Child Welfare Services Need and Access to Supportive Services: A ...

Child Welfare Services Need and Access to Supportive Services: A GIS Analysis Final Report May 1, 2007

Principle Investigator: Bridget Freisthler

Abstract Currently, very little research exists that examines how environmental and structural factors related to service use (i.e. accessibility and availability) may influence a parent’s ability to comply with case service plans developed by DCFS to ensure the safety of children. Improving the ability of families to access services enhances the Department’s ability to use the least restrictive approach available while ensuring the safety and protection of children. Moreover, families’ knowledge of and access to services in their own community helps maintain community ties and increases the likelihood that families will access agencies that are familiar with their needs and are sensitive to local ethnic and cultural issues. The objectives of this study are to: 1. determine if accessibility and availability of social services are related to rates of substantiations and foster care entries in LA County; 2. create maps that show supply of social services across LA County and the demand for services by child welfare families; and 3. provide recommendations on areas where DCFS could actively recruit agencies to contract for services provided to families involved with the child welfare system.

A. Issue The vision of the LA Department of Children and Family Services (DCFS) is that children grow up safe, physically and emotionally healthy, educated and in permanent homes. In recent years there has been an emphasis on minimizing the necessity for removing children from their home, keeping children in their home communities when they are removed, reducing their length of time in foster care, and generally decreasing the number of children who enter foster care. A key factor in determining whether a child can safely remain in the home and when and how reunification occurs is parental compliance with service plan requirements such as participating in parenting classes, attending visitation, and completing substance abuse treatment. This project can lead to recommendations to improve service delivery by making it more efficient for families involved in the child welfare system to comply with these requirements and promote the well-being of children. Moreover, improving the ability of families to access services enhances the Department’s ability to use the least restrictive approach available while ensuring the safety and protection of children. The project will have important implications for a number of DCFS programs and initiatives. As an example, the mission of Kinship Care Services is to provide resources, services and support to relative caregivers and the children they are taking care of with the goal of enhancing the family unit and promoting permanency, safety and decreased reliance on detentions. The quality of CWS referrals and the access of caregivers to community resources are vital to achieving this mission. This project will also benefit the provision of wraparound services at DCFS. The Wraparound approach uses highly individualized Plans of Care that include the strategies, services and supports to offer “whatever it takes” to maintain the child in a safe, nurturing, permanent community-based setting. Families’ knowledge of and access to

1

services in their own community helps maintain community ties and increases the likelihood that families will access agencies that are familiar with their needs and are sensitive to local ethnic and cultural issues. Currently, very little research exists that examines how environmental and structural factors related to service use (i.e. accessibility and availability) may influence a parent’s ability to comply. Moreover, extant research does not address whether accessibility and availability of social services are related to rates of substantiations and foster care entries. Thus, not only does this project have important implications for individual families and the quality of service provided by DCFS, but it also makes important contributions to the child welfare literature. B. Objectives The objectives are to: 1. determine if availability of social services are related to rates of foster care entries in LA County; 2. create maps that show supply of social services across LA County and the demand for services by child welfare families; and 3. provide recommendations on areas where DCFS could actively recruit agencies to provide services to families involved with the child welfare system. The short-term goal of the proposed work is to provide greater understanding of the geographic distributions of social services in high and low risk areas for child maltreatment. The long-term goal of the project is to provide an assessment of service availability (supply and demand) that will enable DCFS to develop policy regulations directed at high risk areas to prevent child maltreatment.

