Laboratory Enhancement Program HIV Laboratory, Public Health Ontario Updated analyses: January 2009 to December Background
|
|
|
- Judith Sanders
- 5 years ago
- Views:
Transcription
1 Laboratory Enhancement Program HIV Laboratory, Public Health Ontario Updated analyses: January 2009 to December 2012 Background To enhance laboratory-based HIV surveillance data and to better understand trends in the HIV epidemic in Ontario, we initiated the Laboratory Enhancement Program (LEP) in October The objectives were: 1) to obtain complete and valid risk factor information among persons testing positive and negative for HIV in Ontario; 2) to collect accurate and more complete information on HIV testing history to better quantity the number of HIV-infected persons who have been diagnosed and; 3) to perform HIV incidence testing to detect recent infections and, in turn, to estimate HIV incidence. We send the LEP questionnaire to all physicians prescribing an HIV test with a positive result and a 1 in 200 random sample of HIV-negative tests. The completed questionnaire is returned to us by mail and fax. In January 2009, the questionnaire was modified to add questions on race/ethnicity, country of birth and year of arrival Canada if born outside Canada. Trends in race/ethnicity In the tables below, we analyzed HIV-positive diagnoses by exposure category, race/ethnicity and year of diagnoses from January 2009 to December Analyses by health region are forthcoming. In the four-year period examined, among HIV-positive diagnoses in Ontario, the proportion of cases classified as White decreased, from 56.6% in to 48.7% in (p=0.0003). The proportion of Latin-American persons diagnosed with HIV also decreased, from 7.6% in to 4.4% in 2012 (p=0.01). In contrast, the proportion comprised by Black cases significantly increased, from 22.5% in to 29.4% in (p=0.0003); in 2012, 31.1% of diagnoses were among Black persons. The proportion of South-Asian persons increased slightly, from 3.4% in to 5.0% in 2012 but this was not statistically significant (p=0.10). Among male cases, trends similar to those seen overall were observed. The proportion of HIV diagnoses among White and Latin-American males significantly decreased while the proportion of cases among Black and East/Southeast Asian persons significantly increased. Among females, the proportion comprised by Black cases significantly increased, from 54.7% in to 67.1% in (p=0.009) while the proportion of East/Southeast Asian persons decreased from 5.2% in to 0.5% in (p=0.003). No obvious trends were observed in other race/ethnicities. Trends in exposure category and race/ethnicity Among MSM diagnosed with HIV, the proportion Black increased from 8.9% in to 13.7% in (p=0.009) and the proportion East/Southeast Asian increased from 4.9% in to 7.6% in (p=0.053). However, the proportion White decreased, from 68.2% in to 61.3% in (p=0.01). Among IDU and MSM-IDU together, the proportion Black increased from 4.3% in to 12.5% in 2012 (p=0.064). Among male IDUs, the proportion Aboriginal increased from 9.6% in to 35.3% in 2012 (p=0.01). Among female IDUs, the proportion Black increased significantly, from 0.0% in to 21.7% in (p=0.02). 1
2 Among low-risk heterosexual cases, the proportion Latin-American decreased, from 10.0% in to 3.7% in (p=0.09), mainly due to a decrease among men (11.0% in to 2.0% in , p=0.08). 2
