Introduction
Over a decade after the implementation of the health care reform in Mexico, its intended impact on the coverage and healthcare utilization is still uncertain. Initial evaluations of Seguro Popular (Popular Health Insurance, SP by its Spanish acronym) showed that SP was reaching short-term objectives on population health and healthcare costs.1 At first, at the household level, SP had positive impact on catastrophic health expenditures, which it reduced by approximately 6.7%.2 Secondly, SP increased access to medical care utilization for people with diabetes and hypertension.3,4 In contrast, recent studies have found that SP had little to no impact on diabetes and hypertension treatment and care among Mexican elders, and even produced an increase in ambulatory care sensitive hospitalizations (ACSH).5,6
Effective preventive care for older adults can reduce health care costs and reduce multimorbidity and mortality.7SP provides a package of services that is limited in scope and availability since patients need to be treated via the SP network of health facilities.8 Compared to those without health insurance, people with SP have access to primary care, preventive screen ing procedures and specialty care, including routine screenings for people aged 20 or more years. In theory, low-income SP beneficiaries would be more likely to use preventive routine services than the uninsured. The main objective of this paper is to examine how the Mexican healthcare reform influences preventive care use among low-income older Mexicans (aged 50 to 75 years), including Pap smears, mammography/ clinical examination, and diabetes and hypertension screening. We focused on these illnesses because: 1) the high rates of these conditions in Mexico are attributed to the aging of the population; 2) these conditions are major causes of death among older adults in Mexico in 2011,9 and 3) there are high costs associated with the management and treatment of these conditions and related complications.10,11
This paper expands the current literature on preven tive care utilization by using pseudo-panel data from Mexico to estimate the impact of SP. Although it has been shown that healthcare behaviors vary by ethnic groups, very little is known in regard to this topic among contemporary adults in Mexico. In an era where policy changes are implemented to improve the health of the aging population, it is imperative to critically discuss what is happening in other countries.
Materials and methods
We used data from three different sources: 1) the Mexi can Health and Nutrition Survey [Encuesta Nacional de Salud y Nutrición, Ensanut]; 2) the Mexican Census [In stituto Nacional de Estadística y Geografía; INEGI], and 3) the Mexican Department of Health Information (Sistema Nacional de Información en Salud; Sinais].
Firstly, individual characteristics were obtained from the Ensanut. We used repeated cross-sectional data from the 2000, 2006 and 2012 surveys. Ensanut uses a proba bilistic multistage stratified cluster sampling design, is nationally representative and includes participants from all the 32 states in Mexico. The protocol of Ensanut was approved by the Research, Ethics and Bio-security committees of the National Institute of Public Health.12 Our sample included 17 640 adults aged 50 to 75 years, 5 506 in 2000, 4 947 in 2006, and 7 187 in 2012, who were enrolled in SP or did not have any type of health insur ance (the latter being a natural control group).13
In order to adjust for local level differences, state and regional characteristics, we obtained data from INEGI (http://www.inegi.org.mx/est/contenidos/Proyectos/ ccpv/default.aspx) and the National Health Information System (Sinais) (http://www.sinais.salud.gob.mx/basesdedatos/index.html) (variables in table I).
Variable | Description | Source | ||
Preventive Care Indicators | Respondents had received preventive care screening test in the 12 months previous to the time of the survey, including: 1) mammigraphy or clinical exploration; 2) cervical cancer; 3)blood pressurescreening, and 4) blood glucose screening | Ensanut | ||
SP Insured | The main independent variable of interest was health insurance for the poor: namely, whether participants were anrolled in SP or uninsured (those with IMSS, ISSSTE, PEMEX; private insurance were completely excluded from the datasets and the analyses) | |||
Age | Age measured in years | |||
Sex | Female versus male | |||
Marital status | Married compared to non-married | |||
Indigenous background | Yes compared to no | |||
Smoking status | Coded as never-smoker versus former/current smoker | |||
Drinking status | Non-drinker versus former drinker/current drinker | |||
Education | Education has three categories: 1) non-education vs. 2) primary education vs. 3) at least secondary education | |||
Employment status | Employed compares to unemployed | |||
Household asset index | Computed based on respondent's house infrastructure and materials, as well as personal/family assets. The principal components method was used. The variable is centered at 0; therefore, those with negative values are less wealthy | |||
