thomson; Catherine Linard; Sabine VanHuysse; Jessica E. Steele; Michal Shimoni; Jose Siri; Waleska Teixeira Caiaffa; Megumi Rosenberg; Eléonore Wolff; Taïs Grippa; Stefanos Georganos; Helen Elsey. 2019. "Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs." Journal of Urban Health 96, no. 4: 514-536.

Preprint
Published: 01 January 2019 in SSRN Electronic Journal
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Background: The methods used in low- and middle-income country (LMIC) household surveys have not changed in four decades; however, LMIC societies have changed substantially. This mismatch may result in unintentional exclusion of vulnerable and mobile urban populations. We compare three survey method innovations with standard survey methods in Kathmandu, Dhaka, and Hanoi, and summarize feasibility of our innovative methods in terms of time, cost, skill requirements, and experiences.Methods: We used descriptive statistics and regression techniques to compare respondent characteristics in samples drawn with innovative versus standard survey designs and household definitions, adjusting for sample probability weights and clustering. Feasibility of innovative methods was evaluated using a thematic framework analysis of focus group discussions with survey field staff, and via survey planner budgets.Findings: We found that a common household definition excluded single adult (46.9%) and migrant headed households (6.7%), as well as non-married (8.5%), unemployed (10.5%), disabled (9.3%), and studying (14.3%) adults. Further, standard two-stage sampling resulted in fewer single adult and non-family households than an innovative one-stage design; however, two-stage sampling resulted in more tent and shack dwellers. Our survey innovations provided good value for money and field staff experiences were neutral or positive. Staff recommended streamlining field tools and pairing technical and survey content experts during fieldwork.Interpretation: This evidence of unintentional exclusion of vulnerable and mobile urban populations in LMIC household surveys is deeply concerning, and underscores the need to modernize survey methods and practices.Funding: UK Medical Research Council and UK Economic and Social Research Council.Declaration of Interest: All authors declare no conflicts of interest.Ethical Approval: Ethics approvals were obtained from the University of Leeds (ref:MREC16-137), University of Southampton (ref:26819), Nepal Health Research Council (ref:1761), Bangladesh Medical Research Council (ref:BMRC/NREC/RP/2016-2019/317), and Hanoi University of Public Health (ref:324/2017/YTCC-HD3).

ACS Style

Dana Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph Paul Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka, and Hanoi. SSRN Electronic Journal 2019, 1 .

AMA Style

Dana Thomson, Radheshyam Bhattarai, Sudeepa Khanal, Shraddha Manandhar, Rajeev Dhungel, Subash Gajurel, Joseph Paul Hicks, Duong Minh Duc, Junnatul Ferdoush, Tarana Ferdous, Nushrat Jahan Urmy, Riffat Ara Shawon, Khuong Quynh Long, Ak Narayan Poudel, Chris Cartwright, Hilary Wallace, Tim Ensor, Sushil Baral, Saidur Mashreky, Rumana Huque, Hoang Van Minh, Helen Elsey. Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka, and Hanoi. SSRN Electronic Journal. 2019; ():1.

Chicago/Turabian Style

Dana Thomson; Radheshyam Bhattarai; Sudeepa Khanal; Shraddha Manandhar; Rajeev Dhungel; Subash Gajurel; Joseph Paul Hicks; Duong Minh Duc; Junnatul Ferdoush; Tarana Ferdous; Nushrat Jahan Urmy; Riffat Ara Shawon; Khuong Quynh Long; Ak Narayan Poudel; Chris Cartwright; Hilary Wallace; Tim Ensor; Sushil Baral; Saidur Mashreky; Rumana Huque; Hoang Van Minh; Helen Elsey. 2019. "Feasibility of Innovative Tools and Methods to Improve Household Surveys in Complex Urban Settings: Multiple Methods Analysis of the Surveys for Urban Equity (SUE) Study in Kathmandu, Dhaka, and Hanoi." SSRN Electronic Journal , no. : 1.

