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Nuredin Ibrahim

Evaluation of factors affecting the chance of survival/death status of HIV-positive people under the antiretroviral treatment program: The case of Adama Hospital. Nuredin Ibrahim. Outline. Background Objectives of the study Limitation of the study Data and methodology

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Nuredin Ibrahim

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  1. Evaluation of factors affecting the chance of survival/death status of HIV-positive people under the antiretroviral treatment program: The case of Adama Hospital Nuredin Ibrahim

  2. Outline • Background • Objectives of the study • Limitation of the study • Data and methodology • Results and discussion • Conclusion and Recommendation

  3. Background • HIV/AIDS is still accountable for economic, social and health crises especially in resource-poor countries. • In just 25 years, HIV has spread relentlessly infecting 65 million people and killing 25 million. • In 2005 an estimated 2.8 million lost their lives due to AIDS. • In the case of Ethiopia, in 2005 it was estimated that a total of 1,320,000 were living with HIV/AIDS.

  4. Background cont’d • In the same year 134,500 (368 a day) AIDS deaths occurred. • The estimated total number of persons requiring ART in 2005 was 277,800 but only 55,000 of them were taking ART. • Thus we need urgent response to AIDS to overcome such great loses.

  5. ART in Ethiopia • Its objective is providing health related support/treatmentfor HIV patients. • Started in July 2003 in 35 hospitals and currently 96 public and 13 private hospitals, and 77 health centers deliver the service across the country. • Among the largest ART service providers in the Oromiya regional state is the Adama Hospital ART clinic. • The research will center this hospital which is among the biggest ART clinics in the country.

  6. Objectives of the study General objective To study some socio-economic, demographic and health factors that influence the survival/death status of HIV-Positives under ART follow up, i.e. to evaluate the association of the factors with survival/death status. Specific objectives • To determine or get some clue on the relative importance of the factors, • to develop a statistical model that predicts the chance of survival/dying among HIV-positives taking ART, • to provide information for policy makers on the factors affecting survival/death status of HIV positives taking ART.

  7. Limitation of the study • The study does not cover HIV-positive individuals who take ART outside Adama ART Clinic. • Lack of much literature on our country related to the subject under study. • Poor data recording on the different patient charts.

  8. Data and methodology The Data • This is a retrospective study, which reviews the patient intake forms and follow up charts of adult HIV/AIDS patients taking ART at Adama Hospital. • Total no. of patients=4121 • Transferred/Lost=670 • Dead=205 • Total no. patients in the target population=3651 • Sample size=259

  9. Data and methodology Sample Design and Sample Size • Sample size: where is the sample size, p the population proportion of death and dis the absolute precisiondefined as: • d= SE, where SE is the standard error. • And if sampling is from a finite population of size N, then

  10. Sample Size Determination • Using alpha=0.05, d= 0.03,p=7% and N=3651 =278 implying that n = 259. • The sampling design used for this study is systematic random sampling. • The sampling frame is the list of patients taking ART at Adama Hospital until February 2007.

  11. Variables in the study • Dependent variable • “Death Status” (dead, alive) • Independent variables • Age • Sex • Marital Status • Educational level • Previous attendance of HIV counseling Session(s) • Residence • Employment Status • Number of rooms • Running water • Running electricity

  12. Independent variables cont’d • TB • Partners HIV status • Base Line Weight • Base Line CD4 Counts • WHO Clinical Stage • Antiretroviral regimen • Condom use • Tobacco • Alcohol • Soft/Hard Drugs • Interactions • Base Line Weight and WHO Clinical Stage • Educational level and Employment Status ..\..\Table 4.1(Independent-Vars)doc.doc

  13. Methodology • There are many situations in which the response of interest is dichotomous rather than continuous. • Examples of variables that assume only two possible values : • disease status (the disease is either present or absent), • survival following surgery (a patient is either alive or dead), • survival after the start of ART (alive or dead) etc.

