A Comparison of Foster Care Entry Risk at Three

Substance Use & Misuse, 43:223–237 Copyright © 2008 Informa Healthcare USA, Inc. ISSN: 1082-6084 (print); 1532-2491 (online) DOI: 10.1080/10826080701690631 A Comparison of Foster Care Entry Risk at Three Spatial Scales BRIDGETTE LERY Chapin Hall Center for Children, University of Chicago, Chicago, Illinois, USA This study addresses the problemof operationalizing neighborhood boundaries by inves- tigating foster care entry risk at three spatial scales. Foster care entries froma California county
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  Substance Use & Misuse , 43:223–237Copyright © 2008 Informa Healthcare USA, Inc.ISSN: 1082-6084 (print); 1532-2491 (online)DOI: 10.1080/10826080701690631 A Comparison of Foster Care Entry Risk at ThreeSpatial Scales BRIDGETTE LERY Chapin Hall Center for Children, University of Chicago, Chicago, Illinois, USA Thisstudyaddressestheproblemofoperationalizingneighborhoodboundariesbyinves-tigatingfostercareentryriskatthreespatialscales.FostercareentriesfromaCaliforniacounty between 2000 and 2003 (n  =  3,311) are geocoded to each of the three scales( N  =  46 zip codes, 320 census tracts, and 983 block groups). Exploratory spatial dataanalysis is used to compare spatial autocorrelation of entry rates among scales. Resultssuggest that depending on how neighborhoods are defined, the geographic pattern of  fostercareincidencechanges.Implicationsforaccuratelytargetingservicestohigh-risk neighborhoods and future research directions are noted. Keywords  spatial scale; child welfare; foster care; spatial analysis; modifiable arealunit problem; risk factors; high risk neighborhood Introduction The push for neighborhood-based child welfare and other social services demands bettertools to investigate the role of location in child and family outcomes. Geographic infor-mation systems (GIS) and spatial analysis are now commonly used to map, for instance,rates of child maltreatment in neighborhoods and to model its relationship to neighbor-hood social conditions such as impoverishment or alcohol outlet density (Coulton, Korbin,Su, and Chow, 1995; Freisthler, 2004). The spatial representation of social phenomenaon GIS maps exposes patterns and relationships that are not easily recognized using con-ventional analytical procedures. However, there is a methodological gap between the de-scriptive mapping of social data across geographic areas and the statistical inference of the relationships of such variables across space. Most studies of neighborhood effectson child maltreatment and subsequent policies promoting community-based interventionshavenotconsideredtheuniquestatisticalbiasesthatoftenariseinmapsandmodelsthatusegeographic data.The problem stems from the fact that there is no consensus on what is the best unitof analysis to call “neighborhood.” Researchers chose administrative units such as censustracts to represent neighborhoods in ecological studies out of convenience in the absenceof an available systematic method for doing so. This creates two related problems. For one,administrative units are usually not very good proxies for neighborhoods. For another, thevariation of a phenomenon over a geographic area may change if the data are representedoveradifferentgeographicscale(Coulton,Cook,andIrwin,2004).Theseissuesareknownas the  zone problem  and the  scale problem , respectively, and fall within a larger definition AddresscorrespondencetoDr.BridgetteLery,ChapinHallCenterforChildrenattheUniversityof Chicago, 1313 East 60th Street, Chicago, IL 60637. E-mail: 223    S  u   b  s   t   U  s  e   M   i  s  u  s  e   D  o  w  n   l  o  a   d  e   d   f  r  o  m   i  n   f  o  r  m  a   h  e  a   l   t   h  c  a  r  e .  c  o  m   b  y   J  o  a  q  u   i  n   I   b  a  n  e  z   E  s   t  e   b  o  n   0   2   /   2   4   /   1   1   F  o  r  p  e  r  s  o  n  a   l  u  s  e  o  n   l  y .  224 Lery of problems in ecological studies, the modifiable areal unit problem (MAUP; Openshawand Taylor, 1979; Robinson, 1950).The zone problem refers to the fact that administrative units are typically poor rep-resentations for neighborhoods because the concentration of a phenomenon in one spa-tial unit will tend to spill over into adjacent units, making the relationships between theunits not independent. In other words, attributes of areal units may be correlated acrossspace. The strength of the correlation depends largely on where the unit boundaries aredrawn, and this correlation may bias the standard errors in multivariate ordinary leastsquares regression models of ecological data, which assume independence among theunits of analysis (Bailey and Gatrell, 1995). Further complicating the matter of choos-ing a reliable unit, neighborhood boundaries are permeable. People move in and out of neighborhoods, making boundaries dynamic over time (Freisthler, Lery, Gruenewald, andChow, 2006). Substance Use and Child Welfare in Spatial Context The explicit