3 Table 1a HIV-positive diagnoses by race/ethnicity and exposure category - both sexes Laboratory Enhancement Program, Ontario, 2009 White % % % 0 0.0% % % % % 0 0.0% % % Black % 1 7.1% 1 2.2% % 0 0.0% % 0 0.0% 0 0.0% % 2 7.4% % Aboriginal 1 0.3% % 4 8.9% 1 1.0% 0 0.0% 1 1.7% 0 0.0% 0 0.0% 0 0.0% 1 3.7% % East/Southeast Asian % 0 0.0% 0 0.0% 4 3.9% % 3 5.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % South Asian % 1 7.1% 0 0.0% 0 0.0% 0 0.0% 5 8.3% % 0 0.0% 0 0.0% % % Latin American % 1 7.1% 1 2.2% 2 2.0% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Arab/West Asian 6 2.1% 0 0.0% 0 0.0% 1 1.0% 0 0.0% 1 1.7% 0 0.0% 0 0.0% 0 0.0% 2 7.4% % Other 5 1.7% 0 0.0% 1 2.2% 2 2.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 8 1.4% Unknown Total % % % % % % % % % % % Table 1b HIV-positive diagnoses by race/ethnicity and exposure category - male Ontario, Laboratory Enhancement Program, 2009 White % % % 0 0.0% % % % % 0 0.0% % % Black % 1 7.1% 1 3.2% % 0 0.0% % 0 0.0% 0 0.0% % 1 4.3% % Aboriginal 1 0.3% % 2 6.5% 1 2.9% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 4.3% 7 1.6% East/Southeast Asian % 0 0.0% 0 0.0% 1 2.9% 0 0.0% 1 2.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % South Asian % 1 7.1% 0 0.0% 0 0.0% 0 0.0% % % 0 0.0% 0 0.0% % % Latin American % 1 7.1% 0 0.0% 2 5.7% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Arab/West Asian 6 2.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 2.7% 0 0.0% 0 0.0% 0 0.0% 2 8.7% 9 2.1% Other 5 1.7% 0 0.0% 1 3.2% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 6 1.4% Unknown Total % % % % % % % % % % % 3
4 Table 1c HIV-positive diagnoses by race/ethnicity and exposure category - female Ontario, Laboratory Enhancement Program, 2009 IDU HIV-end HR hetero LR hetero Transfsd CF MTC Unknown Total No. % No. % No. % No. % No. % No. % No. % No. % No. % White % 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% % % Black 0 0.0% % 0 0.0% % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal % 0 0.0% 0 0.0% 1 4.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 3 2.6% East/Southeast Asian 0 0.0% 3 4.5% % 2 8.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 8 7.0% South Asian 0 0.0% 0 0.0% 0 0.0% 1 4.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.9% Latin American 1 7.1% 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 6 5.2% Arab/West Asian 0 0.0% 1 1.5% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.9% Other 0 0.0% 2 3.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 2 1.7% Unknown Total % % % % 0 0.0% 0 0.0% 0 0.0% % % Note: Male and female tables exclude two cases with unknown sex Proportion expressed as percentage of cases with known race/ethnicity 4
5 Table 2a HIV-positive diagnoses by race/ethnicity and exposure category - both sexes Laboratory Enhancement Program, Ontario, 2010 White % % % 1 1.4% % % % % 0 0.0% % % Black % 0 0.0% 2 5.3% % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 3 1.0% 1 7.1% % 0 0.0% % 4 6.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % East/Southeast Asian % 0 0.0% 0 0.0% 0 0.0% 1 4.0% % 0 0.0% 0 0.0% 0 0.0% 1 4.8% % South Asian 8 2.8% 0 0.0% 0 0.0% 2 2.9% 1 4.0% 4 6.7% 0 0.0% 0 0.0% 0 0.0% 1 4.8% % Latin American % 0 0.0% 0 0.0% 1 1.4% % 4 6.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Arab/West Asian 6 2.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 6 1.2% Other 6 2.1% 0 0.0% 1 2.6% 0 0.0% 1 4.0% 1 1.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 9 1.7% Unknown Total % % % % % % % % 0 0.0% % % Table 2b HIV-positive diagnoses by race/ethnicity and exposure category - male Laboratory Enhancement Program, Ontario, 2010 White % % % 0 0.0% % % % % 0 0.0% % % Black % 0 0.0% 2 7.1% % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 3 1.0% 1 7.1% % 0 0.0% 0 0.0% 1 2.8% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 9 2.1% East/Southeast Asian % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % 0 0.0% 0 0.0% 0 0.0% 1 6.3% % South Asian 8 2.8% 0 0.0% 0 0.0% 1 4.2% 1 8.3% % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Latin American % 0 0.0% 0 0.0% 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Arab/West Asian 6 2.1% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 6 1.4% Other 6 2.1% 0 0.0% 1 3.6% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 7 1.7% Unknown Total % % % % % % % % 0 0.0% % % 5