Rural residency | Rural versus urban | |||
BMI | Coded as 1) underweught/normal versus 2) overweight versus 3) obese | |||
Number of doctors | Number of doctors per 100 000 residents | SINAIS | ||
Number of nurses | Number of nurses per 100 000 residents | SINAIS | ||
Number of Hospitals | Number of hospitals per 100 000 residents | |||
Illiteracy rate | Propotion of people aged 15 or more years who do not know how to read or write a note | INEGI | ||
Population density | People per square kilometer |
Note: Datasets from INEGI and SINAIS were merged with Ensanut to form one dataset with individual, state and local level factors
Ensanut: Encuesta Nacional de Salud y Nutrición
IMSS: Instituto Mexicano del Seguro Social
ISSSTE: Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado
PEMEX: Petróleos Mexicanos
INEGI: Instituto Nacional de Estadística y Geografía
SINAIS: Sistema Nacional de Información en Salud
SP: Seguro Popular
BMI: body mass index
Analytic strategy
Although panel data would be preferred in this case, publicly available data were limited to pseudo-panel data constructed from repeated cross-sections from Ensanut. Pseudo-panel or repeated cross-sectional data, widely used in economics, contain information from individuals at different points in time, obtained using random sampling.14,15,16 For instance, Ensanut collects data approximately every six years. We used the alternative approach proposed by Moffitt in 1993.17 We constructed a pseudo-panel dataset at the individual level using the cross-sections from 2000, 2006 and 2012, with different older adults grouped into cohorts using the year of birth. In order to analyze the impact of insurance on preventive care, a fixed-effect instrumental variable (FEIV) estimation was conducted. Standard fixed-effect models were estimated and are available upon request from the corresponding author.
The instrumental variable used in the present study (the interaction between the logarithm of population density at the municipality level in year 2000 and a dummy for whether Ensanut was conducted in 2005-6) takes into account the fact that the intensity of the roll-out process and the penetration of SP over the study period were not equally distributed across different areas. As explained in other studies,6SP was introduced in 2002 and gradually adopted by all the states by 2005. At first, it was targeted to smaller areas in order to achieve uni versal coverage more quickly;18,19 however, after 2006, SP spread to more urbanized areas.20 This pattern suggests that coverage rates in densely and sparsely populated areas grew in similarity. The direct effect of log of popula tion density was collinear with municipality fixed effects and, therefore, was not included in the model.
This IV captures the expansion process of SP in high-populated municipalities in Mexico. The instrument is defined as the interaction between the logarithm of population density at the municipality level in year 2000 and a dummy for whether Ensanut was conducted in 2005-6. A similar IV has been used in prior studies.6,21 A similar variable with this interac tion for 2011-12 was also explored as a potential IV. To account for temporal and spatial heterogeneity across municipalities, both municipality and year fixed effects were included.
The models of use of preventive care by Seguro Popular (SP imt, the main independent variable) speci fied preventive care use by individual i at municipality m at time period t as given by the following regression equations:
where the dependent variable (Y
imt) was a dichotomous indicator of whether the individual used preventive services for diabetes, hypertension, and/or cervical and breast cancer screenings (each outcome modeled separately). The two-stage least squares (2SLS) approach involves first regressing SP
imt on Z
imt, the instrumental at the municipality level and the survey year), in order to obtain predicted values/
For cases with missing values in SES and demographic independent variables (about 13%), the mean or median municipality value was used.
The relevance of the instrument was tested using the F-test of excluded instruments,22 which ranged from ~10 to ~20. Then, FEIV were obtained using the Stata XTIVREG2 command.
Results
Sample descriptive statistics by insurance status (SP or uninsured) and year of survey (2000, 2006 and 2012) can be seen in table II. The results show that the number of adults aged 50 to 75 years old insured through SP increased from 1 192 in 2006 to 5 037 in 2012. Yet, in 2012 there were 2 150 older people (30%) who were still uninsured. Although the most significant differences for this sample can be found in 2012, there were a few differences in both 2006 and 2012 between those with SP and those without health insurance. SP beneficiaries were more likely to be females and have primary edu cation. For both years, SP enrollees were less likely to be employed, had fewer family assets, and were more likely to reside in rural areas (these differences were significant at p≤ .05).
