Neighborhood Drug Demand and Risk of Foster Care Entries

Abstract

Parental drug abuse has been repeatedly associated with child maltreatment risk. Similarly, rates of drug demand in neighborhoods have been found to be positively associated with rates of child maltreatment. Demand for illicit drugs is difficult to measure as it is illegal behavior. Here, we use survey data from Sacramento, California to estimate demand for illicit drugs at the neighborhood level. Data were analyzed using Bayesian space-time conditional autoregressive models to assess associations between neighborhood drug market potentials and foster care entries. We find that higher drug demand was positively associated with increased drug-related foster care entries (RR 1.06, CI 1.02–1.10). Interventions at the policy level that decrease drug availability in high demand neighborhoods may also decrease rates of drug related foster care entries.

Keywords

drug market potential demand child maltreatment foster care entries Bayesian spatio-temporal analysis

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References

Akin

B. A.

Brook

Lloyd

M. H.

(2015). Examining the role of methamphetamine in permanency: A competing risks analysis of reunification, guardianship, and adoption. American Journal of Orthopsychiatry, 85(2), 119–130. https://doi.org/10.1037/ort0000052

Bivand

Sha

Osland

Thorsen

I. S.

(2017). A comparison of estimation methods for multilevel models of spatially structured data. Spatial Statistics, 21(Part B), 440–459. https://doi.org/10.1016/j.spasta.2017.01.002

Brakman

Garretsen

Van Marrewijk

(2009). The new introduction to geographical economics. Cambridge University Press.

Bruzelius

Levy

N. S.

Okuda

Suglia

S. F.

Martins

S. S.

(2022). Prescription drug monitoring and child maltreatment in the United States, 2004–2018. The Journal of Pediatrics, 241(February), 196–202. https://doi.org/10.1016/j.jpeds.2021.10.014

Cerdá

Mauro

Hamilton

Levy

N. S.

Santaella-Tenorio

Hasin

Wall

M. M.

Keyes

K. M.

Martins

S. S.

(2020). Association between recreational marijuana legalization in the United States and changes in marijuana use and cannabis use disorder from 2008 to 2016. JAMA Psychiatry, 77(2), 165–171. https://doi.org/10.1001/jamapsychiatry.2019.3254

Chapman

(2022). The opioid crisis and child maltreatment across counties and time in the United States, 2007–2017. The ANNALS of the American Academy of Political and Social Science, 703(1), 139–161. https://doi.org/10.1177/00027162221144172

Coulton

C. J.

Crampton

D. S.

Irwin

Spilsbury

J. C.

Korbin

J. E.

(2007). How neighborhoods influence child maltreatment: A review of the literature and alternative pathways. Child Abuse & Neglect, 31(11–12), 1117–1142. https://doi.org/10.1016/j.chiabu.2007.03.023

Cruden

Brown

C. H.

Lanier

Zolotor

Frerichs

Lich

K. H.

(2021). Exploring clusters of risk and association with child maltreatment in North Carolina counties. North Carolina Medical Journal, 82(4), 229–238. https://doi.org/10.18043/ncm.82.4.229

Cunningham

Finlay

(2013). Parental substance use and foster care: Evidence from two methamphetamine supply shocks. Economic Inquiry, 51(1), 764–782. https://doi.org/10.1111/j.1465-7295.2012.00481.x

10.

Diez Roux

A. V.

(2001). Investigating neighborhood and area effects on health. American Journal of Public Health, 91(11), 1783–1789. https://doi.org/10.2105/ajph.91.11.1783

11.

Diez Roux

A. V.

(2007). Neighborhoods and health: Where are we and were do we go from here? Revue d’epidemiologie et de Sante Publique, 55(1), 13–21. https://doi.org/10.1016/j.respe.2006.12.003

12.

Dodge

K. A.

Skinner

A. T.

Godwin

Bai

Lansford

J. E.

Copeland

W. E.

… Bates

J. E.

(2021). Impact of the COVID-19 pandemic on substance use among adults without children, parents, and adolescents. Addictive behaviors reports, 14, 100388.

13.

