Maximum Likelihood Estimation of Individual Inbreeding Coefficients and Null Allele Frequencies

In this paper, we developed and compared several expectation-maximization (EM) algorithms to find maximum likelihood estimates of individual inbreeding coefficients using molecular marker information. The first method estimates the inbreeding coefficient for a single individual and assumes that allele frequencies are known without error. The second method jointly estimates inbreeding coefficients and allele frequencies for a set of individuals that have been genotyped at several loci. The third method generalizes the second method to include the case in which null alleles may be present. In particular, it is able to jointly estimate individual inbreeding coefficients and allele frequencies, including the frequencies of null alleles, and accounts for missing data. We compared our methods with several other estimation procedures using simulated data and found that our methods perform well. The maximum likelihood estimators consistently gave among the lowest root-mean-square-error (RMSE) of all the estimators that were compared. Our estimator that accounts for null alleles performed particularly well and was able to tease apart the effects of null alleles, randomly missing genotypes and differing degrees of inbreeding among members of the datasets we analysed. To illustrate the performance of our estimators, we analysed previously published datasets on mice (Mus musculus) and white-tailed deer (Odocoileus virginianus)

Is the Positive Association Between Middle-Income and Rich Household Wealth and Adult Sub-Saharan African Women\u27s Overweight Status Modified by the Level of Education Attainment? A Cross-Sectional Study of 22 Countries

BACKGROUND: Previous studies show a positive association between household wealth and overweight in sub-Saharan African (SSA) countries; however, the manner in which this relationship differs in the presence of educational attainment has not been well-established. This study examined the multiplicative effect modification of educational attainment on the association between middle-income and rich household wealth and overweight status among adult females in 22 SSA countries. We hypothesized that household wealth was associated with a greater likelihood of being overweight among middle income and rich women with lower levels of educational attainment compared to those with higher levels of educational attainment. METHODS: Demographic and Health Survey (DHS) data from 2006 to 2016 for women aged 18-49 years in SSA countries were used for the study. Overweight was defined as a body mass index (BMI) ≥ 25 kg/m2. Household wealth index tertile was the exposure and educational attainment, the effect modifier. Potential confounders included age, ethnicity, place of residence, and parity. Descriptive analysis was conducted, and separate logistic regression models were fitted for each of the 22 SSA countries to compute measures of effect modification and 95% confidence intervals. Analysis of credibility (AnCred) methods were applied to assess the intrinsic credibility of the study findings and guide statistical inference. RESULTS: The prevalence of overweight ranged from 12.6% in Chad to 56.6% in Swaziland. Eighteen of the 22 SSA countries had measures of effect modification below one in at least one wealth tertile. This included eight of the 12 low-income countries and all 10 middle income countries. This implied that the odds of overweight were greater among middle-income and rich women with lower levels of educational attainment than those with higher educational attainment. On the basis of the AnCred analysis, it was found that the majority of the study findings across the region provided some support for the study hypothesis. CONCLUSIONS: Women in higher wealth strata and with lower levels of educational attainment appear to be more vulnerable to overweight compared to those in the same wealth strata but with higher levels of educational attainment in most low- and middle- income SSA countries

Evaluating the impact of programmatic mass drug administration for malaria in Zambia using routine incidence data.

BACKGROUND NlmCategory: BACKGROUND content: In 2016, the Zambian National Malaria Elimination Centre started programmatic mass drug administration (pMDA) campaigns with dihydroartemisinin-piperaquine as a malaria elimination tool in Southern Province. Two rounds were administered, two months apart (coverage 70% and 57% respectively). We evaluated the impact of one year of pMDA on malaria incidence using routine data. - Label: METHODS NlmCategory: METHODS content: We conducted an interrupted time series with comparison group analysis on monthly incidence data collected at the health facility catchment area (HFCA) level, with a negative binomial model using generalized estimating equations. pMDA was conducted in HFCAs with greater than 50 cases/1,000 people/year. Ten HFCAs with incidence rates marginally above this threshold (pMDA group) were compared to 20 HFCAs marginally below (comparison group). - Label: RESULTS NlmCategory: RESULTS content: "The pMDA HFCAs saw a 46% greater decrease in incidence at the time of intervention than the comparison areas (incidence rate ratio: 0.536 [0.337-0.852]); however, incidence increased toward the end of the season. No HFCAs saw a transmission interruption." - Label: CONCLUSION NlmCategory: CONCLUSIONS content: pMDA, implemented during one year with imperfect coverage in low transmission areas with sub-optimal vector control coverage, significantly reduced incidence. However, elimination will require additional tools. Routine data are important resources for programmatic impact evaluations and should be considered for future analyses

Effect Modification by Baseline Mortality in the MORDOR Azithromycin Trial.

