ARTICLE IN PRESS

Health & Place 12 (2006) 1937

in London, 760 electoral wards as dened in 1998. The analysis controls for age and sex composition and, as a proxy for

www.elsevier.com/locate/healthplace

1353-8292/$ - see front matter r 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.healthplace.2004.07.002

Corresponding author. Tel.: +44-171-975-5400; fax: +44-207-975-5500.

E-mail address: s.e.curtis@qmw.ac.uk (S. Curtis).access to care, spatial proximity to hospitals with psychiatric beds. Poisson regression modeling incorporating random

effects was used to control for both overdispersion in the counts of admissions and for the effects of spatial

autocorrelation. The results for NYC and London showed that local admission rates for all types of condition were

positively and signicantly associated with deprivation and the association is independent of demographic composition

or access to beds. In NYC, social fragmentation showed a signicant association with admissions due to affective

disorders and schizophrenia, and for drug dependency among females. Racial minority concentration was signicantly

and positively associated with admissions for schizophrenia. In London, social fragmentation was associated positively

with admissions for men and women due to schizophrenia and affective disorders. The variable measuring racial/ethnic

minority concentration for London wards showed a negative association with admission rates for drug dependency and

for affective disorders. We discuss the interpretation of these results and the issues they raise in terms of the potential

and limitations of international comparison.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Psychiatric inpatient admissions; New York; London; Ecological analysis; Deprivation indicesThe ecological relationship between deprivation, socialisolation and rates of hospital admission for acute psychiatric

care: a comparison of London and New York City

Sarah Curtisa,, Alison Copelanda, James Fagga, Peter Congdona,Michael Almogb, Justine Fitzpatrickc

aDepartment of Geography, Health Research Group, Queen Mary College, University of London, London, UKbWagner Graduate School of Public Service, New York University, USA

cLondon Health Observatory, London, UK

Received in revised form 28 June 2004

Abstract

We report on comparative analyses of small area variation in rates of acute hospital admissions for psychiatric

conditions in Greater London around the year 1998 and in New York City (NYC) in 2000. Based on a theoretical

model of the factors likely to inuence psychiatric admission rates, and using data from the most recent population

censuses and other sources, we examine the association with area indicators designed to measure access to hospital beds,

socio-economic deprivation, social fragmentation and ethnic/racial composition. We report results on admissions for

men and women aged 1564 for all psychiatric conditions (excluding self-harm), drug-related substance abuse/

addiction, schizophrenia and affective disorders. The units of analysis in NYC were 165 ve-digit Zip Code Areas and,

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 193720Introduction: background to the analysis

In the USA and the UK, mental health care for most

patients is provided according to a model emphasising

care in the community, rather than long-term care in

residential institutions. Nevertheless, inpatient psychia-

tric care in acute hospitals continues to play an

important role in community-based systems and con-

sumes signicant health care resources to meet the costs.

Policy making and nancial planning for mental health

services therefore require information on the factors

associated with use of psychiatric inpatient care.

In this paper, we discuss the signicance of ecological

analyses for mental healthcare planning. By means of a

comparative study of small area variations in the rate of

psychiatric inpatient use by local populations, we

illustrate the ecological factors that relate to psychiatric

inpatient use in New York City (NYC), USA, and

London, UK. While their health systems vary signi-

cantly and there are some differences in their social

geographies, these cities have several features that justify

an international comparative study, considering them

separately from the rest of the country as a whole. They

are both global cities, they are atypical of average

conditions prevailing in the rest of the country, they

show considerable internal diversity and they have

complex healthcare systems. Both cities face challenges

in providing services for seriously mentally ill people.

Both have health information systems providing routine

data on admissions, as well as other sources of small

area data on socio-economic conditions, that allow a

similar approach to ecological variation in psychiatric

hospital use.

The ecological analyses presented here concern

patterns of health care use by geographically dened

populations. This approach does not aim to explain or

predict service use by individual patients and one should

not assume that individual mental health care users have

characteristics like the population average in their area

of residence. On the other hand, there is a large body of

evidence from health geography demonstrating that

individual and ecological characteristics may interact,

such that conditions in small areas may have signicance

for individual experience of illness and health services

use (Macintyre et al., 1993; Kearns and Joseph, 1993;

Jones and Moon, 1993; Duncan et al., 1996; Curtis and

Jones, 1998, Diez-Roux, 1998).

Ecological studies may also illustrate differences

between local communities in service use, which are

signicant for the organization and nancing of mental

health services. In the UK, most psychiatric services

used by the population are provided by the National

Health Service (NHS), a comprehensive, integrated

system in which local NHS trusts use budgets allocated

by central government to commission services for the

population in a specic area. Access to health care issignicantly affected both by the amount of NHS

resource allocated for a local population and by the

ways that this resource is managed at the local level to

commission services. A founding principle of the NHS is

that services should be provided equitably in proportion

to need for populations in all parts of the country. The

information produced in geographical analyses therefore

can help target mental health care resources or inter-

ventions to areas where they are most needed (e.g.,

Smith et al., 1996; Glover et al., 1998; DH, 2003).

The organization of US health care system is more

diversied and care of individual patients may be funded

by one or more of a range of different, independent

private insurance schemes, or publicly funded by

Medicaid or Medicare (McAlpine and Mechanic,

2000). Access to services is therefore largely dependent

on the health care plan providing coverage for the

patient and the system does not allow for national

control of resource allocation to different areas. Never-

theless, there is constant emphasis in health policy on

the requirement to manage health care in order to make

it more cost-effective. Therefore, a focus on small area

variations in mental health care use may have relevance

for mental health service planning in the US situation.

For example, recommendation 2.4 of the Presidents

New Freedom Commission on Mental Health (2003: p. 8,

44) proposes the production of comprehensive State

Mental Health Plans to increase exibility of resource

use, expand options and arrays of services and supports,

and encourage states and localities to develop compre-

hensive health care strategies. It seems likely that, in

order to produce these plans, States and local govern-

ments will need to be informed about local variations in

current mental health care resource use and to judge

whether these variations appear to be justied on

grounds of cost-effectiveness in meeting the needs of

patients with mental illness.

