Client social media data

Appendix

Data Sources for Understanding the Social Determinants of Health: Examples from Two Middle-Income Countries: the 3D Commission

Torres I, Thapa B, Robbins G, Koya SF, Abdalla SM, Arah OA, Weeks WB, Zhang L, Asma S, Vega J, Galea S, Larson HJ, Rhee K

Supplemental List 1. References for Kenya data sources

1. Citizen-Generated Data and Sustainable Development. Evidence from Case Studies in Kenya and Uganda.; 2017. https://www.local2030.org/library/306/Citizen-generated-data-and- sustainable-development-Evidence-from-case-studies-in-Kenya-and-Uganda.pdf

2. Bauer JM, Mburu S. Effects of drought on child health in Marsabit District, Northern Kenya.

Econ Hum Biol. 2017;24. doi:10.1016/j.ehb.2016.10.010

3. De Villiers L, Chetty R. Data in FSP decision-making Findings from six African countries.

Published 2018. https://cenfri.org/publications/data-in-financial-service-provider-decision- making/

4. Fiedler JL, Afidra R, Mugambi G, et al. Maize flour fortification in Africa: markets, feasibility, coverage, and costs. Ann N Y Acad Sci. 2014;1312(1). doi:10.1111/nyas.12266

5. Gao X, Kelley DW. Understanding how distance to facility and quality of care affect maternal health service utilization in Kenya and Haiti: A comparative geographic information system study. Geospatial Health. 2019;14(1). doi:10.4081/gh.2019.690

6. Gold J, Andrews H, Appleford G, et al. Using mobile phone text messages (SMS) to collect health service data: Lessons from social franchises in Kenya, Madagascar and the Philippines. J Health Inform Dev Ctries. 2012;6(2). https://jhidc.org/index.php/jhidc/article/view/87

7. Hjort J, Poulsen J. The Arrival of Fast Internet and Employment in Africa. Am Econ Rev.

2019;109(3). doi:10.1257/aer.20161385

8. Mahabir R, Agouris P, Stefanidis A, Croitoru A, Crooks AT. Detecting and mapping slums using open data: a case study in Kenya. Int J Digit Earth. 2020;13(6):683-707.

doi:10.1080/17538947.2018.1554010

9. Lucas AM, Mbiti IM. Access, Sorting, and Achievement: The Short-Run Effects of Free Primary Education in Kenya. Am Econ J Appl Econ. 2012;4(4). doi:10.1257/app.4.4.226 10. Maina I, Wanjala P, Soti D, Kipruto H, Droti B, Boerma T. Using health-facility data to assess

subnational coverage of maternal and child health indicators, Kenya. Bull World Health Organ.

2017;95(10). doi:10.2471/BLT.17.194399

11. Mekuria LA, de Wit TF, Spieker N, et al. Analyzing data from the digital healthcare exchange platform for surveillance of antibiotic prescriptions in primary care in urban Kenya: A mixed- methods study. PLOS ONE. 2019;14(9). doi:10.1371/journal.pone.0222651

12. Sandefur J, Glassman A. The Political Economy of Bad Data: Evidence from African Survey and Administrative Statistics. J Dev Stud. 2015;51(2):116-132.

doi:10.1080/00220388.2014.968138

13. van Wijk M, Hammond J, Gorman L, et al. The Rural Household Multiple Indicator Survey, data from 13,310 farm households in 21 countries. Sci Data. 2020;7(1). doi:10.1038/s41597- 020-0388-8

14. Wane W. Kenya - Service Delivery Indicators Education Survey 2012 - Harmonized Public Use Data.; 2017. https://microdata.worldbank.org/index.php/catalog/2755/pdf-documentation

15. Wesolowski A, Eagle N, Noor AM, Snow RW, Buckee CO. The impact of biases in mobile phone ownership on estimates of human mobility. J R Soc Interface. 2013;10(81).

doi:10.1098/rsif.2012.0986

16. Wesolowski A, O’Meara WP, Tatem AJ, Ndege S, Eagle N, Buckee CO. Quantifying the Impact of Accessibility on Preventive Healthcare in Sub-Saharan Africa Using Mobile Phone Data. Epidemiology. 2015;26(2). doi:10.1097/EDE.0000000000000239

17. Williams S, Marcello E, Klopp JM. Toward Open Source Kenya: Creating and Sharing a GIS Database of Nairobi. Ann Assoc Am Geogr. 2014;104(1). doi:10.1080/00045608.2013.846157