consideration of spatial scale makes a substantive difference when seeking tounderstand how attributes of place may impact the risk of child maltreatment or foster careentry and when designing interventions to reduce this risk. For example, there is ampleevidence that parental substance abuse * is a posited risk factor for child maltreatment andthat a large portion of children receiving child welfare services are from substance-usingfamilies (Besinger, Garland, Litrownik and Landsverk, 1999). One recent study suggeststhat integrating substance user and child welfare services might be an effective strategyto increase the likelihood of reunification for children placed in out-of-home care (Ryan,Marsh, Testa and Louderman, 2006). Coordinating the location of substance user treat-ment programs nearby to maltreating parents is one way to do this (D’Aunno, 1997).However, in order to target the location of treatment services effectively and efficiently, itis important to know which areas in a jurisdiction are at the highest risk of using fostercare.From an ecological perspective, it is also important to know whether there is anyrelationship between drug and alcohol availability in neighborhoods and risks to children,beyond the known associations at the individual level. Freisthler (2004) found that theavailability of alcohol in neighborhoods is associated with rates of child maltreatment incensus tracts, suggesting that a solution could be to reduce the density of bars and otheralcohol outlets at that spatial scale. However, it is possible that the density of outlets, ratesof maltreatment, and therefore their relationship to each other changes depending on howneighborhoods are defined. Defining Neighborhoods The child welfare literature offers little guidance on how to measure and define neigh-borhoods and, as a consequence, the geographic unit chosen to represent neighborhoodsvaries across studies. A neighborhood can be conceptualized as a geographic place whereresidents share social networks and informal ties. However, such networks are not nec-essarily tied to place, and the distinction between neighborhoods and communities canbecome blurred (Newman and Small, 2001). In addition, residents’ perceptions of their * The journal’s style utilizes the category  substance abuse  as a diagnostic category. Substancesare used or misused; living organisms are and can be  abused  . Editor’s note.    S  u   b  s   t   U  s  e   M   i  s  u  s  e   D  o  w  n   l  o  a   d  e   d   f  r  o  m   i  n   f  o  r  m  a   h  e  a   l   t   h  c  a  r  e .  c  o  m   b  y   J  o  a  q  u   i  n   I   b  a  n  e  z   E  s   t  e   b  o  n   0   2   /   2   4   /   1   1   F  o  r  p  e  r  s  o  n  a   l  u  s  e  o  n   l  y .   A Comparison of Foster Care Entry Risk at Three Spatial Scales 225 neighborhoods are unlikely to correspond to administrative boundaries (Coulton, 2005;Coulton et al., 2004; Coulton, Korbin, Chan, and Su, 1997). Since social networks andresidents’ perceptions are not reliable tools with which to measure homogenous neigh-borhoods, researchers typically select either counties (Albert and Barth, 1996; Fryer andMiyoshi, 1995; Spearly and Lauderdale, 1983), ZIP codes (Drake and Pandey, 1996), cen-sus tracts (Coulton et al., 1995; Deccio, Horner, and Wilson, 1994; Ernst, 2001; Freisthler,2004), or block groups (Coulton, Korbin, and Su, 1999; Young and Gately, 1988) as unitsof analysis. Block groups or census tracts are sometimes grouped to model socializationspaces that better represent neighborhoods than do individual units (Sampson, Raudenbushand Earls, 1997). Census and geographic boundary data are readily available at all of these levels of aggregation, allowing for the calculation of rates of social indicators suchas poverty and, when the data are accessible, rates of child maltreatment and foster careentry.In addition to the zone problem, administrative units may not represent the scale atwhich the social processes of interest operate (Coulton, 2005). When the level of dataaggregation is too large, the spatial pattern is obscured (Can, 1993). Maps are merely amodelofrealityandcanmisrepresentdataandleadtopoorassumptionsaboutrelationshipsbetween variables. People are good at detecting patterns visually, but they also tend toidentify patterns that are not present (MacEachren, 1995).Most neighborhood studies in child welfare limit analysis to one spatial scale withoutformal consideration of the statistical bias that may occur due to spatial autocorrelationamong units of analysis. Recent evidence suggests that an outcome such as child maltreat-ment risk may be affected by neighborhood characteristics differently at different scales(Johnston et al., 2004). Chou (1991) found that Moran’s I (Moran, 1950)—the most com-monlyusedmeasureofspatialautocorrelation—increasessystematicallywiththeresolutionlevel. If resolution changes the degree to which units are correlated across space, which inturn affects the level of