Addressing the Challenge of P-Value and Sample Size when the Significance is Borderline: The Test of Random Duplication of Participants as a New Approach

The issue of borderline p-value seems to divide health scientists into two schools of thought. One school of thought argues that when the p-value is greater than or equal to the statistical significance cut-off level of 0.05, it should not be considered statistically significant and the null hypothesis should be accepted no matter how close the p-value is to the 0.05. The other school of thought believes that by doing so one might be committing a Type 2 error and possibly missing valuable information. In this paper, we discuss an approach to address this issue and suggest the test of random duplication of participants as a way to interpret study outcomes when the statistical significance is borderline. This discussion shows the irrefutability of the concept of borderline statistical significance, however, it is important that one demonstrates whether a borderline statistical significance is truly borderline or not. Since the absence of statistical significance is not necessarily evidence of absence of effect, one needs to double check if a borderline statistical significance is indeed borderline or not. The p-value should not be looked at as a rule of thumb for accepting or rejecting the null hypothesis but rather as a guide for further action or analysis that leads to correct conclusions

HIV hospital admissions attributable to specific opportunistic infections and factors associated with them at a Botswana Referral Hospital

Hospital admissions among people living with HIV (PLWH) in Botswana are high. Opportunistic infections (OIs) are responsible for most of these admissions. Although leading OIs causing these admissions have been identified in the region, their correlates are poorly understood. This study aimed to: 1) evaluate major OIs responsible for admissions among HIV patients at Princess Marina Hospital (PMH) in Botswana; 2) estimate the proportion and identify the most frequent admissions attributable to specific OIs; 3) characterize major correlates of admissions attributable to each specific OIs and identify populations most at risk as a base for effective policy and resource orientation. HIV infected patients were randomly selected from hospital record lists. Biomedical, sociodemographic and economic data were collected from the records and from face-to-face patient interviews and analyzed. Tuberculosis was the most important OI responsible for 234.6 per 1000 HIV admissions. Cryptococcal meningitis accounted for 162.0 per 1000 admissions. Patients with a CD4-cell count 350/µL and females. The risk of admission due to cryptococcal meningitis was also high among patients with low socioeconomic status (SES). Females were more at risk for Cryptosporidium, Bacterial pneumonia (BP), Pneumocystis Carinii Pneumonia (PCP), Herpes and candidiasis-specific admissions than male and, patients not on co-trimoxazole were more likely to be admitted than those on co-trimoxazole.Comprehensive implementation strategies to address OIs among PLWH are needed. To be effective, such strategies should address not only biomedical factors but should also focus on PLWH’s SES