<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0036-3634</journal-id>
<journal-title><![CDATA[Salud Pública de México]]></journal-title>
<abbrev-journal-title><![CDATA[Salud pública Méx]]></abbrev-journal-title>
<issn>0036-3634</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Salud Pública]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0036-36342008000500013</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A predictive model for the utilization of curative ambulatory health services in Mexico]]></article-title>
<article-title xml:lang="es"><![CDATA[Un modelo predictivo de la utilización de servicios de salud ambulatorios curativos en México]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valencia-Mendoza]]></surname>
<given-names><![CDATA[Atanacio]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bertozzi]]></surname>
<given-names><![CDATA[Stefano M]]></given-names>
</name>
<xref ref-type="aff" rid="A01"/>
<xref ref-type="aff" rid="A02"/>
</contrib>
</contrib-group>
<aff id="A01">
<institution><![CDATA[,Instituto Nacional de Salud Pública Centro de Investigación en Evaluación y Encuestas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="A02">
<institution><![CDATA[,Docencia Económicas Haas School of Business Centro de Investigación ]]></institution>
<addr-line><![CDATA[Berkeley ]]></addr-line>
<country>EUA</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>10</month>
<year>2008</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>10</month>
<year>2008</year>
</pub-date>
<volume>50</volume>
<numero>5</numero>
<fpage>397</fpage>
<lpage>407</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0036-36342008000500013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0036-36342008000500013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0036-36342008000500013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[OBJECTIVES: To estimate the degree to which individual and household variables jointly predict utilization of curative ambulatory services in Mexico for four types of health providers. MATERIAL AND METHODS: Patient choice of provider (self-care, Ministry of Health, social security, or private provider) when they become ill is modeled using a nested multinomial logit model that uses household and individual variables as predictors. The data are from the Mexican National Health Survey conducted in 2000. RESULTS: Being a social security beneficiary is one of the most important predictors of utilization. A strong positive relationship between socio-economic status (SES) and demand for services was also found, with the strongest relationship being for private providers, followed by social security. Utilization of Ministry of Health (MoH) services was negatively associated with household SES. CONCLUSIONS: Expansion of health insurance coverage should significantly reduce health inequalities due to reduced care-seeking by non-beneficiaries.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[OBJETIVO: Estimar el grado en el cual variables individuales, del hogar y comunitarias predicen la utilización de servicios ambulatorios curativos en México. MATERIAL Y MÉTODOS: Ante un problema de salud los individuos pueden elegir utilizar servicios médicos, servicios de la Secretaría de Salud (SSa), de la Seguridad Social (SS) o Privados (SP). Esta elección es modelada con datos de la ENSA 2000 mediante un modelo logístico multinomial anidado. RESULTADOS: El predictor más importante de la utilización de servicios de salud fue la derechohabiencia a la SS. Se encontró una fuerte relación positiva entre estatus socioeconómico (ESE) y la utilización de servicios de salud. Dicha relación es mayor para la utilización de SP, seguida de la SS. Se encontró una relación negativa entre el ESE y la utilización de servicios de la SSa. CONCLUSIÓN: Expandir la cobertura de aseguramiento reduciría significativamente las inequidades en salud debidas a la baja utilización de servicios de salud por los no beneficiarios.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[utilization]]></kwd>
<kwd lng="en"><![CDATA[health services]]></kwd>
<kwd lng="en"><![CDATA[Mexico]]></kwd>
<kwd lng="es"><![CDATA[utilización]]></kwd>
<kwd lng="es"><![CDATA[servicios de salud]]></kwd>
<kwd lng="es"><![CDATA[México]]></kwd>
</kwd-group>
</article-meta>
</front><body><![CDATA[ <p align="right"><font size="2" face="Verdana"><b>ART&Iacute;CULO ORIGINAL</b></font></p>     <p>&nbsp;</p>     <p><font size="4" face="verdana"><b>A predictive model for the utilization of    curative ambulatory health services in Mexico</b></font></p>     <p>&nbsp;</p>     <p><font size="3" face="verdana"><b>Un modelo predictivo de la utilizaci&oacute;n    de servicios de salud ambulatorios curativos en M&eacute;xico</b></font></p>     <p>&nbsp;</p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana"><b>Atanacio Valencia&#45;Mendoza, MCES<SUP>I</SUP>;    Stefano M Bertozzi, MD, PhD<SUP>I, II</sup></b></font></p>     <p><font size="2" face="Verdana"><sup>I</sup>Centro de Investigaci&oacute;n en    Evaluaci&oacute;n y Encuestas, Instituto Nacional de Salud P&uacute;blica. M&eacute;xico    <br>   <sup>II</sup>Centro de Investigaci&oacute;n y Docencia Econ&oacute;micas Haas    School of Business. UC Berkeley. EUA</font></p>     ]]></body>
