In developing countries that have lower socioeconomic status, TB is one factors may increase the probability of exposure to infection among. is the relationship between TB and socioeconomic factors. The association of infection have highlighted this relationship.6'10". Attempts have. A cross-sectional study was conducted among TB patients aged ≥18 to Italian TB patients, while indicators of socioeconomic status, income and . The strict relationship between poverty and the risk of TB has been historically recognized Social factors may increase susceptibility to TB infection and.
Continuous variable age was transformed to categorical as: SES of the household was decided according to Kuppuswamy rating system developed for Indian families. A latent variable LC was defined as a complex of i quality of housing and ii available community facilities. It was regarded as an indicator of physical conditions in and around the household. Quality of housing referred to type of houses concrete or mud and adequacy of ventilation.
The community facilities included water, electricity, and sanitation. An index of LC was obtained by applying categorical principal component analysis to this data. Three tertile groups representing LC were labeled as very poor, poor, and better. Low scores represented poor status with mud houses, inadequate ventilation, no sanitation, no electricity, and well as the only source of water.
On the other hand, high scores represented better living status with concrete housing, adequate ventilation, proper sanitation, electricity, and hand pump along with well as the source of water.
During analysis, households with very poor and poor conditions were pooled together due to sample inadequacies, thereby resulting into two groups, that is, poor and better. Accordingly, frequency and percentage of households in each class were obtained as summary statistics. Homogeneity of sample characteristics in two sets Subjects were split into concordant and discordant sets as described earlier.
In the absence of any gold standard for prediction of LTBI, we adopted splitting strategy to determine which test provides consistent results considering their relationship with different factors. The idea was that in concordant set there is agreement on the LTBI outcome either positive or negative by both the tests and hence interpretations of factors association are same for the two tests.
In the discordant set with discrepancies in test outcomes, it is expected that interpretations of factor associations for one of the test could be consistent with that of concordant set.
However, this comparison would be meaningful only under the assumption of homogeneity of sample characteristics in two sets. If so, the consistent test could be regarded as suitable option for diagnosing LTBI in this population. We ensured the validity of assumption through Categorical Principal Component Analysis, with factors comprising the variable set and the two sets as independent groups.
Risk evaluation Initially, the effect of risk factors on test positivity was determined in each set using bivariate analysis. Only those factors showing statistical significance in atleast one of the sets were retained and considered for multivariate analysis. Variables like age and sex, although showed insignificant effect, were retained in the analysis being biologically relevant.
The primary focus of the study was on the modifiable risk factors and their effect on test outcome. OR for each individual level modifiable risk factor was obtained separately in the presence of combinations of covariates.
The relationship between socioeconomic factors and pulmonary tuberculosis.
The model derived using nonmodifiable risk factors confounders such as age, sex, and TB history in family was referred as the basic model, while the one with household level modifiable risk factors along with confounders was referred as advanced model.
Fitness of each model was evaluated using Hosmer-Lemeshow test. ORs for individual level risk factors were compared with the unadjusted crude estimates and changes were observed.
Similar analysis was performed for household level modifiable risk factors, wherein ORs were adjusted with confounders basic model. At the next stage advance type Ibehavioral factors were added to confounders and the ORs were obtained. In the advance type II model, nutritional factor was also considered along with confounders and the ORs were observed.
Apparently in all these models, the interests were to understand i how the modifiable risk factors affect the test positivity in presence of one or more cofactors and ii the relative performance of diagnostic tests in two data sets. Results Out of eligible participants, were eventually considered for the study.
Data relating to demographic, behavioral, biological, socioeconomic factors, and community services were summarized as shown in [Table 1]. Majority of the individuals, that is, The proportion of malnourished individuals in the region was Effects of individual and household level factors Association of each factor with test positivity was studied through bivariate analysis in concordant and discordant data sets with results shown in [Table 2].
In the concordant set 1smoking OR: Surprisingly, BCG vaccination also showed increased likelihood of both tests being positive. In the discordant set, for QFT-G positive outcome 2ORs indicated similar trends to that of concordant set for eight of the ten factors; although the magnitudes differed.
Bivariate analysis to determine effect of individual and household level risk factors Click here to view Multivariate analysis: Effect of modifiable risk factors on test positivity Individual level modifiable risk factors were assessed by adjusting for nonmodifiable risk factors like age, sex, and TB history in family basic model and further augmenting with household level modifiable factors advanced model with the results shown in [Table 3].