Global health
Published: 25 November 2018 in BMJ Open
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IntroductionAs rapid urbanisation transforms the sociodemographic structures within cities, standard survey methods, which have remained unchanged for many years, under-represent the urban poorest. This leads to an overly positive picture of urban health, distorting appropriate allocation of resources between rural and urban and within urban areas. Here, we present a protocol for our study which (i) tests novel methods to improve representation of urban populations in household surveys and measure mental health and injuries, (ii) explores urban poverty and compares measures of poverty and ‘slumness’ and (iii) works with city authorities to understand, and potentially improve, utilisation of data on urban health for planning more equitable services.Methods and analysisWe will conduct household surveys in Kathmandu, Hanoi and Dhaka to test novel methods: (i) gridded population sampling; (ii) enumeration using open-access online maps and (iii) one-stage versus two-stage cluster sampling. We will test reliability of an observational tool to categorise neighbourhoods as slum areas. Within the survey, we will assess the appropriateness of a short set of questions to measure depression and injuries. Questionnaire data will also be used to compare asset-based, consumption-based and income-based measures of poverty. Participatory methods will identify perceptions of wealth in two communities in each city. The analysis will combine quantitative and qualitative findings to recommend appropriate measures of poverty in urban areas. We will conduct qualitative interviews and establish communities of practice with government staff in each city on use of data for planning. Framework approach will be used to analyse qualitative data allowing comparison across city settings.Ethics and disseminationEthical approvals have been granted by ethics committees from the UK, Nepal, Bangladesh and Vietnam. Findings will be disseminated through conference papers, peer-reviewed open access articles and workshops with policy-makers and survey experts in Kathmandu, Hanoi and Dhaka.

ACS Style

Helen Elsey; Ak Narayan Poudel; Timothy Ensor; Tolib Mirzoev; James Nicholas Newell; Joseph Paul Hicks; Christopher Cartwright; David Wong; Caroline Tait; Sushil Baral; Radheshyam Bhattarai; Sudeepa Khanal; Rajeev Dhungel; Subash Gajurel; Shraddha Manandhar; Saidur Mashreky; Junnatul Ferdoush; Rumana Huque; Tarana Ferdous; Shammi Nasreen; Minh Hoang Van; Duong Minh Duc; Bao Ngoc; Dana Thomson; Hilary Wallace. Improving household surveys and use of data to address health inequities in three Asian cities: protocol for the Surveys for Urban Equity (SUE) mixed methods and feasibility study. BMJ Open 2018, 8, e024182 .

AMA Style

Helen Elsey, Ak Narayan Poudel, Timothy Ensor, Tolib Mirzoev, James Nicholas Newell, Joseph Paul Hicks, Christopher Cartwright, David Wong, Caroline Tait, Sushil Baral, Radheshyam Bhattarai, Sudeepa Khanal, Rajeev Dhungel, Subash Gajurel, Shraddha Manandhar, Saidur Mashreky, Junnatul Ferdoush, Rumana Huque, Tarana Ferdous, Shammi Nasreen, Minh Hoang Van, Duong Minh Duc, Bao Ngoc, Dana Thomson, Hilary Wallace. Improving household surveys and use of data to address health inequities in three Asian cities: protocol for the Surveys for Urban Equity (SUE) mixed methods and feasibility study. BMJ Open. 2018; 8 (11):e024182.

Chicago/Turabian Style

Helen Elsey; Ak Narayan Poudel; Timothy Ensor; Tolib Mirzoev; James Nicholas Newell; Joseph Paul Hicks; Christopher Cartwright; David Wong; Caroline Tait; Sushil Baral; Radheshyam Bhattarai; Sudeepa Khanal; Rajeev Dhungel; Subash Gajurel; Shraddha Manandhar; Saidur Mashreky; Junnatul Ferdoush; Rumana Huque; Tarana Ferdous; Shammi Nasreen; Minh Hoang Van; Duong Minh Duc; Bao Ngoc; Dana Thomson; Hilary Wallace. 2018. "Improving household surveys and use of data to address health inequities in three Asian cities: protocol for the Surveys for Urban Equity (SUE) mixed methods and feasibility study." BMJ Open 8, no. 11: e024182.