<body><![CDATA[<p>&nbsp;</p>     <p>&nbsp;</p> <hr size="1" noshade>     <p><font size="2" face="Verdana"><b>ABSTRACT</b></font></p>     <p><font size="2" face="Verdana"><B>OBJECTIVES:</b> To estimate the degree to    which individual and household variables jointly predict utilization of curative    ambulatory services in Mexico for four types of health providers.    <br>   <B>MATERIAL AND METHODS:</B> Patient choice of provider (self&#45;care, Ministry    of Health, social security, or private provider) when they become ill is modeled    using a nested multinomial logit model that uses household and individual variables    as predictors. The data are from the Mexican National Health Survey conducted    in 2000.    <br>   <B>RESULTS:</B> Being a social security beneficiary is one of the most important    predictors of utilization. A strong positive relationship between socio&#45;economic    status (SES) and demand for services was also found, with the strongest relationship    being for private providers, followed by social security. Utilization of Ministry    of Health (MoH) services was negatively associated with household SES.    <br>   <B>CONCLUSIONS:</B> Expansion of health insurance coverage should significantly    reduce health inequalities due to reduced care&#45;seeking by non&#45;beneficiaries.</font></p>     <p><font size="2" face="Verdana"><b>Keywords:</b> utilization; health services;    Mexico</font></p> <hr size="1" noshade>     <p><font size="2" face="Verdana"><b>RESUMEN</b></font></p>     <p><font size="2" face="Verdana"><B>OBJETIVO:</b> Estimar el grado en el cual    variables individuales, del hogar y comunitarias predicen la utilizaci&oacute;n    de servicios ambulatorios curativos en M&eacute;xico.    ]]></body>
<body><![CDATA[<br>   <B>MATERIAL Y M&Eacute;TODOS:</B> Ante un problema de salud los individuos pueden    elegir utilizar servicios m&eacute;dicos, servicios de la Secretar&iacute;a    de Salud (SSa), de la Seguridad Social (SS) o Privados (SP). Esta elecci&oacute;n    es modelada con datos de la ENSA 2000 mediante un modelo log&iacute;stico multinomial    anidado.    <br>   <B>RESULTADOS:</B> El predictor m&aacute;s importante de la utilizaci&oacute;n    de servicios de salud fue la derechohabiencia a la SS. Se encontr&oacute; una    fuerte relaci&oacute;n positiva entre estatus socioecon&oacute;mico (ESE) y    la utilizaci&oacute;n de servicios de salud. Dicha relaci&oacute;n es mayor    para la utilizaci&oacute;n de SP, seguida de la SS. Se encontr&oacute; una relaci&oacute;n    negativa entre el ESE y la utilizaci&oacute;n de servicios de la SSa.    <br>   <B>CONCLUSI&Oacute;N:</B> Expandir la cobertura de aseguramiento reducir&iacute;a    significativamente las inequidades en salud debidas a la baja utilizaci&oacute;n    de servicios de salud por los no beneficiarios.</font></p>     <p><font size="2" face="Verdana"><b>Palabras clave:</b> utilizaci&oacute;n; servicios    de salud; M&eacute;xico</font></p> <hr size="1" noshade>     <p>&nbsp;</p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">The health status of a population is one of the    most important factors in the process of economic development for at least two    reasons. In the first place, health as an indicator of economic development    shows the degree to which a country successfully provides for the basic needs    of its citizens. Secondly, health as human capital is an important factor in    economic development. Health affects labor supply and the labor productivity    of adults, as well as children's school performance.<SUP>1&#45;3 </SUP>The resources    necessary to reach or maintain a certain level of health mainly involve adequate    nutrition, sanitation, and preventive and curative health services.</font></p>     <p><font size="2" face="Verdana">In this paper we implicitly assume the intrinsic    importance of health, its importance as a determinant of socio&#45;economic development,    that access to health services is one of the important determinants of health    and thus that government has an important role to play in ensuring that the    population has appropriate access to health services. Strategies will need to    consider not only the supply of health services but also demand. In particular,    preferences and access barriers faced by different population sub&#45;groups &#150;such    as financial, ethnic, and cultural factors&#150; can make accessing health services    difficult.</font></p>     <p><font size="2" face="Verdana">The concept of preference is an imagined "choice"    between alternatives and the possibility of rank ordering these alternatives    based on the happiness, satisfaction, gratification, or enjoyment they provide.    Therefore, preferences and how they are constructed play an important role when    deciding whether or not to utilize health care. Together with access, the knowledge    of population preferences could be important to designing public policy.</font></p>     <p><font size="2" face="Verdana">In 2000, the Mexican population (around 97 million)    received their health services from a health system composed of three main subsystems:    a) a number of social security institutions financed by employers, employees,    and the government and providing health insurance to formal sector employees    and their families (39.2 million beneficiaries);<SUP>4</SUP> b) health services    provided by the Ministry of Health and limited services provided by non&#45;governmental    organizations (NGOs) for people without health insurance (an estimated 57.1    million people);<SUP>4</SUP> and c) an extensive private sector, heterogeneous    in quality and level of provided services, <SUP>5</SUP> which is almost entirely    financed by out&#45;of&#45;pocket expenses (only approximately 1 million people have    private health insurance)<I>.</I><SUP>4 </sup></font></p>     ]]></body>