2

C. Background Previous research on parents’ compliance with child welfare service plans has largely examined the how a person’s motivation is related to complying with reunification requirements (Smith, 1999). A review conducted by Faver and colleagues (1999) find that lack of compliance is generally measured as a parent’s lack of cooperation and not as barriers to service utilization. Four categories of barriers have been identified as factors that might affect service use: availability (location), accessibility (travel considerations), acceptability (stigma attached), and affordability (Stefl & Prosperi, 1985). In an unpublished report Lery et al. (2004) found that children who were placed further than 11 miles away from their removal address were less likely to be reunified—a potential issue of accessibility in ease of getting to and from a parent’s home address and visitation. Studies of health care services usage such as prenatal care, find that areas with lower densities of prenatal clinics have lower levels service use (McLafferty & Grady, 2004). Thus it is not unreasonable to hypothesize that social service availability and accessibility in local areas may affect rates of maltreatment and foster care entries. Models for examining location-based service availability for families in the child welfare system have been developed and implemented in limited geographic areas throughout the United States. For example New York City has developed neighborhood-based networks to collaborate around service provision in geographically identified community districts (Chahine et al., 2005). The premise behind the strategy is that parents may be more likely to participate in services that are located in areas that are convenient and comfortable. In their model, collaborations of service providers are essential in identifying gaps in service delivery and provision. However, this approach did not include any geographic or spatial assessment of service availability, limiting the information that could be ascertained by networking among professionals alone. A

3

second model for examining service availability is the Matching Needs and Services (MNS) tool. This particular model provides a framework for planning more effective services by determining the types of needs among the target population that are most prevalent, implementing new services or improving existing service delivery, and evaluating the services to see whether the outcomes of interest have improved (Taylor, 2005). In 2003, Los Angeles County DCFS used this approach in a review of a random sample of 100 child welfare case files in eight Service Planning Areas (SPAs) in the county to determine the primary needs of the families (Poverny & Melamid, 2003). This case record review revealed that the primary service needs of families involved with the child welfare system were related to substance abuse (25% of cases), permanency to adoption (23%), family violence (16%), and mental health (14%). These results are further delineated by SPA. The current study builds on both of these approaches by examining the contest of service availability geographically. Thus the primary concern here is to determine locations across the county were there is a low availability of social services but a high need for those services among families involved with the child welfare system. Furthermore, this study utilizes the information from Los Angeles County’s Matching Needs and Services Project to focus on those services that have been identified as the most needed for this population. D. Methods Sample. The sample for this study consists of the universe of zip codes fully contained within Los Angeles County (n = 282). Dependent Variable. Rates of entries into foster care in 2003 in Los Angeles County represented “demand” for CWS. These rates have been obtained from the Center for Social

4

Services Research Center at the University of California, Berkeley. Due to a skewed distribution of this measure, the square root of the rate of foster care entry is used in all multivariate analyses. Independent Variables. Supply (availability) of social services includes agencies that provide services for basic needs, criminal justice and legal needs, health care, public assistance, mental health care and counseling, and city and county resources. The locations of these services have been obtained from the Rainbow Directory of Social Service agencies. The location information for these data sources were geocoded using ArcGIS 9.0 (Environmental Services Research Institute, 2004). Using information from the Matching Needs and Services Project (Poverny & Melamid, 2003), eight types of service categories were identified as those areas that encompassed the majority of needs exhibited by families involved with the child welfare system. These service types included substance abuse, adoption, mental health, domestic violence, independent living/emancipation, pregnant and parenting teens, housing and supportive services, and special needs. Density variables were created (number of services per area) for each of the social service categories listed above which will be used in multivariate analyses as will data on neighborhood characteristics including percent poverty, percent unemployment, percent of residents who moved in the past five years, and percent of foreign-born residents have been obtained from the U.S. Census. Overall 97% of the social service agencies were successfully geocoded. However, this geocoding rate differed by type of social service agency as can be seen below. Most notably, agencies that provide domestic violence services had a substantially lower geocoding rate. Many of these were due to the fact that shelters are unwilling to provide exact locations of shelters in order to maintain the safety of their residents.

5

Geocoding Rate by Social Service Agency Category Geocoding Rate Substance Abuse Services 96.3% Adoption Services 98.7% Mental Health Services 96.9% Domestic Violence Services 82.4% Independent Living Services 95.9% Parenting/Pregnant Teen Services 98.4% Housing and Supportive Services 97.5% Special Needs Services 95.5%

