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AIDS Incidence Rates in Chiang Mai Province, Thailand

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AIDS Incidence Rates in Chiang Mai Province, Thailand Chiang Mai Med J 2010;49(4):161-166. Original article AIDS Incidence Rates in Chiang Mai Province, Thailand Wattanavadee Sriwattanapongse,M.S., Sukon Prasitwattanaseree, D.E.A., and Surin Khanabsakdi, M.S. Department of Statistics, Faculty of Science, Chiang Mai University Abstract The objective of this study was to investigate the epidemic patterns of hospital-diagnosed HIV/ AIDS incidence by year, district and age group for Chiang Mai; a northern province of Thailand. This study was based on individual hospital case records of HIV/AIDS patients routinely reported from 1997 to 2006. Linear regression to forecast HIV/AIDS incidence rates was used by districts and age groups in order to prevent disease epidemics that are likely to occur in these areas in the near future. The model contained additive effects associated with the season of the year, district, age group, and the HIV/AIDS incidence rates of previous years, and it can be used to provide useful short-term forecasts. This study found that all coeffi cients of age group factor were signifi cant, as were all those of district factor, except for Samoeng, Hot, Doi Tao, Chai Prakan and Mae Wang district. All coeffi cients of year time factor were signifi cant, except for the 1998 factor. The linear regression model obtained an r-square of 74.47%.This study also found that the average incidence rate of AIDS appears to be highest at 2.10 per 10,000 people per year in Hang Dong district. Having a model that provides such forecasts of disease outbreaks can provide a useful basis for allocation of resources for disease prevention. Chiang Mai Medical Journal 2010;49(4):161-166. Keywords: AIDS incidence, Chiang Mai province, linear regression model Acquired immune defi ciency syndrome Asia new HIV infections declined by nearly 25% (AIDS) is a set of symptoms and infections result- and in South and South East Asia by 10% in the ing from damage to the human immune system same time period. In Eastern Europe, after a caused by the human immunodefi ciency virus dramatic increase in new infections among inject- (HIV).(1) HIV is transmitted through direct con- ing drug users, the epidemic has leveled off con- tact of a mucous membrane or the bloodstream siderably. However, in some countries there are with a bodily fl uid containing HIV, such as blood, signs that new HIV infections are rising again.(3) semen, vaginal fl uid, preseminal fl uid, and breast Statistics regarding AIDS cases occurring in milk.(2) According to new data in the 2009 AIDS Thailand between 1985 and 2006 show that an epidemic update, new HIV infections have been estimated 1,102,628 people (adults and children) reduced by 17% over the past eight years. Since were infected with HIV, and that 558,578 died 2001, the number of new infections in sub-Saha- of AIDS-related complications (data from the ran Africa is approximately 15% lower, which is Thailand A2 Team cited in AIDS Thailand, about 400,000 fewer infections in 2008. In East 2007).(4) Thailand experienced its fi rst case of Address requests for reprints: Wattanavadee Sriwattanapongse, M.S., Department of Statistics, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand: E-mail:[email protected] Received 26 July 2010, and in revised form 6 August 2010. 162 Sriwattanapongse W, Prasitwattanaseree S, and Khanabsakdi S. AIDS in 1984. By 1995, approximately 800,000 Doi Saket, Mae Taeng, Mae Rim, Samoeng, Fang, Thais were infected with HIV, and 1 million Mae Ai, Phrao, San Pa Tong, San Kamphaeng, Thais became infected by the year 2000. There San Sai, Hang Dong, Hot, Doi Tao, Omkoi, have been fi ve major epidemic waves: among Saraphi, Wiang Haeng, Chai Prakan, Mae Wang, male homosexuals (beginning 1984-85); intrave- Mae On and Doi Lo. Chiang Mai province has nous drug users (1988); female commercial sex been one of Thailand’s major tourist destinations. workers (1989); male clients of commercial sex It is considered one of the most scenic provinces workers (1990); and housewives and the newborn in the country, due to its mountain ranges, valleys, (1991).(5) fl ora and fauna. Unlike most of Thailand, the The fi rst case of an AIDS patient in the north- climate in the north, especially Chiang Mai, is ern part of Thailand was reported in 1987,(6) cool, fresh and misty. marking the outbreak of the epidemic. In a recent cross-sectional, qualitative study, Angelina MATERIALS AND METHODS Arbisi and Panpanich(7) examined the current atti- tudes toward and motives for acupuncture use and Study Design disuse among people with HIV/AIDS (PHA) in Data used in this study were taken from the northern Thailand. AIDS and STD Prevention and Control Program, Sun et al.