Abstract
Taking into account the hierarchical structure of the data, through two-level analysis on infant mortality available under second round of National family Health Survey, the same group of authors recently reported determinants of infant mortality while examining possible changes in results under traditional regression analysis that ignores hierarchical structure of data. They reported that the community (e.g., state) level characteristics still have a major role regarding infant mortality in India. For better epidemiological understanding, the present study is to assess determinants of infant mortality in rural India, where three level considerations were possible. The results indicate that even after consideration of these covariates, variation in infant mortality remains significant not only between States but also between Districts. Further, as an additional observation, the probability of infant mortality is still high in rural areas of districts having health facility beyond three kilometers than their counterparts.
Highlights
The infant mortality rate (IMR) still remains an important public health indicator [2,3]
As true in case of infant mortality in India [1], the distribution of infant deaths in the relation to various socioeconomic and demographic characteristics in rural India (Table 1) reveals that those children are more likely to die before celebrating their first birthday; whose mothers have comparatively lower education [2.13 (1.72 - 2.64)]; did not have exposure to mass media [1.34 (1.19 - 1.50)]; fathers have low level of education [1.52 (1.33 - 1.74)]; who belong to SC/ST/OBC categories [1.37 (119 - 1.59)], low and medium standard of living index 1.93 (1.55 - 2.40)]; births order one [1.30
The additional covariate considered at district level in Rural India data, namely distance to health facilities, entered in the model
Summary
The infant mortality rate (IMR) still remains an important public health indicator [2,3]. As reported earlier, data on IMR need to be dealt with hierarchical/multilevel analysis that takes hierarchical structure into account and makes it possible to incorporate variables from all levels and retains them at their own levels [1]. For better epidemiological understanding in rural areas where the largest population of the country lives, data relate to only rural India. An appropriate epidemiological understanding from time to time may be helpful to policy planners. It may help in testing many hypotheses related to population issues and generate various important clues towards public health programs
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