As the point process analyses (described below) are conducted for Los Angeles County as a whole and by each SPA separately, the following table gives the total number of social service agencies by type of service provided for each SPA. According to this table, it is clear that overall SPA 1 (Antelope Valley) has the fewest overall number of resources, while the Metro area which includes downtown (SPA 4) has the most. The all services category refers to the total number of social service agencies in those areas, not just those services that have been determined from the Matching Needs and Services Project to be the most in need by families involved with the child welfare system. Number by Type of Social Service Agencies with Service Planning Areas of Los Angeles County Service Planning Area Agency Service Type 1 2 3 4 5 6 7 8 Substance Abuse 13 91 100 87 44 61 61 100 Adoption 3 20 21 10 8 1 5 8 Mental Health 38 237 278 340 150 203 200 294 Domestic Violence 3 15 16 31 10 8 11 14 Independent Living 9 70 52 63 25 28 34 67 Pregnant/Parenting Teens 20 96 144 131 52 72 86 117 Housing and Supporting Services 34 129 113 296 95 191 79 139 Special Needs 8 134 94 76 56 49 58 97 All Services 240 1,737 1,857 2,051 810 979 1,172 1,594

6

Analyses. Point process analyses consider the location and availability of resources, and local population characteristics to determine use of services by likely target populations by mapping demand and supply. Demand. Zip code quantile maps were created using child maltreatment rates (as measured by foster care entries discussed above), where each point in the map received a value of 1 through 10. A “1” represented an area with low demand (i.e. low rates of maltreatment) whereas a “10” represented an area with high demand (i.e. high rates). Supply. Kernel density procedures were used to assess social services availability (Bailey & Gatrell, 1995). Kernel density calculates the density of points by which the influence of a neighboring point decreases as function of its distance from the focal point. Ten levels of density were create where level 1 refers to those areas with low supply (i.e., few social service agencies) and 10 to those areas with high supply (i.e., many social service agencies). Final Point Process Analyses. The final step combined the supply and demandmeasures to give a description of how availability of social services are related to child maltreatment in zip codes. Specifically, the final maps were the result of an overlay where: Service Need = demand – supply

(1)

The maps generated from this procedure suggest locations where increased service provision may reduce maltreatment or enhance reunification rates. Local Indicators of Spatial Autocorrelation (LISA) and Spatial Regression Procedures. One problem in analyzing areal data is spatial autocorrelation. Spatial autocorrelation refers to the fact that spatial units located close in space may share characteristics. In other words, measures from adjacent spatial units (i.e., neighborhoods that share a boundary) are often correlated and that errors in measurement from statistical models may also be correlated between

7

spatial units (Bailey & Gatrell, 1995). The biases that such correlations introduce into spatial models are important to diagnose and control. LISA statistics assess the level of spatial autocorrelation for each individual spatial unit comparing it to neighboring areas (Anselin, 2003). Through this procedure the Moran coefficient (an assessment of spatial autocorrelation) and its significance level is calculated for each spatial unit. Spatial clusters are identified as those spatial units that have high levels of spatial autocorrelation and are surrounded by units that also have high levels of spatial autocorrelation or those units with low levels of spatial autocorrelation surrounded by units with low spatial autocorrelation. Spatial outliers are those spatial units that are may have high LISA but is surrounded by spatial units with low levels of spatial autocorrelation and vice versa. Once LISA descriptive statistics are conducted, multivariate spatial regression approaches are then implemented that address issues of spatial autocorrelation. One approach to dealing with spatial autocorrelation is to assume that spatial dependence is found to be a nuisance, related only to correlated error and otherwise unrelated to the independent and dependent measures (Bailey & Gatrell, 1995). In this model, spatial autocorrelation is controlled for by using the connection matrix discussed above as part of the error term. This model has three equations that takes the following form: Y = Xb + (I – ρW)-1ε

(2)

b = (X'QX)-1 X'Qy

(3)

Q = (I – ρW)'(I- ρW)

(4)

where Y refers to the dependent variable (foster care entries), X to the n * k matrix of independent variables, n is the number of cases, k is the number of independent variables, b is the vector of coefficients for each independent variable, and I is the I is the identity matrix. W is

8

a row stochastic n * n matrix representing connections between spatial units. It is based on a binary connection matrix, C, where 1 indicates that two units share a boundary or a 0 for places that are not adjacent. Each element in C is divided by the sum of the elements in that particular row so that they then sum to 1. This transformation of the connection matrix enables estimation of Moran coefficients and other measures of spatial autocorrelation (ρ) that remain approximately bounded between -1.0 and +1.0 (Cliff & Ord, 1973, 1981). The estimate of spatial autocorrelation (ρ) obtained from the GLS model is corrected for by the weight matrix Q. E. Results Point Process Analyses. The results for the point process analyses of supply and demand for Los Angeles County and each of the eight Service Planning Areas are described in the following pages. Each analysis for the SPAs includes three maps, (1-2) two descriptive maps showing the size of the population by zip code and the location of all eight types of social service agencies overlayed onto the rates of entry into foster care and (3) a map showing the areas with high demand but low supply of social services.