Evans

M. F.

Harris

M. C.

Kessler

L. M.

(2022). The hazards of unwinding the prescription opioid epidemic: Implications for child maltreatment. American Economic Journal: Economic Policy, 14(4), 192–231. https://doi.org/10.1257/pol.20200301

14.

Fong

(2020). Getting eyes in the home: Child protective services investigations and state surveillance of family life. American Sociological Review, 85(4), 610–638. https://doi.org/10.1177/0003122420938460

15.

Freisthler

Kranich

Price Wolf

Boyd

Gruenewald

P. J.

(2023). Neighborhood market potentials for alcohol use and rates of child abuse and neglect. Alcoholism: Clinical and Experimental Research, 47(1), 143–154. https://doi.org/10.1111/acer.14975

16.

Freisthler

Merritt

D. H.

LaScala

E. A.

(2006). Understanding the ecology of child maltreatment: A review of the literature and directions for future research. Child Maltreatment, 11(3), 263–280. https://doi.org/10.1177/1077559506289524

17.

Freisthler

Michaels

Wolf

J. P.

(2022). Families in crisis: The relationship between opioid overdoses and child maltreatment in neighborhood areas. Journal of Studies on Alcohol and Drugs, 83(1), 145–152. https://doi.org/10.15288/jsad.2022.83.145

18.

Geolytics . (2022). [Demographic data, US census and GIS software]. https://www.geolytics.com/

19.

Ghertner

(2022). Children living with parents using substances 2015–2019. U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation. https://aspe.hhs.gov/reports/children-living-parents-using-substances

20.

Ghertner

Waters

Radel

Crouse

(2018). The role of substance use in child welfare caseloads. Children and Youth Services Review, 90(July), 83–93. https://doi.org/10.1016/j.childyouth.2018.05.015

21.

Grubesic

T. H.

(2008). Zip codes and spatial analysis: Problems and prospects. Socio-Economic Planning Sciences, 42(2), 129–149. https://doi.org/10.1016/j.seps.2006.09.001

22.

Gruenewald

P. J.

Freisthler

Remer

LaScala

E. A.

Treno

(2006). Ecological models of alcohol outlets and violent assaults: Crime potentials and geospatial analysis. Addiction, 101(5), 666–677. https://doi.org/10.1111/j.1360-0443.2006.01405.x

23.

Guy

G. P.

Zhang

Bohm

M. K.

Losby

Lewis

Young

Murphy

L. B.

Dowell

(2017). Vital signs: Changes in opioid prescribing in the United States, 2006–2015. MMWR. Morbidity and Mortality Weekly Report, 66(26), 697–704. https://doi.org/10.15585/mmwr.mm6626a4

24.

Henry

Liner-Jigamian

Carnochan

Taylor

Austin

M. J.

(2018). Parental substance use: How child welfare workers make the case for court intervention. Children and Youth Services Review, 93(October), 69–78. https://doi.org/10.1016/j.childyouth.2018.07.003

25.

Herrenkohl

T. I.

Leeb

R. T.

Higgins

(2016). The public health model of child maltreatment prevention. Trauma, Violence, & Abuse, 17(4), 363–365. https://doi.org/10.1177/1524838016661034

26.

Jones

C. M.

Zhang

Han

Guy

G. P.

Losby

Einstein

E. B.

Delphin-Rittmon

Volkow

N. D.

Compton

W. M.

(2024). Estimated number of children who lost a parent to drug overdose in the US from 2011 to 2021. JAMA Psychiatry.

27.

Kepple

N. J.

(2017). The complex nature of parental substance use: Examining past year and prior use behaviors as correlates of child maltreatment frequency. Substance Use & Misuse, 52(6), 811–821. https://doi.org/10.1080/10826084.2016.1253747

28.

Kepple

N. J.

(2018). Does parental substance use always engender risk for children? Comparing incidence rate ratios of abusive and neglectful behaviors across substance use behavior patterns. Child Abuse & Neglect, 76, 44–55. https://doi.org/10.1016/j.chiabu.2017.09.015

29.

Kepple

N. J.

Freisthler

(2020). All drugs aren’t created equal: Exploring the general and specific effects of psychoactive substances to understand child maltreatment risk by drug type. In The Routledge handbook of social work and addictive behaviors (pp. 381–396). Routledge.

30.