We examined whether baseline mortality risk, as a function of child age and site, modified the azithromycin mortality-reduction effect in the Macrolide Oraux pour Réduire les Décès avec un Oeil sur la Résistance (MORDOR) clinical trial. We used the Cox proportional hazards model with an interaction term. Three models were examined representing three sources for the baseline-risk covariate: two using sources external to MORDOR and the third leveraging data within MORDOR. All three models provided moderate evidence for the effect becoming stronger with increasing baseline mortality (P = 0.02, 0.02, and 0.07, respectively) at the rate of approximately 6-12% additional mortality reduction per doubling of baseline mortality. Etiological and programmatic implications of these findings are discussed

Intraperitoneal drain placement and outcomes after elective colorectal surgery: international matched, prospective, cohort study

Despite current guidelines, intraperitoneal drain placement after elective colorectal surgery remains widespread. Drains were not associated with earlier detection of intraperitoneal collections, but were associated with prolonged hospital stay and increased risk of surgical-site infections.Background Many surgeons routinely place intraperitoneal drains after elective colorectal surgery. However, enhanced recovery after surgery guidelines recommend against their routine use owing to a lack of clear clinical benefit. This study aimed to describe international variation in intraperitoneal drain placement and the safety of this practice. Methods COMPASS (COMPlicAted intra-abdominal collectionS after colorectal Surgery) was a prospective, international, cohort study which enrolled consecutive adults undergoing elective colorectal surgery (February to March 2020). The primary outcome was the rate of intraperitoneal drain placement. Secondary outcomes included: rate and time to diagnosis of postoperative intraperitoneal collections; rate of surgical site infections (SSIs); time to discharge; and 30-day major postoperative complications (Clavien-Dindo grade at least III). After propensity score matching, multivariable logistic regression and Cox proportional hazards regression were used to estimate the independent association of the secondary outcomes with drain placement. Results Overall, 1805 patients from 22 countries were included (798 women, 44.2 per cent; median age 67.0 years). The drain insertion rate was 51.9 per cent (937 patients). After matching, drains were not associated with reduced rates (odds ratio (OR) 1.33, 95 per cent c.i. 0.79 to 2.23; P = 0.287) or earlier detection (hazard ratio (HR) 0.87, 0.33 to 2.31; P = 0.780) of collections. Although not associated with worse major postoperative complications (OR 1.09, 0.68 to 1.75; P = 0.709), drains were associated with delayed hospital discharge (HR 0.58, 0.52 to 0.66; P < 0.001) and an increased risk of SSIs (OR 2.47, 1.50 to 4.05; P < 0.001). Conclusion Intraperitoneal drain placement after elective colorectal surgery is not associated with earlier detection of postoperative collections, but prolongs hospital stay and increases SSI risk

Space-Time Smoothing Models for Surveillance and Complex Survey Data

Thesis (Ph.D.)--University of Washington, 2016-06Area and time-specific estimates of disease rates, cause-specific mortality rates and other key health indicators are of great interest for health care and policy purposes. Such estimates provide the information needed to identify areas with increased risk, effectively allocate resources, and target interventions. A wide variety of data, such as vital statistics, complex surveys, demographic surveillance sites, and disease registries, are used for these purposes. Unfortunately, the sample size of data available at a granular space-time scale is often too small to provide reliable estimates and uncertainty intervals. Using data from multiple sources and spatial and temporal smoothing is beneficial to alleviate problems of data scarcity. The purpose of the work described herein is to use Bayesian space-time models, to combine data from multiple sources to provide reliable area-based estimates. This work is motivated by estimating rates of health indicators (e.g. diabetes, smoking) by health reporting areas in King County from the Behavioral Risk Factor Surveillance Survey, child mortality by regions in Tanzania from Demographic and Health Surveys and demographic surveillance sites, and cancer-specific incidence and mortality rates in Europe from government data and local registries

Exploring the Hyak Real-time Health and Population Measurement Platform in Tanzania using DHS Surveys and HDSS Data

Hyak is an idea for a statistical data collection and analysis platform that leverages the advantages of traditional sample survey and demographic surveillance methods. The overall goal is to produce a varying and diverse array of indicator values in near real- time that describe large populations and can identify differences across relatively small distances in space. To accomplish this Hyak combines and builds on existing knowledge and experience with sampling, follow-up, fieldwork, spatial modeling, estimation and other statistical methods. This project explores the foundational ideas of the Hyak system. The key aim is to inves- tigate one of the important pre-conditions necessary to make Hyak possible – whether or not traditional sample surveys and demographic surveillance methods produce similar estimates of a key demographic indicator, and further, if the differences can be exploited to improve the usefulness of the indicator. Addition aims address spatial models, a sim- ulation study of the whole Hyak idea and further development of detailed ideas about innovative sampling methods and statistical approaches to integrating data from several structurally difference sources to estimate demographic indicators. In our main comparison we focused on child mortality (5q0) using data from the 2010 de- mographic and health survey (DHS) of Tanzania and from two health and demographic surveillance system (HDSS) sites in Tanzania – Ifakara and Rufiji. Over the period 1990 – 2000 estimates of child mortality from the two data sources are generally similar but dif- ferent in useful ways. The HDSS estimates are accurate (low bias) and precise (small vari- ance) measurements for comparatively small, geographically-defined populations, and the DHS estimates are less accurate and much less precise but representative of large populations. Altogether this is exactly what we need for Hyak to work. We developed several spatio-temporal smoothing models of the DHS data for the regions of Tanzania, and we developed one ‘merged’ model that combines data from DHS and HDSS in the regions where they are close to each other. These clearly demonstrate the utility of smoothing and integration of data from multiple sources. The key result for the regions of Tanzania is to dampen noisy fluctuations in time and space and greatly reduce variance, and in areas near HDSS sites, to adjust overall estimates to more closely match the HDSS. We present additional ideas relating to spatio-temporal modeling of survey data, var- ious sampling methods, informed sampling in particular, and statistical approaches to integrating data from several sources into unified models for demographic indicators.\u