Whereas ecological associations do not necessarily

provide explanations for differences in use of health

services between areas, they can be used to predict local

differences in use of psychiatric care and, as argued

above, they may help to inform service planning and

provision in different urban settings. A large body of

theoretical and empirical evidence from the UK, the

USA and elsewhere suggests that several types of

ecological factors are associated with varying use of

psychiatric care at the small area level. It may be

possible to argue that such associations are suggestive of

direct or indirect causal processes producing variation

among populations in different places in need for, and

access to psychiatric care. In North America these

relationships were discussed by Dear and Wolch (1987),

who proposed a general model of the service-dependent

ghetto, describing the typical geographical concentra-

tion of mental health service users. In NYC, Schweitzer

and Kierszenbaum (1978) examined variations in

admission rates in Brooklyn (Kings County) and Almog

et al. (2004) reported time trends in small area variation

in NYC during the 1990s. In the UK, hospital admission

rates have been examined using ecological analysis by

Giggs (1986) in Nottingham, by Dean and James (1981)

in Plymouth, and by several groups of authors in

London and the surrounding area (Thornicroft, 1991;

Harvey et al., 1996; Glover et al., 1998; Johnson et al.,

1998). Congdon et al. (1998) also found associations

between socio-economic conditions and small area

variation in rates of use of community services for

serious mental illness in London. Nationwide analysis of

small area variation in psychiatric hospital use was also

reported by Carr-Hill et al. (1994) and Smith et al.

(1996). There are examples of similar studies in cities in

other parts of Europe (Dekker et al., 1997 for

Amsterdam; Driessen et al., 1998 for Maastricht;

Maylath et al., 1999, 2000 for Hamburg; Thornicroft

et al., 1993 for Verona).

There is some variability in the ndings reported, and

this may be partly due to differences among these studies

an

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 1937 21in the type of urban setting, the type of statistical

approach used to test for ecological associations and the

measures of social conditions and of hospital use

examined. Some studies have shown that in the same

setting, admission rates for different types of psychiatric

condition have different ecological relationships with

social indicators (Thornicroft et al., 1993; Tansella et al.,

1993; Harrison et al., 1995; Boardman et al., 1997;

Koppel and McGufn, 1999). For example, hospitaliza-

tion rates due to disorders related to schizophrenia or

substance abuse are more consistently associated with

socio-economic deprivation than affective or neurotic

disorders1.

1For example, in Verona, Italy, Thornicroft et al. (1993) and

Tansella et al. (1993) showed that while indicators of area

deprivation predicted rates of mental health service use for

schizophrenic conditions, these associations were not consis-

tently related with use of services for neurotic disorders.

Harrison et al. (1995) showed that for districts in North West

England, deprivation was more strongly associated with area

variation in admission for psychotic disorders than for non-

psychotic conditons. In North Staffordshire, Boardman et al.

(1997) analysed the ecological relationship at ward level

between deprivation indicators and standardized admission

rates for schizophrenic, affective, neurotic, substance abuse

disorders and found that both psychotic and non-psychotic

conditions were signicantly and positively associated with

deprivation indicators. The correlations were weaker for

affective disorders than for other causes of admission. A study

in Cardiff by Koppel and McGufn (1999) found associations

between psychiatric admission rates at ward level and

composite measures of deprivation as well as with specic

poverty measures like unemployment. These varied by cause of

admission in that drug related disorders and schizophrenia

showed stronger associations than affective and neurotic

disorders.affordable accommodation and where some specialist

psychiatric and social support services are available.

Breeder and drift effects operating together are thought

to cause greater concentrations of people with severe

mental illness in areas where on average the population

is relatively poor, accommodation is cheap or where

communities lack strong social support systems and

social cohesion.

Varying access to care may also affect the pattern.

This is supported by the nding that populations living

close to hospitals are more likely to use them than

populations living at a greater distance (the distance

decay effect in hospital use). Thus some local popula-

tions within a city may have higher levels of hospitaliza-

tion just because they are closer to the supply of beds.

Historically, large hospitals in major cities like London

and New York were established in inner city areas, andwe

(usople tend to be excluded from some (usually

althier) parts of the city and will move towards other

ually poor inner city) areas where they can ndtor

peereby deprived environments with high levels of

omie cause or exacerbate mental illness. Commenta-

s also refer to drift effects, by which mentally illcau

whsal pathways may operate through breeder effects,LaSome researchers have also compared the predictive

power of different social indicators. For example,

Thornicroft (1991) and Koppel and McGufn (1999)

demonstrated that certain specic area indicators that

are updated annually, such as small area unemployment

rates, predict psychiatric admissions as effectively as

composite socio-economic indicators that require de-

cennial census data. This may be important for service

planning because during the period between censuses,

social indicators that can be regularly updated may

more accurately reect changing socio-economic condi-

tions at the local level.

Taken together, these empirical studies suggest that at

the local level, within cities, several groups of ecologi-

cal factors are often positively associated with higher

levels of psychiatric hospital use. These are:

relatively high levels of poverty and socio-economicdeprivation in the local population; relatively high levels of social fragmentation andindividual isolation in the local community (also

described by Durkheim (1897) as anomie); locally high concentrations of minority ethnic groups; close spatial proximity of the area to hospitals withpsychiatric beds.

Explanations for these associations are thought to be

complex, and they may be summarized as a combination

of different processes (as discussed for example by Smith

and Hanham, 1981; Giggs, 1988, Dear and Wolch, 1987;

Dohwenrend et al., 1992; Takahashi and Gaber, 1998;

mont et al., 2000; Almog et al., 2004). One set of

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 193722they are now often located in relatively deprived areas,

so that socio-economic factors and proximity to beds

may be spatially correlated. Some ecological studies of

varying hospitalization rates therefore include indicators

of spatial proximity to services (eg Carr-Hill et al., 1994;

Smith et al., 1996; Almog et al., 2004; Maylath et al.,

1999) and try to establish whether associations with

socio-economic conditions are independent of spatial

proximity to the supply of beds.

Access to primary and ambulatory services may also

be variable for the populations of small areas, which

may affect their use of hospitals. The Commission on

Mental Health in the US observed that:

Too often the short term inpatient care and

emergency services in hospitals are used as the rst

contact for uninsured and underinsured populations.

(Commission on Mental Health, 2003, p. 71).

The comments by the commission seem to conrm that

emergency inpatient hospital care is being used as a rst

response service for some people with mental illness

because of lack of access to ambulatory care alternatives.

