Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania.

BACKGROUND: Drug prescription practices depend on several factors related to the patient, health worker and health facilities. A better understanding of the factors influencing prescription patterns is essential to develop strategies to mitigate the negative consequences associated with poor practices in both the public and private sectors. METHODS: A cross-sectional study was conducted in rural Tanzania among patients attending health facilities, and health workers. Patients, health workers and health facilities-related factors with the potential to influence drug prescription patterns were used to build a model of key predictors. Standard data mining methodology of classification tree analysis was used to define the importance of the different factors on prescription patterns. RESULTS: This analysis included 1,470 patients and 71 health workers practicing in 30 health facilities. Patients were mostly treated in dispensaries. Twenty two variables were used to construct two classification tree models: one for polypharmacy (prescription of ≥3 drugs) on a single clinic visit and one for co-prescription of artemether-lumefantrine (AL) with antibiotics. The most important predictor of polypharmacy was the diagnosis of several illnesses. Polypharmacy was also associated with little or no supervision of the health workers, administration of AL and private facilities. Co-prescription of AL with antibiotics was more frequent in children under five years of age and the other important predictors were transmission season, mode of diagnosis and the location of the health facility. CONCLUSION: Standard data mining methodology is an easy-to-implement analytical approach that can be useful for decision-making. Polypharmacy is mainly due to the diagnosis of multiple illnesses

Predictors of Antibiotics Co-prescription with Antimalarials for Patients Presenting with Fever in Rural Tanzania.

Successful implementation of malaria treatment policy depends on the prescription practices for patients with malaria. This paper describes prescription patterns and assesses factors associated with co-prescription of antibiotics and artemether-lumefantrine (AL) for patients presenting with fever in rural Tanzania. From June 2009 to September 2011, a cohort event monitoring program was conducted among all patients treated at 8 selected health facilities in Ifakara and Rufiji Health and Demographic Surveillance System (HDSS).It included all patients presenting with fever and prescribed with AL. Logistic regression was used to model the predictors on the outcome variable which is co-prescription of AL and antibiotics on a single clinical visit. A cohort of 11,648 was recruited and followed up with 92% presenting with fever. Presumptive treatment was used in 56% of patients treated with AL. On average 2.4 (1 -- 7) drugs was prescribed per encounter, indicating co-prescription of AL with other drugs. Children under five had higher odds of AL and antibiotics co-prescription (OR = 0.63, 95% CI: 0.46 -- 0.85) than those aged more than five years. Patients testing negative had higher odds (OR = 2.22, 95%CI: 1.65 -- 2.97) of AL and antibiotics co-prescription. Patients receiving treatment from dispensaries had higher odds (OR = 1.45, 95% CI: 0.84 -- 2.30) of AL and antibiotics co-prescription than those from served in health centres even though the deference was not statistically significant. Regardless the fact that Malaria is declining but due to lack of laboratories and mRDT in most health facilities in the rural areas, clinicians are still treating malaria presumptively. This leads them to prescribe more drugs to treat all possibilities

Vulnerability to high risk sexual behaviour (HRSB) following exposure to war trauma as seen in post-conflict communities in eastern uganda: a qualitative study

