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Abstract
This study examines the geographic variation in the incidence of HIV/AIDS in Chiang Rai, one of the northern districts of Thailand with a very high rate of HIV infection and AIDS. A related goal of this study is to understand the socioeconomic impact of the disease. First, spatial analysis is used to estimate the effects of local determinants on the incidence of HIV and AIDS. Second, standardized questionnaire surveys of patients are conducted to understand the individual context of the disease. Finally, in-depth qualitative interviews are used to examine the socioeconomic impact of the disease at the individual level. Results show that localities with a relatively high percentage of households engaged as laborers, localities close to municipal areas, and those with a high concentration of commercial sex workers are significantly correlated with high incidence rates. Places close to municipal areas are typically more urbanized, with diverse income groups and businesses in their vicinity. These areas have higher levels of risk factors compared to places that are remote. The interview-based analysis shows that HIV/AIDS patients, from diverse education and income backgrounds and different levels of comfort with disclosure and disease intensity, have shown equally diverse levels of suffering and coping strategies. Their response is not only dictated by the intensity of the disease but by their own acceptance of their disease status, an understanding of the disease and treatment options, their fear of stigma, and the reaction of family, friends, partners, and health workers.
Este estudio examina la variación geográfica de la incidencia de HIV/SIDA en Chiang Rai, uno de los distritos del norte de Tailandia que registra altas tasas de infección de HIV y SIDA. Otro de los propósitos del estudio es comprender el impacto socioeconómico de la enfermedad. Primero, se utilizó el análisis espacial para calcular los efectos de determinantes locales sobre la incidencia de HIV y SIDA. Segundo, se llevaron a cabo observaciones sistemáticas de pacientes por medio de cuestionarios estandarizados, para entender el contexto individual de la enfermedad. Y, finalmente, se aplicaron entrevistas cualitativas a fondo para examinar el impacto socioeconómico de la enfermedad a nivel individual. Los resultados muestran que aquellas localidades con un porcentaje relativamente alto de familias de trabajadores, localidades cercanas a las áreas municipales y aquellas con una alta concentración de trabajadoras sexuales, están significativamente correlacionadas con tasas de alta incidencia. Los lugares cercanos a los poblados son típicamente más urbanizados, tienen grupos de ingresos diversificados y negocios en la vecindad. Estas áreas tienen los factores de riesgo de más alto nivel, en comparación con los lugares remotos. El análisis de los datos generados en entrevistas muestra que los pacientes de HIV/SIDA con antecedentes variados en educación e ingreso y diferentes niveles de confort, con conocimiento revelado de la enfermedad e intensidad de la misma, indican también diferentes niveles de sufrimiento y de estrategias para enfrentar el problema. Sus respuestas no están solamente dictadas por la intensidad de la patología sino por su propia aceptación del estatus de su enfermedad, comprensión de la naturaleza de la enfermedad y las opciones de tratamiento, sus temores a la estigmatización, y por las reacciones de la familia, amigos, socios y trabajadores de la salud.
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Acknowledgments
We would like to thank Dr. Tanarak Plipat for his help with understanding the data from Thailand. We are indebted to the patients who came forward to help with the study. Our special thanks are extended to the nurses at hospitals and staff members at the Thailand Ministry of Public Health, Chiang Rai Ministry of Public Health, Chiang Rai Development Information Center, and Chiang Rai Municipal Office. We acknowledge partial support of this research from the Mark Diamond Research Fund at the University at Buffalo. Comments from four anonymous referees, the editor Dr. Mei-Po Kwan, and colleagues Dr. Jared Aldstadt and Dr. Peter Rogerson are also acknowledged.
Notes
az value of spatial lagged coefficient is 2.277.
bA psuedo-R 2 measure is shown for spatial lag model.
* p ≤ 0.05.
** p ≤ 0.01.
1. This study uses mixed methods, defined as follows: “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study. … Mixed-method research also is an attempt to legitimate the use of multiple approaches in answering research questions, rather than restricting or constraining researchers' choices. … It is an expansive and creative form of research, not a limiting form of research, it is inclusive, pluralistic, and complementary, and it suggests that researchers take an eclectic approach to method selection and the thinking about and conduct of research” (CitationJohnson and Onwuegbuzie 2004, 17). “As noted by CitationGreene et al. (1989), there are five major purposes or rationales for conducting mixed methods research: (a) triangulation (i.e., seeking convergence and corroboration of results from different methods and designs studying the same phenomenon); (b) complementarity (i.e., seeking elaboration, enhancement, illustration, and clarification of the results from one method with results from the other method); (c) initiation (i.e., discovering paradoxes and contradictions that lead to a re-framing of the research question); (d) development (i.e., using the findings from one method to help inform the other method); and (e) expansion (i.e., seeking to expand the breadth and range of research by using different methods for different inquiry components)” (CitationJohnson and Onwuegbuzie 2004, 21–22).
2. All case interviews focused on fifteen- to twenty-four-year-olds. All procedures for recruiting participants and conducting interviews were approved by the institutional review board at the State University of New York at Buffalo and Chiang Rai Hospital in Thailand. To ensure confidentiality, the patients' names appear only on the signed copy of the consent/assent form.
3. Mixed-method design matrix (adapted from CitationJohnson and Onwuegbuzie 2004, 22):
4. Families and individuals are defined as poor if average annual income for an individual fell below 20,000 baht (1,666 baht per month), based on Thai government directives (CitationCommunity Development Information Center 2003). In this study, the criteria are slightly adjusted to match with the fact that most participants are very young, reside with their parents, and are not full-time employees. The participants usually work on and off throughout the year, which leads to lower and unstable income. Therefore, the income data of participants are used to categorize 2,000 to 5,000 baht monthly income as the medium-income group, and participants who have income lower or higher than this range are categorized as low-income or high-income groups, accordingly.
5. Thai officials would not allow the researchers to contact household members of participants because many participants might not have disclosed their status to their relatives or family members. Recruiting youth participants was a difficult task for two reasons: (1) there are much lower numbers of youths in the hospital databases, and (2) many cannot be reached because of missing address or contact information. In addition, institutional review boards prohibited direct recruiting and as a result self-selection was the only process by which interviewees could be recruited.
6. The increase of log-likelihood from –240.417 from the OLS model to –238.279 is noted. The AIC is also lower, from 500.835 in the OLS model to 498.558, which suggests an improvement of fit in the spatial lag specification. The order of three classic specification tests (Wald, likelihood ratio, and Lagrange multiplier-lag tests) also supports the spatial lag model. The proper order of three classical tests on spatial autoregressive coefficient is as follows: Wald test (the square of asymptotic t or z value) is more than the likelihood ratio test, and the likelihood ratio test is more than the Lagrange multiplier-lag test. In our data, the Wald test is 2.2772 = 5.18, the likelihood ratio test is 4.27, and the Lagrange multiplier-lag is 4.13. This suggests that the spatial lag estimation is the optimal model specification.
7. Thailand achieved universal coverage of health insurance with the following three major insurance schemes: the Civil Servant Medical Benefit Scheme (CSMBS), Social Security Scheme (SS), and Universal Coverage Scheme (UC). CSMBS covers civil servants, employees of government enterprises, and their family members. SS covers employees of private companies. UC covers the rest of the population. All of them cover medical expenses for the treatment of opportunistic infections due to HIV/AIDS. Only CSMBS covered the expense of antiretroviral therapy when it was given as part of inpatient care (CitationKitajima et al. 2005).