This may occur in the event of crises arising when mental

illness has not been managed effectively in the commu-

nity. It raises questions about whether such use of

inpatient care is effective or efcient in a mental health

system based on a model of care in the community.

Commentators from European countries have also

commented that community mental health services need

to be better developed and that high levels of hospitaliza-

tion in some areas may be compensating for a lack of

access to other forms of social or community support

(Johnson et al., 1998; Maylath et al., 1999).

This is all the more important because it seems that

such variation in use of inpatient and outpatient care

relates to poverty and to race or ethnic group. Several

observers have criticized the US mental health care

system for what is apparently a lack of access to good

ambulatory mental health care for poor patients without

insurance cover (e.g. Frank and Morlock, 1997; Grob,

1994). This is reported especially for black Americans,

and may be partly related to differences in the way that

health services diagnose and treat patients from this

racial group. Jones and Gray (1986) suggested that there

are discrepancies in the pattern of psychiatric diagnoses

for black and white patients such that there may be

relative overdiagnosis for black patients of schizophre-

nia-related disorders, and an overdiagnosis of whites

for personality and affective disorders. This seems to be

associated with relatively low usage of outpatient care

and high rates of use of inpatient services by black

populations, often funded by the Medicaid program

(Snowden, 1999; Dixon et al., 2001; Wang & Demler,

2002). For example, Dixon et al. (2001) report a large

study of service use for schizophrenia in the populationunder 65 years of age, and found that, although

incidence of diagnosed schizophrenia and associated

use of hospital care were relatively high among black

Americans, whites were about 1.5 times as likely as

blacks to have received outpatient care and 1.3 times as

likely to have received individual therapy. Reports of

relatively high rates of psychiatric illness requiring

hospital treatment among African-Carribean British

men include: Van Os et al., (1996), Callan (1996),

Harrison et al. (1997), Bhui and Bhugra (2001).

Thus if ecological studies reveal that in certain areas

the rate of acute hospital use is relatively high, this could

be open to different interpretations. Such variations

might be appropriate if the prevalence, nature and

severity of mental illness in some areas necessitate higher

rates of inpatient care to provide effective medical

treatment. On the other hand, they might reect varying

access to hospital beds and to effective primary and

ambulatory care which might prevent admission. The

latter scenario suggests that there may be some inap-

propriate use of inpatient services as a rst resort and as

an alternative to ambulatory, community-based-care.

Method to compare geographical differences in

hospitalization rates in New York City and London

We aimed to make a comparative empirical study to

investigate the relationships discussed above in NYC

and London. We analysed small area data, for both

cities, on acute admissions of men and women aged

1564, collated for selected psychiatric conditions, which

we have dened as far as possible in similar terms for

both cities. We have examined the relationship between

local admission rates and a set of predictor variables,

which we also attempted to dene in similar ways for

both cities.

Hospitalization data for NYC were derived from

administrative data in the Statewide Planning and

Research Co-operative System (SPARCS) produced by

the New York State Department of Health. The data

used here represent almost all psychiatric admissions of

NYC residents to non-federal public, non-prot and

proprietary general hospitals in New York State

included in the SPARCS system. Admissions to

Veterans Administration hospitalizations as well as

those outside of New York State are not included in

this analysis. For London, the data were derived from

the Hospital Episode Statistics (HES), which are

compiled for all parts of the country and record all

episodes of inpatient psychiatric care funded by the

National Health Service. The relatively small proportion

of hospital use that is privately funded is excluded

because suitable data are not available. The HES data

used here were for residents of Greater London.

We focus on acute admissions (lasting less than 90

days) for which the main cause of admission is classied

as one of categories 290319 inclusive of the World

Health Organization (WHO, 1978) International Classi-

fication of Diseases Version 9 (ICD-9), three-digit

classication. We refer to these here as all psychiatric

conditions (though they exclude, for example, self-harm

categories associated with para-suicide). All psychiatric

conditions are therefore a broad category, including

admissions due to drug and alcohol dependency, organic

problems such as Alzheimers disease, mental retarda-

tion and developmental disorders, as well as illness due

to schizophrenia and affective disorders. We have also

psychiatric beds and socio-economic conditions within

the area. These data were derived from the most recent

population censuses (for the year 2000 in US and 2001 in

UK) and also from other UK sources discussed below.

The demographic data for NYC ZCAs were derived

from the 2000 population census giving the numbers of

males and females in 5-year age groups. For London,

ward level population estimates for 1998 produced by

the London Research Centre were used to estimate

person-years for the relevant period. These population

data were used in combination with the admissions data

to calculate expected numbers of admissions for each

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 1937 23examined the pattern for selected, more specic causes

of mental illness: schizophrenic conditions (coded 295 in

the ICD-9 3 digit classication), affective disorders

(coded 296), and drug dependency (coded 304). We have

selected these because other ecological studies (reviewed

above) found that while they were all correlated with

small area social structure, the different diagnoses

showed varying associations with socio-economic vari-

ables. Although we aimed for consistency in diagnostic

cause of admission there is some difculty due to

changes in classication between ICD version 9, used in

the NYC data, and ICD version 10 (WHO, 1992), used

in the London data2.

In NYC data were collated for 165 ve-digit Zip Code

Areas (ZCAs). Total population size ranges from 4226

to 106,154. In London the spatial units were 760

electoral wards (as dened in 1998) population ranges

from 2257 to 21,162. We examined admissions for men

and women separately in the age groups 1564. Hospital

stays/episodes exceeding 90 days were excluded from the

analysis because we are interested in acute psychiatric

care (which can be seen as part of the spectrum of care

provided in a community based care model involving

ambulatory services and relatively brief inpatient stays

when necessary). The data for NYC are for the 12-

month period April 1999March 2000. In London

hospitalization rates are lower than in New York and

the spatial units are smaller, and as a consequence the

counts of admissions are fewer, so we combined data for

the 3-year period: April 1996March 1999.

Admissions data for small areas were linked to

information relating to the demographic prole of area

populations, the proximity of areas to hospitals with

2The following ICD-10 codes were used, in the London

admissions data, to approximate ICD-9 categories in the NYC

data. This conversion was based on: NHSCCC (2000).

All psychiatric causes: ICD-10 categories F00-99 (approx-

imating to 290-319 of ICD-9).

Schizophrenic disorders ICD-10 codes F20, F21X, F23,

F25, F25x (approximating to ICD-9 295).