Supplemental List 2. References for the Philippines data sources

1. Asian Development Bank. Mapping Poverty through Data Integration and Artificial Intelligence: Special Supplement of the Key Indicators for Asia and the Pacific.; 2020.

doi:10.22617/FLS200215-3

2. Estoque RC, Ooba M, Seposo XT, et al. Heat health risk assessment in Philippine cities using remotely sensed data and social-ecological indicators. Nat Commun. 2020;11.

doi:10.1038/s41467-020-15218-8

3. Google Flu Trends Data. Accessed October 8, 2020. https://www.google.org/flutrends/about/

4. Uy FAA, Vea LA, Binag MG, et al. The Potential of New Data Sources in a Data-Driven Transportation, Operation, Management and Assessment System (TOMAS). In: IEEE; 2020.

doi:10.1109/SusTech47890.2020.9150505

5. Fatehkia M, Tingzon I, Orden A, et al. Mapping socioeconomic indicators using social media advertising data. EPJ Data Sci. 2020;9(1). doi:10.1140/epjds/s13688-020-00235-w

6. Gold J, Andrews H, Appleford G, et al. Using mobile phone text messages (SMS) to collect health service data: Lessons from social franchises in Kenya, Madagascar and the Philippines. J Health Inform Dev Ctries. 2012;6(2). https://jhidc.org/index.php/jhidc/article/view/87

7. Jongman B, Wagemaker J, Romero B, de Perez E. Early Flood Detection for Rapid

Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals. ISPRS Int J Geo-Inf. 2015;4(4). doi:10.3390/ijgi4042246

8. Philippines Statistical Authority (PSA). Use of Citizen Generated Data for SDG Reporting in the Philippines: A Case Study.; 2020.

9. Read L, Atinc TM. Investigations into Using Data to Improve Learning: Philippines Case Study. Brookings Institute: Global Economy and Development.; 2017.

10. Tingzon I, Orden A, Go KT, et al. Mapping Poverty in the Philippines Using Machine Learning, Satellite Imagery and Crowd-Sourced Geospatial information. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci. 2019;XLII-4/W19. doi:10.5194/isprs-archives-XLII-4-W19-425-2019

11. Travaglia C, Profeti G, Aguilar Manjarrez J, Lopez NA. Mapping coastal aquaculture and fisheries structures by satellite imaging radar. FAO. Fisheries Technical Paper 459.

12. World Bank. Getting a Grip on Climate Change in the Philippines : Extended Technical Report.; 2013. https://openknowledge.worldbank.org/handle/10986/

13. World Bank Group. Transport and ICT. Open Data for Sustainable Development. Policy Note ICT01.; 2015. http://pubdocs.worldbank.org/en/904051440717425994/Open-Data-for-

Sustainable-development-Final-New.pdf

14. World Bank Group. World Development Report 2016: Digital Dividends. Published 2016.

https://openknowledge.worldbank.org/handle/10986/23347

15. World Bank Group. Big Data Innovation Challenge: Pioneering Approaches to Data-Driven Development.; 2016.

16. Dayrit M, Lagrada L, Picazo O, Pons M, Villaverde M. Philippines Health System Review.

World Health Organization. Regional Office for South-East Asia.; 2018.

https://apps.who.int/iris/handle/10665/274579

Supplemental Table 1. Data sources for Kenya

A. Data Source Type = Traditional

Data Sources Name of the specific data source [where / if applicable]

Use of data / information captured Surveys 1. Demographic and Health Survey (DHS)

2. Maternal and Child Health Indicator Survey

3. Kenya malaria indicator survey 4. Kenya AIDS Indicator Survey

5. USAID Demographic and Health Survey program

6. Service Provision Assessment

7. Kenya Service Delivery Indicators (World Bank, African Economic Research Consortium & African Development Bank)

8. Rural Household Multiple Indicator Survey

9. Welfare Monitoring Survey (WMS) 10. Kenya Integrated Household Budget

Survey (KIHBS) 11. Afrobarometer Survey

12. World Bank Enterprise Survey

 Health facility services

 Health facilities human resources, infrastructure & supplies

 Health insurance coverage

 Employment & socio-economic data

 Residence

 Healthcare access and utilization

 Food security indicators such as the Probability of Poverty Index, the Household Food Insecurity Access Scale, and household dietary diversity