Research article
Published: 21 August 2018 in PLOS Medicine
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South Africa has the highest tuberculosis incidence globally (781/100,000), with an estimated 4.3% of cases being rifampicin resistant (RR). Control and elimination strategies will require detailed spatial information to understand where drug-resistant tuberculosis exists and why it persists in those communities. We demonstrate a method to enable drug-resistant tuberculosis monitoring by identifying high-burden communities in the Western Cape Province using routinely collected laboratory data. We retrospectively identified cases of microbiologically confirmed tuberculosis and RR-tuberculosis from all biological samples submitted for tuberculosis testing (n = 2,219,891) to the Western Cape National Health Laboratory Services (NHLS) between January 1, 2008, and June 30, 2013. Because the NHLS database lacks unique patient identifiers, we performed a series of record-linking processes to match specimen records to individual patients. We counted an individual as having a single disease episode if their positive samples came from within two years of each other. Cases were aggregated by clinic location (n = 302) to estimate the percentage of tuberculosis cases with rifampicin resistance per clinic. We used inverse distance weighting (IDW) to produce heatmaps of the RR-tuberculosis percentage across the province. Regression was used to estimate annual changes in the RR-tuberculosis percentage by clinic, and estimated average size and direction of change was mapped. We identified 799,779 individuals who had specimens submitted from mappable clinics for testing, of whom 222,735 (27.8%) had microbiologically confirmed tuberculosis. The study population was 43% female, the median age was 36 years (IQR 27–44), and 10,255 (4.6%, 95% CI: 4.6–4.7) cases had documented rifampicin resistance. Among individuals with microbiologically confirmed tuberculosis, 8,947 (4.0%) had more than one disease episode during the study period. The percentage of tuberculosis cases with rifampicin resistance documented among these individuals was 11.4% (95% CI: 10.7–12.0). Overall, the percentage of tuberculosis cases that were RR-tuberculosis was spatially heterogeneous, ranging from 0% to 25% across the province. Our maps reveal significant yearly fluctuations in RR-tuberculosis percentages at several locations. Additionally, the directions of change over time in RR-tuberculosis percentage were not uniform. The main limitation of this study is the lack of unique patient identifiers in the NHLS database, rendering findings to be estimates reliant on the accuracy of the person-matching algorithm. Our maps reveal striking spatial and temporal heterogeneity in RR-tuberculosis percentages across this province. We demonstrate the potential to monitor RR-tuberculosis spatially and temporally with routinely collected laboratory data, enabling improved resource targeting and more rapid locally appropriate interventions.

ACS Style

Avery I. McIntosh; Helen E. Jenkins; Laura F. White; Marinus Barnard; Dana R. Thomson; Tania Dolby; John Simpson; Elizabeth M. Streicher; Mary B. Kleinman; Elizabeth J. Ragan; Paul D. Van Helden; Megan B. Murray; Robin M. Warren; Karen R. Jacobson. Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis. PLOS Medicine 2018, 15, e1002638 .

AMA Style

Avery I. McIntosh, Helen E. Jenkins, Laura F. White, Marinus Barnard, Dana R. Thomson, Tania Dolby, John Simpson, Elizabeth M. Streicher, Mary B. Kleinman, Elizabeth J. Ragan, Paul D. Van Helden, Megan B. Murray, Robin M. Warren, Karen R. Jacobson. Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis. PLOS Medicine. 2018; 15 (8):e1002638.