9

Los Angeles County

The above map shows one geographically large area (in northern LA) that has a high need (as determined by foster care entries) but low supply of social service agencies. There are other smaller areas interspersed throughout the county, including those in the South Service Planning Area and along the central coast of the Pacific Ocean in Los Angeles County. It is important to note that although the Antelope Valley region of Los Angeles County exhibits a relatively high unmet need for services, the map is a bit misleading as this area is also less densely populated than other portions of Los Angeles County. Although it encompasses a large geographic region, it may not be the most cost-effective approach to focus solely on this area for

10

intervention. The following maps which denote both population size and the high need areas will provide better information on those areas most likely at risk for continuing problems due to child maltreatment. Service Planning Area 1 – Antelope Valley

Several zip code areas in SPA 1 have relatively high rates of entries into foster care (as evidenced visually by both the Los Angles County zip code map and the above map). However, the type of services in this area are generally limited in scope (e.g. primarily mental health, substance abuse, and housing/supporting services) and located in a very limited area (i.e. downtown areas within the SPA). Although the service type is limited in its diversity within SPA 1, the Matching Needs and Services analysis for SPA 1 show that substance abuse (49%),

11

permanent and temporary safe environments (21%), and mental health stability (10%) are the three highest presenting needs in the case file review for SPA 1 (Poverny & Melamid, 2003). However, there are relatively few agencies providing services for domestic violence (n = 3) but 10% of cases presented with this as the primary presenting problem.

Given the high rate of foster care entries throughout SPA 1, there are really only one area throughout the where the demand for services does not match the availability of social services (“supply”). This area encompasses the Lake Los Angeles area and the eastern portions of Lancaster and far Palmdale areas. A word of caution: this need for introducing services in this area needs to be weighed against the size of population (as compared to other areas within Los Angeles County). Thus rather than introducing new agencies to the area and potentially having not enough “demand” to support the supply, social service agencies in this area may consider

12

opening satellite offices or providing services in more hard to reach on a regular basis. Agencies may choose to share costs by jointly renting space so that individual agencies providing various services are available each day of the week, while minimizing the overhead cost for any one agency. Service Planning Area 2 – San Fernando Valley

According to the Matching Needs and Services Survey case file review, the primary needs of families were substance abuse (25%), mental health services (21%) and domestic violence (18%) (Poverny & Melamid, 2003). However, the primary services available in SPA 2 are mental health (n = 237), services for individuals with special needs (n = 134) and housing and supportive services (n = 129). There are only 15 agencies within this area that specifically

13

target services to families where domestic violence is an issue. This mismatch in social service density (see map below) is particularly apparent in the Burbank area. By either creating additional agencies that service substance abusing clients or victims of domestic violence or increasing the capacity of agencies that serve these populations, entries into foster care in this area may be reduced.

14

Service Planning Area 3 – San Gabriel Valley

Permanency through adoption/custody (26%), domestic violence (20%), and substance abuse (13%) were the highest rates needs from the case file review in SPA 3 (Poverny & Melamid, 2003); yet adoption and domestic violence services are the least prevalent social service types in this area. The service types with the highest density of services include mental health services, housing and supportive services, and substance abuse services. Quite possibly because of the difference between the services that are available and those that are needed, much of SPA 3 has a high “demand” for services (as evidenced by relatively high rates of foster care entries) but low supply of services.