Census 1. Census of Population and Housing  Housing information Administrativ

e

1. Health facility reporting through web system (DHIS 2)

2. Index-based Livestock Insurance child and household panel data

3. Administrative records of the Kenya National Examination Council, 4. Education Management Information

System (EMIS) of the Ministry of Education

5. Kenyan National Examination 6. Official maps

 Aggregated patient level information

 School type

 Student enrollment, absenteeism, transition and dropout

 Educational statistics

(performance, toilet facilities, access to safe water, # and type of classrooms, student/teacher ratios, human resources and budget)

 Individual and household characteristics

 Income

 Households receiving food aid

 Education of household

 Livestock insurance and diversity to determine food availability

B. Data Source Type = New Data

Sources

Name of the specific data source [where / if applicable]

Use of data / information captured Open data 1. Kenya Open Data

Repository  Location of private primary schools

 Location of private health care facilities

 Road quality

 Places of worship as slum indicator Search

engine

1. Online identification of real estate agencies

2. Local news

 Real estate activity

 Mentions of slums/informal settlements Digital

platform

1. Payment platform (“health wallet”)

2. Credit bureau



Customer transactional data



Customer interaction data



Client utility bills



Client social media data

phone

1. Text messages (SMS) 2. Mobility records 3. Call detail

records/communication logs

 Digital healthcare claims data

 Digital medical prescriptions

 Health service delivery reported data

 Stock orders of health facilities

 Enquiry messages (health services)

 Payment data

 Airtime expenditure

 Mobility estimates & travel-time data Social

media data

1. Flickr API  Location of slums or informal settlements Satellite

imagery

1. Digital Earth Africa

2. Landsat 8  Texture measures and vegetation cover (to identify slums)

 Road density GIS data 3. GIS data available via DHS

survey

4. OpenStreetMap 5. Google Map Maker 6. Google Earth Engine 7. Landscan (ORNL), 8. Majidata

9. Road network GIS data files

10. Land use GIS data files

 Health facility mapping

 Information on # of buildings and housing clusters

 Scanned georegistered version of maps

 Population density,

 Road density and quality

 Street intersections

 Pit latrines

 Water kiosks

 Hazardous locations

 Individually and spatially aggregated travel patterns

Remote sensing

1. Normalized difference vegetation index (NDVI) satellite data



Photosynthetic activity data to gauge vegetation cover as a drought indicator (which in a pastoral context reflects food availability)

Citizen- generated data

1. Citizen-generated data via National Taxpayers Association of Kenya

 School safety and protection School facilities

 Access to textbooks

 School processes

 School management performance

 Parental involvement

 Water, sanitation and health

Supplemental Table 2. Data sources for Philippines A. Data Source Type = Traditional

Data Sources Name of the specific data source [where / if applicable]

Use of data / information captured Surveys 13. Demographic and Health Survey (DHS)

14. Family Health Survey, Maternal and Child Health Survey (MCHS) 15. Functional Literacy, Education and

Mass Media Survey (FLEMMS) 16. Labor Force Survey

17. Agriculture Labor Survey 18. Occupational Wages Survey

19. Family Income and Expenditure Survey (FIES)

20. Annual Poverty Indicators Survey (APIS)

21. Integrated Survey on Labor and Employment (ISLE)

22. Labor Turnover Survey (LTS) 23. Crops Production Survey (CPS) 24. Farm Prices Survey (FPS), Agriculture

Labor Survey (ALS)

25. Household Energy Consumption Survey, Survey on Energy Consumption of Establishments

 Healthcare access and utilization

 School attendance; out-of-school children; drop- out rates

 Labor market information (including info on wages, employment)

 Housing ownership; housing conditions

 Household/individual level information on income, consumption and poverty

 Information on agricultural land, crops production and prices, farm labor and wages

 Sources of energy at the household; perceptions about climate change

Census 2. Census of Population and Housing

-2015 (the latest census)  Housing information Administrativ

e

1. Community Health Information Tracking System (CHITS) based EMR and field health service information system

2. Enhanced Basic Education Information System; Education Management Information System

3. The labor market information (LMI) system maintained by the Department of Labor and Employment (DOLE)

4. Administrative data available from rail, road, maritime, and air transportation authorities.

 Aggregated patient level information; health facility information

 Student enrollment information;

school completion information;

school information (including info on infrastructure)

 Labor market information

 Information on road, rail, air, and maritime transport

B. Data Source Type = New Data

Sources

Name of the specific data source [where / if applicable]

Use of data / information captured

Cell phone data

1. Open Roads platform 2. Open Traffic

platform

 Cell phone records used in conjunction with internet data to do natural language processing (NPL) with an end goal of matching people for jobs