Affective psychoses:ICD-10 codes F30-F39 (approximating

to ICD-9 296).

Drug dependency: ICD-10, F11F16, F18, F19 (approx-

imating for ICD-9 304).New York ZCA or London ward. This is the number

that would occur if the annual rate of admissions for

each sex and age group in the small area were the same

as in the general population of the whole city where it is

situated. This information is used to produce age

standardized admission ratios (SARs) for men and

women (comparing the observed and expected admis-

sions to show whether small area rates of admission are

higher or lower than expected given their demographic

prole). The reference population for SARs of NYC

ZCAs is therefore the general population of the ve

NYC counties, while for London the reference is the

population of Greater London.

To measure spatial proximity to psychiatric beds we

used data on the location and number of psychiatric

beds in hospitals. These were supplied for NYC by New

York State Department of Health and for London by

the Adult Mental Health Service Mapping project,

University of Durham, UK (http://www.dur.ac.uk/

service.mapping/). We used the postcode of individual

hospitals to derive an approximate estimate of geo-

graphic location (based on the centroid of the small area

in which it was situated). This information was used to

generate two measures of spatial proximity to psychia-

tric beds for each small area. One indicator shows

whether or not there is a hospital with psychiatric beds

located within the small area (this measures proximity to

psychiatric beds for the population located in the

immediate vicinity of the hospital). The second is a

general measure of spatial access opportunity3 to all

beds in the city, based on a gravity model (Carr-Hill

3The access opportunity measure used here was calculated

as follows:

Ai SkBkgdik=SmPmgdmk;where Ai is the access score for ith Zip Code Area; Bk the no. of

psychiatric beds at kth hospital; dik and dmk the distances from

kth hospital to ith and mth Zip Code Areas, respectively; Pm the

population in mth Zip Code Area; and g is a function of

distance. An inverse function of distance is used to reect the

distance decay effect on access. Following Carr-Hill et al.

(1994) the function used is g(d)=e0.2d.For further discussion of gravity models in the analysis of

hospital distribution, see (Congdon, 1996b).

et al., 1994). This assumes that the population of every

area competes for access to all the available beds in the

city, and that in each area, mentally ill people are more

likely to use beds which are geographically closer than

beds which are further away. As discussed above, it was

important to examine local variations in the supply of

psychiatric beds because in both London and NYC many

hospitals have been located in what are now poorer inner

city areas and, as discussed above, this may confound the

association with social and economic variables.

We would have liked to include information on use of

outpatient services, which might have been indicative of

use of alternatives to inpatient care, but these data were

not available for this research. This limitation is

considered in the discussion that concludes this paper.

We used three composite indicators of socio-economic

conditions to reect deprivation, anomie and concentra-

tion of racial/ethnic minority groups. As shown in the

theoretical and empirical evidence summarized above,

these represent different population factors likely to be

independently associated with psychiatric admissions.

The deprivation indicators for NYC and London

were based on components4 dened in similar ways.

Most previous analyses of socio-economic factors

associated with psychiatric hospital use in Britain (e.g.

Glover, 1998; Carr-Hill et al., 1994) have used census-

based socio-economic indicators. For our analysis of

London wards, we employed the Index of Multiple

Deprivation (IMD) which is disseminated by the Ofce

of the Deputy Prime Minister (ODPM) and is widely

used to measure local variation in deprivation and to

inform area-based social policy (DETR, 2000). Several

of the elements of this indicator are derived from sources

that do not depend on the decennial census and can be

compiled on an annual basis for wards throughout the

country. Moreover, unlike the population census (which

does not provide direct information on income poverty),

the IMD indicator includes several measures of relative

numbers of the population qualifying for welfare

benets because of low income, disability or unemploy-

ment. It also includes detailed measures of educational

qualications of the local population, housing depriva-

tion, morbidity and disability and access to essential

services. However, the IMD does not include data on

racial/ethnic composition of the population or variables

relating to anomie. The following analysis shows the

ARTICLE IN PRESS

4The indicator of deprivation for London wards which we

have adopted here was compiled for the Ofce of the Deputy

Prime Minister from a variety of sources and includes

components relating to several different domains. These are

dened as follows (IMD, 2000):

Income domain:

Adults (and children) in income support households, 1998.

Adults (and children) in income-based job seekers allowance

(footnote continued)

HE, 1997-8.

KS2 primary school performance indicators, 1998.

Primary school children with English as an additional

language, 1998.

Absenteeism at primary school, 1998.

Housing domain:

S. Curtis et al. / Health & Place 12 (2006) 193724households, 1998.

Adults (and children) in family credit households, 1999.

Adults (and children) in disability working allowance

households, 1999.

Non-earning, non-IS pensioner and disabled Council Tax

Benet recipients, 1998.

Employment domain:

Unemployment claimant counts, 199899.

People out of work but in TEC government supported

training.

People aged 1824 on New Deal options.

Incapacity benet recipients aged 1659, 1998.

Severe disablement allowance claimants aged 1659, 1999.

Health deprivation and disability domain

Mortality ratios under 65, 199798

Proportion of population receiving attendance allowance or

disability living allowance, 1998.

People of working age receiving incapacity benet or severe

disablement allowance, 199899.

Age/sex standardized morbidity ratio (self reported long

term limiting illness), 1991 census.

Proportion of births with birthweight o2500 g, 199397.Education skills and training domain:

Working age adults with no qualications, 199598.

Children aged 16+ not in full time education, 1999.

Proportions of 17-19 year olds not successfully applied forHomeless households in temporary accommodation,

19978.

Houshold overcrowding 1991 census.

Poor private sector housing.

Access domain:

Access to a post ofce.

Access to food shops.

Access to a GP.

Access to a primary school.

To produce an indicator of deprivation for ZCAs NYC

(data from 2000 census), z-score transformations of the

following variables were used:

% population over 16 years old unemployed.

% with income less than $10,000.

inverse of median income in 1999.

% of households qualifying for social security payments.

% households with supplemental income.

% households with public assistance income.

% population over 25 years of age with less than 9th grade

educational level.

% population aged 2164 with a disability.

% individuals over 18 years below poverty level.

% housing units with more than 1 person per room.

% housing lacking complete plumbing facilities.