<body><![CDATA[<p><font size="2" face="Verdana">Such a patchwork of health systems, common in    Latin America, complicates the analysis of health system access, coverage, and    effectiveness, especially when individuals commonly receive care from multiple    sources. Each of the public subsystems collects utilization data but they have    almost no information regarding their clients' use of services in other subsystems;    in addition, the private sector is atomized into thousands of different independent    providers and institutions.</font></p>     <p><font size="2" face="Verdana">Fortunately, Mexico has a tradition of conducting    population&#45;based, nationally&#45;representative health surveys, the most recent    of which is the <I>Encuesta Nacional de Salud y Nutrici&oacute;n 2006</I> (National    Health and Nutrition Survey, ENSANUT). The <I>Encuesta Nacional de Salud</I>    2000 (National Health Survey, ENSA&#45;2000) was the most recently available health    survey at the time of this analysis, that surveyed 45 870 households and 190    214 individuals. We used the data from the ENSA&#45;2000 to explore the relationship    between individual and household characteristics and utilization of health services    when individuals perceived a health problem. What characteristics are associated    with not obtaining care from a health professional? What characteristics predict    choice of provider/subsystem among those who seek professional care? Answers    to these questions can inform the development of policies and programs to improve    access for those who are not receiving care, as well as to reduce the number    of low&#45;income families who only have access to low&#45;quality private providers    who typically serve the poor.</font></p>     <p><font size="2" face="Verdana">More specifically, we estimate the degree to    which individual variables (characteristics of the specific disease episode,    as well as socio&#45;economic and demographic characteristics) and household level    variables predict utilization of curative ambulatory services for four types    of health providers (self&#45;care, Ministry of Health, social security institutes,    and private sector), conditional upon a change in an individual's health status.    We decided to focus this analysis on the utilization of curative ambulatory    services because of the conditioning that occurs from utilization due to "health    care need" (people reporting having had a health problem in the two weeks    preceding the survey), which makes the motivation for seeking health care more    homogeneous than the motivation to seek preventive health services or hospitalization.    When modeling the responses that people have to a health problem, with heterogeneous    motivation for seeking health care, it is not possible to avoid the situation    in which some variables used as predictors could be correlated with that motivation,    and consequently result in incorrect estimates.</font></p>     <p><font size="2" face="Verdana">There are just two published works in Mexico    modeling health care utilization at the national level, G&oacute;mez <I>et al</I>.<SUP>    6</SUP> (1995) and Zamudio (1997).<SUP>7</SUP> Both were based on the 1994 National    Health Survey; none have been published based on the 2000 National Health Survey.    Given that many health programs that potentially have a positive impact on health    services utilization were established since 2000, this analysis provides a baseline    against which changes from 2000 to 2006 can be assessed with the forthcoming    2006 National Health and Nutrition Survey.</font></p>     <p>&nbsp;</p>     <p><font size="3" face="verdana"><b>Material and Methods</b></font></p>     <p><font size="2" face="Verdana"><b>Data</b></font></p>     <p><font size="2" face="Verdana">Data from the nationally representative ENSA&#45;2000    were used for this study. The ENSA is a rich source of information about household    characteristics and service utilization of 190214 individuals living in 45870    households.<SUP>4 </sup></font></p>     <p><font size="2" face="Verdana">In the remainder of the paper, "utilization"    refers to "curative ambulatory health care utilization". To estimate    the probability of utilization by people who had a "health care need,"    we restricted the sample to individuals who reported having had a health problem    in the two weeks preceding the survey.</font></p>     <p><font size="2" face="Verdana">Fourteen percent (27177) of individuals reported    having had a health problem in the two weeks before the survey. We excluded    anybody who reported having received care from an herbalist or other traditional    healer (0.37% of the sample). Additional individuals were excluded because of    missing data in one or more variables, bringing the sample used for the analysis    to 22581.</font></p>     ]]></body>
<body><![CDATA[<p><font size="2" face="Verdana">The variables used in the analysis are shown    in <a href="#tab01">Table I</a>. The provider variable, which is our outcome    variable in this study, is a categorical variable that can take four possible    values: 0 corresponds to self&#45;care (i.e. not receiving care from a health professional),    1 corresponds to receiving care from the Ministry of Health, 2 to Social Security,    and 3 to private providers. In the Ministry of Health services we included services    financed by the government and provided by different institutions: (1) Ministry    of Health, which has a sliding&#45;scale fee for co&#45;payments which varies from zero    for families below the poverty line to the estimated full cost of the service    for families in the upper socioeconomic deciles, (2) IMSS <I>Solidaridad</I>,    which provides free services to the poorest households entitled to the <I>Oportunidades</I>    program aimed at fighting poverty; (3) services from the <I>Sistema para el    Desarrollo Integral de la Familia</I>, which is the national program for the    comprehensive development of families; (4) National Institute for Indigenous    People; (5) the Red Cross; and (6) the National Institutes of Health. In the    Social Security institutes we included all the institutions that provide services    to employees in the formal sector of the economy. In the Social Security services    we included the services provided by different social security institutions    for employees in the formal sector of the economy in Mexico: Mexican Institute    of Social Security (IMSS), Social Security Institute for Government Workers    (ISSSTE), Social Security for Oil Workers (Pemex), Social Security for Army    Forces (SEDENA) and Social Security for Navy Forces (SEMAR).