Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses

Warren, Joshua L.; Perez-Heydrich, Carolina; Burgert, Clara R.; Emch, Michael

doi:10.1007/s40980-015-0014-0

Cited by 18 publications

(10 citation statements)

References 38 publications

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“…The Global Forest Change data are downloadable as tiff panels; we downloaded those panels covering the spatial extent of our 15 study countries in SSA (Hansen et al, 2013). The georeferenced DHS cluster locations are randomly displaced in order to protect the confidentiality of the survey respondents (Warren et al, 2016). The large majority (99%) of the locations are displaced by 0-5 km, with a remaining 1% of rural clusters displaced to a maximum of 10 km (Measure DHS/ICF International, 2012).…”

Section: Independent Variablementioning

confidence: 99%

Deforestation and Household- and Individual-Level Double Burden of Malnutrition in Sub-saharan Africa

Acharya¹,

Naz²,

Galway³

et al. 2020

Front. Sustain. Food Syst.

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Introduction: Although forests and forest-based ecosystems have been shown to influence health and sustainable diets, there is limited evidence on how deforestation affects the current nutrition transition and the double burden of malnutrition. We examined the relationship between deforestation and the individual-and household-level double burden of malnutrition in 15 countries in Sub-Saharan Africa. Materials and methods: We combined data from geolocated Demographic and Health Surveys and the Global Forest Change dataset. We defined household-level double burden of malnutrition as the co-occurrence of an overweight woman of childbearing age (WCBA) and a stunted preschool child (PSC) within the same household. We defined individual-level double burden in two ways: (1) as the co-occurrence of overweight and anemia within an individual WCBA, and (2) as the co-occurrence of overweight and stunting within a PSC. We used logistic regression analysis to examine the association between forest cover loss and these three measures after adjusting for potential confounders. We also assessed the mechanisms linking forest cover loss and nutritional status, such as livestock ownership and access to clean water. Results: In our sample, the prevalence rates of the three measures of the double burden were: overweight and anemic WCBA: 8.4%, overweight WCBA and stunted PSC: 6.9%, overweight and stunted PSC: 2.7%. After adjusting for the confounders as well as country fixed effects and the month of the survey, forest cover loss was marginally associated with a higher odds of an overweight WCBA and stunted PSC [odds ratio (95% CI): 4.80 (0.82, 28.25)]. We found no association between forest cover loss and odds of an overweight and stunted PSC [odds ratio (95% CI): 2.47 (0.80, 7.60)] or the odds of an anemic and overweight WCBA [odds ratio (95% CI): 0.71 (0.15, 3.32)]. Discussion: Deforestation does not seem to be an important driver of the double burden of malnutrition in SSA. However, deforestation influences several intermediate factors which, in turn, may influence the double burden. The overall weak association between forest cover loss and double burden measures mask significant heterogeneity across regions within SSA. Future research should unpack the mechanisms behind these regional differences.

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Section: Independent Variablementioning

confidence: 99%

Deforestation and Household- and Individual-Level Double Burden of Malnutrition in Sub-saharan Africa

Acharya¹,

Naz²,

Galway³

et al. 2020

Front. Sustain. Food Syst.

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“…The preliminary analysis presented here has been motivated by concern with facilitating socio-ecological research while preserving the confidentiality of social science survey respondents. Several studies have emerged over the past several years examining the potential error introduced by geomasking techniques, but most such research has examined error with regard to the creation of distance-based measures such as distance to health clinic (e.g., Warren et al, 2016). Results suggest that the creation of these distance-based covariates can yield consequential measurement error.…”

Section: Discussionmentioning

confidence: 99%

Working toward effective anonymization for surveillance data: innovation at South Africa’s Agincourt Health and Socio-Demographic Surveillance Site

et al. 2021

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Linking people and places is essential for population-health-environment research. Yet, this data integration requires geographic coding such that information reflecting individuals or households can appropriately be connected with characteristics of their proximate environments. However, offering access to such geocoding greatly increases the risk of respondent identification and, therefore, holds the potential to breach confidentiality. In response, a variety of "geographic masking" techniques have been developed to introduce error into geographic coding and thereby reduce the likelihood of identification. We report findings from analyses of the error introduced by several masking techniques applied to data from the Agincourt Health and Socio-Demographic Surveillance System in rural South Africa. Using a vegetation index (Normalized Difference Vegetation Index (NDVI)) at the household scale, comparisons are made between the "true" NDVI values and those calculated after masking. We also examine the tradeoffs between accuracy and protecting respondent privacy. The exploration suggests that in this study setting and for NDVI, geomasking approaches that use buffers and account for population density produce the most accurate results. However, the exploration also clearly demonstrates the tradeoff between accuracy and privacy, with more accuracy resulting in a higher level of potential respondent identification. It is important to note that these analyses illustrate a process that should characterize spatially informed research but within which particular decisions must be shaped by the research setting and objectives. In the long run, we aim to provide insight into masking's potential and perils to facilitate population-environment-health research.

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“…For example, although it can be shown that the expected measurement error in distances has positive mean and is bounded when measuring the distance to a single fixed point (Elkies et al ., ), when measuring distance to the nearest of a set of facilities, it is possible that the ‘nearest’ facility may change when noise is added to the household location, and the distribution of the errors that are induced depends on the precise locations of the facilities. If we assume that the distributions of actual distances and of the measurement errors in distances that are induced by using perturbed locations are independent and normally distributed, then we can apply standard regression calibration methods (Warren et al ., ). However, none of these assumptions are true (Arbia et al ., ; Elkies et al ., ), which makes this method unlikely to solve the problem.…”

Section: Introductionmentioning

confidence: 97%

Adding Measurement Error to Location Data to Protect Subject Confidentiality While Allowing for Consistent Estimation of Exposure Effects

Karra

Canning

Sato

2020

Journal of the Royal Statistical Society Series C: Applied Statistics

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In public use data sets, it is desirable not to report a respondent's location precisely to protect subject confidentiality. However, the direct use of perturbed location data to construct explanatory exposure variables for regression models will generally make naive estimates of all parameters biased and inconsistent. We propose an approach where a perturbation vector, consisting of a random distance at a random angle, is added to a respondent's reported geographic coordinates. We show that, as long as the distribution of the perturbation is public and there is an underlying prior population density map, external researchers can construct unbiased and consistent estimates of location-dependent exposure effects by using numerical integration techniques over all possible actual locations, although coefficient confidence intervals are wider than if the true location data were known. We examine our method by using a Monte Carlo simulation exercise and apply it to a real world example using data on perceived and actual distance to a health facility in Tanzania.

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Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses

Cited by 18 publications

References 38 publications

Deforestation and Household- and Individual-Level Double Burden of Malnutrition in Sub-saharan Africa

Deforestation and Household- and Individual-Level Double Burden of Malnutrition in Sub-saharan Africa

Working toward effective anonymization for surveillance data: innovation at South Africa’s Agincourt Health and Socio-Demographic Surveillance Site

Adding Measurement Error to Location Data to Protect Subject Confidentiality While Allowing for Consistent Estimation of Exposure Effects

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