  14. Methodology cont’d • We use Multiple Logistic Regression (MLR) for the analysis. • The logistic regression is preferred from multiple regression and discriminant analysis as • it results in a biologically meaningful interpretation • it is mathematically flexible and easily used distribution • it requires fewer assumptions

  15. The Multiple Logistic Regression Model • Consider a collection of k independent variables denoted by the vector =(x1, x2, …, xk). • Let the conditional probability that the outcome is present be denoted by P(Y=1| X) = p(X). Then the logit of the multiple logistic regression is given by the equation g(X)= + 1 x1+ 2 x2 + …+ k xk and the odds in favor of success for the multivariate logistic regression will be ln =ln = + 1 x1+ 2 x2 + …+ k xk

  16. In which case p(X)= =

  17. Fitting the Logistic Regression Model • ML method provides the foundation for our approach to estimation with the logistic regression model. • This method yields values for the unknown parameters which maximize the probability of obtaining the observed set of data. • The likelihood function is given by And the log likelihood is defined as

  18. Fitting the Logistic Regression Model cont’d • The likelihood equations in logistic regression are non linear in the vector and, and thus require special methods for their solution. • These methods are iterative in nature and have been programmed into available logistic regression packages like SPSS.

  19. Model Building Strategies/Variable Selection • The following steps are recommended to aid in the selection of variables for a logistic regression. • The selection process should begin with bivariate analysis. • Selection of variables for the MLR analysis will follow based on the results in the bivariate analysis along with all variables of known biologic importance. • The importance of each variable included in the MLR model should be verified by different model assessment techniques.

  20. Assessing the Fit of the Model • The tests/techniques used in this study include • Likelihood-ratio test • Hosmer-Lemeshow Test • Wald statistics • Cook’s Distance and dfbeta are also used to check the model for Influential observations.

  21. Results and discussion • The results of this study are presented in three steps: • Summary statistics • Bivariate analysis and • The multiple logistic regression analysis.

  22. Summary statistics I-Socio-Economic factors and HIV death ..\..\Table 5-1(Soc-Econmc).doc II-Demographic and Health factors in relation to HIV/AIDS death ..\..\Table 5-2(Demg-Health).doc III-Risk Behavior factors and HIV/AIDS death ..\..\Table 5-3(RiskBehv).doc

  23. Bivariate Analysis • It is used to see the association death status with each independent variable proposed (using Pearson chi-square test). • Note that the p-value used here as a criterion for significance is 0.25. ..\..\Table 5-4(Bivariate).doc

  24. Multiple Logistic Regression Analysis • Bivariate approach ignores the possibility that a collection of variables, each of which is weakly associated with the outcome, can become an important predictor of the outcome when taken together. • In such cases, we should choose a significance level large enough (e.g. 25%) to allow the suspected variables to become candidates for inclusion in the Multiple Logistic Regression (MLR)model. ..\..\Table 5-5(Cat var Coding).doc

  25. Estimates from MLR Analysis The variables that are found to be significant in the MLR analysis are 1-CONDOM 2- ALCOH 3-BASE_CD4 4- BASE_WEIG 5-DRUG 6- NO_ROOMS 7-PART_HIV • And this is somehow in line with the results obtained from the bivariate analysis. ..\..\Table 5-6(Vars in the final model).doc

  26. Discussion and Interpretation of results • The negative sign for the odds ratio of the variable condom use implies that death risk is lower for those patients who use condom during sexual intercourse. • Its magnitude indicates that, the odds of being at risk of death for those patients who use condom during sexual intercourse is 2.7% less than for those who do not use it or for those in the non response group. • This is due to the scientific reason that, the condom prevents the transfer of stronger HIV virus to the patient. • Alcohol and drug abusers are usually accused of ARV non-adherence by clinicians in addition to the complications they bring in to one’s health. ..\..\Table 5-6(Vars in the final model).doc

  27. Discussion and Interpretation • The results of this study support this claim: Non alcoholics are 3.2% less likely for the risk of death than those who didn’t explain their behavior towards alcohol. • On the other hand patients who responded they take more alcohol are still at a lower risk of death than those who didn’t explain their alcoholic behavior. • This lesser risk might be attributed to the fact that those who explain their status will get counseling services on how to avoid their bad addictions from the clinics. ..\..\Table 5-6(Vars in the final model).doc