% housing lacking kitchen facilities.

value of the IMD measure as predictor of admissions

and whether better prediction can be obtained by using

social isolation and ethnicity indicators in addition to

the IMD measure. Another advantage of the IMD

measure, for the purposes of this study, is that several of

the components are dened in a similar way to variables

in the US census for 2000. Footnote 4 lists the census

data selected for NYC ZCAs to approximate, as far as

possible, the IMD index. In the IMD index the

component variables are standardized and weighted

according to the results of a factorial analysis. For the

NYC deprivation measure we standardized the variables

(z-scores) and combined the resulting components

without weighting them.

The indicators of anomie used in London and

NYC5 were previously employed in ecological

studies of variation in suicide rates in London by

themselves as belonging to the white racial/ethnic

category, because there may be other minority groups

with distinctive patterns of use of mental health care,

which are not as frequently reported in the literature

(Burr, 2002; Lu et al., 2002).

Poisson regression modeling incorporating random

effects was used to control for both overdispersion in the

counts of admissions and for the effects of spatial

autocorrelation6. The analysis was carried out in the

WINBUGS analysis package (Spiegelhalter et al., 2000).

This employs Bayesian modeling, which is more suitable

than conventional Poisson modeling for data from small

areas that includes small counts. The dependent variable

in these models was the count of the observed number of

inpatient events (admissions). The expected number of

events was included as an offset term to standardize for

the effect of age and sex. This means that the outcome

variable can be interpreted as the ratio of observed to

expected events (the Standardized Admission Ratios

described above). The deviance was examined in relation

to the number of units in the analysis to check for

overdispersion7.

The analysis produced mean values for beta coef-

cients for the predictor variables in the model. These

were used to assess the signicance of association

ARTICLE IN PRESS

6We have chosen to use a regression model that takes spatial

auto-correlation into account, in preference to a more conven-

tional Poisson regression model. The Poisson model is often

S. Curtis et al. / Health & Place 12 (2006) 1937 25Congdon (1996a, 1997, 2001) and in Britain by Whitley

et al. (1999). We have included components which

separately measure the proportions of the population

who live alone, who are not married (which at the

population level we take to represent household level

isolation), who live in privately rented accommodation

or who had moved in the 12 months preceding the

census (reecting residentially mobile populations that

may generally be less likely to form stable neighbour-

hood ties).

We included a variable labelled race, to reect the

concentration of minority racial/ethnic populations. For

this we used census data for London and NYC on self-

dened racial or ethnic category. We used data on the

proportion of the population who identied themselves

in the census as belonging to black or African racial/

ethnic groups (which we have selected because of the

large literature suggesting that this group tend to have

particularly high rates of hospital admission for some

psychiatric disorders). In addition, we have included the

proportion of the population who did not identify

5Indicators of social isolation or social fragmentation were

calculated by combining standardized (z-score) transformations

of the following components:

For ZCAs in New York City (data from 2000 population

census):

% population living alone.

% population not unmarried or separated.

% population in rented accommodation.

% population who moved house in the previous year.

For wards in London (data from 2001 population census):

% population living alone.

% population not unmarried or separated.

% population in rented accommodation.

Measure of racial composition, NYC:

% of population in households with people of Black or

African American race (alone or in combination)

% of population in households with people of white race

(alone or in combination).suitable for data made up of counts, but here there are possible

problems of overdispersion in the count data (Cameron and

Trivedi, 1990). With spatially dened units there are likely to be

issues of spatial correlation in the extraneous variation (e.g.

Cliff and Ord, 1981). This was apparent in this analysis, because

we found that a conventional Poisson model gave deviances

considerably exceeding the degrees of freedom, indicating that

the signicance of the regression coefcients of independent

variables may be overstated. Solutions may be to introduce the

random effects either multiplicatively (e.g. leading to a negative

binomial) or to additively in the regression link. Here we have

adopted the mixed model of Besag et al. (1991) which adds two

kinds of random effects in the regression link, one spatially

structured, the other being spatially unstructured effects. Our

approach differed from the Besag et al. model because spatial

effects were modelled via the specication of Sun et al. (1999),

which involves a specic spatial correlation parameter.7To conrm that the model is appropriate to the data

structure we have examined the size of the deviance at the

posterior mean of the coefcients, which should be similar to

the number of degrees of freedom in the model. The degrees of

freedom in a model without random effects is Np where

N=165 and p is the number of independent variables (constant

included). In a model with random effects the degrees of

freedom is problematic because the number of parameters has

to be estimated. Here we have estimated the effective number of

parameters in deriving the Deviance Information Criterion

(Spiegelhalter et al., 2002) which indicates the goodness of t of

the model.

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 193726between the predictor variable and the rate of admission

(dependent variable). The ratio of the mean beta value

to the standard deviation indicates the signicance of the

association. Where the mean of the beta coefcient has a

value more than 1.96 the standard deviation, and where

the 95% credible interval (the condence interval in

Bayesian analysis) does not include zero, the association

is signicant (indicated by in the tables of results).The sign of the coefcient shows whether the correlation

is positive or negative. Relatively high ratios of mean to

standard deviation indicate that there is a relatively large

variability in admission ratios associated with variation

in the predictor variable. We also report the deviance at

posterior mean and the deviance information criterion

(DIC), which are indicators of the goodness of t of the

model and the rho coefcient, indicating the degree of

spatial auto-correlation in the regression errors (Sun

et al., 1999).

Results: local variation in hospitalization for psychiatric

conditions in NYC and London

The patterns of admissions show some variation

between New York and London. For example, crude

Fig. 1. ODPM index of deprivarates of admission per 1000 men aged 1564 are much

higher in New York than in London (25/1000 compared

with 11/1000). Admission rates for women are more

similar between the two cities (11/1000 in NYC and 8/

1000 in London). The large differential between male

and female rates of admission in NYC is most evident

for admissions due to conditions such as drug depen-

dency. (Male admission rates for ICD-9 304 are three

times those for women). These results suggest differences

between the two cities in treatment of some psychiatric

conditions, or possibly variations in prevalence of these

conditions in the population.

Fig. 1 shows a ward level map for London of

variation in the deprivation indicator, showing more

deprived areas (shaded darker) especially in central and

in eastern areas, as well as in some western areas. Ward

level SARs for all psychiatric causes for males aged

1564 in London are shown in Fig. 2, and the higher

levels of admission follow a broadly similar spatial

pattern to that for higher levels of deprivation. Almog et

al. (2004) reported a similar spatial association between

poverty and admission rates in NYC. Figs. 3 and 4,

which show patterns for admissions in London due to

affective disorders and for schizophrenia, demonstrate

that there are some differences in the geographical

tion 2000; London wards.

