Literature review of estimation of prevalence rate of HIV/AIDS in India using population survey and sentinel surveillance based data

HIV/AIDS is a treatable although incurable disease that presents immense challenges to those infected including physical, social and psychological effects. As of 2009, an estimated 2.4 million people were living with HIV or AIDS in India, 0.3% of the country's population. In India, it is difficult to not only treat but also to track because it is associated with socio-economic factors such as illiteracy, social biases, poor sanitation, malnutrition and social class. Nevertheless, it is important to know the prevalence of HIV/AIDS for several reasons. At the individual level, the quality of life of people living with HIV/AIDS is markedly lower than their counterparts without the disease and is associated with challenges. At the community level, it is important to identify high risk groups, monitor prevention efforts, and allocate appropriate resources to target programs for the reduction of transmission of HIV. ^

Estimation of the incidence of a rare genetic disease through a two-tier mutation survey.

Recent attempts to detect mutations involving single base changes or small deletions that are specific to genetic diseases provide an opportunity to develop a two-tier mutation-screening program through which incidence of rare genetic disorders and gene carriers may be precisely estimated. A two-tier survey consists of mutation screening in a sample of patients with specific genetic disorders and in a second sample of newborns from the same population in which mutation frequency is evaluated. We provide the statistical basis for evaluating the incidence of affected and gene carriers in such two-tier mutation-screening surveys, from which the precision of the estimates is derived. Sample-size requirements of such two-tier mutation-screening surveys are evaluated. Considering examples of cystic fibrosis (CF) and medium-chain acyl-CoA dehydrogenase deficiency (MCAD), the two most frequent autosomal recessive disease in Caucasian populations and the two most frequent mutations (delta F508 and G985) that occur on these disease allele-bearing chromosomes, we show that, with 50-100 patients and a 20-fold larger sample of newborns screened for these mutations, the incidence of such diseases and their gene carriers in a population may be quite reliably estimated. The theory developed here is also applicable to rare autosomal dominant diseases for which disease-specific mutations are found.

Population genetics of VNTR loci and their applications in evolutionary studies

Variable number of tandem repeats (VNTR) are genetic loci at which short sequence motifs are found repeated different numbers of times among chromosomes. To explore the potential utility of VNTR loci in evolutionary studies, I have conducted a series of studies to address the following questions: (1) What are the population genetic properties of these loci? (2) What are the mutational mechanisms of repeat number change at these loci? (3) Can DNA profiles be used to measure the relatedness between a pair of individuals? (4) Can DNA fingerprint be used to measure the relatedness between populations in evolutionary studies? (5) Can microsatellite and short tandem repeat (STR) loci which mutate stepwisely be used in evolutionary analyses?^ A large number of VNTR loci typed in many populations were studied by means of statistical methods developed recently. The results of this work indicate that there is no significant departure from Hardy-Weinberg expectation (HWE) at VNTR loci in most of the human populations examined, and the departure from HWE in some VNTR loci are not solely caused by the presence of population sub-structure.^ A statistical procedure is developed to investigate the mutational mechanisms of VNTR loci by studying the allele frequency distributions of these loci. Comparisons of frequency distribution data on several hundreds VNTR loci with the predictions of two mutation models demonstrated that there are differences among VNTR loci grouped by repeat unit sizes.^ By extending the ITO method, I derived the distribution of the number of shared bands between individuals with any kinship relationship. A maximum likelihood estimation procedure is proposed to estimate the relatedness between individuals from the observed number of shared bands between them.^ It was believed that classical measures of genetic distance are not applicable to analysis of DNA fingerprints which reveal many minisatellite loci simultaneously in the genome, because the information regarding underlying alleles and loci is not available. I proposed a new measure of genetic distance based on band sharing between individuals that is applicable to DNA fingerprint data.^ To address the concern that microsatellite and STR loci may not be useful for evolutionary studies because of the convergent nature of their mutation mechanisms, by a theoretical study as well as by computer simulation, I conclude that the possible bias caused by the convergent mutations can be corrected, and a novel measure of genetic distance that makes the correction is suggested. In summary, I conclude that hypervariable VNTR loci are useful in evolutionary studies of closely related populations or species, especially in the study of human evolution and the history of geographic dispersal of Homo sapiens. (Abstract shortened by UMI.) ^

Using small area estimation and geographic information systems technology to target health services for the uninsured