The estimate for alcohol dropped below one OR: Effect of individual level modifiable risk factors on test positivity in two data sets Click here to view In the discordant set, for QFT-G positive outcome 3the basic model resulted into increased odds for smoking and tobacco compared to their crude estimates.
The interpretations for smoking, tobacco, and malnourishment were similar to concordant set, while the effect due to alcohol consumption was unchanged after basic adjustment OR: The findings for smoking, alcohol consumption, and malnourishment matched with that of concordant set.
Similar analysis was performed for TST positive outcome. In the same manner, household level modifiable factors SES and LC were assessed in the presence of other cofactors with the results shown in [Table 4].
Despite this, all models ascertained that improved SES reduces the likelihood of test positivity.
LC was analyzed on similar lines [Table 4]. In concordant set 1the ORs remained unchanged in all the three models and matched with the crude estimate OR: Further, the small OR suggested that the likelihood of test positivity decreases with the improved LC.
Earlier, poor LC and nutritional deficiencies have been linked with the prevalence of TB. Bivariate analysis without any covariate adjustment revealed that the individual level modifiable risk factors like smoking, alcohol, tobacco consumption, and malnourishment have increased odds in favor of LTBI positivity by both the tests. These factors have previously been reported for decreased immunity, along with risk of progression of TB in other studies.
Smoking and alcohol consumption has been regarded as major risk factors that predispose TB. Malnutrition in tribal population impairs immune functions, which is responsible for host sensitivity to various infectious diseases including TB.
The relationship between socioeconomic factors and pulmonary tuberculosis.
Among the three confounders, effect of TB history in family was pronounced suggesting that malnourished population with family history is more likely to get detected positively by both the tests. Individual level modifiable risk factors were further adjusted to study the impact of house-hold level modifiable risk factors SES and LC. Interestingly, it was observed that the combination of these factors had a mediating effect on the modifiable risk factors, indicating their roles in explaining part of the association between individual level factor and test positivity in concordant set.
Earlier, we thought malnourishment to be an only and major influencing factor for LTBI in this population. There is substantial evidence that poverty is a determinant of TB, both at the macroscale and in individual and hierarchical analyses  however, the association of same with LTBI is a matter of concern.
The results showed that transmission rate is higher in poor communities than in the rich ones due to overcrowding, poor nutrition, reduced treatment uptake and lower socioeconomic status 7.
Santos M, et al. The socioeconomic factors that were studied were Schooling, Income, and Number of Residents. The results showed that in poorest areas the disease prevalence was higher 8. The higher rate of this disease is among migrant groups. If migrant groups move from areas with low TB rates to one of the high rates, the risk of the face of primary infections is increased.
If migration was occurred from areas with high rate of TB to areas with a low rate of TB, the migrant group transmissions this high rate of TB to those areas. Even if the migration occurs between areas with similar incidence, due to the change in the living environment, and exposure to undesirable situations, the probability of contact with TB for the first time increases, so it is likely that TB is prevalent among migrant groups 9.
Jose Leopold et al. In their study, the relationship between immigration rate for foreigners and mortality rate of TB and also the relationship between immigration rates for the other Brazilian States with a mortality rate of TB was significant The counts data are typically modeling with log-linear Poisson model. With this model, the relationship between covariates and the mean of the response can be estimated However, this model cannot be applied to evaluate the effects of covariates on the other aspects of the response distribution, such as the quartiles.
Therefore based on the log-linear model the picture of the relationship is incomplete, on the other hand, the assumption of the log-linear model is the equality of mean and variance of the response distribution, whereas in real, the variance of the data may be bigger than the mean. This issue is named overdispersion. The overdispersion may lead to the significant relationship between a response variable and covariates that is not significant in fact and also with overdispersion the variance of the data may be an underestimate To solve this problem of the log-linear model, we can use the quantile regression for count data that investigate how covariates affect the entire distribution of the count responses and also unlike the log-linear model, quantile regression model for count data, has a separate scale parameter, and overdispersion does not occur The aim of this study was to investigate the relationship between the socioeconomic factors such as immigration rate, unemployment rate, urbanization rate, the sum of physicians to the number of population points and illiteracy rate with the number of tuberculosis patients.