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 1937 27pattern of admission rates according to the type of

diagnosis considered.

Tables 1 to 4 record the results from the regression

analyses for each city and for males and females,

respectively.

The regression analyses of male admission rates for

NYC are shown in Table 1. In most of the models, the

measure of access showing that there was a hospital

located in the same ZCA had a positive and signicant

association with admission rates. Admissions due to

drug dependency, however, were positively associated

with the alternative indicator measuring spatial access to

beds across the city as a whole. After controlling for the

access indicators, deprivation was signicantly and

positively associated with all the causes of admission

considered here for males in NYC. The association with

deprivation was stronger (larger beta coefcient) for

admissions due to drug dependency than for affective

disorders or schizophrenia. For males in NYC, anomie

(social fragmentation) showed a signicant association

with admissions due to all causes and admissions due to

affective disorders and schizophrenia but not for drug

dependency. Racial minority concentration was signi-

cantly and positively associated with admissions for

Fig. 2. SARs for all psychiatric causes;schizophrenia but not for affective disorders or drug

dependency admissions for males.

Admissions for women in NYC (Table 2) showed

similar associations with predictor variables, except that

beta coefcients for the access variables show weaker

associations which are not always signicant. Also the

anomie indicator was signicantly and positively related

to admissions for drug dependency, which was not the

case for male admissions.

Results for London (Tables 3 and 4) showed variable

associations with the measure of access opportunity.

Most of the signicant associations were positive,

especially for admissions due to schizophrenia and drug

dependency, reecting relatively high rates for wards

which are closer to the overall supply of hospitals with

psychiatric beds across the city. However, for affective

disorders, there was no important association with the

overall access opportunity measure, and female admis-

sion rates for affective disorders were signicantly lower

in wards within which psychiatric hospital beds are

located.

Independently of the measures of access, deprivation

was positively associated with admission rates in

London for all the types of psychiatric conditions

London, males 1564, 19961999.

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 193728considered here and for both males and females. Anomie

was associated positively with admissions for men and

women due to schizophrenia and affective disorders, and

for all psychiatric conditions, including these. How-

ever, for drug dependency the association was insignif-

icant. When controlling for deprivation and anomie, the

variable measuring racial/ethnic minority concentration

for London wards showed a negative association with

admission rates for drug dependency and for affective

disorders. For schizophrenia the association showed no

signicant association independent of deprivation and

social fragmentation. This was unexpected, given the

evidence from the UK reviewed above concerning

relatively high rates of admission for African and

Carribean Black men due to conditions such as

schizophrenia. In our data, the association between

racial composition and deprivation in London wards is

quite strong (Pearsons R=0.46), so it is possible that

the strong association with deprivation is masking a

positive link with ethnic composition in these data.

Alternative variables relating to ethnicity were also

tested in the model (e.g. a variable which specically

indicated the proportion of the population belonging to

Black African and Caribbean groups), and they gave

similar results. We discuss the interpretation of this

nding below.

Fig. 3. Standardized admission ratios: acute psychiatric admissioDiscussion

There are other examples of international compara-

tive studies of psychiatric care in urban settings (e.g.,

Goldberg and Thornicroft, 1998) and some have

employed a standardized methodology to improve the

potential for comparative measurement of variations in

psychiatric care (e.g., Becker et al., 1999). However, we

are not aware of other studies that have taken such a

standardized approach to comparison of psychiatric

hospitalization in NYC and London. This paper

demonstrates that there are some limits to the scope

for direct statistical comparisons using a common

approach to ecological analysis of hospitalization for

these two cities. For example, there are differences

between the two cities in: the average size of the small

areas for which analysis is feasible; the ways in which

causes of admission are coded (using different versions

of the ICD); and the inclusion of data on hospitaliza-

tions paid by commercial insurance in NYC, which is

not provided by HES data for London. There are slight

differences in the years for which data have been

collected (though we do not consider this large enough

to affect the comparison). Also, the socio-economic data

available from sources like the census are similar, but

not dened in exactly the same way. Furthermore, some

ns, London wards c. 1998: males 1564, affective disorders.

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 1937 29socio-economic variables may have different social

meaning in the two cities, even though they appear to

be dened in rather similar ways. For example, the

ethnic/racial categories do not relate to groups with

exactly the same origins.

It is also important to note that this analysis has

considered data on hospital admissions. Some of these

will be repeat admissions of the same patients for

ongoing mental health problems and they are therefore

not indications of the incidence or prevalence of mental

illness in the population. Furthermore, service use is not

a very reliable indication of the pattern of mental illness

in the population, and this is not an aetiological study of

factors that may be causing psychiatric conditions. We

have not reported here analyses which take into account

variations in length of stay in hospital which are also

relevant in assessing the health care burden associated

with different patient groups. However, in a separate

paper we show that local variation in the volume of

hospital use (beddays) in NYC shows associations with

socio-economic conditions and proximity to services

that are broadly similar to those found for admissions

(Almog et al., 2004). For this analysis we only had

inpatient data available and we were not able to include

data on rates of use of outpatient hospital services or

other forms of ambulatory psychiatric care. The

Fig. 4. Standardized admission ratios: acute psychiatric admissaddition of such data would give a clearer impression

of the spectrum of mental health services consumed by

local populations and we plan further research to derive

this information from other sources and include these in

later analyses. It is also important to note that, as this

study relies on routine data sources, any biases in data

recording will affect the results. One possible problem

here concerns the possible under-enumeration of resi-

dent populations in some areas of large cities, which

would tend to result in inated calculations of admission

rates. Under-enumeration may be particularly proble-

matic in areas with large numbers of marginalized and

highly mobile populations, so this might partly explain

our ndings of higher admission rates in deprived areas.

On the other hand, the rates of under-enumeration

would need to be very large (well in excess of 30%) to

fully account for the differences we report here between

observed and expected rates of admission.

Allowing for these caveats about the comprehensive-

ness and comparability of the data, some interesting

observations can be made about the results of the

analyses reported here.

