Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Population and Environment
Erschienen in: Population and Environment 4/2020

27.05.2020 | Brief Report

Contextual data in IPUMS DHS: physical and social environment variables linked to the Demographic and Health Surveys

verfasst von: Elizabeth Heger Boyle, Miriam L. King, Sarah Garcia, Corey Culver, Jordan Bourdeaux

Erschienen in: Population and Environment | Ausgabe 4/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The Demographic and Health Surveys (DHS) are the most important source of comparative information on the health of women and young children in low- and middle-income countries and are well-suited for studies of the relationship between environmental factors and health. However, barriers have limited the use of the DHS for these purposes. IPUMS DHS, an online data dissemination tool, overcomes these barriers, simplifying comparative analyses with DHS. IPUMS DHS recently incorporated environmental variables that can easily be attached to individual or household records, facilitating the use of DHS data for the study of population and environment issues. We provide a brief introduction to IPUMS DHS, describe the current and anticipated environmental variables and how to use them, and provide an example of the novel research possibilities facilitated by this latest IPUMS DHS development. IPUMS-DHS is available free online at dhs.​ipums.​org.
Vorheriger Artikel Climate anomalies, land degradation, and rural out-migration in Uganda

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
1
Much of the source data was acquired as raster or vector files. Source raster files with a resolution greater than 500 m and vector files were converted to raster files with a resolution of 500 m. We used the Focal Statistics tool to update each pixel value within a 5- or 10-km circular buffer around the sample cluster GPS location.
 
2
For studies pairing NDVI data with DHS data, two publications (Grace et al. 2017; Davenport et al. 2017) used a 10-km buffer zone, while two others (Ickowitz et al. 2014; Brown et al. 2014) used a 5-km buffer zone. For both the 5- and 10-km buffer zones, these authors cited DHS recommendations and typical practice to justify their choice. A study pairing precipitation data with DHS data (Davenport et al. 2017) used a 5-km buffer zone. A study pairing temperature data with DHS data (Grace et al. 2012) used 10-km buffer zones. That some authors use 5- versus 10-km buffer zones in different articles using the same environmental sources suggests an openness to using either of these scales with DHS data.
 
3
For example, Pelillo (2012) used ACLED data with DHS data using buffer zones of 50, 100, and 200 km.
 
4
Once IPUMS DHS releases contextual variables for a given sample, the values for contextual variables for that sample are not recalculated, even if additional historical information is added by the producers of the original supplemental dataset (e.g., for ACLED data on conflicts). Revising the contextual variables after their original release would undermine the scientific replicability of publications incorporating contextual variables from IPUMS DHS. When new supplemental data become public (e.g., observed rather than estimated population counts for the year 2020), IPUMS DHS will calculate and release new contextual variables based on more recent data.
 
5
IPUMS DHS variable names are listed in bold font throughout the article.
 
6
Data on the 60 months prior to a survey provide information on climatic conditions during the lives of children under age 5 in that survey but admittedly do not capture conditions when the earliest-born young children were in utero. If we learn that many users desire this further information, we will reconsider this time frame and potentially add information for an additional 9 months before the survey start date.
 
7
We chose to use Gridded Population of the World data, rather than WorldPop data, because GPW-v4 does not rely on ancillary sources, such as land use and transportation data, when allocating population data from census units to grid cells. If researchers choose to use other data on transportation or land use in conjunction with GPW-4 data, they do not face endogeneity problems. Some researchers understandably consider WorldPop results more reliable due to its reliance on ancillary data and may prefer to construct their own measures of population density using WorldPop.
 
8
According to records of how frequently variables are downloaded, the DHS’s original urban (V025) variable on urban versus rural residence is among the most widely used variables. Popdensity supplies a supplemental measure that is comparable across countries.
 
9
Another source of conflict data is the Uppsala Conflict Data Program (UCDP; Cunningham et al. 2009), which measures fatalities in countries at war. We plan to incorporate the UCDP into IPUMS DHS in the near future.
 
10
Burundi 2016, Cameroon 2011, Democratic Republic of Congo 2013, Cote D’Ivoire 2011, Kenya 2014, Malawi 2016, Mali 2012, Mozambique 2011, Nigeria 2013, Rwanda 2014, Zimbabwe 2015, Tanzania 2015, and Zambia 2013. These countries were selected because they had all experienced some low levels of conflict, and the surveys included questions on IPV. Enumeration areas where there is intense conflict, as in parts of the Democratic Republic of Congo, are generally omitted, for interviewer security reasons, from DHS samples.
 
