Barriers to Maternal Health Seeking Behavior
6.6. Conclusion
This chapter discusses the factors influencing health seeking behavior among sample population. The result shows that the health seeking behavior of an individual is influenced by organizational factors such as non-availability of female health providers and ambulances, cultural factors viz. ignorance, hesitations and socioeconomic factors such as long queue, non-availability of persons at home, heavy workloads.
Sometimes distinction between the various factors are blurred, or point to some other problems. Long queue, for example, can be result of inadequate provision of medical facility compared to number of patients. Non availability of female health providers could be a problem with organization as well as a cultural factor. Cultural factors point to a social setting where the ―value‖ of women is less as a person due to (already entrenched) patriarchal biases. Such patriarchal biases work through heavy workload which nobody is willing to share because the workload is a woman‘s job and duty at the same time.
From the discussion, it is clear that health promotion action needs to expand the focus beyond individual behavior to the people‘s social interactions and immediate environments. Considering the availabilitiy, accessibility and affordability of health
services, local and context-specific needs to be taken care of. In addition to encourage utilzation, ensuring availability of physical infrastructure and female health providers, community level awareness and maternal education are needed. This will help the population to upgrade their knowledge and perception towards women reproductive health issues. Along with that, frequent visit of ASHA workers during last trimester of pregnancy is important, so that women can get assistantance at the time of emergency.
This will reduce the problem of non-availability of the person at home.
Appendix
Table 6.3.A: Effective Sample Sizes of all Variables
ESS Corr.time Efficiency
Seeking care
Far from home 2113.61 5.68 0.1761
Bad roads 2709.58 4.43 0.2258
Doctors not responsive 1941.9 6.18 0.1618
Non-Availability of ambulance 2158.74 5.56 0.1799
Non-Availability of female health provider 1357.69 8.84 0.1131
Husband restriction 2125.13 5.65 0.1771
Hesitation 1587.17 7.56 0.1323
Ignorance 1973.57 6.08 0.1645
Cost of drugs 1896.86 6.33 0.1581
Cost of transportation 1511.27 7.94 0.1259
Long queue 1123.91 10.68 0.0937
Non availability of person at home 1873.47 6.41 0.1561
Heavy workload 1729.66 6.94 0.1441
Land ownership 1313.51 9.14 0.1095
Literacy 787.4 15.24 0.0656
Note: ESS = effective sample size i.e. the number of effectively independent samples from the total number of posterior samples collected that the Markov chain is equivalent to.
Corr. time: Correlation times i.e. an estimated lag after which autocorrelation in an MCMC sample is small.
Efficiency: the lower the correlation times are and the higher the efficiencies are the better.
Chi-Squared Test for Variable Selection:
Given the variables, we would like to remove variables which are significantly associated with the others. We propose that perception about roads, perceptions about distance, non- availability of ambulance and cost of transportation are likely to be related with each other. To see the association between categorical variables, we propose a chi square test, the results of which are given below (Table 6.4.A)
Table 6.4 A: Chi-Squared (p-Value ) for Selected Variables
FH
BR
NAmb CoTFar from home (FH) 0 0.86 0.69 0.02
Bad roads (BR) 0.86 0 0.86 0.63
Non-availability of ambulance (NAmb) 0.69 0.86 0 0.27
Cost of transportation (CoT) 0.02 0.63 0.00 0
Since cost of transportation is significantly associated with far from home (distance perception) and non-availability of ambulance facilities, we can drop cost of transportation.
In a similar fashion, perceptions about organization such as non-availability of female health provider, unresponsive doctors are likely to be associated with each other. At the same time, such organizational shortfalls may spill over cultural factors like husband restriction, hesitation, and ignorance about health facilities. The results are reported in Table 6.5. A:
Table 6.5 A: Chi-Squared (p-Value ) for Selected Variables
DnR NAFhP HR Hesi Ign
Doctors not responsive (DnR) 0 0.80 0.01 0.74 0.79
Non-availability Female health provider (NAFhP) 0.80 0 0.00 0.19 0.93
Husband restriction (HR) 0.01 0.00 0 0.59 0.66
Hesitation (Hesi) 0.74 0.19 0.59 0 0.55
Ignorance (Ign) 0.79 0.93 0.66 0.55 0
Result indicates that husband restriction is significantly associated with non-availability of female health providers and unresponsive doctors. We drop husband restriction from the model.
Similarly, non-availability of person at home, long queue and heavy workload are likely to be related. So, we run chi square test with these variables (Table 6.6.A). Result shows that none of the variables are significantly associated. We keep all these three variables in our model.
Table 6.6 A: Chi-Squared (p-Value ) for Selected Variables
LQ NAPaH HW
Long queue (LQ) 0 0.23 0.92
Non-availability of person at home (NAPaH) 0.23 0 0.24
Heavy workload (HW) 0.92 0.24 0
Traditional Logit model:
note: farfromhome != 0 predicts success perfectly farfromhome dropped and 5 obs not used
note: badroads != 0 predicts success perfectly badroads dropped and 1 obs not used
note: doctorsnotresponsive != 0 predicts success perfectly doctorsnotresponsive dropped and 2 obs not used
note: availabilityofambulance != 0 predicts success perfectly availabilityofambulance dropped and 5 obs not used
note: femalehealthprovider != 0 predicts success perfectly femalehealthprovider dropped and 19 obs not used note: hesitation != 0 predicts success perfectly hesitation dropped and 9 obs not used
note: ignorance != 0 predicts success perfectly ignorance dropped and 5 obs not used
note: costofdrugs != 0 predicts success perfectly
costofdrugs dropped and 18 obs not used note: longqueue != 0 predicts success perfectly longqueue dropped and 13 obs not used
note: nonavailabilityofpersonathome != 0 predicts success perfectly nonavailabilityofpersonathome dropped and 19 obs not used
note: heavyworkload != 0 predicts success perfectly heavyworkload dropped and 6 obs not used
Table 6.11 A: Logit regression for not seeking care with associate variables Variables Not Seeking care
Odd ratio (Std. err.) z Far from home 1 (Omitted)
Bad roads 1 (Omitted)
Doctors not
responsive
1 (Omitted) Ambulance facilities 1 (Omitted) Female health
provider
1 (Omitted) Husband restriction 1 (Omitted) Hesitation 1 (Omitted)
Ignorance 1 (Omitted)
Cost of drugs 1 (Omitted) Cost of transportation 1 (Omitted)
Long Queue 1 (Omitted)
NA person at home 1 (Omitted) Heavy household
work
1 (Omitted)
Literacy 1.54 (1.381) 0.49
Landownership 1.23 (.3281) 0.80
No of observation 67