15

Service Planning Area 4 - Metro

In general, SPA 4 can be considered a “resources rich” area. SPA 4 is one of the smaller service planning areas in geographic area, yet it has the largest number of social service agencies within its boundaries (n = 2,051). This is likely due to the fact that SPA 4 encompasses the downtown area of Los Angeles city. The Matching Needs and Services project identified family protection (26%), permanency through relatives (24%), and sexual abuse trauma (15%) as the three highest needs of child welfare families in this area. None of these service types are consistent with the service categories used in the current study (although it could be argued that mental health services encompass dealing with issues related to sexual abuse trauma). This may

16

be a result of the fact that so many services are available within this area, those needs that continue to be problematic for families living in SPA 4 are those were agencies providing these types of services may not be easily identifiable or the types of services needed to reduce these problems are not as well articulated. Despite not being able to quantify the availability of these types of services, SPA 4 has a relatively small area that has a high “demand” and relatively low social service availability. The high risk area that is identified is adjacent to SPA 6 which has a higher level of social service need overall (see page 20). This area “spillover” may be one where agencies serving both SPAs 4 and 6 may want to focus on identifying and delivering additional services. As so many social service agencies are located within this SPA, it would be useful to focus identify agencies that provide permanency and family protection services to make sure that families with these specific needs are not being overlooked and are not continuing to be at risk for child maltreatment.

17

Service Planning Area 5 - West

Located within SPA 5 are the relatively wealthy communities of Beverly Hills, Malibu, and Bel Air as compared to many other SPAs. The three top needs identified all include some aspect of mental health services (permanent placement with intensive mental health needs – 25%, mental health in-home supportive services – 16%, and permanency with independent living program and mental health – 14%). Mental health service agencies are the primary type of social service that is available in this area (n = 150). Although the supply-demand map shown below does some areas that can be classified as high demand, low supply, this is partially an artifact of the analysis procedures. Examining this map in conjunction with the map of supply-demand across Los Angeles County identifies some areas of Santa Monica as having high demand and

18

low supply of services, rather than the much larger area identified by the SPA 5-only map. Social service agencies should focus on this smaller area to increase service delivery.

19

Service Planning Area 6 - South

Permanency, including adoption (33%), substance abuse (22%), and mental health (16%) were identified as the three primary needs of families involved with the child welfare system from this SPA (Poverny & Melamid, 2003). The highest three service types available in SPA 6 include mental health (n = 203), housing and supportive services (n = 191), and substance abuse (n = 61). The relatively low number of mental health and substance abuse agencies (compared with many of the other SPAs) may be one reason that much of SPA 6 can be considered high risk areas (as evidenced by high demand for services and low supply, see map below). Further the lack of adoption agencies (n = 1) may make finding permanent placements for children involved with the child welfare system living in this area difficult. For mental health and substance abuse

20

agencies increasing the current capacity of these programs may be one way to reduce child maltreatment in the area. SPA 4 covers a relatively small geographic area (about 4 – 6 miles wide) so accessibility to these services should not necessarily be a problem if adequate numbers of services are available within the area.

21

Service Planning Area 7 - East

Child welfare families in SPA 7 need services that address substance abuse (41%), permanency through adoption (14%), and dealing with the effects of sexual abuse (14%) according to the Matching Needs and Services Project (Poverny & Melamid, 2003). Social service agencies are primarily available to serve mental health issues, including sexual abuse (n = 200), housing and supportive services (n = 79), and substance abuse problems (n = 61). This strong overlap between the services needed and the service categories provided can be seen in the relatively few areas that exhibit high demand and low supply in the map below. The areas

22

where children may be particularly vulnerable are in Commerce and parts of East Los Angeles. Enhancing capacity for service delivery in these particular areas of SPA 7 may be warranted.

23

Service Planning Area 8 – South Bay

In SPA 8, the Matching Needs and Services Project identified substance abuse (24%), issues around abusive behaviors (16%), and special needs (12%) as the primary needs of families involved with the child welfare system (Poverny & Melamid, 2003). In terms of service availability, SPA 5 is tied with SPA 3 for the highest number of social service agencies that serve substance abuse problems (n = 100) and has the second highest number of agencies that serve special needs (n = 97). Combined with the large number of agencies providing mental health services (n = 294) means that SPA 8 has comparatively fewer areas of high demand for services among the foster care population with low supply of services available. The major exceptions are the Compton, Manhattan Beach, and El Segundo areas (see map below).