The three articles that comprise this dissertation describe how small area estimation and geographic information systems (GIS) technologies can be integrated to provide useful information about the number of uninsured and where they are located. Comprehensive data about the numbers and characteristics of the uninsured are typically only available from surveys. Utilization and administrative data are poor proxies from which to develop this information. Those who cannot access services are unlikely to be fully captured, either by health care provider utilization data or by state and local administrative data. In the absence of direct measures, a well-developed estimation of the local uninsured count or rate can prove valuable when assessing the unmet health service needs of this population. However, the fact that these are “estimates” increases the chances that results will be rejected or, at best, treated with suspicion. The visual impact and spatial analysis capabilities afforded by geographic information systems (GIS) technology can strengthen the likelihood of acceptance of area estimates by those most likely to benefit from the information, including health planners and policy makers. ^ The first article describes how uninsured estimates are currently being performed in the Houston metropolitan region. It details the synthetic model used to calculate numbers and percentages of uninsured, and how the resulting estimates are integrated into a GIS. The second article compares the estimation method of the first article with one currently used by the Texas State Data Center to estimate numbers of uninsured for all Texas counties. Estimates are developed for census tracts in Harris County, using both models with the same data sets. The results are statistically compared. The third article describes a new, revised synthetic method that is being tested to provide uninsured estimates at sub-county levels for eight counties in the Houston metropolitan area. It is being designed to replicate the same categorical results provided by a current U.S. Census Bureau estimation method. The estimates calculated by this revised model are compared to the most recent U.S. Census Bureau estimates, using the same areas and population categories. ^

HPV vaccine coverage in Texas counties: An application of multilevel, small area estimation

Health departments, research institutions, policy-makers, and healthcare providers are often interested in knowing the health status of their clients/constituents. Without the resources, financially or administratively, to go out into the community and conduct health assessments directly, these entities frequently rely on data from population-based surveys to supply the information they need. Unfortunately, these surveys are ill-equipped for the job due to sample size and privacy concerns. Small area estimation (SAE) techniques have excellent potential in such circumstances, but have been underutilized in public health due to lack of awareness and confidence in applying its methods. The goal of this research is to make model-based SAE accessible to a broad readership using clear, example-based learning. Specifically, we applied the principles of multilevel, unit-level SAE to describe the geographic distribution of HPV vaccine coverage among females aged 11-26 in Texas.^ Multilevel (3 level: individual, county, public health region) random-intercept logit models of HPV vaccination (receipt of ≥ 1 dose Gardasil® ) were fit to data from the 2008 Behavioral Risk Factor Surveillance System (outcome and level 1 covariates) and a number of secondary sources (group-level covariates). Sampling weights were scaled (level 1) or constructed (levels 2 & 3), and incorporated at every level. Using the regression coefficients (and standard errors) from the final models, I simulated 10,000 datasets for each regression coefficient from the normal distribution and applied them to the logit model to estimate HPV vaccine coverage in each county and respective demographic subgroup. For simplicity, I only provide coverage estimates (and 95% confidence intervals) for counties.^ County-level coverage among females aged 11-17 varied from 6.8-29.0%. For females aged 18-26, coverage varied from 1.9%-23.8%. Aggregated to the state level, these values translate to indirect state estimates of 15.5% and 11.4%, respectively; both of which fall within the confidence intervals for the direct estimates of HPV vaccine coverage in Texas (Females 11-17: 17.7%, 95% CI: 13.6, 21.9; Females 18-26: 12.0%, 95% CI: 6.2, 17.7).^ Small area estimation has great potential for informing policy, program development and evaluation, and the provision of health services. Harnessing the flexibility of multilevel, unit-level SAE to estimate HPV vaccine coverage among females aged 11-26 in Texas counties, I have provided (1) practical guidance on how to conceptualize and conduct modelbased SAE, (2) a robust framework that can be applied to other health outcomes or geographic levels of aggregation, and (3) HPV vaccine coverage data that may inform the development of health education programs, the provision of health services, the planning of additional research studies, and the creation of local health policies.^

A categorical data analysis approach to estimation of demographic characteristics for Texas county populations