  28. Discussion and Interpretation • The odds of death risk for Drug (Soft/Hard) abusers is much higher (odds ratio=4.073) than those who don’t use drugs. • The increased risk here arises again due to the problem of non-adherence of patients with this behavior to ART. ..\..\Table 5-6(Vars in the final model).doc

  29. Discussion and Interpretation • Partners’ HIV status affects survival since the wellbeing of one’s partner has economic, social and psychological advantages that may influence health positively. • The odds of death risk for patients with HIV negative Partner is less by 56.0% than those who don’t know their partner’s HIV status. • Compared to those who don’t know their partner’s HIV status, patients with HIV-positive partner are less likely to survive (odds ratio=11.881). • The other two health factors, base line CD4 and base line weight determine one’s resistance to different opportunistic diseases. • Thus, the larger their number/value, the lower the danger of being at risk of HIV death. • The outcomes of this study support this fact as the odds of death risk are high for those with lower CD4 count and weight. ..\..\Table 5-6(Vars in the final model).doc

  30. Discussion and Interpretation • The last variable, number of rooms, is used here as an indicator of economic status. • Patients with more number of rooms have better economic status than those who live perhaps in a room with their families. • Patients living in less than 2 rooms are more likely not to survive than those living in more than 2 rooms with their family (odds ratio=4.297).

  31. Discussion and Interpretation • Patients of higher economic class have better chance of survival under ART follow up. • From clinical point of view, better economic class implies better nutrition which increases the resistance of patients to opportunistic diseases thus lowering the patient’s risk of death.

  32. Model Checking • The Likelihood ratio test gives a model chi square of 107.242 with p-value 0.00 implying a good fit of the model. • Hosmer – Lemeshow Test also agrees the model fits the data well...\..\Table 5-7(Hosme - Lemeshow Test).doc • Cook’s distance and dfbeta indicate there are no suspicious influential observations.

  33. Conclusion and Recommendation Conclusion • In this study an attempt has been made to identify the factors affecting death status of the patients. • According to the MLR analysis , factors that affect the death status of HIV patients include i- Condom use v- Base Line Weight ii- Alcohol vi- Base Line CD4 Counts iii- Soft/Hard Drugs vii- Partners HIV status iv- Number of rooms

  34. Conclusion Cont’d • The results also indicated that survival/death status doesn’t show differences for different age groups and educational levels. • It also doesn’t depend on gender and employment status. • TB is not a risk factor perhaps due to the consideration given to control the disease through the supply of drugs and treatment facilities at clinical level. • At clinical level, 65.6% of the patients who were taking ART were TB-positive and about 86% of them have survived.

  35. Conclusion Cont’d • The factors that influence survival/death status can be grouped as • Risk behavior factors (CONDOM, ALCOH and DRUG) • Health factors (BASE_WEIG, BASE_CD4 and PART_HIV) • Economic factor (NO_ROOMS)

  36. Conclusion cont’d • Based on such grouping patients • involved in risky behaviors will have higher risk of death perhaps due to their tendency of non-adherence. • with poor health indicators like small baseline CD4 and weight who have HIV-positive partner are less likely to survive. • living in a single room with their family have also less chance of survival. Poor nutrition because of low economic status makes them vulnerable to opportunistic diseases.

  37. Recommendations • From the results of this study, the following recommendations are made for health policy workers and clinicians: • The outcomes of the study underlined behavioral (risk) factors as important predictors of survival/death status. • Thus, clinicians are expected to work hard to bring about behavioral change. • Currently the emphasis is on bringing behavioral change for prevention purposes. But we should also consider it with respect to HIV-positive persons taking ART. • ART programs will not be successful unless we can change the behavior of patients under ART follow up.

  38. Recommendations cont’d • Health workers should be cautious when a patient has lower CD4 and weight at baseline and an HIV-positive partner. • When this is the case appropriate clinical and non-clinical measures like medicine and support (can be home-based) should be provided. • For those patients with low economic status, health workers/stakeholders need to find ways of supporting the patients with respect to improving their nutrition in particular and other assistance in general.

  39. Thank You!

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