For admissions of men due to schizophrenia and drug

dependency (in London and in NYC), we found that the

rate of hospitalization is partly determined by supply of

hospital beds. This is consistent with commentaries on

ions, London wards c.1998: males 1564, Schizophrenia.

ARTIC

LEIN

PRESS

Table 1

Regression results for males 1564 in NYC (controlling spatial autocorrelation)

Dependent variable

(cause of

admission)

Deviance at

posterior mean

DIC rho Variable *Signicant b-coeff; mean Standard

deviation

Credible interval

0.025 0.975

All psychiatric 163.5 320.6 0.944 Constant 0.420 0.067 0.540 0.283Access opportunity 0.092 0.061 0.029 0.205Whether hospital in

area

* 0.258 0.058 0.134 0.378

Deprivation * 0.051 0.005 0.041 0.062

Anomie * 0.050 0.017 0.017 0.081

Race * 0.049 0.023 0.007 0.092

ICD-9 304 162.2 304.9 0.956 Constant 0.529 0.129 0.773 0.274Drug dependence Access opportunity * 0.237 0.095 0.039 0.418

Whether hospital in

area

0.054 0.086 0.120 0.230

Deprivation * 0.072 0.008 0.056 0.088

Anomie 0.041 0.029 0.019 0.094Race 0.034 0.031 0.030 0.094

ICD-9 295 161.0 301.5 0.816 Constant 0.353 0.077 0.500 0.186Schizophrenia Access opportunity 0.007 0.101 0.180 0.196

Whether hospital in

area

* 0.267 0.098 0.083 0.464

Deprivation * 0.028 0.007 0.015 0.041

Anomie * 0.065 0.024 0.015 0.110

Race * 0.153 0.028 0.094 0.203

ICD-9 296 162.5 280.6 0.844 Constant 0.191 0.047 0.286 0.099Affective disorders Access opportunity 0.081 0.054 0.186 0.027

Whether hospital in

area

* 0.212 0.067 0.088 0.353

Deprivation * 0.025 0.005 0.016 0.035

Anomie * 0.075 0.020 0.033 0.110

Race 0.030 0.019 0.007 0.067

S.

Cu

rtiset

al.

/H

ealth

&P

lace

12

(2

00

6)

19

3

730

ARTIC

LEIN

PRESS

Table 2

Regression results for females 1564 in NYC

Dependent variable

(cause of

admission)

Deviance at

posterior mean

DIC rho Predictor variable *Signicant b-coeff; mean Standard

deviation

Credible interval

0.025 0.975

All psychiatric 166.20 308.69 0.96 Constant 0.211 0.080 0.381 0.075Access opportunity 0.015 0.056 0.104 0.121Whether hospital in

area

0.090 0.052 0.014 0.197

Deprivation * 0.041 0.005 0.031 0.049

Anomie * 0.065 0.015 0.037 0.098

Race * 0.034 0.017 0.000 0.069

ICD-9 304 165.90 290.89 0.92 Constant 0.572 0.161 0.888 0.270Drug dependence Access opportunity 0.144 0.105 0.080 0.334

Whether hospital in

area

0.021 0.098 0.172 0.209

Deprivation * 0.066 0.009 0.049 0.084

Anomie * 0.065 0.029 0.010 0.121

Race 0.062 0.037 0.004 0.137ICD-9 295 165.20 297.78 0.78 Constant 0.231 0.061 0.348 0.105Schizophrenia Access opportunity 0.075 0.088 0.225 0.123

Whether hospital in

area

0.177 0.106 0.025 0.392

Deprivation * 0.027 0.007 0.013 0.041

Anomie * 0.070 0.024 0.023 0.118

Race * 0.070 0.028 0.017 0.125

ICD-9 296 164.90 278.02 0.93 Constant 0.148 0.040 0.226 0.067Affective disorders Access opportunity 0.051 0.048 0.141 0.048

Whether hospital in

area

0.077 0.060 0.035 0.203

Deprivation * 0.028 0.004 0.021 0.036

Anomie * 0.066 0.015 0.036 0.095

Race 0.011 0.016 0.042 0.020

S.

Cu

rtiset

al.

/H

ealth

&P

lace

12

(2

00

6)

19

3

731

ARTIC

LEIN

PRESS

Table 3

Regression results for males 1564 in London

Dependent variable

cause of admission

Deviance at

posterior mean

DIC rho Predictor variable *Signicant Mean Standard

deviation

Credible interval

0.025 97.5%

All psychiatric 754 1366 0.95 Constant 0.201 0.026 0.254 0.148Access opportunity 0.052 0.030 0.005 0.111Whether hospital in

area

0.035 0.041 0.047 0.114

Deprivation * 0.594 0.032 0.533 0.655

Anomie * 0.151 0.026 0.098 0.199

Race * 0.148 0.030 0.209 0.093ICD-9 304 630 932 0.92 Constant 0.311 0.051 0.411 0.208Drug dependence Access opportunity * 0.266 0.057 0.146 0.372

Whether hospital in

area

0.086 0.062 0.031 0.211

Deprivation * 0.444 0.085 0.306 0.628

Anomie 0.051 0.057 0.153 0.063Race * 0.128 0.053 0.231 0.023

ICD-9 295 718 1185 0.83 Constant 0.495 0.064 0.657 0.390Schizophrenia Access opportunity * 0.131 0.067 0.017 0.297

Whether hospital in

area

0.058 0.084 0.225 0.104

Deprivation * 0.731 0.061 0.612 0.846

Anomie * 0.338 0.069 0.190 0.450

Race 0.096 0.059 0.202 0.037ICD-9 296 723 1141 0.89 Constant 0.321 0.051 0.430 0.215Affective disorders Access opportunity 0.014 0.074 0.100 0.194

Whether hospital in

area

0.023 0.076 0.177 0.121

Deprivation * 0.694 0.086 0.515 0.836

Anomie * 0.211 0.059 0.091 0.312

Race * 0.267 0.052 0.365 0.159

S.

Cu

rtiset

al.