Literatur
Alemu, Z. A., Ahmed, A. A., Yalew, A. W., Birhanu, B. S., & Zaitchik, B. F. (2017). Individual and community level factors with a significant role in determining child height-for-age Z score in East Gojjam zone, Amhara regional state, Ethiopia: A multilevel analysis. Archives of Public Health, 75(1), 27–40.CrossRef
Amoah, B., Giorgi, E., Heyes, D. J., Burren, S., & Diggle, P. J. (2018). Geostatistical modelling of the association between malaria and child growth in Africa. Int J Health Geogr, 17(1), 7–19.CrossRef
Bakhtsiyarava, M., Grace, K., & Nawrotzki, R. J. (2018). Climate, birth weight, and agricultural livelihoods in Kenya and Mali. Am J Public Health, 108(S2), S144–S150.CrossRef
Balk, D., Storeygard, A., Levy, M., Gaskell, J., Sharma, M., & Flor, R. (2005). Child hunger in the developing world: An analysis of environmental and social correlates. Food Policy, 30(5–6), 584–611.CrossRef
Balogum, M. O., Owoaje, E. T., & Fawole, O. I. (2012). Intimate partner violence in southwestern Nigeria: Are there rural-urban differences? Women Health, 52(7), 627–645.CrossRef
Bhatt, S., Weiss, D., Cameron, E., Bisanzio, D., Mappin, B., Dalrymple, U., … Eckhoff, P. (2015). The effect of malaria control on plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572), 207–224.
Bouffard, L.A. & Muftic, L.R. (2006) The ‘rural mystique’: Social disorganization and violence beyond urban communities. Western Criminological Review, 7(3).
Brown, M. E., Grace, K., Shively, G., Johnson, K. B., & Carroll, M. (2014). Using satellite remote sensing and household survey data to assess human health and nutrition response to environmental change. Popul Environ, 36(1), 48–72.CrossRef
Burgert, C., Colston, J., Roy, T., & Zachary, B. (2013) Georeferenced displacement procedures and georeferenced data release policy for the Demographic and Health Surveys. DHS spatial analysis reports 7. ICF.
Butz, W. P., & Torrey, B. B. (2006). Some frontiers in social science. Science, 312(5782), 1898–1900.CrossRef
Carranza, E. (2014). Soil endowments, female labor force participation, and the demographic deficit of women in India. Am Econ J Appl Econ, 6(4), 197–225.CrossRef
Corsi, D. J., Neuman, M., Finlay, J. E., & Subramanian, S. (2012). Demographic and Health Surveys: A profile. Int J Epidemiol, 41(6), 1602–1613.CrossRef
Cuadros, D. F., Branscum, A. J., & Crowley, P. H. (2011a). HIV–malaria co-infection: Effects of malaria on the prevalence of HIV in East sub-Saharan Africa. Int J Epidemiol, 40(4), 931–939.CrossRef
Cuadros, D. F., Branscum, A. J., & García-Ramos, G. (2011b). No evidence of association between HIV-1 and malaria in populations with low HIV-1 prevalence. PLoS One, 6(8), e23458.CrossRef
Cunningham, D. E., Gleditsch, K. S., & Salehyan, I. (2009). It takes two: A dyadic analysis of civil war duration and outcome. J Confl Resolut, 53, 570–597.CrossRef
Darrouzet-Nardi, A. F. (2016). Nonviolent civil insecurity is negatively associated with subsequent height-for-age in children aged< 5 y born between 1998 and 2014 in rural areas of Africa, 2. Am J Clin Nutr, 105(2), 485–493.CrossRef
Davenport, F., Grace, K., Funk, C., & Shukla, S. (2017). Child health outcomes in sub-Saharan Africa: A comparison of changes in climate and socio-economic factors. Glob Environ Chang, 46, 72–87.CrossRef
Delavande, A., & Menezes Cordeiro, R. (2012). Violent conflicts and risky sexual behavior in Uganda (MICROCON Research Working Paper 60). Brighton: MICROCON.
Edwards, K. M. (2015). Intimate partner violence and the rural–urban–suburban divide: Myth or reality? A critical review of the literature. Trauma, Violence, & Abuse, 16(3), 359–373.CrossRef
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., … Hoell, A. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2, 150066.
Gallup-Black, A. (2005). Twenty years of rural and urban trends in family and intimate partner homicide: Does place matter? Homicide Stud, 9(2), 149–173.CrossRef
Grace, K., & Nagle, N. N. (2015). Using high-resolution remotely sensed data to examine the relationship between agriculture and fertility in Mali. Prof Geogr, 67(4), 641–654.CrossRef