Year 2000 (Pre-SP) | Year 2006 | Year 2012 | ||||||||||||
Uninsured (n=5506) | Uninsured ± (n=3755) | SP Insured (n=1192) | p-value | Uninsured ± (n=2150) | SP Insured (n=5037) | p-value | ||||||||
Age | 59.75 (7.15) | 60.18 (7.44) | 60.25 (7.46) | 0.7570 | 59.66 (7.25) | 60.06 (7.23) | 0.0340 | |||||||
Female | 0.63 (0.48) | 0.55 (0.50) | 0.60 (0.49) | 0.0028 | 0.47 (0.50) | 0.55 (0.50) | <.0001 | |||||||
Married | 0.54 (0.50) | 0.55 (0.50) | 0.58 (0.49) | 0.0808 | 0.45 (0.50) | 0.55 (0.50) | <.0001 | |||||||
Indigenous background | 0.13 (0.34) | 0.13 (0.34) | 0.13 (0.34) | 0.9631 | 0.11 (0.32) | 0.18 (0.39) | <.0001 | |||||||
No education | 0.09 (0.24) | 0.29 (0.45) | 0.30 (0.46) | 0.2554 | 0.22 (0.41) | 0.26 (0.44) | 0.0003 | |||||||
Elementary education | 0.79 (0.35) | 0.59 (0.49) | 0.63 (0.48) | 0.0124 | 0.54 (0.50) | 0.61 (0.49) | <.0001 | |||||||
Employed | 0.40 (0.49) | 0.42 (0.49) | 0.37 (0.48) | 0.0020 | 0.54 (0.50) | 0.45 (0.50) | <.0001 | |||||||
Family assets | -0.94 (2.43) | -0.61 (1.48) | -0.93 (1.49) | <.0001 | -0.57 (2.11) | -1.10 (1.87) | <.0001 | |||||||
BMI_1 | 0.36 (0.47) | 0.40 (0.49) | 0.39 (0.48) | 0.4290 | 0.39 (0.41) | 0.39 (0.45) | 0.6145 | |||||||
BMI_2 | 0.30 (0.45) | 0.32 (0.46) | 0.35 (0.47) | 0.0207 | 0.35 (0.41) | 0.34 (0.43) | 0.4909 | |||||||
Smoker | 0.33 (0.47) | 0.26 (0.44) | 0.27 (0.44) | 0.8027 | 0.39 (0.48) | 0.33 (0.47) | <.0001 | |||||||
Drinker | 0.80 (0.84) | 0.72 (0.87) | 0.65 (0.85) | 0.0194 | 1.16 (0.80) | 1.06 (0.80) | <.0001 | |||||||
Physicians | 55.90 (19.33) | 63.04 (19.12) | 73.85 (25.36) | <.0001 | 80.51 (25.51) | 85.90 (27.36) | <.0001 | |||||||
Nurses | 73.72 (24.51) | 79.02 (23.04) | 93.82 (31.22) | <.0001 | 103.11 (32.92) | 109.37 (35.34) | <.0001 | |||||||
Hospital | 14.43 (6.06) | 14.81 (6.40) | 15.31 (6.32) | 0.0194 | 14.95 (7.19) | 16.49 (7.34) | <.0001 | |||||||
Illiteracy | 0.08 (0.06) | 0.07 (0.06) | 0.08 (0.05) | 0.1397 | 0.06 (0.07) | 0.08 (0.06) | <.0001 | |||||||
Rural | 0.55 (0.50) | 0.60 (0.49) | 0.46 (0.50) | <.0001 | 0.66 (0.47) | 0.46 (0.50) | <.0001 |
Notes: The table presents mean values and standard deviations in parentheses for all participants aged 50 to 75 years
SP= Seguro Popular. SP, was launched in 2001; therefore, the 2000 survey does not have an SP column and serves as a “pre-treatment” observation. ± The comparison groups in 2006 and 2012 are the uninsured (76% in 2006 and 30% in 2012 from this sample); that is, those without any private, IMSS, ISSSTE, PEMEX, SEDENA, or any other type of health insurance
Physicians, nurses and hospitals are per 100 000 people
BMI_1=Body mass index overweight (>25 kg/m2)
BMI_2=Body mass index obese (>30 kg/m2)
Family asset index based on the following assets: household infrastructure, household materials and assets. This is a proxy for household’s wealth. It was calculated using principal components analysis. This measure is centered at 0, and negative values indicate lower household wealth
BMI: body mass index
IMSS:Instituto Mexicano del Seguro Social
ISSSTE: Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado
PEMEX:Petróleos Mexicanos
SEDENA:Secretaría de la Defensa Nacional
Table III presents a summary of those who per formed screening tests by year of survey and insurance status. Overall, it appears that the number of people performing preventive screening for diabetes, hyper tension, cervical and breast cancer has increased. There were significant differences between the uninsured and SP enrollees for most screening rates in 2006 and 2012, except for breast cancer screening, for which the rates differed only in 2006.
