Tag Archives: Public Health

My Spatial Problem – Elevated Blood Lead Levels in Bangladeshi Children: What is to blame?

Background:

No level of lead exposure is considered safe, and humans have experienced its toxicity to nearly every organ system, especially the central nervous system (1,2).  Exposures to lead, especially for the developing neurological system of children, are known to be hazardous and greatly impact children (2).

Environmental contamination is a widespread public health issue throughout the country of Bangladesh. Within the 1990s, widespread arsenic poisoning was described in populations which used groundwater wells for drinking water and were contaminated with naturally-occurring arsenic (3). In addition, Bangladesh is a developing country with increasing industry and continued burning of gasoline pointing to high exposures of lead through air pollution (4). Children also make up about 13% of the labor force in Bangladesh which include pottery glazing, lead melting, welding, car repair, and ship breaking which are all known exposures of lead (5). Based on historical air pollution and industries within urban areas of Bangladesh, the exposures to lead and known elevated childhood blood levels alarm health authorities and more information is needed as to determine exact exposures within communities.

I will utilize a 16-year developed maternal cohort study (R01ES015533 PI:Christiani/CoI Kile; P42ES16454: Bellinger/CoI: Kile; R01ES023441,PI: Kile) in Pabna and Sirajdikhan Upazilas in Bangladesh to examine the possible spatial relationships of lead exposures. Children born from the participating mothers in the maternal study were followed from early childhood and were tested for blood lead levels at 4 to 5 years old. Many children within this cohort showed elevated blood lead levels above the U.S. recommendation level of 5 ug/dL (Table 1).

Table 1: Summary results of blood lead levels (ug/dL) of children aged 4-5 years in Pabna and Sirajdikhan (n=348)

Location

 Minimum Mean Maximum Above 5 ug/dL and percentage
Combined

0.00

 3.86 19.6

165, 47%
Pabna (n=183)

0.00

 1.32 10.8

51, 28%
Sirajdikahn (n=165) 0.00 6.67 19.6

114, 69%

Research question:

What are the potential spatial relationships of blood lead levels in children aged 4-5 years old within two areas in Bangladesh and association with possible sources of exposure from high traffic or urban areas?

Dataset analyzing:

Blood lead concentrations (ug/dL) of children at ages 4-5 years old only taken at one timepoint with coordinates of their homes (n=348). There is one other categorical variable of if the children are above or below the 5 ug/dL US recommendation for lead exposure. The resolution is fine scale within meters of one another over the span of square kilometers. I will also be employing base maps of the two districts where the children live. This will allow network review for the major traffic and highway areas and distance to urban centers. I have a slight hesitation for the level of information I will have within the country of Bangladesh for understanding the street network. My pilot data from analysis in R made it difficult to gather these data. I am looking to import the data into Arc for more extensive data analysis.

Hypotheses:

I hypothesize that the blood lead levels of the children are spatially correlated and increased blood lead levels in either specific hot spots and/or locations closer to roads and/or urban centers will be due to inhalation and dermal exposure from air pollution.

Approaches:

I would first like to perform spatial autocorrelation to determine if the blood lead levels of children are spatially correlated.  Secondly, I would like to employ the hot-spot analysis tool from Arc to determine if within the sample of children there are specific hotspots of higher blood lead levels. We do know that between the two districts of samples taken, the district closer to urban landscape has higher, more prevalent levels of lead in children (Table 1). To try and pinpoint more specific exposures, I would like to intersect the locations of the children’s homes with a buffer (100 m) from major highway and traffic areas. I would then aggregate the intersected homes and determine if those blood lead levels are higher comparatively to the children that live greater than 100 m. The first attempt would be crude analysis of within or outside the buffer, but if we do find that individuals within the buffer have significantly higher blood lead levels, I would like to see if there is a drop off of between 100-250 m, 250 – 500 m, and 500 m + in distance from high traffic roadways. Lastly, if we do not find trends with distance to roadways and blood lead levels, I will overlay base maps of industry related areas and identify if they overlap with hot spots within our spatial spread of the blood lead levels.

