br Methods and procedures br Results

Methods and procedures
Results In Table 1 we present the distribution of the independent variables. In 2010, 45% of women reported that they don’t watch TV while 19% reported that they watch TV less than weekly or at least weekly and 35% said that they watch TV almost daily. This prevalence of TV watching is similar to that observed in the BDHS 2011. Access to TV is a function of socioeconomic conditions. In Appendix A, we show logistic regression results for the determinants of TV watching. The following characteristics of women are associated with higher likelihood of watching TV: Young age, education, living in wealthier households, being a non-Muslim, living in urban areas or in the western or central regions of the country. Most of these characteristics are also associated with better health behavior.
Discussion Our findings are derived from cross-sectional associations between TV watching and reproductive health behaviors so we cannot determine the extent to which these association are causal. The multivariate analysis controls for important observed confounding factors. We tested the robustness of the multivariate findings by refitting models using propensity score weights as an alternative way to control for selective TV watching based on observable confounders (data not presented). The results of the propensity score weights analysis were consistent with the results from the multivariate analysis. However, both these methods only control for selection based on observable characteristics; there are likely to be additional unobserved variables that affect both TV watching and health behaviors. For example, it jnk inhibitors is possible that social innovators adopt more modern health behaviors such as lower fertility, seeking ANC or delivering with SBA; the same social innovators may also be TV watchers. Our options for dealing with selection on unobservables are limited by the data we have. We explored using an instrumental variables approach but were unable to find strong instruments for these models. Another common approach for dealing with selection on unobservables is to use a difference in differences approach but that requires longitudinal data (see Lance et al., 2014 for descriptions of these methods). Our ability to make definitive causal statements about the associations observed is therefore limited. It is also possible that our analysis underestimates the total effect of TV on health behavior in the population. The acquired behavior due to observational learning through TV can spread to non-watchers through diffusion. The diffusion model argues that behavior change occurs through ideational change by observing other individuals’ behavior which is thought to be ideal or regarded as a role model (Palloni, 2001). Fishbein and Azjen (2010) argued that mass media, in our case TV, can reach large audiences, by changing behavior that becomes norms within an individual’s social network which can then influence others who have not been directly exposed to the information dissemination. Hughes (1980) hinted at the possibility of the association of TV with cultural diffusion in the American society. In Bangladesh, it is common to seek advice from peers, neighbors, and relatives about health or related issues or for people to volunteer advice facilitating the spread of knowledge and ideas obtained from watching TV to people who do not watch TV. To the extent that diffusion is operating in the effects of TV on health behavior, comparing TV watchers and non-watchers will underestimate the total effect of TV on health behaviors. To the extent that our findings reflect causal associations, inequity in access to TV could exacerbate inequities in reproductive health behaviors and outcomes in the short term. Lack of availability of electricity is an infrastructural barrier to the spread of TV, especially in the rural areas where 70% of Bangladesh population reside; electricity has not yet reached to 35% of rural households (NIPORT et al., 2016). The growth in solar power in Bangladesh is among the fastest in the world and has potential to reduce electricity availability as a barrier to TV access (The Daily Star, March 8, 2015 and July 1, 2014). Solar-power generation in Bangladesh currently covers 11% of households, mostly in low-lying and difficult to reach rural areas.