<p>Abstract</p> <p>Background</p> <p>Much of the literature on the relationship between conflict-related trauma and high risk sexual behaviour (HRSB) often focuses on refugees and not mass in-country displaced people due to armed conflicts. There is paucity of research about contexts underlying HRSB and HIV/AIDS in conflict and post-conflict communities in Uganda. Understanding factors that underpin vulnerability to HRSB in post-conflict communities is vital in designing HIV/AIDS prevention interventions. We explored the socio-cultural factors, social interactions, socio-cultural practices, social norms and social network structures that underlie war trauma and vulnerability to HRSB in a post-conflict population.</p> <p>Methods</p> <p>We did a cross-sectional qualitative study of 3 sub-counties in <it>Katakwi </it>district and 1 in <it>Amuria </it>in Uganda between March and May 2009. We collected data using 8 FGDs, 32 key informant interviews and 16 in-depth interviews. We tape-recorded and transcribed the data. We followed thematic analysis principles to manage, analyse and interpret the data. We constantly identified and compared themes and sub-themes in the dataset as we read the transcripts. We used illuminating verbatim quotations to illustrate major findings.</p> <p>Results</p> <p>The commonly identified HRSB behaviours include; transactional sex, sexual predation, multiple partners, early marriages and forced marriages. Breakdown of the social structure due to conflict had resulted in economic destruction and a perceived soaring of vulnerable people whose propensity to HRSB is high. Dishonour of sexual sanctity through transactional sex and practices like incest mirrored the consequence of exposure to conflict. HRSB was associated with concentration of people in camps where idleness and unemployment were the norm. Reports of girls and women who had been victims of rape and defilement by men with guns were common. Many people were known to have started to display persistent worries, hopelessness, and suicidal ideas and to abuse alcohol.</p> <p>Conclusions</p> <p>The study demonstrated that conflicts disrupt the socio-cultural set up of communities and destroy sources of people's livelihood. Post-conflict socio-economic reconstruction needs to encompass programmes that restructure people's morals and values through counselling. HIV/AIDS prevention programming in post-conflict communities should deal with socio-cultural disruptions that emerged during conflicts. Some of the disruptions if not dealt with, could become normalized yet they are predisposing factors to HRSB. Socio-economic vulnerability as a consequence of conflict seemed to be associated with HRSB through alterations in sexual morality. To pursue safer sexual health choices, people in post-conflict communities need life skills.</p

Global, regional, and national disability-adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015.

BACKGROUND: Healthy life expectancy (HALE) and disability-adjusted life-years (DALYs) provide summary measures of health across geographies and time that can inform assessments of epidemiological patterns and health system performance, help to prioritise investments in research and development, and monitor progress toward the Sustainable Development Goals (SDGs). We aimed to provide updated HALE and DALYs for geographies worldwide and evaluate how disease burden changes with development. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2015 (GBD 2015) for all-cause mortality, cause-specific mortality, and non-fatal disease burden to derive HALE and DALYs by sex for 195 countries and territories from 1990 to 2015. We calculated DALYs by summing years of life lost (YLLs) and years of life lived with disability (YLDs) for each geography, age group, sex, and year. We estimated HALE using the Sullivan method, which draws from age-specific death rates and YLDs per capita. We then assessed how observed levels of DALYs and HALE differed from expected trends calculated with the Socio-demographic Index (SDI), a composite indicator constructed from measures of income per capita, average years of schooling, and total fertility rate. FINDINGS: Total global DALYs remained largely unchanged from 1990 to 2015, with decreases in communicable, neonatal, maternal, and nutritional (Group 1) disease DALYs offset by increased DALYs due to non-communicable diseases (NCDs). Much of this epidemiological transition was caused by changes in population growth and ageing, but it was accelerated by widespread improvements in SDI that also correlated strongly with the increasing importance of NCDs. Both total DALYs and age-standardised DALY rates due to most Group 1 causes significantly decreased by 2015, and although total burden climbed for the majority of NCDs, age-standardised DALY rates due to NCDs declined. Nonetheless, age-standardised DALY rates due to several high-burden NCDs (including osteoarthritis, drug use disorders, depression, diabetes, congenital birth defects, and skin, oral, and sense organ diseases) either increased or remained unchanged, leading to increases in their relative ranking in many geographies. From 2005 to 2015, HALE at birth increased by an average of 2·9 years (95% uncertainty interval 2·9-3·0) for men and 3·5 years (3·4-3·7) for women, while HALE at age 65 years improved by 0·85 years (0·78-0·92) and 1·2 years (1·1-1·3), respectively. Rising SDI was associated with consistently higher HALE and a somewhat smaller proportion of life spent with functional health loss; however, rising SDI was related to increases in total disability. Many countries and territories in central America and eastern sub-Saharan Africa had increasingly lower rates of disease burden than expected given their SDI. At the same time, a subset of geographies recorded a growing gap between observed and expected levels of DALYs, a trend driven mainly by rising burden due to war, interpersonal violence, and various NCDs. INTERPRETATION: Health is improving globally, but this means more populations are spending more time with functional health loss, an absolute expansion of morbidity. The proportion of life spent in ill health decreases somewhat with increasing SDI, a relative compression of morbidity, which supports continued efforts to elevate personal income, improve education, and limit fertility. Our analysis of DALYs and HALE and their relationship to SDI represents a robust framework on which to benchmark geography-specific health performance and SDG progress. Country-specific drivers of disease burden, particularly for causes with higher-than-expected DALYs, should inform financial and research investments, prevention efforts, health policies, and health system improvement initiatives for all countries along the development continuum. FUNDING: Bill & Melinda Gates Foundation