</font></p>     <p><a name="tab01"></a></p>     <p>&nbsp;</p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13tab01.gif"></p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">Based on Andersen's framework of health services    utilization<SUP>8</SUP> we included individual, household, and community variables    to predict utilization of curative ambulatory services. According to this framework,    utilization of health services is considered to be a function of three characteristics:    (1) predisposing factors, which include education, occupation, ethnicity, social    networks, social interactions, culture, attitudes, values, knowledge that people    have concerning and towards the health care system, age, and sex; (2) enabling    factors, which include personal/family and community characteristics; and (3)    need factors which include perceived need.</font></p>     <p><font size="2" face="Verdana">At the individual level, the variables used as    predictors in our model were sex, age, type of health problem in the two weeks    preceding the survey, disability, social security beneficiary, private insurance    beneficiary, kinship with the head of household, and self&#45;reported severity    of the health problem. At the household level the variables used were sex of    the head of household, female labor status (head of household or wife), indigenous    language of the head of household, and per capita household expenditure. At    the community level we used geographical region and municipal poverty level.    The remainder of this section is devoted to clarifying the way we constructed    and used the variables that are not self&#45;explanatory in <a href="#tab01">table    I</a>, such as type of health problem, geographical zones, per capita household    expenditure, age, and municipal poverty level.</font></p>     <p><font size="2" face="Verdana">Health problems were classified into three categories:    acute problems, chronic problems, and injuries. The <I>acute </I>category includes    diarrhea, respiratory infections, intestinal parasites, viral infections, and    fever without other symptoms. Migraine, diabetes, hypertension, arthritis, asthma,    gout, hypercholesterolemia and cholecystitis were categorized as <I>chronic</I>.    The <I>injury</I> category consists of intentional or unintentional physical    injuries.</font></p>     <p><font size="2" face="Verdana">Mexico was divided in five geographical zones:    The northern zone, the central zone, the metropolitan zone of Mexico City, the    southeastern/gulf zone and a zone formed by the PASSPA&#45;states (<I>Programa de    Apoyo a los Servicios de Salud para Poblaci&oacute;n Abierta</I>).<a name="tx01"></a><a href="#nt01"><sup>*</sup></a>    While not strictly contiguous, this grouping is the most commonly used in Mexico    for sub&#45;national analyses of this type. It is a compromise between geographical    proximity and social/cultural/economic similarity.</font></p>     <p><font size="2" face="Verdana">Household expenditure was used as it is a better    measure of permanent income than self&#45;reported income because it is less sensitive    to short&#45;term fluctuations, better captures the value of household production,    and more accurately reflects unearned income.<SUP>9</SUP></font></p>     ]]></body>
<body><![CDATA[<p><font size="2" face="Verdana">Since household expenditure was not captured    in the ENSA&#45;2000, household expenditure was imputed using housing characteristics    and household assets following Moshiro <I>et al</I>.'s approach.<SUP>10</SUP>    We used data from the 2000 National Household Income and Expenditure Survey    (ENIGH&#45;2000)<SUP> 11</SUP> to regress the log transformed household expenditure    per trimester on: the number of rooms; total number of people living in the    household; ownership of a radio, television, VHS player, refrigerator, gas or    electric hot water heater, blender, washing machine, telephone, and/or vehicle;    presence of running water, a flush toilet, and/or electricity in the house;    and finally, the type of floor material and the type fuel used for cooking.    The <I>R<SUP>2</sup></I> for this model was 0.55, indicating a good fit. The    expenditure per trimester was then imputed for the households in the ENSA&#45;2000    using this estimated regression equation and the information available on the    same housing characteristics and household assets in our sample.</font></p>     <p><font size="2" face="Verdana">Previous studies have described a non&#45;linear    relationship between health care utilization and age,<SUP>7&#45;9,12,13</SUP> and    per capita household expenditure.<SUP>7,14,15</SUP> For both variables, linear    and quadratic model specifications were tested. Both specifications were rejected    in favor of a spline specification.<SUP>16</SUP> Splines are based on a special    class of linear parametric functions and permit much greater flexibility with    respect to the specification of the relationship between the dependent and independent    variables.