Grace, K., Davenport, F., Funk, C., & Lerner, A. M. (2012). Child malnutrition and climate in sub-Saharan Africa: An analysis of recent trends in Kenya. Appl Geogr, 35(1–2), 405–413.CrossRef
Grace, K., Brown, M., & McNally, A. (2014). Examining the link between food prices and food insecurity: A multi-level analysis of maize price and birthweight in Kenya. Food Policy, 46, 56–65.CrossRef
Grace, K., Davenport, F., Hanson, H., Funk, C., & Shukla, S. (2015). Linking climate change and health outcomes: Examining the relationship between temperature, precipitation and birth weight in Africa. Glob Environ Chang, 35, 125–137.CrossRef
Grace, K., Frederick, L., Brown, M. E., Boukerrou, L., & Lloyd, B. (2017). Investigating important interactions between water and food security for child health in Burkina Faso. Popul Environ, 39(1), 26–46.CrossRef
Grace, K., Nagle, N. N., Burgert-Brucker, C. R., Rutzick, S., Van Riper, D. C., Dontamsetti, T., & Croft, T. (2019). Integrating environmental context into DHS analysis while protecting participant confidentiality: A new remote sensing method. Popul Dev Rev, 45(1), 197–218.CrossRef
Headey, D. D., & Hoddinott, J. (2016). Agriculture, nutrition and the green revolution in Bangladesh. Agric Syst, 149, 122–131.CrossRef
Hengl, T., de Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M., Blagotić, A., et al. (2017). SoilGrids250m: Global gridded soil information based on machine learning. PLoS One, 12(2), e0169748.CrossRef
Hirvonen, K., & Hoddinott, J. (2017). Agricultural production and children’s diets: Evidence from rural Ethiopia. Agric Econ, 48(4), 469–480.CrossRef
Hyland, M., & Russ, J. (2019). Water as destiny–the long-term impacts of drought in sub-Saharan Africa. World Dev, 115, 30–45.CrossRef
Ickowitz, A., Powell, B., Salim, M. A., & Sunderland, T. C. (2014). Dietary quality and tree cover in Africa. Glob Environ Chang, 24, 287–294.CrossRef
Jankowska, M. M., Lopez-Carr, D., Funk, C., Husak, G. J., & Chafe, Z. A. (2012). Climate change and human health: Spatial modeling of water availability, malnutrition, and livelihoods in Mali, Africa. Appl Geogr, 33, 4–15.CrossRef
Johnson, K., & Brown, M. E. (2014). Environmental risk factors and child nutritional status and survival in a context of climate variability and change. Appl Geogr, 54, 209–221.CrossRef
Jones, A. D., Shrinivas, A., & Bezner-Kerr, R. (2014). Farm production diversity is associated with greater household dietary diversity in Malawi: Findings from nationally representative data. Food Policy, 46, 1–12.CrossRef
Joy, E. J., Broadley, M. R., Young, S. D., Black, C. R., Chilimba, A. D., Ander, E. L., … Watts, M. J. (2015). Soil type influences crop mineral composition in Malawi. Science of the Total Environment, 505, 587–595.
Kazembe, L. N., Appleton, C. C., & Kleinschmidt, I. (2007). Spatial analysis of the relationship between early childhood mortality and malaria endemicity in Malawi. Geospat Health, 2(1), 41–50.CrossRef
Kelly, J. T., Colantuoni, E., Robinson, C., & Decker, M. R. (2018). From the battlefield to the bedroom: A multilevel analysis of the links between political conflict and intimate partner violence in Liberia. BMJ Glob Health, 3(2), e000668.CrossRef
Kunnuji, M. O. (2016). Population density and armed robbery in Nigeria: An analysis of variation across states. African Journal of Criminology and Justice Studies, 9(1), 62–73.
León, F. R. (2012). The latitudinal tilts of wealth and education in Peru: Testing them, explaining them, and reflecting on them. Economía, 35(70), 60.
Lillepold, K., Aimone, A., Keino, S., & Braitstein, P. (2018). Assessing the effect of drought severity on height-for-age z-score in Kenyan children: A secondary analysis. The Lancet Planetary Health, 2, S13.CrossRef
Liu, J., Fritz, S., Van Wesenbeeck, C., Fuchs, M., You, L., Obersteiner, M., & Yang, H. (2008). A spatially explicit assessment of current and future hotspots of hunger in sub-Saharan Africa in the context of global change. Glob Planet Chang, 64(3–4), 222–235.CrossRef
Mabaso, M., Appleton, C., Hughes, J., & Gouws, E. (2003). The effect of soil type and climate on hookworm (Necator americanus) distribution in KwaZulu Natal, South Africa. Tropical Med Int Health, 8(8), 722–727.CrossRef