24

Summary of Point Process Models. The results of the point process models clearly show areas in Los Angeles County as a whole and within each Service Planning Area where social services are needed that may work to prevent entries into foster care. Further, when comparing the supply-demand maps of each SPA with the results of the Matching Needs and Services Project, those areas that have a fewer number of services that address the most frequent category of needs have higher “high risk” areas where increased in the number or capacity of agencies is warranted. Spatial Regression Model. Although the point process models are illustrative in showing where gaps in service agencies, and potentially service coverage, exist across Los Angeles County, the do not provide information on how the density of specific types of services are

25

related to rates of entry into foster care. In this project, an analysis of the local indicators of spatial autocorrelation (LISA) and spatial regression models are used to examine the relationship between zip code demographic characteristics and densities of service types. Local Indicators of Spatial Autocorrelation. As described on pages 7-8, LISA show areas of spatial clusters and spatial outliers. In the map presented below the only spatial outliers are those areas that zip codes with low rates of entry into foster care located next to areas with high rates of foster care (light blue in color). There also exist several areas with spatial clusters. SPAs 2, 5, and 7 all have zip codes areas with significantly low rates of foster care entries located next to similar types of zip codes. In SPAs 1 and 6 there are zip code areas with clustering of high rates of foster care entries. Children in these areas are at particularly high risk for entering foster care (and also represent areas where social service agency availability is low.)

Map of Local Areas of Spatial Associations of Rates of Foster Care in Los Angeles County

26

Spatial Regression. Two spatial regression models were conducted to examine the statistical relationship between density of various types of social service agencies and rates of foster care entries. The first model regresses only the measures of density of social service agencies (by type of service provided) whereas the second model adds several demographic measures that have been shown to be related to child maltreatment rates in previous research. The following table presents those results. Spatial Regression Model of Rates of Entry into Foster Care by Density of Social Service Agencies and Demographics by Zip Code (n = 282) Model 1 Model 2 Variables B SE B SE Constant Substance Abuse Services Adoption Services Mental Health Services Domestic Violence Services Independent Living Services Parenting/Pregnant Teen Services Housing and Supportive Services Special Needs Services % Unemployment % Poverty % Moved in past 5 years % Foreign Born

Spatial Autocorrelation R-squared

.943 .082 -.017 -.050 .220 .027 -.004 .016 .057

.844 .382

.184 .037 .129 .019 .090 .046 .015 .017 .044

.035

*** * ** *

***

.332 .046 .136 -.064 .223 -.005 .019 .020 .034 -.020 .020 .022 -.006

.244 .036 .124 .018 .085 .044 .014 .016 .042 .006 .006 .005 .004

.852 .456

.034

*** **

*** *** ***

***

***p < .001, **p < .01, *p < .05

Model 1 shows that density of mental health service agencies is related to lower rates of foster care entries in zip code areas. On the other hand, paradoxically areas with higher densities of substance abuse and domestic violence agencies have higher rates of entries into foster care. In Model 2, the associations between density of mental health and domestic violence service agencies remain the same. In addition to these findings, higher percentages of poverty and foreign born residents are related to higher rates of entry into foster care while percent of

27

unemployment is related to lower rates of foster care entries. The finding related to domestic violence agencies and foster care rates may be an artifact of the low geocoding rate for these types of agencies. Thus the secrecy of locations of domestic violence shelters results in an undercount of the density of these types of agencies by zip code. F. Conclusions The current study identified areas within Los Angeles County zip codes that demonstrate a high demand for social services among child welfare families but do not have enough of those services available to meet the demand. Specifically areas with higher densities of substance abuse and mental health services have fewer “high risk” areas for entry into foster care according to the point process analyses conducted. Examining the density of these service types across zip code areas shows that only the density of mental health services is negatively related to rates of foster care entry. That is, areas with a higher density of mental health services have lower rates of foster care entry. Density of domestic violence agencies was positively related to foster care entry rates. While interesting, this finding should be interpreted with caution given the low geocoding rate of these types of agencies. With regards to demographic measures of the zip code areas, this study finds similar findings to previous research. For example, areas with higher rates of poverty and housing stress (measured here by residential instability) have been consistently found to be related higher rates of child maltreatment (Freisthler et al., 2006). Limitations. There are several limitations to the current study. First, crude measures of service availability are used. This study focuses solely on location and service type of agencies but does not include information on agency capacity, availability of other supportive services (e.g., translation, transportation) that may enhance service utilization by particular segments of