6 Table 2c HIV-positive diagnoses by race/ethnicity and exposure category - female Laboratory Enhancement Program, Ontario, 2010 IDU HIV-end HR hetero LR hetero Transfsd CF MTC Unknown Total No. % No. % No. % No. % No. % No. % No. % No. % No. % White % 1 2.2% % % 0 0.0% 0 0.0% 0 0.0% % % Black 0 0.0% % 0 0.0% % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal % 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 9 9.3% East/Southeast Asian 0 0.0% 0 0.0% 1 7.7% 2 8.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 3 3.1% South Asian 0 0.0% 1 2.2% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % 2 2.1% Latin American 0 0.0% 1 2.2% 1 7.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 2 2.1% Arab/West Asian 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Other 0 0.0% 0 0.0% 1 7.7% 1 4.2% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 2 2.1% Unknown Total % % % % 0 0.0% 0 0.0% 0 0.0% % % Note: Male and female tables exclude one case with unknown sex Proportion expressed as percentage of cases with known race/ethnicity 6
7 Table 3a HIV-positive diagnoses by race/ethnicity and exposure category - both sexes Laboratory Enhancement Program, Ontario, 2011 White % % % 0 0.0% % % % 0 0.0% 0 0.0% % % Black % 1 6.7% 2 5.6% % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 3 1.1% % % 0 0.0% % 2 5.4% % 0 0.0% 0 0.0% 1 4.3% % East/Southeast Asian % 1 6.7% 0 0.0% 0 0.0% 0 0.0% % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % South Asian 7 2.5% 0 0.0% 0 0.0% 1 1.0% % 2 5.4% 0 0.0% 0 0.0% 0 0.0% % % Latin American % 1 6.7% 0 0.0% 4 3.9% 1 6.7% 2 5.4% 0 0.0% 0 0.0% 0 0.0% 2 8.7% % Arab/West Asian 5 1.8% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 4.3% 6 1.2% Other 8 2.8% 0 0.0% 2 5.6% 2 2.0% 0 0.0% 2 5.4% 0 0.0% 0 0.0% 0 0.0% 1 4.3% % Unknown Total % % % % % % % 0 0.0% 0 0.0% % % Table 3b HIV-positive diagnoses by race/ethnicity and exposure category - male Laboratory Enhancement Program, Ontario, 2011 White % % % 0 0.0% % % % 0 0.0% 0 0.0% % % Black % 1 6.7% 0 0.0% % 0 0.0% 2 9.1% 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 3 1.1% % 2 8.3% 0 0.0% % 1 4.5% % 0 0.0% 0 0.0% 0 0.0% % East/Southeast Asian % 1 6.7% 0 0.0% 0 0.0% 0 0.0% % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % South Asian 7 2.5% 0 0.0% 0 0.0% 1 2.4% % 1 4.5% 0 0.0% 0 0.0% 0 0.0% % % Latin American % 1 6.7% 0 0.0% 2 4.8% 0 0.0% 1 4.5% 0 0.0% 0 0.0% 0 0.0% % % Arab/West Asian 5 1.8% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 5.6% 6 1.5% Other 8 2.8% 0 0.0% 2 8.3% 0 0.0% 0 0.0% 2 9.1% 0 0.0% 0 0.0% 0 0.0% 1 5.6% % Unknown Total % % % % % % % 0 0.0% 0 0.0% % % 7
8 Table 3c HIV-positive diagnoses by race/ethnicity and exposure category - female Laboratory Enhancement Program, Ontario, 2011 IDU HIV-end HR hetero LR hetero Transfsd CF MTC Unknown Total No. % No. % No. % No. % No. % No. % No. % No. % No. % White % 0 0.0% % % % 0 0.0% 0 0.0% % % Black % % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal % 0 0.0% 1 9.1% 1 6.7% 0 0.0% 0 0.0% 0 0.0% % 5 4.8% East/Southeast Asian 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% South Asian 0 0.0% 0 0.0% 0 0.0% 1 6.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 1.0% Latin American 0 0.0% 2 3.3% 1 9.1% 1 6.7% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 4 3.8% Arab/West Asian 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Other 0 0.0% 2 3.3% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 2 1.9% Unknown Total % % % % % 0 0.0% 0 0.0% % % Note: Proportion expressed as percentage of cases with known race/ethnicity 8