Kepple

N. J.

Wolf

J. P.

Freisthler

(2022). Substance use and child maltreatment: Providing a framework for understanding the relationship using current evidence. In Handbook of child maltreatment (pp. 259–278). Springer.

31.

Kilgore

McClellan

Teigland

Pulungan

(2018). Using aggregate data to proxy individual-level characteristics in health services research: 9-Digit zipcode vs. Census block group. Value in Health, 21(Supplement 1), S218–S219. https://doi.org/10.1016/j.jval.2018.04.1482

32.

Kim

Song

E.-J.

Windsor

(2023). Longitudinal changes in the county-level relationship between opioid prescriptions and child maltreatment reports, United States, 2009–2018. American Journal of Orthopsychiatry.

33.

Korn

A. E.

(2016). Detoxing the child welfare system. Virginia Journal of Social Policy & the Law, 23(3), 293.

34.

Krieger

Waterman

Chen

J. T.

Soobader

M.-J.

Subramanian

S. V.

Carson

(2002). Zip code caveat: Bias due to spatiotemporal mismatches between zip codes and US census-defined geographic areas-the public health disparities geocoding project. American Journal of Public Health, 92(7), 1100–1102. https://doi.org/10.2105/ajph.92.7.1100

35.

Kuppens

Moore

S. C.

Gross

Lowthian

Siddaway

A. P.

(2020). The enduring effects of parental alcohol, tobacco, and drug use on child well-being: A multilevel meta-analysis. Development and Psychopathology, 32(2), 765–778. https://doi.org/10.1017/S0954579419000749

36.

Lee

Brown

E. R.

Grant

Belin

T. R.

Brick

J. M.

(2009). Exploring nonresponse bias in a health survey using neighborhood characteristics. American Journal of Public Health, 99(10), 1811–1817. https://doi.org/10.2105/AJPH.2008.154161

37.

Lloyd

M. H.

Akin

B. A.

Brook

(2017). Parental drug use and permanency for young children in foster care: A competing risks analysis of reunification, guardianship, and adoption. Children and Youth Services Review, 77(June), 177–187. https://doi.org/10.1016/j.childyouth.2017.04.016

38.

Lloyd

M. H.

Brook

(2019). Drug testing in child welfare: A systematic review. Children and Youth Services Review, 104(September), 104389. https://doi.org/10.1016/j.childyouth.2019.104389

39.

Logan

J. R.

Stults

B. D.

(2016). Validating population estimates for harmonized census tract data, 2000–2010. Annals of the American Association of Geographers, 106(5), 1013–1029. https://doi.org/10.1080/24694452.2016.1187060

40.

Lunn

D. J.

Thomas

Best

Spiegelhalter

(2000). WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and computing, 10, 325–337.

41.

Mair

Frankeberger

Gruenewald

P. J.

Morrison

C. N.

Freisthler

(2019). Space and place in alcohol research. Space and place in alcohol research, 6(4), 412–422. https://doi.org/10.1007/s40471-019-00215-3

42.

Martins

T. G.

Simpson

Lindgren

Rue

(2012). Bayesian computing with INLA: New features. Computational Statistics and Data Analysis.

43.

Mathews

Hamilton

B. E.

(2002). Mean age of mother, 1970–2000.

44.

Meinhofer

Angleró-Díaz

(2019). Trends in foster care entry among children removed from their homes because of parental drug use, 2000 to 2017. JAMA Pediatrics, 173(9), 881–883. https://doi.org/10.1001/jamapediatrics.2019.1738

45.

Moraga

(2018). Small area disease risk estimation and visualization using R. R J, 10(1), 495. https://doi.org/10.32614/rj-2018-036

46.

Morris

M. C.

Marco

Bailey

Ruiz

Goodin

(2019). Opioid prescription rates and risk for substantiated child abuse and neglect: A Bayesian spatiotemporal analysis. Drug and Alcohol Dependence, 205(December), 107623. https://doi.org/10.1016/j.drugalcdep.2019.107623

47.

Morrison

Gruenewald

P. J.

Freisthler

Ponicki

W. R.

Remer

L. G.

(2014). The economic geography of medical cannabis dispensaries in California. International Journal of Drug Policy, 25(3), 508–515. https://doi.org/10.1016/j.drugpo.2013.12.009

48.