28

the population. Second, this study focuses on a subset of social service agencies. Although the types of services were determined based on previous research examining the needs of families in the child welfare system, it is certainly possible that areas high in other types of service availability (i.e., service-rich areas) may have positive effects on reducing out-of-home placement. Finally, the location of services was determined based on a resource and referral source. Agencies may have satellite offices or provide services in multiple locations that are not reflected in the data used here. More comprehensive data on the locations of social service agencies, including multiple office locations or off-site service delivery would provide a better understanding of the relationship between locations of social services and entry into foster care. Recommendations. Despite these limitations, this study has several recommendations 1 to enhance service availability with the ultimate goal of reducing child maltreatment. 1. Several areas throughout Los Angeles County have been identified as having low availability of social services, but have a high demand for these services. The primary areas include parts of SPA 1 and SPA 6. The addition of services in these areas, particularly as they relate to mental health services, is recommended. However there is a need to think creatively about how this might look in these two demographically and geographically diverse areas. For example, SPA 1 social service agencies may consider holding mental health services in additional sites throughout the area given the extent to which the SPA covers a larger geographic area. SPA 6, which has a large diverse population, may find that there is a need for culturally relevant mental health services within that area.

1

In addition to the recommendations listed here, additional recommendations are given for each SPA when discussing the results of the point process analyses for those areas.

29

2. Service-rich areas (such as SPA 4) continue to have demand for social services but those needs appear to be qualitatively different from areas with lower densities of social services (e.g., services related to permanency or child protection). Service providers need to determine the specific services that are needed to serve these families or develop new service delivery models to assist these families leave the child welfare system. 3. Services that help to reduce and/or eliminate poverty and promote housing stability continue to be a key to reduce entry into foster care. Future research needs to examine the extent to which service availability affects the ability of child welfare clients to comply with mandated service plans. This information, in conjunction with more detailed data on service capacity and service delivery would provide a more comprehensive picture of the relationship between service availability, utilization, and child maltreatment.

30

References Anselin, L. (2003). GeoDa 0.9 User's Guide. Urbana: Spatial Analysis Laboratory, Department of Agricultural and Consumer Economics, University of Illinois, Urbana-Champaign and Center for Spatially Integrated Social Science. Bailey, T.C. & Gatrell, A.C. (1995). Interactive Spatial Data Analysis. Essex: Addison Wesley Longman. Chahine, Z., van Straaten, J., Williams-Isom, A. (2005). The New York City neighborhoodbased services strategy. Child Welfare, 84(2), 141-152. Cliff, A.D. & Ord, J.K. (1973). Spatial autocorrelation, monographs in spatial environmental systems analysis. London: Pion Limited. Faver, C.A., Crawford, S.L., & Combs-Orme, T. (1999). Services for child maltreatment: Challenges for research and practice. Children and Youth Services Review, 21(2), 89-109. Freisthler, B., 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. Lery, B., Webster, D., & Chow, J. (2004). Far from home: The effect of geographic distance on likelihood of reunification for children in out-of-home care. Paper presented at the Society for Social Work Research annual meeting. McLafferty, S. & Grady, S. (2004) Prenatal care need and access: A GIS analysis. Journal of Medical Systems, 28(3), 321-333. Poverny, L.M. & Melamid, E. (2003). Matching Needs and Services, Study Results. Los Angeles County Department of Children and Family Services.

31

Smith, B.A. (1999). Parents’ compliance with child welfare service plan requirements: A literature review and critique. Final Report for the Children and Family Research Center. Urbana, Illiniois: School of Social Work, University of Illinois. Stefl, M.E., & Prosperi, D.C. (1985). Barriers to mental health services utilization. Community Mental Health Journal, 21(3), 167-178. Taylor, K.I. (2005). Understanding communities today: Using Matching Needs and Services to assess community needs and design community-based services. Child Welfare, 84(2), 251264.

32