</font></p>     <p><font size="2" face="Verdana">The spline method entails dividing the ranges    of variation of an independent variable (age and expenditure, in this case)    into <I>k</I>+1 discrete regions (with k being the number of internal thresholds    or "knots" in the covariable). In each region, polynomial regression    is used to fit the outcome variable. This essentially corresponds to fitting    a linear association with utilization but allowing for a different slope in    each of the discrete regions of the independent variable, which is reflected    in the estimation of <I>k</I>+1 separate coefficients. Based on previous findings    about the relationship between age and utilization of health services, and based    on our bivariate data analysis, the age variable was divided into four categories:    0 to 4, 5 to 20, 21 to 59, and 60+, while quintiles were used for per&#45;capita    expenditure.</font></p>     <p><font size="2" face="Verdana">Municipal poverty level was described using the    federal <I>&Iacute;ndice de Marginaci&oacute;n, 2000</I> (Conapo),<SUP> 17</SUP>    a composite measure that includes education levels, housing conditions, income    and rurality, and the factors related to living in small villages.<a name="tx02"></a><a href="#nt02"><sup>**</sup></a></font></p>     <p><font size="2" face="Verdana"><b>Estimation strategy</b></font></p>     <p><font size="2" face="Verdana">As in much of the published literature on demand    for health services, we employed the Nested Multinomial Logit Model (NMLM),    introduced by McFaden, 1981. NMLM relaxes the IAI assumption by allowing correlations    across sub&#45;groups of alternatives. This means that NMLM permits the grouping    of similar alternatives or alternatives that may be close substitutes.</font></p>     <p><font size="2" face="Verdana">In Equation 1 we define <I>V<SUB>ij</sub></I>    as a function of X<I><SUB>i</sub></I>, a vector of variables for individual    <I>i, </I>which includes the variables presented in <a href="#tab01">Table I</a>    as described in the data section, and </font><font>&#946;</font><font size="2" face="verdana"><I><SUB>j, </sub></I></font><font>&#947;</font><font size="2" face="verdana"><I><SUB>j</SUB>,    </I>and </font><font>&#949;</font><font size="2" face="verdana"><I><SUB>ij</sub></I> parameters to be estimated for provider    type <I>j</I>, which are assumed to be equal for all individuals<I>.</i></font></p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13frm01.gif"></p>     <p><font size="2" face="Verdana">According to the outcome variable definition    in the data section, j=0 corresponds to self&#45;care; j=1 receiving care from the    Ministry of Health, j=2 from the Social Security, and j=3 from a private provider.    Choices 0 and 3 were specified to be independent, whereas 2 and 4 (Ministry    of Health and of Social Security) were specified to be correlated. Models with    other nesting structures were estimated; we present this model because it resulted    in the best fit as measured by the estimated value of the log&#45;likelihood function.</font></p>     <p><font size="2" face="Verdana">The probability of not using care or using private    services is given by:</font></p>     ]]></body>
<body><![CDATA[<p align="center"><img src="/img/revistas/spm/v50n5/a13frm02.gif"></p>     <p><font size="2" face="Verdana">The probability of using Ministry of Health services    (<I>j</I>=1) or Social Security (<I>j</I>=2) is given by:</font></p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13frm03.gif"></p>     <p><font size="2" face="Verdana">The parameter </font><font>&#945;</font><font size="2" face="verdana"> is a measure of the degree    of association between alternatives and thus only relevant when the alternatives    are not independent (1 and 2). </font><font>&#945;</font><font size="2" face="verdana">=1 corresponds to the complete independence    and thus to the simple multinomial logist model. </font><font>&#945;</font><font size="2" face="verdana">=0 would imply that    individuals consider the nested alternatives (services of the Ministry of Health    and Social Security) as being perfect substitutes.</font></p>     <p><font size="2" face="Verdana">The corresponding log&#45;likelihood function is:</font></p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13frm04.gif"></p>     <p><font size="2" face="Verdana">Where <I>D<SUB>ij</sub></I> is a dichotomous    variable taking the value of 1 if individual <I>i</I> chooses alternative <I>j</I>.</font></p>     <p><font size="2" face="Verdana">The NMLM was estimated using the maximum likelihood    method for Equation 5 with STATA 8.2. Self&#45;care was chosen as the reference    category for the dependent variable.</font></p>     <p>&nbsp;</p>     <p><font size="3" face="verdana"><b>Results</b></font></p>     ]]></body>
<body><![CDATA[<p><font size="2" face="Verdana">The sample characteristics are shown in <a href="#tab01">Table    I</a>. <a href="#tab02">Table II</a> shows the estimated results of the model    and their significance. The estimated value for </font><font>&#945;</font><font size="2" face="verdana"> is 0.7 and is statistically    different from both zero and one, thus rejecting the MLM specification in favor    of NMLM. The estimated value for </font><font>&#945;</font><font size="2" face="verdana"> indicates that the Ministry of Health    and Social Security health services are to some extent substitutable.</font></p>     <p><a name="tab02"></a></p>     <p>&nbsp;</p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13tab02.gif"></p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">The magnitude of the coefficients presented in    II cannot be easily interpreted in terms of probabilities, although, when significant,    the signs of the coefficients indicate the direction of the effect in comparison    to the reference category. To facilitate the presentation and interpretation    of the results, the coefficients were transformed into marginal effects (e.g.    the % change in the dependent variable for 1% change in the independent variable    from the mean value of the independent variable) and are presented in <a href="#tab03">table    III</a>. All marginal effects for the continuous variables were calculated at    the variable mean. For categorical variables the marginal effect is the change    in the dependent variable as a consequence of moving from the reference category    to the category in question.</font></p>     <p><a name="tab03"></a></p>     <p>&nbsp;</p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13tab03.gif"></p>     <p>&nbsp;</p>     ]]></body>