9 Table 4a HIV-positive diagnoses by race/ethnicity and exposure category - both sexes Laboratory Enhancement Program, Ontario, 2012 White % % % 1 1.0% % % 0 0.0% 0 0.0% 0 0.0% % % Black % % % % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 1 0.3% % % 0 0.0% 0 0.0% 3 6.7% 0 0.0% 0 0.0% 0 0.0% 1 3.7% % East/Southeast Asian % 0 0.0% 0 0.0% 1 1.0% 0 0.0% 2 4.4% 0 0.0% 0 0.0% 0 0.0% 2 7.4% % South Asian % 0 0.0% 0 0.0% 0 0.0% % 4 8.9% 0 0.0% 0 0.0% % % % Latin American % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 2.2% 0 0.0% 0 0.0% 0 0.0% 1 3.7% % Arab/West Asian 3 1.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 4 8.9% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 7 1.3% Other 8 2.6% 0 0.0% 0 0.0% 1 1.0% 0 0.0% 2 4.4% % 0 0.0% 0 0.0% 0 0.0% % Unknown Total % % % % % % % 0 0.0% % % % Table 4b HIV-positive diagnoses by race/ethnicity and exposure category - male Laboratory Enhancement Program, Ontario, 2012 White % % % 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% % % Black % % 0 0.0% % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 1 0.3% % % 0 0.0% 0 0.0% 1 3.6% 0 0.0% 0 0.0% 0 0.0% 1 4.0% % East/Southeast Asian % 0 0.0% 0 0.0% 1 3.3% 0 0.0% 1 3.6% 0 0.0% 0 0.0% 0 0.0% 2 8.0% % South Asian % 0 0.0% 0 0.0% 0 0.0% % % 0 0.0% 0 0.0% 0 0.0% % % Latin American % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 4.0% % Arab/West Asian 3 1.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 7 1.6% Other 8 2.6% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 3.6% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 9 2.1% Unknown Total % % % % % % 0 0.0% 0 0.0% 0 0.0% % % 9
10 Table 4c HIV-positive diagnoses by race/ethnicity and exposure category - female Laboratory Enhancement Program, Ontario, 2012 IDU HIV-end HR hetero LR hetero Transfsd CF MTC Unknown Total No. % No. % No. % No. % No. % No. % No. % No. % No. % White % 1 1.4% % % 0 0.0% 0 0.0% 0 0.0% 0 0.0% % Black % % % % 0 0.0% 0 0.0% 0 0.0% % % Aboriginal 0 0.0% 0 0.0% 0 0.0% % 0 0.0% 0 0.0% 0 0.0% 0 0.0% 2 1.8% East/Southeast Asian 0 0.0% 0 0.0% 0 0.0% 1 5.9% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.9% South Asian 0 0.0% 0 0.0% % 1 5.9% 0 0.0% 0 0.0% % 0 0.0% 3 2.7% Latin American 0 0.0% 0 0.0% 0 0.0% 1 5.9% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 1 0.9% Arab/West Asian 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% 0 0.0% Other 0 0.0% 1 1.4% 0 0.0% 1 5.9% % 0 0.0% 0 0.0% 0 0.0% 3 2.7% Unknown Total % % % % % 0 0.0% % % % Note: Male and female tables exclude one case with unknown sex Proportion expressed as percentage of cases with known race/ethnicity 10
DEMOGRAPHICS OF HIV INFECTIONS AMONG INDIVIDUALS RESIDING IN ALABAMA AT DIAGNOSIS
DEMOGRAPHICS OF HIV INFECTIONS AMONG INDIVIDUALS RESIDING IN ALABAMA AT DIAGNOSIS CHARACTERISTIC Prevalent Cases Cumulative Cases Race/Ethnicity Cases % of Total Cases % of Total Cases % of Total Cases
2011/04 LEUKAEMIA IN WALES Welsh Cancer Intelligence and Surveillance Unit
2011/04 LEUKAEMIA IN WALES 1994-2008 Welsh Cancer Intelligence and Surveillance Unit Table of Contents 1 Definitions and Statistical Methods... 2 2 Results 7 2.1 Leukaemia....... 7 2.2 Acute Lymphoblastic
PROC LOGISTIC: Traps for the unwary Peter L. Flom, Independent statistical consultant, New York, NY
Paper SD174 PROC LOGISTIC: Traps for the unwary Peter L. Flom, Independent statistical consultant, New York, NY ABSTRACT Keywords: Logistic. INTRODUCTION This paper covers some gotchas in SAS R PROC LOGISTIC.