 Combined mobile phone data with geo-tagged video data, image data, and official road data for information on traffic management and ensuring accountability in road investment Satellite

imagery

1. Open Roads platform 2. Open Traffic

platform 3. Night time

imagery data

 Access to public transportation

 Combination of mobile phone data+ geo- tagged video data+

image data + official road data for information on traffic management and ensuring accountability in road investment

 Poverty prediction by combining night time image data with income data from survey

Social media data

1. Facebook based advertising data 2. Twitter activity

data Google

trends data

1. Google trends

search  Mapping the spread of dengue

GIS data 1. GIS data available via DHS survey

2. Open Roads platform 3. Open Traffic platform

 Health facility mapping

 Combination of mobile phone data+ geo tagged video data+

image data + official road data for information on traffic management and ensuring accountability in road investment

 Information on # of buildings and housing clusters through Open Street Maps

 Satellite flood signal data combined with twitter activity data Remote

sensing data

1. Satellite imaging radar (SAR) data

 Mapping coastal aquaculture and fisheries structures

Citizen- generate d data

1. Citizen- generated data via CSOs and NGOs, most of which operate at the sub- national level but some operate at the national level

 Information on sexual and reproductive health (SRH), nutrition

 Information on scholarships, training, seminars, day care center support

 Livelihood (including employment) opportunities for those living in poverty

 People living in slums or in sub-optimal housing conditions

 Financial and employment opportunities for people in poverty, providing means for access to basic services

 Sustainable forestry and fishing practices

 Monitoring of fish catch, reefs, forest cover; disaster risk reduction and solid waste management; pollution monitoring

 Geo-tagged video data, image data, and official road data

Supplemental Table 3.

Search strategy

Question MeSH terms Synonymous keywords/phrases

Data Collection/ Data sources

"Citizen Science"[Mesh]

"Remote Sensing Technology"[Mesh]

"Big Data"[Mesh]

"Cell Phone"[Mesh]

"Data Mining"[Mesh]

"Medical Records Systems, Computerized"[Mesh]

"Electronic Health Records"[Mesh]

"Geographic Information Systems"[Mesh]

"Data Collection"[Mesh]

"Citizen Science"

"Crowd Sourc*"

"Citizen-collected data"

"Citizen collected data"

"Remote Sensing Technolog*"

"Remote Tracking data" "Wearable tech"

"Wearable Technology"

"Big Data"

"Mobile Phone*"

"Cellular Phone*"

"Cell Phone*"

"Cell phone data"

"Mobile phone data"

"Cellular Data"

"Social network data"

"Web scraping"

"Mining, Data"

"Text Mining"

"Mining, Text"

"Geographic Information System"

"Information System, Geographic"

"Information Systems, Geographic"

"Geographical Information Systems"

"Geographical Information System"

"Information System, Geographical"

"Information Systems, Geographical"

"System, Geographical Information"

"Systems, Geographical Information"

"Global Positioning Systems"

"Positioning System, Global"

"Positioning Systems, Global"

"System, Global Positioning"

"Systems, Global Positioning"

"Global Positioning System"

"GIS Mapping"

"Data Sources”

“Data Source”

Social Determinants of Health

("Social Determinants of Health"[Mesh]

"Social Determinants of health"

Health Care "Health Services Accessibility"[Mesh]

"Delivery of Health Care"[Mesh]

"Access to Health Care"

"Accessibility of Health Services"

“Delivery of Healthcare"

"Healthcare Delivery"

"Delivery of Health Care"

Education "Educational Status"[Mesh] "Educational Status"

Access to Healthy Choices

“Social Environment"[Mesh]

"Health Promotion"[Mesh]

"Healthy Lifestyle"[Mesh]

"Social Environment"

"Health Promotion"

"Wellness Program*”

"Healthy Lifestyles"

"Healthy Life Style"

Labor/ Employment "Employment"[Mesh] "Employment"

"Employment Status"

"Status, Employment"

"Status, Occupational"

"Occupational Status"

Housing "Housing" [Mesh]

“Homeless Persons” [Mesh]

“Slums”

“Slum”

“Persons, Homeless”

“Person, Homeless”

“Homelessness”

Transportation "Transportation"[Mesh]

"Walking"[Mesh]

"Transportation"

"Commuting"

"Commute"

"Environment Design"

"Environmental Design"

"walking"

"public transportation"

"Public transport"

Income "Socioeconomic

Factors"[Mesh]

"Social Class"[Mesh]

"Poverty"[Mesh]

"Income"[Mesh]

"Social Class*"

"Socioeconomic Status"

"Poverty"

"Income"

"Socioeconomic factor*"