/H

ealth

&P

lace

12

(2

00

6)

19

3

732

ARTIC

LEIN

PRESS

Table 4

Regression results for females 1564 in London

Dependent variable

(cause of

admission)

Deviance at

posterior mean

DIC rho Predictor variable *Signicant b-coeff; mean Standard

deviation

Credible interval

0.025 0.975

All psychiatric 751 1346 0.95 Constant 0.214 0.052 0.350 0.113Access opportunity * 0.099 0.058 0.006 0.252

Whether hospital in

area

0.023 0.049 0.122 0.072

Deprivation * 0.552 0.046 0.465 0.642

Anomie 0.081 0.046 0.007 0.164Race * 0.239 0.037 0.311 0.170

ICD-9 304 466 662 0.92 Constant 0.432 0.098 0.657 0.248Drug dependence Access opportunity * 0.374 0.100 0.176 0.559

Whether hospital in

area

0.091 0.097 0.294 0.095

Deprivation * 0.500 0.139 0.237 0.751

Anomie 0.062 0.097 0.256 0.117Race * 0.278 0.082 0.446 0.126

ICD-9 295 687 1086 0.90 Constant 0.424 0.070 0.579 0.281Schizophrenia Access opportunity * 0.163 0.082 0.035 0.374

Whether hospital in

area

0.061 0.083 0.228 0.097

Deprivation * 0.611 0.067 0.474 0.739

Anomie * 0.202 0.073 0.060 0.331

Race 0.080 0.053 0.186 0.022ICD-9 296 755 1209 0.92 Constant 0.251 0.042 0.329 0.158Affective disorders Access opportunity 0.035 0.063 0.076 0.175

Whether hospital in

area

* 0.186 0.087 0.365 0.029

Deprivation * 0.604 0.076 0.464 0.751

Anomie * 0.135 0.052 0.035 0.235

Race * 0.268 0.047 0.359 0.180

S.

Cu

rtiset

al.

/H

ealth

&P

lace

12

(2

00

6)

19

3

733

ARTICLE IN PRESSS. Curtis et al. / Health & Place 12 (2006) 193734hospital service use in London (Smith et al., 1996;

Johston et al., 1998) and also agrees with ndings by

Maylath et al. (1999) for Hamburg. We also found that

this does not apply equally to all types of condition

because for affective disorders in London (and also for

females in NYC) the indicators of spatial access do not

show a positive relationship with rates of hospitalization

when socio-economic factors are included in the model.

In this respect our ndings differ from those of Maylath

et al. (1999). This raises a question about whether

proximity to supply is really an important factor for all

types of hospital use, or only for certain mental illnesses

such as schizophrenia and drug dependency.

Another key point from our analysis is that there are

signicant independent associations between the socio-

economic characteristics of the local population and the

rate of hospitalization. These associations are indepen-

dent of variations in the local supply of beds. It seems

possible that poverty and anomie are rather consistently

related to psychiatric hospital use in large cities around

the world. However the signicance of relationships with

socio-economic variables differs according to the cause

of admission. This might be partly because some

diagnoses tend to give rise to more admissions than

others, which might affect the statistical signicance of

results based on larger or smaller counts. However, our

results may reect real differences in the geography of

admissions for different conditions. Such differences

might be due to admissions policy, bed availability and

treatment of different conditions, or variations in the

aetiology of different types of mental illness (or at least

the social conditions which precipitate admission). The

results suggest that poverty is more predictive for drug

dependency while anomie (social fragmentation) is more

strongly associated with affective disorders. These

ndings are broadly similar in both cities and are fairly

consistent with research from other cities, reviewed in

the introduction.

Ethnic/racial composition of the population also has

an independent association for schizophrenia admis-

sions in New York City, but not in London. Our results

for New York City are consistent with other research

that has highlighted high rates of diagnosed schizo-

phrenia and associated hospital use among Black and

African populations. Also, we have shown that the

variables representing the racial composition of the local

population are particularly important for admissions

due to schizophrenia in New York, but less so for other

conditions. However, our results for the race variable in

London, showing that Schizophrenia SARs are not

signicantly higher in areas with large Black and African

populations, seem contrary to some other studies which

have indicated high rates of hospitalization for African

Caribbean populations in Britain. We also found that

SARs for drug-related and affective disorders are

negatively associated with populations with largeminority groups. It is possible that the ecological data

in London do not pick out Black Carribean and African

populations very effectively. For example, census data

for London do not show such extreme concentrations of

Black and African populations as are found in NYC.

In the data we have used for London wards, the

proportion of Black and African residents does not

exceed 62%, however 15% of NYC ZCAs have

concentrations in excess of this gure, and in some

ZCAs, concentrations occur of over 80% or even 90%,

so it may be easier to detect the experience of Black and

African populations using ecological data in New York.

Our results for London may also be due to intercorrela-

tions between poverty and race at the ecological level,

so that higher rates of admission among individuals of

Black Caribbean or African origin is not detectable

once poverty, proximity to hospitals and spatial

auto-correlation effects are taken fully into account.

Most of the previous ecological studies in London

showing high rates of admission in areas with

greater concentration of minority ethnic groups were

based on older data and so it is also possible that there

has been some change in the pattern of admissions

over time. Our results suggest that areas with

large populations from Black, African and Asian

minority ethnic groups are now more distinguished

from the White majority population by their relatively

low use of hospital care for affective disorders and drug

dependency (especially among women). Our results

suggest that relationships between ethnic composition

of the population and use of psychiatric hospitals may

be continguent on other factors and variable between

cities.

The ecological relationships shown here show broadly

similar associations for admissions for males and

females. However, there are some differences in the

strength of the associations with some variables.

Because we have examined cause specic admissions, it

seems unlikely that these differences are simply due to

variations in the types of mental illness for which men or

women are hospitalized. For example, deprivation

appears to be more strongly associated with male

admissions generally and in New York this is most

evident for drug-related diagnoses, while in London the

difference is more apparent for schizophrenia and for

affective disorders.

The application of these results for service planning

would probably be interpreted somewhat differently in

London and in NYC. In Britain, ecological models of

the type we have discussed here are used to predict the

demand for psychiatric inpatient care and to direct NHS

resources for mental health services. Differences in

relative levels of poverty seem to have the biggest

impact on admission rates and we have shown that, for

London, it is possible to predict local rates of admission

using the IMD measures of poverty and deprivation that

the introduction.

UK.

issue. Science NS 255, 946952.

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The ecological relationship between deprivation, social isolation and rates of hospital admission for acute psychiatric care: a comparison of London and New York CityIntroduction: background to the analysisMethod to compare geographical differences in hospitalization rates in New York City and LondonResults: local variation in hospitalization for psychiatric conditions in NYC and LondonDiscussionAcknowledgementReferences