OBJECTIVE: To examine the time trends, socio-economic and regional inequalities of under-five mortality rate (U5MR) in Nepal.
METHODS: We analyzed the data from complete birth histories of four Nepal Demographic and Health Surveys (NDHS) done in the years 1996, 2001, 2006 and 2011. For each livebirth, we computed survival period from birth until either fifth birthday or the survey date. Using direct methods i.e. by constructing life tables, we calculated yearly U5MRs from 1991 to 2010. Projections were made for the years 2011 to 2015. For each NDHS, U5MRs were calculated according to child's sex, mother's education, household wealth index, rural/urban residence, development regions and ecological zones. Inequalities were calculated as rate difference, rate ratio, population attributable risk and hazard ratio.
RESULTS: Yearly U5MR (per 1000 live births) had decreased from 157.3 (95% CIs 178.0-138.9) in 1991 to 43.2 (95% CIs 59.1-31.5) in 2010 i.e. 114.1 reduction in absolute risk. Projected U5MR for the year 2015 was 54.33. U5MRs had decreased in absolute terms in all sub groups but relative inequalities had reduced for gender and rural/urban residence only. Wide inequalities existed by wealth and education and increased between 1996 and 2011. For lowest wealth quintile (as compared to highest quintile) hazard ratio (HR) increased from 1.37 (95% CIs 1.27, 1.49) to 2.54 ( 95% CIs 2.25, 2.86) and for mothers having no education (as compared to higher education) HR increased from 2.55 (95% CIs 1.95, 3.33) to 3.75 (95% CIs 3.17, 4.44). Changes in regional inequities were marginal and irregular.
CONCLUSIONS: Nepal is most likely to achieve MDG-4 but eductional and wealth inequalities may widen further. National health policies should address to reduce inequalities in U5MR through 'inclusive policies'.
MATERIALS AND METHODS: The OncoCarta(™) panel v1.0 assay was used to characterize oncogenic mutations. In addition, exons 4-11 of the TP53 gene were sequenced. Statistical analyses were conducted to identify associations between mutations and selected clinico-pathological characteristics and risk habits.
RESULTS: Oncogenic mutations were detected in PIK3CA (5.7%) and HRAS (2.4%). Mutations in TP53 were observed in 27.7% (31/112) of the OSCC specimens. Oncogenic mutations were found more frequently in non-smokers (p = 0.049) and TP53 truncating mutations were more common in patients with no risk habits (p = 0.019). Patients with mutations had worse overall survival compared to those with absence of mutations; and patients who harbored DNA binding domain (DBD) and L2/L3/LSH mutations showed a worse survival probability compared to those patients with wild type TP53. The majority of the oncogenic and TP53 mutations were G:C > A:T and A:T > G:C base transitions, regardless of the different risk habits.
CONCLUSION: Hotspot oncogenic mutations which are frequently present in common solid tumors are exceedingly rare in OSCC. Despite differences in risk habit exposure, the mutation frequency of PIK3CA and HRAS in Asian OSCC were similar to that reported in OSCC among Caucasians, whereas TP53 mutations rates were significantly lower. The lack of actionable hotspot mutations argue strongly for the need to comprehensively characterize gene mutations associated with OSCC for the development of new diagnostic and therapeutic tools.