About the project


Motivation and problem statement

Our understanding of the many health risks that lead toxicity pose and their outsized impact on young children are well described elsewhere. There are several primary goals for this project:

  1. Help equip Malden decisionmakers to understand where risks are the highest so that the limited resources set aside for lead service line replacement can be informed by actual public health risk given the data available.
  2. Help quantify the public health impact of applying additional resources to lead service line replacement in Malden, MA. (In other words, we'd like to answer the question: "What could be accomplished with an additional X% of funding devoted to remediation efforts?")
  3. Following work by Potash et al, demonstrate a broadly applicable and relatively simple methodology to evaluate and inform lead mitigation efforts in other geographic communities and with other toxicity modalities (e.g. lead paint).

Data

There are several data sets involved, each of which answers a particular question:

  • Which parcels in Malden have lead service lines? The City of Malden maintains an ArcGIS web application with periodically updated data on what type of service lines exist at each residential parcel in the city. The web application is available here. There is also a GIS web application which has a clean list of Malden property parcels, available here.
  • Which parcels with lead service lines have children living in the house? Anonymized enrollment data from Malden public schools connects student ages to street addresses. These street addresses must be geocoded so that they can be cleaned up and connected with a specific parcel from the city parcels data. This data is updated once per year in the fall after the academic year rolls have been finalized.
  • What street segments contain parcels with children and lead lines? The City of Malden maintains a GIS shapefile called "Malden centerlines" that splits long streets into chunks that are roughly the size of a public works project. This data set changes from time to time, and should be updated periodically.

Once we have access to the datasets above, we can overlay parcels with children and lead on the pieces of each street that City considers a reasonably sized segment of road.

Assumptions and limitations

Here are some assumptions involved in the project, and some simplifcations and limitations that result:

  • Exposure years. To calculate years of exposure, we will assume that a child will live at a certain address until the age of 18. That means that a five year old child living at a parcel with a lead service line is assigned 13 exposure years (18 minus 5). This is not perfect, because the family could move to a different address or the service line could be replaced in that time.
  • Age from school grade. Data from schools was anonymized and did not contain birthdays, only school grade. Assuming that most United States schoolchildren started kindergarten at age 5, we calculate age as the grade plus five, where grade would be 0 for kindergarten, -1 for pre-K, and -2 for preschool.
  • Location of school children, not infants. There is not a simple way to associate infants with particular houses. In theory, birth certificates could be viewed or somehow digitized but these are only a snapshot in time when the child is born and are not updated. Therefore, we miss out on some of the most vulnerable children who are not yet in preschool. On the other hand, the best predictor of where babies might live is where there are already young children indicating parents of childbearing age.
  • Accuracy of lead service line data. The service line data is imperfect, and certain parcels may not have been updated in a long time, or may have been replaced but not updated in the dataset. Over time, this data tends to continue improving as the city replaces lines or conducts physical inspections. For now, some parcels in the Malden data are marked with status UNKNOWN. These tend to be identified over time as the city visits more parcels. Here are the counts as of August, 2019:
    PRIVATE_SERVICE BRASS CAST IRON COPPER DUCTILE IRON GALVANIZED LEAD PVC STEEL UNKNOWN
    CITY_SERVICE
    CAST IRON 0 11 65 0 0 4 0 2 69
    COPPER 5 6 5,182 0 5 1,016 2 18 1,309
    DUCTILE IRON 0 0 10 26 0 0 0 0 21
    GALVANIZED 0 0 0 0 0 0 0 0 3
    LEAD 2 0 1,545 0 1 317 0 4 1,169
    UNKNOWN 0 1 31 0 0 7 0 0 142
  • Accuracy of street segments. Not all street segments are the same size, and they might not be the ideal size for a lead service line replacement project. In the table, we can normalize out length by giving 'exposure years per 100 yards' to try to make apples-to-apples comparisons.
  • Partial replacements. In recent years, it has been observed that replacing only one side (city or private) can be worse than leaving the pipes alone. This is because the service lines tend to build up a protective layer of "scaling" on the inside; disruption of built-up scaling in the pipes may allow more lead to leach into the water. The city can't just unilaterally decide to replace all the city side lines, they must consider which private lines they can persuade owners to cooperate in replacing at the same time. Homeowners in Malden are required by local ordinance to replace lead service lines before selling their house, but without a forcing function its can be difficult to persuade owners to spend upwards of $2,000 to have their private side line replaced. Recent work by Clean Water Action in Chelsea, MA demonstrates that offering to pay for replacement of the private side could help expedite the process of getting owners to replace private side lines.
  • Street disruptions. The City tries to be a good steward of road quality, and given limited resources the DPW is understandably reluctant to tear up streets which have been recently paved.