Available online at J. Math. Comput. Sci. 2 (2012), No. 6, ISSN:
Available online at http://scik.org J. Math. Comput. Sci. 2 (2012), No. 6, 1671-1684 ISSN: 1927-5307 A MATHEMATICAL MODEL FOR THE TRANSMISSION DYNAMICS OF HIV/AIDS IN A TWO-SEX POPULATION CONSIDERING COUNSELING
Jun Tu. Department of Geography and Anthropology Kennesaw State University
Examining Spatially Varying Relationships between Preterm Births and Ambient Air Pollution in Georgia using Geographically Weighted Logistic Regression Jun Tu Department of Geography and Anthropology Kennesaw
Section 4. Test-Level Analyses
Section 4. Test-Level Analyses Test-level analyses include demographic distributions, reliability analyses, summary statistics, and decision consistency and accuracy. Demographic Distributions All eligible
Biology 8 Learning Outcomes
Biology 8 Learning Outcomes CELLS (Bio 8-1) I can connect the names, diagrams, and functions of organelles in a cell I know the major differences between plant and animal cells I can explain cell theory
Abstract. Keywords. 1. Introduction. Sarisak Soontornchai
The Educational Review, USA, 28, 2(4), 245-252 http://www.hillpublisher.com/journals/er Opinions of the University Students towards Two Learning Object Modules (LOM) on Globally Harmonized System of Classification
European harmonisation of product notification- Status 2014
European harmonisation of product notification- Status 2014 Ronald de Groot Dutch National Poisons Information Center University Medical Center Utrecht The Netherlands Poisons Centers Informing the public
GIS and Health Geography. What is epidemiology?
GIS and Health Geography { What is epidemiology? TOC GIS and health geography Major applications for GIS Epidemiology What is health (and how location matters) What is a disease (and how to identify one)
Supplemental Table 48. Studies reporting the prevalence of rheumatoid arthritis in multiple sclerosis
Supplemental Table 48. Studies reporting the prevalence of rheumatoid in multiple sclerosis Study Region/ Source/ Diagnostic Diagnostic Crude Overall Age- Crude Standardized Quality (Year) Sub-Region Population
CHAPTER 2: KEY ISSUE 1 Where Is the World s Population Distributed? p
CHAPTER 2: KEY ISSUE 1 Where Is the World s Population Distributed? p. 45-49 Always keep your vocabulary packet out whenever you take notes. As the term comes up in the text, add to your examples for the
Dynamic pair formation models
Application to sexual networks and STI 14 September 2011 Partnership duration Models for sexually transmitted infections Which frameworks? HIV/AIDS: SI framework chlamydia and gonorrhoea : SIS framework
Person-Time Data. Incidence. Cumulative Incidence: Example. Cumulative Incidence. Person-Time Data. Person-Time Data
Person-Time Data CF Jeff Lin, MD., PhD. Incidence 1. Cumulative incidence (incidence proportion) 2. Incidence density (incidence rate) December 14, 2005 c Jeff Lin, MD., PhD. c Jeff Lin, MD., PhD. Person-Time
A Mathematical Analysis on the Transmission Dynamics of Neisseria gonorrhoeae. Yk j N k j
North Carolina Journal of Mathematics and Statistics Volume 3, Pages 7 20 (Accepted June 23, 2017, published June 30, 2017 ISSN 2380-7539 A Mathematical Analysis on the Transmission Dynamics of Neisseria
Geozones: an area-based method for analysis of health outcomes
Statistics Canada www.statcan.gc.ca Geozones: an area-based method for analysis of health outcomes CIQSS Montreal, Quebec October 19, 2012 Paul A. Peters, PhD Health Analysis Division, Statistics Canada
Modular Program Report
Modular Program Report The following report(s) provides findings from an FDA initiated query using its Mini Sentinel pilot. While Mini Sentinel queries may be undertaken to assess potential medical product
Table 1a. Vector Index for current week Mean abundance of females per trap night 1
D. Reporting templates Based on the need to now report data not only for FC citywide but also separately for each of the four zones (NW, NE, SE, SW; broken down using College and Drake) the city has been
Key elements An open-ended questionnaire can be used (see Quinn 2001).
Tool Name: Risk Indexing What is it? Risk indexing is a systematic approach to identify, classify, and order sources of risk and to examine differences in risk perception. What can it be used assessing
Lecture 6: Linear Regression (continued)
Lecture 6: Linear Regression (continued) Reading: Sections 3.1-3.3 STATS 202: Data mining and analysis October 6, 2017 1 / 23 Multiple linear regression Y = β 0 + β 1 X 1 + + β p X p + ε Y ε N (0, σ) i.i.d.
