Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths

<p>Abstract</p> <p>Background</p> <p>The most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.</p> <p>Methods</p> <p>Between March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.</p> <p>Results</p> <p>HCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.</p> <p>Conclusion</p> <p>Both the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.</p

Expanding Assisted Partner Services (APS) to Partners of Index Partners in Western Kenya.

Thesis (Master's)--University of Washington, 2022Objectives: To investigate the uptake, characteristics and outcomes of Assisted Partner Services (APS) when expanded to identify, test and treat female sexual partners of male partners identified through the APS scale up program. Design: Longitudinal study nested in the APS Scale-up Implementation Study (R01AI134130) Materials and Methods: We utilized data from 31 health facilities offering APS in Homa Bay and Kisumu Counties in Kenya from November 2018 – March 2020. Male sexual partners of female index participants were traced and tested for HIV. Male partners who tested HIV-positive were provided APS and, asked to provide contact information for their female sexual partners so that they could be offered HIV testing and linkage to care if positive. Based on the outcome of their HIV test, these female partners of male partners (FPP) were categorized as Known Positive (previously aware of their HIV positive status before APS), New Positive (new HIV diagnosis), or Negative. We evaluated socio-demographic characteristics of FPP by HIV status using chi-squared and fisher’s exact tests. We further compared New Positive FPP with index females (enrolled females who tested positive for HIV in the facility and provided contact information for their male sexual partners) in terms of socio-demographic characteristics, linkage to care at 6 weeks, viral suppression outcomes at 12 months, and intimate partner violence (IPV). Univariable and multivariable logistic regression was used to evaluate associations between FPP demographics and new HIV positivity. Results: Overall 4951 FPP were identified and enrolled. Among these, 291 (5.9%) were new positives, 1745 (35.2%) were known positive, and 2915 (58.9%) were negative. FPP and female index clients were similar in terms of age, marital status and income. FPP had a 1.72 (1.38-2.14) higher likelihood of having completed secondary school and nearly 6-fold increased likelihood of being self-employed (5.87 (4.20-8.21) compared to female index clients. Similar proportions of FPP living with HIV were in care at 12 months compared to index females (90% vs 89%). Follow-up and HIV viral load outcomes, including report of IPV, were also similar for both populations. No IPV experience was reported in either group. Conclusion: One in twenty female partners of male partners identified through expanded APS were newly diagnosed with HIV. FPP with HIV had high rates of linkage to HIV care and low IPV outcomes. Expanded APS also identified a large number of negative FPP at risk of HIV infection and link them to prevention interventions

Prevalence, intensity and risk factors of tungiasis in Kilifi County, Kenya: I. Results from a community-based study

<div><p>Background</p><p>Tungiasis is a neglected tropical disease caused by female sand fleas (<i>Tunga penetrans</i>) embedded in the skin. The disease is associated with important morbidity. Tungiasis is endemic along the Coast of Kenya with a prevalence ranging from 11% to 50% in school-age children. Hitherto, studies on epidemiological characteristics of tungiasis in Africa are scanty.</p><p>Methods</p><p>In a cross-sectional study 1,086 individuals from 233 households in eight villages located in Kakuyuni and Malanga Sub-locations, Kilifi County, on the Kenyan Coast, were investigated. Study participants were examined systematically and the presence and severity of tungiasis were determined using standard methods. Demographic, socio-economic, environmental and behavioral risk factors of tungiasis were assessed using a structured questionnaire. Data were analyzed using bivariate and multivariate regression analysis.</p><p>Results</p><p>The overall prevalence of tungiasis was 25.0% (95% CI 22.4–27.5%). Age-specific prevalence followed an S-shaped curve, peaking in the under-15 year old group. In 42.5% of the households at least one individual had tungiasis. 15.1% of patients were severely infected (≥ 30 lesions). In the bivariate analysis no specific animal species was identified as a risk factor for tungiasis. Multivariate analysis showed that the occurrence of tungiasis was related to living in a house with poor construction characteristics, such as mud walls (OR 3.35; 95% CI 1.71–6.58), sleeping directly on the floor (OR 1.68; 95% CI 1.03–2.74), the number of people per sleeping room (OR = 1.77; 95% CI 1.07–2.93) and washing the body without soap (OR = 7.36; 95% CI 3.08–17.62). The odds of having severe tungiasis were high in males (OR 2.29; 95% CI 1.18–44.6) and were very high when only mud puddles were available as a water source and lack of water permitted washing only once a day (OR 25.48 (95% CI 3.50–185.67) and OR 2.23 (95% CI 1.11–4.51), respectively).</p><p>Conclusions</p><p>The results of this study show that in rural Kenya characteristics of poverty determine the occurrence and the severity of tungiasis. Intra-domiciliary transmission seems to occur regularly.</p></div

Correlation between age-specific prevalence and age-specific frequency of high intensity of infection (> 30 lesions); rho = 0.90, p = 0.006.

<p>Correlation between age-specific prevalence and age-specific frequency of high intensity of infection (> 30 lesions); rho = 0.90, p = 0.006.</p

Flow chart of study population.

<p>Flow chart of study population.</p

Age-specific prevalence and intensity of tungiasis in the study area.

<p>Column heights indicate overall prevalence in age groups.</p

Bivariate analysis of demographic, housing, economic and behavioral risk factors (n = 1,086).

<p>Bivariate analysis of demographic, housing, economic and behavioral risk factors (n = 1,086).</p

Population attributable fractions for exposure variables amenable to modification.

<p>Population attributable fractions for exposure variables amenable to modification.</p

Risk factors of tungiasis/severe tungiasis after multivariate analysis.

<p>Risk factors of tungiasis/severe tungiasis after multivariate analysis.</p

Bivariate analysis of educational, occupational and environmental risk factors (n = 1,086).

<p>Bivariate analysis of educational, occupational and environmental risk factors (n = 1,086).</p