MacVicar, S. (2016). The impacts of meteorological exposures on perinatal health: A mixed methods study of Kanungu District, Uganda (Doctoral Dissertation, McGill University Libraries).
Malcomb, D. W., Weaver, E. A., & Krakowka, A. R. (2014). Vulnerability modeling for sub-Saharan Africa: An operationalized approach in Malawi. Appl Geogr, 48, 17–30.CrossRef
Matthews, S. A. (2008). Advanced spatial analysis training program for population scientists—Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) R25 proposal. Population Research Institute, Pennsylvania State University.
Mohammed, M. A. (2016). Patterns of children’s work during schooling and its association with school attendance in coffee and non-coffee picking seasons in intense coffee growing regions in Sidama Zone, Southern Ethiopia: A mixed-methods study (Master’s Thesis). Retrieved from http://​hdl.​handle.​net/​10642/​3298.
Monfreda, C., Ramankutty, N., & Foley, J. A. (2008). Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Global Biogeochemical Cycles, 22(1).
Mulmi, P., Block, S. A., Shively, G. E., & Masters, W. A. (2016). Climatic conditions and child height: Sex-specific vulnerability and the protective effects of sanitation and food markets in Nepal. Economics & Human Biology, 23, 63–75.CrossRef
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V., Underwood, E. C., et al. (2001). Terrestrial ecoregions of the world: A new map of life on earth. BioScience, 51(11), 933–938.CrossRef
Østby, G. (2016). Violence begets violence: Armed conflict and domestic sexual violence in sub-Saharan Africa. Paper presented at the workshop on sexual violence and armed conflict: New Research Frontiers held at the Harvard Kennedy School, Harvard University.
Perez-Heydrich, C., Warren, J. L., Burgert, C. R., & Emich, M. E. (2016). Influence of demographic and health survey point displacements on raster-based analyses. Spatial Demography, 4(2), 135–153.CrossRef
Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: An armed conflict location and event dataset: Special data feature. J Peace Res, 47(5), 651–660.CrossRef
Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008). Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles, 22(1).
Rosenberg, A. M., Maluccio, J. A., Harris, J., Mwanamwenge, M., Nguyen, P. H., Tembo, G., & Rawat, R. (2018). Nutrition-sensitive agricultural interventions, agricultural diversity, food access and child dietary diversity: Evidence from rural Zambia. Food Policy, 80, 10–23.CrossRef
Slavchevska, V. (2015). Agricultural production and the nutritional status of family members in Tanzania. J Dev Stud, 51(8), 1016–1033.CrossRef
Wayoro, D. (2018). Impact of armed conflicts on child welfare in Côte d’Ivoire. Paper presented at the meeting of the American Economic Association, Philadelphia, PA.
Woodruff, B. A., Wirth, J. P., Bailes, A., Matji, J., Timmer, A., & Rohner, F. (2017). Determinants of stunting reduction in Ethiopia 2000–2011. Maternal & Child Nutrition, 13(2), e12307.CrossRef
Metadaten
Titel
Contextual data in IPUMS DHS: physical and social environment variables linked to the Demographic and Health Surveys
verfasst von
Elizabeth Heger Boyle
Miriam L. King
Sarah Garcia
Corey Culver
Jordan Bourdeaux
Publikationsdatum
27.05.2020
Verlag
Springer Netherlands
Erschienen in
Population and Environment / Ausgabe 4/2020
Print ISSN: 0199-0039
Elektronische ISSN: 1573-7810
DOI
https://doi.org/10.1007/s11111-020-00348-4

Weitere Artikel der Ausgabe 4/2020

Population and Environment 4/2020 Zur Ausgabe

Original Paper

Climate anomalies, land degradation, and rural out-migration in Uganda

Original Paper

Economic outlook and the gender gap in attitudes about climate change

Original Paper

Investigating the linkages between pregnancy outcomes and climate in sub-Saharan Africa

Original Paper

Natural hazard information and migration across cities: evidence from the anticipated Nankai Trough earthquake

Original Paper

Using the theory of planned behavior to identify key beliefs underlying heat adaptation behaviors in elderly populations