bias in the statistical model, then the choice of which spatial scaleto use in a neighborhood analysis is a nontrivial one.If neighborhoods can be defined in a variety of ways, then what criteria should beused to examine the geographic distribution of a problem? While most previous studieschoose one spatial scale to approximate neighborhoods, this study systematically comparesfoster care entry rates across three commonly used operational definitions of neighbor-hood to address the question, “  How can we define neighborhoods operationally so that researchersandadministratorscanchooseanappropriateunitofanalysiswhenevaluatingchild welfare outcomes and designing interventions ?” A related goal is to demonstrate thedangers of arbitrarily choosing units of analysis in neighborhood research. Determininga practical and reliable unit of analysis to serve as a proxy for neighborhood will im-prove the reliability of research on neighborhoods and the efficiency of place-based serviceallocation. Method Exploratory spatial data analysis (ESDA; Anselin, 2005) is used to compare spatial auto-correlation in foster care entry rates at three geographic scales within Alameda County,California. Counties are the administrative jurisdictions for child welfare agencies in Cali-fornia. Foster care entry rates vary considerably within the county, making it an illustrativechoiceforstudy.Table1showsfostercareentriesandchildpopulationbyyearforAlamedaCounty.    S  u   b  s   t   U  s  e   M   i  s  u  s  e   D  o  w  n   l  o  a   d  e   d   f  r  o  m   i  n   f  o  r  m  a   h  e  a   l   t   h  c  a  r  e .  c  o  m   b  y   J  o  a  q  u   i  n   I   b  a  n  e  z   E  s   t  e   b  o  n   0   2   /   2   4   /   1   1   F  o  r  p  e  r  s  o  n  a   l  u  s  e  o  n   l  y .  226 Lery Table 1 Alameda County first entries to foster care, child population, and entry rates per 1,000children by year: 2000–2003Total 2000 2001 2002 2003First entries to foster care 1 3,739 1,005 1,033 860 841Child population < 18 years 2 360,7793 3 354,572 358,221 361,862 368,462Entry rate per 1,000 children 2.6 2.8 2.9 2.4 2.3 1 Data source: California Children’s Services Archive, CWS/CMS 2004 Quarter 1 Extract. Basedon the first placement episode of 5 days or more even if it is not the first actual episode. 2 Population data source: 2000–2003 U.S. Census and Claritas, Inc. (2003) population projections. 3 Average population 2000–2003.  Dependent Variable Thisstudyexaminesall2000through2003firstentriesto fostercarefromAlamedaCountyin California that lasted longer than 4 days (n  =  3,311). Limiting the population to first-time entries eliminates the problem of overrepresenting children who reenter foster carerepeatedly over the study period. Children who return home within 4 days are arguablychildren who should never have been removed from home at all, so they are not consideredin the study sample.Thedataaredrawnfromthefirstquarter2004extractofCalifornia’sChildWelfareSer-vicesCaseManagementSystem(CWS/CMS).Underthetermsofaninteragencyagreementwith the California Department of Social Services, quarterly extracts from this system arehousedattheCenterforSocialServicesResearchattheUniversityofCaliforniaatBerkeleyand constitutes the California Children’s Services Archive. Along with a unique identifier,the data include street addresses from where each child was removed by child protectiveservices. The removal addresses of children entering foster care were geocoded using Ar-cGIS 8.3 software (Environmental Systems Research Institute [ESRI], 2003). Overall, 94%of all removal addresses were assigned a geographic location and were then associated witha spatial unit at each of the three scales. Foster care entry rates per 1,000 children in thepopulationwerethencalculatedwithineachspatialunit.Table2showsdescriptivestatisticsfor the rates at each scale.Foster care entry rates within individual geographic units do not have any absolutemeaning in this study because the numerator (total entries) is summed across years, whilethe denominator (child population) is calculated using only Census 2000 figures. Childpopulation data after 2000 are not available at the block group level, so 2000 data werechosen to standardize the denominator within each spatial scale. Therefore, the rates are Table 2 Foster care entry rates and spatial autocorrelation by spatial scalen Mean Standard deviation Minimum Maximum Moran’s IZIP codes 46 10.3 8.8 0.0 35.0 .34 ∗ Census tracts 320 17.6 80.3 0.0 1,200.0 .24 ∗ Block groups 983 12.7 47.7 0.0 1,200.0 .18 ∗ ∗ Significant at the .01 level.    S  u   b  s   t   U  s  e   M   i  s  u  s  e   D  o  w  n   l  o  a   d  e   d   f  r  o  m   i  n   f  o  r  m  a   h  e  a   l   t   h  c  a  r  e .  c  o  m   b  y   J  o  a  q  u   i  n   I   b  a  n  e  z   E  s   t  e   b  o  n   0   2   /   2   4   /   1   1   F  o  r  p  e  r  s  o  n  a   l  u  s  e  o  n   l  y .


Jul 28, 2017


Jul 28, 2017
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