Pair Formation and Disease Dynamics: Modeling HIV and HCV Among Injection Drug Users in Victoria, BC. Jennifer Frances Lindquist
Pair Formation and Disease Dynamics: Modeling HIV and HCV Among Injection Drug Users in Victoria, BC by Jennifer Frances Lindquist B.Sc., University of Victoria, 2007 B.Sc., University of Victoria, 2006
Announcements. Topics: To Do:
Announcements Topics: In the Probability and Statistics module: - Sections 1 + 2: Introduction to Stochastic Models - Section 3: Basics of Probability Theory - Section 4: Conditional Probability; Law of
Date: Wed 12 July 2017 Reference Number: FOI Category: Stats - Crime
Date: Wed 12 July 2017 Reference Number: 20171145 FOI Category: Stats - Crime Title: Hate Crimes Request Date: Friday, 23 June, 2017 Response Date: Wednesday, 12 July, 2017 Request Details: Q1: The total
1 and 2 proportions starts here
1 and 2 proportions starts here Example: Would You Pay Higher Prices to Protect the Environment? In 2000, the GSS asked: Are you willing to pay much higher prices in order to protect the environment? Of
Stochastic Modelling of Specific modes of HIV/AIDS Epidemic. Olorunsola E. Olowofeso
Stochastic Modelling of Specific modes of HIV/AIDS Epidemic Olorunsola E. Olowofeso Mathematical Sciences Department, Federal University of Technology, P.M.B. 704, Akure, Ondo State, Nigeria. Abstract
Yuxin Zhang. September 21st, 2018
*Department of Information, Risk, and Operational Management McCombs School of Business The University of Texas at Austin September 21st, 2018 1 / 21 We propose a novel approach to model mortality of dependent
By Geri Flanary To accompany AP Human Geography: A Study Guide 3 rd edition By Ethel Wood
Session 1 By Geri Flanary To accompany AP Human Geography: A Study Guide 3 rd edition By Ethel Wood WHAT IS DEMOGRAPHY? It is the scientific or statistical study of population. It comes from the Greek
EXPONENTIAL SMOOTHING MODELING AND FORECASTING FOR INCIDENCE OF TUBERCULOSIS IN INDIA
Global and Stochastic analysis Special Issue: 25th International Conference of Forum for Interdisciplinary Mathematics EXPONENTIAL SMOOTHING MODELING AND FORECASTING FOR INCIDENCE OF TUBERCULOSIS IN INDIA
Extreme Temperature Protocol in Middlesex-London
Clean Air Partnership Webinar March 17, 2009 Extreme Temperature Protocol in Middlesex-London Presented by Iqbal Kalsi, MBA, CPHI(C), CCEP iqbal.kalsi@mlhu.on.ca 1 History Informal monitoring, tracking
The World Bank Ghana - Maternal, Child Health and Nutrition Project (P145792)
Public Disclosure Authorized AFRICA Ghana Health, Nutrition & Population Global Practice IBRD/IDA Investment Project Financing FY 2014 Seq No: 5 ARCHIVED on 21-Aug-2016 ISR24840 Implementing Agencies:
COMMISSION ON ACCREDITATION 2014 ANNUAL REPORT ONLINE
COMMISSION ON ACCREDITATION 2014 ANNUAL REPORT ONLINE SUMMARY DATA: POSTDOCTORAL PROGRAMS ^Clicking a table title will automatically direct you to that table in this document *Programs that combine two
NSHE DIVERSITY REPORT
University of Nevada, Las Vegas University of Nevada, Reno NSHE DIVERSITY REPORT 2006-07 Nevada State College at Henderson College of Southern Nevada December 2007 Prepared by the Office of Academic and
UNIVERSITY OF OTAGO EXAMINATIONS 2008
UNIVERSITY OF OTAGO EXAMINATIONS 2008 STATISTICS Paper STAT 242/342 Multivariate Methods (TIME ALLOWED: THREE HOURS) This examination paper comprises 25 pages. Candidates should answer questions as follows:
Mediation for the 21st Century
Mediation for the 21st Century Ross Boylan ross@biostat.ucsf.edu Center for Aids Prevention Studies and Division of Biostatistics University of California, San Francisco Mediation for the 21st Century
Using GIS to Brief New York City Public Officials after September 11
Using GIS to Brief New York City Public Officials after September 11 Presented by Zvia Segal Naphtali, Ph.D. and Leonard M. Naphtali, Ph.D. Presented at the ESRI International Health GIS Conference, May
Chapter 2: Describing Contingency Tables - I
: Describing Contingency Tables - I Dipankar Bandyopadhyay Department of Biostatistics, Virginia Commonwealth University BIOS 625: Categorical Data & GLM [Acknowledgements to Tim Hanson and Haitao Chu]