Parker

V. L.

Liechty

J. M.

Kopels

Adams

M. L.

Janssen

K. C.

Kim

S. S.

Cochrane

T. L.

Salisbury

A. A.

Harris

(2021). Changing cannabis policies and social work: Implications for students, families, and schools. School Social Work Journal, 45(2), 34–60.

49.

Pawlowsky-Glahn

Egozcue

J. J.

(2006). Compositional data and their analysis: An introduction. Geological Society, London, Special Publications, 264(1), 1–10. https://doi.org/10.1144/gsl.sp.2006.264.01.01

50.

Quast

(2018). State-level variation in the relationship between child removals and opioid prescriptions. Child Abuse & Neglect, 86(December), 306–313. https://doi.org/10.1016/j.chiabu.2018.10.001

51.

Quast

Storch

E. A.

Yampolskaya

(2018). Opioid prescription rates and child removals: Evidence from Florida. Health Affairs, 37(1), 134–139. https://doi.org/10.1377/hlthaff.2017.1023

52.

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

53.

Rebbe

Bishop

A. S.

Ahn

Mienko

J. A.

(2020). Opioid overdose events and child maltreatment indicators: Differential county-level associations. Children and Youth Services Review, 119, 105671. https://doi.org/10.1016/j.childyouth.2020.105671

54.

Rue

Martino

Chopin

(2009). Approximate Bayesian inference for latent Gaussian models using inte- grated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society: Series B, 71(2), 105671.

55.

Sedlak

A. J.

Mettenburg

Basena

Peta

McPherson

Greene

(2010). Fourth national incidence study of child abuse and neglect (NIS-4). US Department of Health and Human Services.

56.

Spencer

M. R.

Warner

Cisewski

J. A.

Miniño

Dodds

Perera

Ahmad

F. B.

(2023). Estimates of drug overdose deaths involving fentanyl, methamphetamine, cocaine, heroin, and oxycodone: United States, 2021.

57.

Spiegelhalter

D. J.

Best

N. G.

Carlin

B. P.

Van Der Linde

(2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B, 64(4), 583–639. https://doi.org/10.1111/1467-9868.00353

58.

StataCorp . (2009). Stata multivariate statistics. StataCorp LP.

59.

Sumetsky

Burke

J. G.

Mair

(2022). Relationships between opioid-related hospitalizations and intimate partner violence and child maltreatment hospitalizations in Pennsylvania across space and time. Journal of Interpersonal Violence, 37(5–6), NP3474–NP3491. https://doi.org/10.1177/0886260520948525

60.

Tobin

(1958). Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society, 26(1), 24–36. https://doi.org/10.2307/1907382

61.

Treno

A. J.

Gruenewald

P. J.

Lee

J. P.

Remer

L. G.

(2007). The Sacramento neighborhood alcohol prevention project: Outcomes from a community prevention trial. Journal of Studies on Alcohol and Drugs, 68(2), 197–207. https://doi.org/10.15288/jsad.2007.68.197

62.

Wang

Yue

Y. R.

Faraway

J. J.

(2018). Bayesian regression modeling with INLA. CRC Press.

63.

Wolf

J. P.

(2021). Prescription drug misuse and child maltreatment among high-risk families. Journal of Interpersonal Violence, 36(7–8), 3006–3016. https://doi.org/10.1177/0886260518772109

64.

Wolf

J. P.

Ponicki

W. R.

Kepple

N. J.

Gaidus

(2016). Are community level prescription opioid overdoses associated with child harm? A spatial analysis of California zip codes, 2001–2011. Drug and Alcohol Dependence, 166(September), 202–208. https://doi.org/10.1016/j.drugalcdep.2016.07.014

65.

Wolf

J. P.

Ponicki

W. R.

Kepple

N. J.

Gaidus

(2016). Are community level prescription opioid overdoses associated with child harm? A spatial analysis of California zip codes, 2001–2011. Drug and alcohol dependence, 166, 202–208.

66.

Yang

Qiu

(2016). The impact of education on income inequality and intergenerational mobility. China Economic Review, 37(February), 110–125. https://doi.org/10.1016/j.chieco.2015.12.009