Year 2000 (Pre-SP) | Year 2006 | Year 2012 | ||||||||||||
Uninsured | Uninsured ± | SP Insured | p-value | Uninsured ± | SP Insured | p-value | ||||||||
Screening test | ||||||||||||||
Diabetes | n=5360 | n=3755 | n=1192 | <.0001 | n=1831 | n=4025 | <.0001 | |||||||
N=16 163 | 0.13 (0.34) | 0.24 (0.42) | 0.30 (0.46) | 0.22 (0.41) | 0.34 (0.47) | |||||||||
Hypertension | n=5360 | n=3755 | n=1192 | <.0001 | n=1666 | n=3561 | <.0001 | |||||||
N=15 534 | 0.18 (0.38) | 0.24 (0.43) | 0.33 (0.47) | 0.24 (0.43) | 0.38 (0.48) | |||||||||
Cervical cancer | n=3396 | n=2064 | n=714 | <.0001 | n=676 | n=2229 | <.0001 | |||||||
N=9 079 | 0.23 (0.42) | 0.33 (0.47) | 0.49 (0.48) | 0.36 (0.48) | 0.46 (0.50) | |||||||||
Breast cancer | n=3394 | n=2064 | n=714 | <.0001 | n=89 | n=332 | <.0001 | |||||||
N=6 593 | 0.05 (0.22) | 0.13 (0.33) | 0.18 (0.39) | 0.43 (0.50) | 0.52 (0.50) |
Notes: The table presents mean values and standard deviations in parentheses for all participants aged 50 to 75 years
SP= Seguro Popular. SP was launched in 2001; therefore, the survey carried out in 2000 does not have an SP column and serves as a “pre-treatment” observation.
± The comparison groups in 2006 and 2012 are the uninsured (76% in 2006, and 30% in 2012 from this sample); that is, those without any private, IMSS, ISSSTE, PEMEX, SEDENA, or any other type of health insurance
IMSS: Instituto Mexicano del Seguro Social
ISSSTE: Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado
PEMEX:Petróleos Mexicanos
SEDENA: Secretaría de la Defensa Nacional
Table IV23-25compares the results from the FEIV esti mations. We found that the main variable of interest SP insurance has no significant effect in the utilization of screening tests. Contrary to what one would expect based on evidence from other countries,26,27SP beneficia ries were not significantly different from those without health insurance.
Breast cancer | Cervical cancer | Diabetes | Hypertension | |||||
SP enrollee | -0.31 | -0.21 | -0.29 | -0.24 | ||||
(0.359) | (0.352) | (0.216) | (0.220) | |||||
Age | -0.00* | -0.01* | -0.00‡ | -0.00 | ||||
(0.001) | (0.001) | (0.000) | (0.001) | |||||
Female | N/A | N/A | -0.10* | -0.09* | ||||
(0.015) | (0.015) | |||||||
Married | -0.01 | -0.04§ | -0.03§ | -0.03‡ | ||||
(0.009) | (0.011) | (0.011) | (0.012) | |||||
Indigenous background | 0.02 | -0.02 | -0.01 | -0.02 | ||||
(0.023) | (0.026) | (0.015) | (0.017) | |||||
No education | 0.02 | 0.02 | -0.02 | -0.03 | ||||
(0.025) | (0.025) | (0.021) | (0.020) | |||||
Elementary education | 0.01 | 0.02 | -0.01 | -0.01 | ||||
(0.019) | (0.022) | (0.019) | (0.018) | |||||
Employed | -0.00 | -0.01 | -0.02‡ | -0.02‡ | ||||
(0.012) | (0.015) | (0.009) | (0.009) | |||||
Family assets | 0.00 | 0.00 | 0.00 | 0.00 | ||||
(0.003) | (0.005) | (0.003) | (0.003) | |||||
BMI_1 | 0.00 | 0.01 | 0.02‡ | 0.01 | ||||
(0.012) | (0.015) | (0.009) | (0.010) | |||||
BMI_2 | 0.04§ | 0.03 | 0.05* | 0.03§ | ||||
(0.015) | (0.017) | (0.010) | (0.010) | |||||
Smoker | -0.00 | 0.00 | 0.02‡ | 0.01 | ||||
(0.011) | (0.014) | (0.009) | (0.010) | |||||
Drinker | -0.01 | 0.00 | 0.01 | 0.02* | ||||
(0.006) | (0.008) | (0.005) | (0.005) | |||||
Physicians | -0.00 | 0.00 | 0.00 | -0.00 | ||||
(0.004) | (0.002) | (0.002) | (0.002) | |||||
Nurses | 0.00‡ | 0.00 | 0.00 | 0.00 | ||||
(0.001) | (0.001) | (0.001) | (0.001) | |||||
Hospitals | -0.02 | -0.02 | -0.01 | -0.00 | ||||
(0.014) | (0.010) | (0.006) | (0.007) | |||||
Illiteracy | 0.29 | 0.03 | -0.02 | -0.07 | ||||
(0.171) | (0.229) | (0.135) | (0.147) | |||||
Rural | -0.02 | -0.08* | -0.04‡ | 0.03 | ||||
(0.018) | (0.025) | (0.017) | (0.017) | |||||
Log Pop Density‡ 2012 | 0.29 | 0.03 | -0.02 | -0.07 | ||||
(0.021) | (0.011) | (0.007) | (0.009) | |||||
2006 | 0.16‡ | 0.17‡ | 0.18* | 0.14§ | ||||
(0.073) | (0.082) | (0.049) | (0.050) | |||||
2012 | 0.45‡ | 0.37 | 0.35‡ | 0.35‡ | ||||
(0.183) | (0.240) | (0.152) | (0.161) | |||||
Observations | 6478 | 8992 | 16134 | 15506 | ||||
ID number | 645 | 854 | 948 | 947 |
Notes: Robust standard errors in parentheses
* p<0.001
‡ p<0.05
§ p<0.01
