Spatial Clustering of Tuberculosis Incidence in the North of Iran

Abstract

Background and Purpose:Tuberculosis (TB) poses a serious threat to public health throughout the world but disproportionately afflicts low-income nations. The aim of this study is to identify the high-risk areas in Mazandaran province (North of Iran) in helping the heath programmer for the best intervention.Materials and Methods:This is an ecological study conducted from 1999 through 2008. The sample included 2444 Tuberculosis (TB) patients. The variables were age, gender, type of disease and residential location, analyzed by descriptive statistical methods and spatial analysis to identify cluster of disease incidence. Geographical information system software applied to map of smooth rate of TB.Results:Of 2444 registered patients, 1283 (52.5%) were male. The data showed 61% urban and 96.4% of them with the Iranian nationality. There was insignificant difference between genders, but the main difference was observed between locations that are the incidence rate in the Tonekabon and Behshahr cities were 30% higher than mean incidence rate of Mazandaran province (P-value<0.05). The comprising chance of acquiring infection between urban and rural was 1.46 with confidence interval of 95% (1.35, 1.59).Conclusion:Geostatistical method showed spatial variability of TB incidence rate in all districts and identifying high-risk area (core areas). The most important core of TB incidence has been noticed in the eastern boundary of Mazandaran in the city of Behshahr which is due to proximity to Golestan Province. The incidence rate of TB in Behshahr city is about two times more than the number observed in Mazandaran province. Lower TB incidence rate has been observed in Golestan province is because there is usually a delay in the diagnosis of the disease especially in the positive smear patients.

Spatial transmission and meteorological determinants of tuberculosis incidence in Qinghai Province, China: a spatial clustering panel analysis

“…Positive/negative spatial autocorrelation occurs when Moran’s I is close to +1/-1, which indicates that areas with similar (high-high or low-low)/dissimilar (high-low or low-high) incidence of TB are clustered together [ 29 ]. Monte Carlo randomization (9999 permutations) was employed to assess the significance of Moran’s I , with the null hypothesis being that the distribution of TB incidence in Qinghai province is completely spatially random [ 27 ]; in other words, that the counties with high and low TB incidence are randomly distributed across the study area [ 30 ]. If the test is significant ( P ≤ 0.05), this suggests a clustering/dispersing of the TB incidence [ 2 , 16 ].…”

Socio-Environmental Factors and Tuberculosis: an Exploratory Spatial Analysis in Peninsular Malaysia

“…Empirical global studies have shown the common environmental (endogenous or extrinsic) risk factor affects TB risk vulnerability are the biophysical or ecological environment, socioeconomic status, human population and demographic characteristic. TB spatial clustering and variation, the high-risk areas are coimpacted by areal proximity or geographical factor [1][2] [12] especially in urban area [1][2] [3], and socio-economic status [2] [3].…”

“…Previous studies have shown the environmental risk factors in high burden countries of TB can be divided into two main indicators, including ecological environment, human or social environment. The ecological environment is another term of the environment, relating the biotic and abiotic surrounding of an organism or population, For example, TB high-risk prevalence areas are co-impacted by spatial proximity or geographical factor [1][2] [12] especially in urban area [1] [12][3] [38], poor housing quality, crowded and small living condition [14]. Low SES also contributes to the global TB occurrences [2][12] such as unemployment, low educational level and poverty.…”

Territorial Differentiation of the Tuberculosis Morbidity and Mortality Trend in the Republic of Crimea

“…Сложная социально-экономическая ситуация и снижение финансирования фтизиатрической службы с начала 90-х гг. XX века, как по России в целом, так и в Республике Крым, привели к ухудшению эпидемиологической ситуации по туберкулезу [3,4,7] Кластерный анализ используется отечественными и зарубежными исследователями для выделения закономерностей развития эпидемического процесса [6,9,10], прогнозирования исходов [2], диагностики туберкулеза [8]. Для анализа эпидемиологии туберкулеза и разработки управленческих решений в системе фтизиатрической службы в Республике Крым представляет интерес использование многомерного анализа для идентификации территорий с однородными показателями здоровья населения по туберкулезу.…”