<body><![CDATA[<p><font size="2" face="Verdana">Ambulatory health care was used less frequently    by children from 0 to 4 years of age. A similar effect, albeit smaller, was    found for 5 to 20 year&#45;olds. The coefficients for Social Security and private    providers changed sign for 21 to 59 year&#45;olds, indicating that the probability    of utilizing these services increases with age. Thus, the highest probabilities    for health care utilization were found among the youngest and oldest individuals.    (<a href="#tab02">Tables II</a> and <a href="#tab03">III</a>.)</font></p>     <p><font size="2" face="Verdana">The utilization of Ministry of Health and Social    Security services increased (but at a diminishing rate) with higher education    levels for the head of household. Private health care utilization increased    with education as well but at an increasing rate.</font></p>     <p><font size="2" face="Verdana">Sex was only significantly associated with the    use of Ministry of Health services, with women more likely to use these services.    Male&#45;headed households were more likely to seek care, but this was only significant    for private providers. The presence of a working woman (irrespective of whether    she was head of the household or married to the head) reduced the probability    of health care utilization. Disabled individuals were less likely to utilize    any type of health care, but this was also only significant for private providers.    The likelihood of utilization was lower for the head of household than for his    children or spouse. Type of health problem was significantly correlated with    utilization: the probability being lower across all alternatives for acute problems    than for other types.</font></p>     <p><font size="2" face="Verdana">The probability of using Ministry of Health and    the Social Security services was higher when the head of household spoke an    indigenous language. The opposite was true for private health care, even though    the effect was small and not statistically significant (<a href="#tab02">tables    II</a> and <a href="#tab03">III</a>).</font></p>     <p><font size="2" face="Verdana">Being a Social Security beneficiary was, as expected,    strongly positively associated with utilization of Social Security services    (1299%) and strongly negatively associated with self care (28%). It was also    negatively associated with utilizing private providers and the Ministry of Health,    even though the latter did not reach statistical significance. Having private    health insurance was positively associated with using both private health care    and Social Security and negatively associated with self&#45;care.</font></p>     <p><font size="2" face="Verdana">The differences between the northern region and    the others were only significant for Social Security and private services. The    probability of using Social Security services was higher in the northern region,    controlling for beneficiary status, whereas the probability of using private    services was higher in the other regions, except for the Mexico City metropolitan    area.</font></p>     <p><font size="2" face="Verdana">As one would expect, health care utilization    increased with increasing perceived severity of the health problem, except for    private health care services where the opposite association was seen when going    from a serious to a very serious health problem. Although this might appear    counterintuitive, it is consistent with local conventional wisdom because households    with very serious health problems anticipate private expenditures that may exhaust    their ability to pay and thus seek public services. The probability of self&#45;care    decreased rapidly with increasing perceived severity (<a href="#tab02">tables    II</a> and <a href="#tab03">III</a>).</font></p>     <p><font size="2" face="Verdana">Utilization of Social Security services was inversely    associated with poverty level, even adjusting for beneficiary status. The opposite    was seen for the Ministry of Health. Additionally, private health care utilization    increased rapidly with decreasing poverty level (<a href="#tab02">tables II</a>    and <a href="#tab03">III</a>).</font></p>     <p><font size="2" face="Verdana">Simulations were used to explore the associations    between variations in household socio&#45;economic status (SES) and the utilization    of different types of health services. The probability of using each type of    health care was estimated using the coefficients obtained in the NMLM and equations    2 and 3. We let household expenditure vary from the 5<SUP>th</SUP> to the 95<SUP>th</SUP>    percentile distribution, while all other variables were set to the sample mean.    The results of this exercise are shown in <a href="#fig01">figures 1</a> and    <a href="#fig02">2</a>.</font></p>     <p><a name="fig01"></a></p>     ]]></body>