Chicago/Turabian Style

Avery I. McIntosh; Helen E. Jenkins; Laura F. White; Marinus Barnard; Dana R. Thomson; Tania Dolby; John Simpson; Elizabeth M. Streicher; Mary B. Kleinman; Elizabeth J. Ragan; Paul D. Van Helden; Megan B. Murray; Robin M. Warren; Karen R. Jacobson. 2018. "Using routinely collected laboratory data to identify high rifampicin-resistant tuberculosis burden communities in the Western Cape Province, South Africa: A retrospective spatiotemporal analysis." PLOS Medicine 15, no. 8: e1002638.

Journal article
Published: 09 August 2018 in Data
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Whether evaluating gridded population dataset estimates (e.g., WorldPop, LandScan) or household survey sample designs, a population census linked to residential locations are needed. Geolocated census microdata data, however, are almost never available and are thus best simulated. In this paper, we simulate a close-to-reality population of individuals nested in households geolocated to realistic building locations. Using the R simPop package and ArcGIS, multiple realizations of a geolocated synthetic population are derived from the Namibia 2011 census 20% microdata sample, Namibia census enumeration area boundaries, Namibia 2013 Demographic and Health Survey (DHS), and dozens of spatial covariates derived from publicly available datasets. Realistic household latitude-longitude coordinates are manually generated based on public satellite imagery. Simulated households are linked to latitude-longitude coordinates by identifying distinct household types with multivariate k-means analysis and modelling a probability surface for each household type using Random Forest machine learning methods. We simulate five realizations of a synthetic population in Namibia’s Oshikoto region, including demographic, socioeconomic, and outcome characteristics at the level of household, woman, and child. Comparison of variables in the synthetic population were made with 2011 census 20% sample and 2013 DHS data by primary sampling unit/enumeration area. We found that synthetic population variable distributions matched observed observations and followed expected spatial patterns. We outline a novel process to simulate a close-to-reality microdata census geolocated to realistic building locations in a low- or middle-income country setting to support spatial demographic research and survey methodological development while avoiding disclosure risk of individuals.

ACS Style

Dana R. Thomson; Lieke Kools; Warren C. Jochem. Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia. Data 2018, 3, 30 .

AMA Style

Dana R. Thomson, Lieke Kools, Warren C. Jochem. Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia. Data. 2018; 3 (3):30.

Chicago/Turabian Style

Dana R. Thomson; Lieke Kools; Warren C. Jochem. 2018. "Linking Synthetic Populations to Household Geolocations: A Demonstration in Namibia." Data 3, no. 3: 30.

Journal article
Published: 22 June 2018 in International Journal Of Epidemiology
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ACS Style

Ann C Miller; Andres Garchitorena; Victor Rabeza; Marius Randriamanambintsoa; Hery-Tiana Rahaniraka Razanadrakato; Laura Cordier; Mohammed Ali Ouenzar; Megan B Murray; Dana R Thomson; Matthew H Bonds. Cohort Profile: Ifanadiana Health Outcomes and Prosperity longitudinal Evaluation (IHOPE). International Journal Of Epidemiology 2018, 1 .

AMA Style

Ann C Miller, Andres Garchitorena, Victor Rabeza, Marius Randriamanambintsoa, Hery-Tiana Rahaniraka Razanadrakato, Laura Cordier, Mohammed Ali Ouenzar, Megan B Murray, Dana R Thomson, Matthew H Bonds. Cohort Profile: Ifanadiana Health Outcomes and Prosperity longitudinal Evaluation (IHOPE). International Journal Of Epidemiology. 2018; ():1.

Chicago/Turabian Style

Ann C Miller; Andres Garchitorena; Victor Rabeza; Marius Randriamanambintsoa; Hery-Tiana Rahaniraka Razanadrakato; Laura Cordier; Mohammed Ali Ouenzar; Megan B Murray; Dana R Thomson; Matthew H Bonds. 2018. "Cohort Profile: Ifanadiana Health Outcomes and Prosperity longitudinal Evaluation (IHOPE)." International Journal Of Epidemiology , no. : 1.