(8) discussed the regression analysis in which case information was collected routinely of such studies, and a simple estimating equation in the province by the Chiang Mai Provincial Pub- approach was proposed under the proportional lic Health Offi ce. Data for the years after 1997- hazards model. This method can be easily imple- 2006 are available on computer fi les. There is a mented and does not involve any iteration among record for each case, with that fi elds consist of unknown parameters, as do full likelihood ap- characteristics of each subject including age, gen- proaches proposed in the literature. The asymptot- der, and location (district). ic properties of the proposed regression coeffi cient After cleaning to correct or impute data-entry estimates are derived and an AIDS cohort study is errors, the records for the province were stored analyzed to illustrate the proposed approach. in a Structured Query Language (SQL) database. SQL programs were used to create AIDS case OBJECTIVES counts by year (1997-2006), age group (0-9, 10- 19, 20-29, 30-39, 40-49, 50-59 and 60+ years), This study aimed to fi nd a suitable statistical and district. Incidence rates were computed as the model for predicting the yearly incidence rates of number of cases per 10,000 residents in the dis- reported HIV/AIDS cases by age group, districts trict, according to the 2000 Population and Hous- and year in Chiang Mai province, northern Thai- ing Census of Thailand. land, where a high risk of the disease exists, based on routinely collected data available from Chiang Linear regression model Mai provincial health offi ces. More specifi cally, This method describes data structures with the study aimed to ascertain whether any district multi-categorical determinants (i.e., a multi- had experienced an unusually high incidence of categorical determinant regression model). If HIV/AIDS, so that preventive disease-control variables are denoted by xx 12 , ,....... x c the extended measures could be put in place. model for Chiang Mai uses the form: Chiang Mai occupies an area of 20,107 square c kilometers and has a total population of 1,500,127 (1) ya=+∑ bxjj people. Neighboring provinces comprise (from j=1 the northeast, clockwise) Chiang Rai, Lampang, Lamphun, Tak, and Mae Hong Son, most of The constants b 1 ,b 2 ,..... b c are called regression which border Myanmar’s Shan state. Chiang Mai coeffi cients. These measure the province consists of 24 districts: Muang Chiang associations between the determinants and the Mai , Chom Thong, Mae Chaem, Chiang Dao, outcome variable.(9) For this paper, the outcome is AIDS incidence rate in Chiang Mai 163 an incidence rate, as the determinants are district, this model the parameters were constrained in this β age group, and year. study so that α1= 0, 1= 0 and η1= 0. While linear The simplest model is based on linear regres- time trends could be included in the model, they sion, with the outcome variable defi ned as the are less useful for short-term forecasting purposes incidence rate in a cell indexed by district, age in the presence of high serial correlations, and not group, and calendar month (allowing for a sea- considered in this study. sonal effect) as categorical determinants. Such incidence rates generally have positively skewed RESULTS distributions, so it is conventional to transform them by taking logarithms. Since monthly dis- There was a total of 17,535 HIV/AIDS cases ease counts based on small regions are often zero, reported in Chiang Mai province during the ten it is necessary to make some adjustment to avoid year period of 1997-2006. The number of cases taking logarithms of zero. for a particular age group and district varied from The method used to defi ne the outcome is: 0 to 143; and the corresponding maximum disease rates were 11.04 cases per 1,000. The time series of average yearly age-specifi c HIV/AIDS rates ⎛⎞nijt YK ijt =+ ln ⎜⎟ 1 (2) per 1,000 population at risk in all districts of Chi- P ⎝⎠ ang Mai are plotted in Figure 1. where n is the number of disease cases in the cell, In Figure 1, the incidence rates show a marked P is the population at risk, and K is a specifi ed seasonal periodicity, and decrease from very high constant. Then levels in age groups 20-29, 30-39 and 40-49 over the 10-year period; whereas in age groups 0-9, 10- Y P DE K H ijt i j t ijt (3) 19, 50-59 and 60+ years, the reported HIV/AIDS incidence rate shows a slightly less decreas over the same period. where Nijt is a random variable denoting the Figure 2 shows how the average HIV/AIDS reported number of disease cases in age group incidence rate varied by district in Chiang Mai i, district j and year t for the region of interest, province.
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