<body><![CDATA[<p>&nbsp;</p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13fig01.gif"></p>     <p>&nbsp;</p>     <p><a name="fig02"></a></p>     <p>&nbsp;</p>     <p align="center"><img src="/img/revistas/spm/v50n5/a13fig02.gif"></p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">Increasing household expenditures (i.e. higher    SES) was associated with decreasing probability of using Ministry of Health    services with the negative slope being steeper among poorer households. On the    other hand, as SES increased, the probability of using Social Security increased    initially and leveled off early, from around US$ 352, while for private services    the initial increase was more pronounced and continued to about US$ 779 before    plateauing (<a href="#fig01">figure 1</a>).</font></p>     <p><font size="2" face="Verdana"><a href="#fig02">Figure 2</a> shows the sum of    the curves in <a href="#fig01">figure 1</a>, i.e. the probability of using any    type of health care (the complement is the probability of self&#45;care). The graph    suggests that utilization by the poorest and wealthiest households was relatively    unresponsive to changes in SES, with the probability of utilizing professional    services increased rapidly from about US$ 139 to US$ 566.</font></p>     <p>&nbsp;</p>     ]]></body>
<body><![CDATA[<p><font size="3" face="Verdana"><b>Discussion</b></font></p>     <p><font size="2" face="Verdana">The association between the economic status of    the household and the utilization of formal health care is consistent with the    findings of other studies conducted in Mexico,<SUP>7</SUP> Finland,<SUP>18</SUP>    Mali,<SUP>19</SUP> and the United States.<SUP>20</SUP> However, controlling    for beneficiary status and for the other covariates reveals differential effects    for the different alternatives. The strongest increase in utilization with increasing    levels of household expenditure was found for private services, followed by    Social Security services. Utilization of Ministry of Health services was negatively    associated with household expenditure. This suggests that with increasing economic    status, utilization of Ministry services is replaced by private and Social Security    services, implying that the former are considered an inferior good. Interestingly,    increasing levels of municipal SES have similar effects upon the behavior of    households in the municipality, even when correcting for household SES. This    could be a social norm effect (the same household, living in a wealthier community,    is likely to imitate the utilization behavior of its neighbors) but more likely    reflects the correlation of supply of health services (quantity, choice, and    quality) with municipal poverty level.</font></p>     <p><font size="2" face="Verdana">These findings provide valuable information about    individual preferences, which have important consequences for the health care    system and the reforms that are currently being implemented. Even though health    care services through the Ministry of Health are less costly than private services    and two recent studies suggest that private services for the poor are of lower    technical quality than those in the public sector (S. Barber, personal communication    based on preliminary data analysis), our findings show that with increasing    economic status, individuals replace the former by the latter. This effect is    strongest among poorer families for whom the sacrifice of not consuming other    goods in order to pay for private health services is the largest. This suggests    that individuals may perceive the services of the Ministry of Health as significantly    lower in quality or convenience.</font></p>     <p><font size="2" face="Verdana">Not using health services (self&#45;care) in case    of a change in individual health status is largely explained by the lack of    beneficiary status either with a social security institute or with private insurance.    The poor are the least likely to be beneficiaries and the most likely to experience    catastrophic health expenditures (as a proportion of income).<SUP>4</SUP> This    finding, together with the results discussed in the previous paragraph, suggests    that expansion of health insurance coverage, as currently being undertaken by    the Mexican government, may significantly reduce health inequalities that are    due to a lack of seeking medical attention.</font></p>     <p><font size="2" face="Verdana">We found that after controlling for economic    status, education, municipal poverty level, and beneficiary status, indigenous    people use health care services more frequently in response to a perceived alteration    in their health status. This result contradicts conventional wisdom and the    findings of other studies. We hypothesize that the negative association between    health care utilization and belonging to an indigenous group found in bivariate    analyses is actually due to the association between health care utilization    and poverty, urbanization, low educational level, and lack of beneficiary status    with the social security institutes, rather than to the mere fact of being indigenous.</font></p>     <p><font size="2" face="Verdana">In conclusion, this finding implicitly suggests    that programs or policies that seek to promote curative ambulatory health care    service utilization should use poverty, educational level, locality size, and    beneficiary status for targeting rather than ethnicity. However, an alternative    explanation for the difference may lie in differential perception of illness    episodes between indigenous and non&#45;indigenous peoples, given that our analysis    examined utilization in response to a recent self&#45;reported illness episode and    most previous analyses have examined overall utilization.