Expected outcomes:

  1. Spatial autocorrelation: I hope to describe the blood lead level data as spatially autocorrelated by producing linear plots as a visual representation of their autocorrelated relationships
  2. Hot spot analysis: From Arc, I will produce maps through the hot-spot analysis tool to hone into different areas of interest that might have the highest levels comparatively. This map would be useful to understand blood lead level relationships within the two different districts, and if there are areas of interest for further analysis.
  3. Distance to roadways: From Arc, I want to produce maps that show a 100 m buffer around major roadway systems and intersection of locations of children’s homes from the cohort. I would then like to graphically produce a simple boxplot of blood lead levels within the buffer and outside of the buffer zone to compare the mean blood lead levels. If I do find differences, I would like to produce another map of the gradient change of going farther away from high traffic roadways and blood lead levels (further buffer/intersection analysis).
  4. Intersection of Hot Spots and Industry: I would like to produce a map similar to the hot-spot analysis which overlays the areas of known industry from a base map with the potential hot-spots of children’s homes.

Significance:

In a 16+ year cohort understanding the consequences of chronic arsenic exposures, my work is significant to help communities better protect children from undue burden of environmental pollution. I hope to bring a better understanding to the communities as to why their children have high blood lead levels compared to US averages. My research will help explain if the areas the children live in are correlated with increased exposures and lead to more help public health actions on how to limit lead exposures.

Level of preparation:

a) Intermediate knowledge of ArcGIS Pro

b) I will concurrently be taking a GIS programming course in Python, and I have taken intro Python coding coursework.

c)I have completed coursework (GEOG 561) programming in R with these data to understand spatial relationships. I also have extensive statistical modeling experience in R.

Works Cited:

  1. Tong S, Schirnding YE von, Prapamontol T. Environmental lead exposure: a public health problem of global dimensions. Bull World Health Organ. 2000;78:1068–77.
  2. WHO | Lead [Internet]. WHO. [cited 2019 Mar 17]. Available from: http://www.who.int/ipcs/assessment/public_health/lead/en/
  3. Smith AH, Lingas EO, Rahman M. Contamination of drinking-water by arsenic in Bangladesh: a public health emergency. Bull World Health Organ. 2000;78(9):1093–103.
  4. Kaiser R, Henderson A K, Daley W R, Naughton M, Khan M H, Rahman M, et al. Blood lead levels of primary school children in Dhaka, Bangladesh. Environ Health Perspect. 2001 Jun 1;109(6):563–6.
  5. Mitra AK, Haque A, Islam M, Bashar SAMK. Lead Poisoning: An Alarming Public Health Problem in Bangladesh. Int J Environ Res Public Health. 2009 Jan;6(1):84–95.

Faye Andrews, MPH

andrewsf@oregonstate.edu

Seth Rothbard My Spatial Problem

A description of the research question that you are exploring

Of the 31 pathogens known to cause foodborne illness, Salmonella is estimated to contribute to the second highest number of illnesses, the most hospitalizations, and the highest number of deaths in the US when compared to other domestically acquired foodborne illnesses1. Salmonellosis is the bacterial illness caused by Salmonella infection. It is estimated there are approximately 1.2 million cases of salmonellosis and around 450 deaths every year in the US due to Salmonella1. Over time there has been marked variability in the number of reported cases per year. Salmonellosis is a mandatory reportable illness in Oregon and available information indicates that incidence rates of this disease have been stable since the new millennium2. The objective of this study is to perform spatial analysis of lab-confirmed Salmonella in Oregon counties for the years 2008-2017 for which county level data are available and determine whether some counties have a higher risk of Salmonella infection compared to others. I also wish to explore the socioeconomic factors associated with high incidence rate counties. My research question that I wish to explore is:How are spatial patterns of Salmonella related to spatial patterns of socioeconomic factors? Certain socioeconomic patterns such as lower levels of education and income may increase rates of Salmonella in these populations as a result of improperly preparing/cooking foods, less strict sanitation practices, and/or higher rates of eating high risk foods.

