Smokeless tobacco use: A meta-analysis of risk and attributable mortality estimates for India.

BACKGROUND: Use of smokeless tobacco (SLT) is widely prevalent in India and Indian subcontinent. Cohort and case–control studies in India and elsewhere report excess mortality due to its use. OBJECTIVE: The aim was to estimate the SLT use‑attributable deaths in males and females, aged 35 years and older, in India. MATERIALS AND METHODS: Prevalence of SLT use in persons aged 35 years and older was obtained from the Global Adult Tobacco Survey in India and population size and deaths in the relevant age‑sex groups were obtained from UN estimates (2010 revision) for 2008. A meta‑relative risk (RR) based population attributable fraction was used to estimate attributable deaths in persons aged 35 years and older. A random effects model was used in the meta‑analysis on all‑cause mortality from SLT use in India including four cohort and one case–control study. The studies included in the meta‑analysis were adjusted for smoking, age and education. RESULTS: The prevalence of SLT use in India was 25.2% for men and 24.5% for women aged 35 years and older. RRs for females and males were 1.34 (1.27–1.42) and 1.17 (1.05–1.42), respectively. The number of deaths attributable to SLT use in India is estimated to be 368127 (217,076 women and 151,051 men), with nearly three‑fifth (60%) of these deaths occurring among women. CON CLUSION: SLT use caused over 350,000 deaths in India in 2010, and nearly three‑fifth of SLT use‑attributable deaths were among women in India. This calls for targeted public health intervention focusing on SLT products especially among women.

A simple nomogram for sample size for estimating sensitivity and specificity of medical tests.

Sensitivity and specificity measure inherent validity of a diagnostic test against a gold standard. Researchers develop new diagnostic methods to reduce the cost, risk, invasiveness, and time. Adequate sample size is a must to precisely estimate the validity of a diagnostic test. In practice, researchers generally decide about the sample size arbitrarily either at their convenience, or from the previous literature. We have devised a simple nomogram that yields statistically valid sample size for anticipated sensitivity or anticipated specificity. MS Excel version 2007 was used to derive the values required to plot the nomogram using varying absolute precision, known prevalence of disease, and 95% confidence level using the formula already available in the literature. The nomogram plot was obtained by suitably arranging the lines and distances to conform to this formula. This nomogram could be easily used to determine the sample size for estimating the sensitivity or specificity of a diagnostic test with required precision and 95% confidence level. Sample size at 90% and 99% confidence level, respectively, can also be obtained by just multiplying 0.70 and 1.75 with the number obtained for the 95% confidence level. A nomogram instantly provides the required number of subjects by just moving the ruler and can be repeatedly used without redoing the calculations. This can also be applied for reverse calculations. This nomogram is not applicable for testing of the hypothesis set-up and is applicable only when both diagnostic test and gold standard results have a dichotomous category.

The Methods of Survival Analysis for Clinicians.

The methods of survival analysis are required to analyze duration data but their use is restricted possibly due to lack of awareness and the intricacies involved. We explain common methods of survival analysis, namely, life-table, Kaplan- Meier, log-rank and Cox model, in a simple and friendly language so that the medical fraternity can use them with confidence where applicable.

Elements of medical research.

Most medical research is empirical based on evidence rather than hunches or preferences. It follows a series of specific steps. There are no short cuts. Collection of evidence and its analysis should follow a carefully drawn protocol. Most of the modern medical research requires biostatistical tools to reach to a valid and reliable conclusion. Researcher must have an adequate knowledge and skill to be really effective. The endeavours should be consistent with the accepted medical and research ethics. Medical research can provide immense satisfaction when conducted on scientific lines, and can be occasionally frustrating when years of efforts fail to produce expected results. This article focuses on aspects that can increase the credibility of research. It is addressed to all interested in medical research, and seeking answers to questions such as what actually is research, what are its types, what specific steps should be followed, what a research protocol should contain, and what makes research credible etc.