The need for timely population data for health planning and Indicators of need has Increased the demand for population estimates. The data required to produce estimates is difficult to obtain and the process is time consuming. Estimation methods that require less effort and fewer data are needed. The structure preserving estimator (SPREE) is a promising technique not previously used to estimate county population characteristics. This study first uses traditional regression estimation techniques to produce estimates of county population totals. Then the structure preserving estimator, using the results produced in the first phase as constraints, is evaluated.^ Regression methods are among the most frequently used demographic methods for estimating populations. These methods use symptomatic indicators to predict population change. This research evaluates three regression methods to determine which will produce the best estimates based on the 1970 to 1980 indicators of population change. Strategies for stratifying data to improve the ability of the methods to predict change were tested. Difference-correlation using PMSA strata produced the equation which fit the data the best. Regression diagnostics were used to evaluate the residuals.^ The second phase of this study is to evaluate use of the structure preserving estimator in making estimates of population characteristics. The SPREE estimation approach uses existing data (the association structure) to establish the relationship between the variable of interest and the associated variable(s) at the county level. Marginals at the state level (the allocation structure) supply the current relationship between the variables. The full allocation structure model uses current estimates of county population totals to limit the magnitude of county estimates. The limited full allocation structure model has no constraints on county size. The 1970 county census age - gender population provides the association structure, the allocation structure is the 1980 state age - gender distribution.^ The full allocation model produces good estimates of the 1980 county age - gender populations. An unanticipated finding of this research is that the limited full allocation model produces estimates of county population totals that are superior to those produced by the regression methods. The full allocation model is used to produce estimates of 1986 county population characteristics. ^

AN ESTIMATION OF IN UTERO DRUG EXPOSURE BY DETERMINANTS OF FREQUENCY AND DISTRIBUTION OF MATERNAL DRUG CONSUMPTION

Data derived from 1,194 gravidas presenting at the observation unit of a city/county hospital between October 11, 1979 through December 7, 1979 were evaluated with respect to the proportion ingesting drugs during pregnancy. The mean age of the mother at the time of the interview was 22.0 years; 43.0 percent were Black; 34.0 percent Latin-American, 21.0 percent White and 2.0 percent other; mean gravida was 2.5 pregnancies; mean parity was 1.0; and mean number of previous abortions was 0.34. Completed interview data was available for 1,119 gravida, corresponding urinalyses for 997 subjects. Ninety and one-tenth percent (90.1 percent) of the subjects reported ingestion of one or more drug preparation(s) (prescription, OTC, or substances used for recreational purposes) during pregnancy with a range of 0 to 11 substances and a mean of 2.7. Dietary supplements (vitamins and minerals) were most frequently reported followed by non-narcotic analgesics. Seventy-six and one tenth percent (76.1 percent) of the population reported consumption of prescription medication, 42.5 percent reported consumption of over-the-counter medications, 45.7 percent reported consumption of a substance for recreational purposes and 4.3 percent reported illicit consumption of a substance. For selected substances, no measurable difference was found between obtaining the information from the interview method or from a urinalysis assay. ^

POPULATION ASSESSMENTS OF INFECTION WITH SCHISTOSOMA MANSONI: EFFECTS OF BIAS CAUSED BY THE RELATIVE SENSITIVITY OF DIAGNOSTIC TECHNIQUES (PUERTO RICO)

This study establishes the extent and relevance of bias of population estimates of prevalence, incidence, and intensity of infection with Schistosoma mansoni caused by the relative sensitivity of stool examination techniques. The population studied was Parcelas de Boqueron in Las Piedras, Puerto Rico, where the Centers for Disease Control, had undertaken a prospective community-based study of infection with S. mansoni in 1972. During each January of the succeeding years stool specimens from this population were processed according to the modified Ritchie concentration (MRC) technique. During January 1979 additional stool specimens were collected from 30 individuals selected on the basis of their mean S. mansoni egg output during previous years. Each specimen was divided into ten 1-gm aliquots and three 42-mg aliquots. The relationship of egg counts obtained with the Kato-Katz (KK) thick smear technique as a function of the mean of ten counts obtained with the MRC technique was established by means of regression analysis. Additionally, the effect of fecal sample size and egg excretion level on technique sensitivity was evaluated during a blind assessment of single stool specimen samples, using both examination methods, from 125 residents with documented S. mansoni infections. The regression equation was: Ln KK = 2.3324 + 0.6319 Ln MRC, and the coefficient of determination (r('2)) was 0.73. The regression equation was then utilized to correct the term "m" for sample size in the expression P ((GREATERTHEQ) 1 egg) = 1 - e('-ms), which estimates the probability P of finding at least one egg as a function of the mean S. mansoni egg output "m" of the population and the effective stool sample size "s" utilized by the coprological technique. This algorithm closely approximated the observed sensitivity of the KK and MRC tests when these were utilized to blindly screen a population of known parasitologic status for infection with S. mansoni. In addition, the algorithm was utilized to adjust the apparent prevalence of infection for the degree of functional sensitivity exhibited by the diagnostic test. This permitted the estimation of true prevalence of infection and, hence, a means for correcting estimates of incidence of infection. ^