A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent

The Oregon Health Authority has created a database called the Oregon Public Health Epidemiology User System (ORPHEUS) as a repository for relevant exposure and geospatial data related to disease cases reported to public health departments all across the state. This database has been maintained by the state since 1989 and includes information regarding various diseases. The dataset I will be using is a collection of every single reported non-typhoidal Salmonella case within Oregon from 2008-2017. The distinction between typhoidal Salmonella and non-typhoidal is that the typhoidal variety of Salmonella causes typhoid fever while non-typhoidal Salmonella causes salmonellosis (a common gastrointestinal disease and a type of “food poisoning” as it is usually referred to). The spatial resolution of this data has been obscured to the county level to protect personal privacy and confidentiality. I will also be using data from the American Community Survey and the CDC’s Social Vulnerability Index. These datasets contain social vulnerability related variables for Oregon at the county level. In the case of the American Community Survey, data is available for the years 2009-2017 and the Social Vulnerability Index has data available for 2014 and 2016. Yearly county population estimates will also be used from Portland State University’s Population Research Center. Because of the high amounts of available data I will choose to start my exploratory analysis for Oregon in 2014 as all data is reported for that year.

Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

I expect counties with younger populations (higher proportions of infants and newborns) as well as counties with higher proportions of females to have higher adjusted incidences of Salmonella. Prior surveillance suggests that children under the age of 5 are at the highest risk for Salmonella infection likely due to their developing immune system and how they interact with their environment. Specifically, many young children do not/are unable to wash their hands prior to touching their mouths. Females are also known to have a higher risk of Salmonella infection, however the mechanism behind this is relatively unknown with some explanations suggesting that it is due to that females are more likely to have more interactions with young children. I also expect counties with lower Social Vulnerability scores to have higher rates of Salmonella infections. Higher rates of poverty and lower amounts of education are often associated with more negative health outcomes.

Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

I would like to calculate age and sex adjusted rates of disease for each county in Oregon. I am also interested in undertaking cluster analysis and calculate spatial autocorrelation among Oregon counties over time. Finally, I would like to perform a regression of county disease incidence rates by the different socio-economic factors found in the American Community Survey and Social Vulnerability Index. I would be interested in learning about spatial Poisson regression to assess which variables are significantly associated with the presence of disease. I would also be interested in learning about hotspot analysis to evaluate if there are areas of Oregon with significantly higher disease rates. Ideally, all of my analyses will be performed in R and ArcGIS.

Expected outcome: what do you want to produce — maps? statistical relationships? other?

I would like to produce choropleth maps of adjusted Salmonella infection rates as well as for hotspot analysis. I want to produce regression models to describe how incidence rates of Salmonella vary across different socioeconomic indicators. I also want to create graphs to describe spatial autocorrelation patterns as well as to show disease rates over time.

Significance. How is your spatial problem important to science? to resource managers?

This analysis will be helpful to identify county populations which are at higher risk for Salmonella infections. The inclusion of social vulnerability variables will be useful for state/local policy makers. Reforms can be proposed or further studied to assess how addressing the needs of particularly vulnerable populations will affect the incidence of Salmonella. This research will be beneficial for further public health research as trends found here may also hold true for other foodborne illness. The aim of this research is to benefit the health of communities in Oregon by highlighting the association between social vulnerability and the risk of foodborne illness.

Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R, (d) image processing, (e) other relevant software

I have no experience with Arc-Info, programming in Python, and image processing. I have some limited experience within Modelbuilder. I am very comfortable performing statistical analyses within R and have some experience using the software to create maps using various packages.

References

  1. Estimates of Foodborne Illness in the United States. Centers for Disease Control and Prevention. https://www.cdc.gov/foodborneburden/2011-foodborne-estimates.html#modalIdString_CDCTable_0. Published July 15, 2016. Accessed July 31, 2018.
  2. Oregon Health Authority. Salmonellosis 2016 Report. Oregon Public Health Division. Available at: https://www.oregon.gov/OHA/PH/DISEASESCONDITIONS/COMMUNICABLEDISEASE/DISEASESURVEILLANCEDATA/ANNUALREPORTS/Documents/2016/2016-Salmon.pdf. Accessed July 31, 2018.