</font></p>     <p><font size="2" face="Verdana">In addition, our findings could be useful for    the design and evaluation of new programs and for public policy. The 2006 National    Health Survey will enable analysis of the simultaneous effect on propensity    to utilize health services through the various programs that have been started    since 2000. These programs include (1) the National Crusade for Health Care    Quality, which aims to improve the quality of care, the responsiveness of providers,    and the negative image with regard to quality of care in Ministry of Health    facilities, (2) the <I>Seguro Popular</I> health insurance program for the uninsured    population which aims to provide limited prepaid insurance to those without    access to Social Security facilities, and (3) the expansion of the <I>Oportunidades</I>    health/nutrition/education anti&#45;poverty program to urban areas, which is expected    to increase utilization of Ministry of Health services by the poorest quintile    of the population.</font></p>     <p><font size="2" face="Verdana">The largest limitation of our study is the omission    from the model of variables capturing the characteristics and attributes of    the alternative types of health care such as availability, distance, cost, and    quality, because these variables are not available within the ENSA&#45;2000 and    the location data for the households is not sufficiently detailed to enable    reconstruction of distances to the relevant alternatives. The absence of these    data could generate biased estimates if variation in supply&#45;side variables is    correlated with the independent variables in the model. To minimize such biases    we included the municipal poverty level generated by Conapo<SUP>17</SUP> and    the geographic region in our model because previous studies have shown that    these two variables explain a significant proportion of the municipal&#45;level    variation in supply and characteristics of health services.<SUP>21,22</sup></font></p>     <p>&nbsp;</p>     <p><font size="3" face="Verdana"><b>References</b></font></p>     ]]></body>
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Soc Sci Med 2002;55(9):1523&#45;1537.</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&#160;<a href="javascript:void(0);" onclick="javascript: window.open('/scielo.php?script=sci_nlinks&ref=9257169&pid=S0036-3634200800050001300018&lng=','','width=640,height=500,resizable=yes,scrollbars=1,menubar=yes,');">Links</a>&#160;]<!-- end-ref --><!-- ref --><p><font size="2" face="Verdana">19. Mamadou M. Quality of care and the demand    for health services in Bamako, Mali: the specific roles of structural, process,    and outcome components. Soc Sci Med 2003;56(6):1183&#45;1196.</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&#160;<a href="javascript:void(0);" onclick="javascript: window.open('/scielo.php?script=sci_nlinks&ref=9257170&pid=S0036-3634200800050001300019&lng=','','width=640,height=500,resizable=yes,scrollbars=1,menubar=yes,');">Links</a>&#160;]<!-- end-ref --><!-- ref --><p><font size="2" face="Verdana">20. Meer J, Rosen HS. 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Income&#45;Related Inequality in the Use of Medical    Care in 21 OECD Countries. In: OECD (ed). Towards High&#45;Performing Health Systems.    Paris: OECD Health Policy Studies, 2004.</font>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[&#160;<a href="javascript:void(0);" onclick="javascript: window.open('/scielo.php?script=sci_nlinks&ref=9257173&pid=S0036-3634200800050001300022&lng=','','width=640,height=500,resizable=yes,scrollbars=1,menubar=yes,');">Links</a>&#160;]<!-- end-ref --><p>&nbsp;</p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">Received on: October 19, 2007    <br>   Accepted on: April 11, 2008</font></p>     <p>&nbsp;</p>     <p>&nbsp;</p>     <p><font size="2" face="Verdana">Address reprint requests to: Atanacio Valencia&#45;Mendoza.    Centro de Investigaci&oacute;n en Evaluaci&oacute;n y Encuestas, Instituto Nacional    de Salud P&uacute;blica.    <br>   Av. Universidad 655, col. Santa Mar&iacute;a Ahuacatitl&aacute;n. 62508 Cuernavaca,    Morelos, M&eacute;xico.    ]]></body>
<body><![CDATA[<br>   e&#45;mail: <a href="mailto:avalencia@correo.insp.mx">avalencia@correo.insp.mx</a>    <br>   <a name="nt01"></a><a href="#tx01">*</a> The Northern Zone includes the following    states: Baja California, Baja California Sur, Coahuila, Chihuahua, Durango,    Nuevo Le&oacute;n, Sinaloa, Sonora, Tamaulipas and Zacatecas. The Central Zone    is formed by Aguascalientes, Colima, Guanajuato, Jalisco, M&eacute;xico, Michoac&aacute;n,    Nayarit, Quer&eacute;taro, San Luis Potos&iacute; and Tlaxcala. The Metropolitan    Zone of Mexico city only includes the Federal District. The South&#45;Eastern/Gulf    Zone is formed by Campeche, Morelos, Puebla, Quintana Roo, Tabasco, Veracruz    and Yucat&aacute;n. The PASSPA Zone is formed by the country's four least developed    states: Chiapas, Guerrero, Hidalgo and Oaxaca.    <br>   <a name="nt02"></a><a href="#tx02">**</a> The variables used to construct the    poverty level are: illiteracy and percent who have not completed primary school    among those 15 years and older, percent living in houses without running water,    percent living in houses without sewage, dirt floors, or electricity, percent    with an income less than twice the minimum wage and percent residing in localities    (towns) with fewer than 5,000 inhabitants.<SUP>17</sup></font></p>      ]]></body><back>
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