Future work

Given the assumptions and limitations above, many extensions to this work are possible. For example:

  • More school-age children could be added to the dataset. There are children who live in Malden that are not students enrolled at participating schools. The voluntary participation of other private, charter, or parochial schools could greatly add to the resolution of this data.
  • Infants can be added to the dataset. As discussed above, no data exists on which parcels are home to babies that are not old enough to be enrolled in preschool. Electronic surveys, mailed questionnaires, or door-knocking to solicit voluntary data from parents of infant children would increase the resolution of this data.
  • Differentiate the severity of child exposure years. How much worse is a given unit of lead exposure at age 5 as opposed to age 17? Currently, we treat both of these years of exposure as equally bad. Finding an absolute (in health terms) or relative (in arbitrary units) measure of the comparative severity of exposure at different ages could help re-weight exposure years as a function of a child's age. We could simply pick an arbitrary decay curve, but basing the decay on a thorough review of the literature would be a more principled and data-informed approach.

Why is my child or other children on my street missing from the data?

There are a few reasons why this might be:

  • Only kids living at parcels with lead are counted here. Houses with no lead service lines are not relevant to the project.
  • Not every school in Malden has agreed to provide data, and some children that live in Malden attend school elsewhere.
  • Even if data was shared, not every child's address was able to be connected with a parcel in the lead data set. See above for more detail.
  • Some of the Malden street segments aren't perfect. If you find an error, please feel free to contact with a description of what is wrong so that the City GIS office can be notified.

If you would like you supply information about children who are younger than school age or who are missing from this data, you may voluntarily submit anonymous age information and it will be evaluated the next time the data is assembled.

Who made this?

This is an independent research project by Isaac Slavitt, a data scientist and Malden resident. Please feel free to contact me at .

Acknowledgements

This project would not have been possible without the cooperation of the City of Malden, particularly Maria Luise in the office of Mayor Gary Christenson for greenlighting the research and Steve Fama in the GIS office for providing many data sets and insight into city recordkeeping. Malden City Councillors Steve Winslow and Ryan O'Malley have been strong proponents of this work and energetic advocates for informed public health promotion and infastructure modernization in the City of Malden.

References

[1] “Introduction to Lead and Lead Service Line Replacement,” LSLR Collaborative. [Online]. Available: https://www.lslr-collaborative.org/intro-to-lsl-replacement.html.

[2] N. C. for E. Health, “CDC - Lead Poisoning Prevention Program,” 04-Feb-2019. [Online]. Available: https://www.cdc.gov/nceh/lead/default.htm.

[3] “Lead in U.S. Drinking Water,” SciLine. [Online]. Available: https://www.sciline.org/evidence-blog/lead-drinking-water.

[4] “10 Policies to Prevent and Respond to Childhood Lead Exposure | NCHH.” [Online]. Available: https://nchh.org/information-and-evidence/healthy-housing-policy/10-policies/.

[5] “Optimal Corrosion Control Treatment Evaluation Technical Recommendations for Primacy Agencies and Public Water Systems (Final Draft),” p. 141.

[6] Read “Drinking Water Distribution Systems: Assessing and Reducing Risks” at NAP.edu. .

[7] G. R. Boyd, N. K. Tarbert, R. J. Oliphant, G. J. Kirmeyer, B. M. Murphy, and R. F. Serpente, “Lead pipe rehabilitation and replacement techniques for drinking water service: review of available and emerging technologies,” Tunnelling and Underground Space Technology, vol. 15, pp. 13–24, Jan. 2000.

[8] P. Goovaerts, “How geostatistics can help you find lead and galvanized water service lines: The case of Flint, MI,” Science of The Total Environment, vol. 599–600, pp. 1552–1563, Dec. 2017.

[9] C. on E. Health, “Prevention of Childhood Lead Toxicity,” Pediatrics, vol. 138, no. 1, p. e20161493, Jul. 2016.

[10] “Health Effects of Low-level Lead Evaluation.” [Online]. Available: https://ntp.niehs.nih.gov/pubhealth/hat/noms/lead/index.html.

[11] “Lead in Drinking Water and Human Blood Lead Levels in the United States.” [Online]. Available: https://www.cdc.gov/mmwr/preview/mmwrhtml/su6104a1.htm.

[12] J. P. Wright et al., “Association of Prenatal and Childhood Blood Lead Concentrations with Criminal Arrests in Early Adulthood,” PLOS Medicine, vol. 5, no. 5, p. e101, May 2008.

[13] H. Needleman, “Lead Poisoning,” Annu. Rev. Med., vol. 55, no. 1, pp. 209–222, Jan. 2004.

[14] M. A. Del Toral, A. Porter, and M. R. Schock, “Detection and evaluation of elevated lead release from service lines: a field study,” Environmental science & technology, vol. 47, no. 16, pp. 9300–9307, 2013.

[15] Centers for Disease Control and Prevention, “Guidelines for the identification and management of lead exposure In pregnant and lactating women.” Centers for Disease Control and Prevention, 2010.

[16] “Lead water pipes still a concern in Boston area - The Boston Globe.” [Online]. Available: https://www.bostonglobe.com/metro/2016/02/11/lead-declines-boston-area-water-supply-but-caution-still-advised/0bZoJcOeeQyVgbehkDuHkJ/story.html.

[17] E. Potash et al., “Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning,” 2015. Available: https://dssg.uchicago.edu/wp-content/uploads/2016/01/p2039-potash.pdf.