Using classification tree modelling to investigate drug prescription practices at health facilities in rural Tanzania

Abstract Background Drug prescription practices depend on several factors related to the patient, health worker and health facilities. A better understanding of the factors influencing prescription patterns is essential to develop strategies to mitigate the negative consequences associated with poor practices in both the public and private sectors. Methods A cross-sectional study was conducted in rural Tanzania among patients attending health facilities, and health workers. Patients, health workers and health facilities-related factors with the potential to influence drug prescription patterns were used to build a model of key predictors. Standard data mining methodology of classification tree analysis was used to define the importance of the different factors on prescription patterns. Results This analysis included 1,470 patients and 71 health workers practicing in 30 health facilities. Patients were mostly treated in dispensaries. Twenty two variables were used to construct two classification tree models: one for polypharmacy (prescription of ≥3 drugs) on a single clinic visit and one for co-prescription of artemether-lumefantrine (AL) with antibiotics. The most important predictor of polypharmacy was the diagnosis of several illnesses. Polypharmacy was also associated with little or no supervision of the health workers, administration of AL and private facilities. Co-prescription of AL with antibiotics was more frequent in children under five years of age and the other important predictors were transmission season, mode of diagnosis and the location of the health facility. Conclusion Standard data mining methodology is an easy-to-implement analytical approach that can be useful for decision-making. Polypharmacy is mainly due to the diagnosis of multiple illnesses.</p

A Comparison of suspected signals detected by the two data mining algorithms.

<p>AL = Artemether-Lumefantrine; ASAQ = Artesunate-Amodiaquine; DHAPQ = Dihydroartemisinin- Piperaquine; WBC = White Blood Cells; ALT = Alanine amino Transferase.</p

A two by two table for the adverse event-drug pair.

<p>*AE = Adverse Event; <i>a = </i>the number of reports involving the drug of interest <i>j</i> and adverse event of interest <i>i</i> combination; <i>b = </i>reports of adverse event of interest <i>i</i> observed with other drugs; <i>c = </i>reports of all other AEs with drug <i>j; d = </i>reports of all other AEs with the other drugs; and <i>a+b+c+d</i> = the total number of reports in the dataset.</p

Ranking of drug-event combinations according to the signal index of the PRR (Upper panel) and BCPNN (Lower panel) methods.

<p>Ranking of drug-event combinations according to the signal index of the PRR (Upper panel) and BCPNN (Lower panel) methods.</p

Paediatric pharmacovigilance: use of pharmacovigilance data mining algorithms for signal detection in a safety dataset of a paediatric clinical study conducted in seven african countries

Pharmacovigilance programmes monitor and help ensuring the safe use of medicines which is critical to the success of public health programmes. The commonest method used for discovering previously unknown safety risks is spontaneous notifications. In this study we examine the use of data mining algorithms to identify signals from adverse events reported in a phase IIIb/IV clinical trial evaluating the efficacy and safety of several Artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria in African children