Authors’ analysis of the 2000, 2006 and 2012 Mexican Health and Nutrition Surveys, the Mexican Census and the Mexican Department of Health Information. ENSA 2000 was created as part of the System for National Health Surveys conducted during the last months of 1999 and the first three of 2000, with households sampled from the 32 states.23 Similarly, Ensanut 2006 and 2012 were implemented from October 2005 through May 2006, and from October 2011 through May 2012, respectively24,25
The table presents results from a fixed-effects instrumental variables model with pseudo-panel data at the state level, using the cross-sections from 2000, 2006 and 2012, constructed from individuals described in tables I and Table II
F-test of excluded instrument(s) in the IV first stage regression
Discussion
This is the first study that used pseudo-panel data fixed-effects and instrumental-variables fixed-effects models to evaluate the impact of the SP program on preventive screening among older Mexican adults. In the initial results, SP appeared to be headed in the right direction in closing the gap in health care coverage for the poor. Descriptive statistics for 2012 showed that a higher percentage of older adults enrolled in SP were poor, unemployed and less educated. In addition, FE showed significant effects for SP (results not shown). Nevertheless, once a correction for endogeneity was implemented using a valid and relevant instrumental variable, the FEIV showed no difference for those with SP or the uninsured.
Our results are consistent with other researchers who have discussed poor impact of SP on population health due to organizational and structural issues in SP.28,29,30 The federal and state governments have faced challenges when implementing SP -such as limited institutional capacity and information systems to over see productivity and quality-; these may be reflected in the perceived quality of care and impact healthcare utilization.31SP enrollees have reported dissatisfaction with their care because of the long distances they must travel to get to the clinics, the long waiting times to see a physician; the short duration of visits, the lack of bedside manner; and the short supply of medicines.32,33 Older SP beneficiaries may not want to deal with these issues and delay seeking healthcare.
The current study has some limitations. Firstly, we used pseudo-panel data since longitudinal data are not available. However, we used a well-established approach proposed by Moffit.17 Secondly, Ensanut participants reported healthcare utilization measures and may have introduced recall bias. Furthermore, the questionnaire asked participants about their insurance status at the time of the survey, but healthcare utilization patterns referred to the previous year. Thirdly, due to the nature of the Ensanut, we were not able to separate mammograms from clinical examinations; instead, a composite measure was used. Finally, cervical and breast cancer screening guidelines have changed since 2000 and are continuously being updated worldwide;34 Mexico has undergone similar changes.35 However, in our search to assess the impact of SP across years, we were only able to compare screening utilization in the past 12 months of the survey. This limits our ability to accurately report the utilization of services.
Despite the limitations, the methods used are suf ficiently strong, and we found that preventive care for older low-income adults in Mexico did not differ be tween the SP-insured and the uninsured. These results have policy implications beyond Mexico. Although, SP may have made some progress in improving health care access and reducing out-of-pocket expenditures, effective access of health care and health care utilization remains a major issue.36SP has achieved nearly universal health care coverage in Mexico; yet, as shown in this sample, there are still people who have no insurance. Furthermore, preventive services utilization is lower and not significantly different from that of older adults without health insurance. Additional efforts are needed to increase insurance coverage and healthcare quality, as well as to decrease healthcare disparities among older adults with a low income.