1996 Report on Occupational Radiation Exposures in Canada
Health Canada Santé Canada 1996 Report on Occupational Radiation Exposures in Canada 1996 Report on Occupational Radiation Exposures in Canada Environmental Health Directorate Health Protection Branch
Biopower and Sexuality
Module Outline for Course Participants Biopower and Sexuality Created by: Dr Mark Davis, Monash University Melbourne, Australia Introduction to Advancing Sexuality Studies: A short course on sexuality
Notes for Math 324, Part 12
72 Notes for Math 324, Part 12 Chapter 12 Definition and main properties of probability 12.1 Sample space, events We run an experiment which can have several outcomes. The set consisting by all possible
AP Human Geography Chapter 2: Population
Population Concentrations AP Human Geography Key Issue 1 Where is the World s Population Distributed? Pgs. 44 53 1. The world s population is highly clustered, or concentrated in certain regions. FOUR
STAC51: Categorical data Analysis
STAC51: Categorical data Analysis Mahinda Samarakoon January 26, 2016 Mahinda Samarakoon STAC51: Categorical data Analysis 1 / 32 Table of contents Contingency Tables 1 Contingency Tables Mahinda Samarakoon
MAT 2379, Introduction to Biostatistics, Sample Calculator Questions 1. MAT 2379, Introduction to Biostatistics
MAT 2379, Introduction to Biostatistics, Sample Calculator Questions 1 MAT 2379, Introduction to Biostatistics Sample Calculator Problems for the Final Exam Note: The exam will also contain some problems
Manitoba Weekly West Nile virus Surveillance Report
Manitoba Weekly West Nile virus Surveillance Report Week 26 (June 25 to July 1, 2017) Communicable Disease Control Active Living, Population and Public Health Branch Active Living, Indigenous Relations,
Solutions to First Midterm Exam, Stat 371, Spring those values: = Or, we can use Rule 6: = 0.63.
Solutions to First Midterm Exam, Stat 371, Spring 2010 There are two, three or four versions of each question. The questions on your exam comprise a mix of versions. As a result, when you examine the solutions
TECHNICAL DOCUMENT. Guidelines for presentation of surveillance data. Tables, graphs, maps.
TECHNICAL DOCUMENT Guidelines for presentation of surveillance data Tables, graphs, maps www.ecdc.europa.eu ECDC TECHNICAL DOCUMENT Guidelines for presentation of surveillance data Tables, graphs and maps
Impact of Heterosexuality and Homosexuality on the transmission and dynamics of HIV/AIDS
IOSR Journal of Mathematics (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X. Volume 12, Issue 6 Ver. V (Nov. - Dec.216), PP 38-49 www.iosrjournals.org Impact of Heterosexuality and Homosexuality on the
Sampling approaches in animal health studies. Al Ain November 2017
Sampling approaches in animal health studies Al Ain 13-14 November 2017 Definitions Populations and Samples From a statistical point of view: Population is a set of measurements, that may be finite or
QUESTIONNAIRE: REACH INTRODUCTION
QUESTIONNAIRE: REACH INTRODUCTION Note: For some of the questions there is more than one correct answer. 1. What does the abbreviation REACH mean? Please complete the letters.! R! E! A! CH 2. Who is responsible
emerge Network: CERC Survey Survey Sampling Data Preparation
emerge Network: CERC Survey Survey Sampling Data Preparation Overview The entire patient population does not use inpatient and outpatient clinic services at the same rate, nor are racial and ethnic subpopulations
emerge Network: CERC Survey Survey Sampling Data Preparation
emerge Network: CERC Survey Survey Sampling Data Preparation Overview The entire patient population does not use inpatient and outpatient clinic services at the same rate, nor are racial and ethnic subpopulations
What is Statistics? Statistics is the science of understanding data and of making decisions in the face of variability and uncertainty.
What is Statistics? Statistics is the science of understanding data and of making decisions in the face of variability and uncertainty. Statistics is a field of study concerned with the data collection,
NATIONAL PRISON ENTRANTS BLOODBORNE VIRUS AND RISK BEHAVIOUR SURVEY REPORT
NATIONAL PRISON ENTRANTS BLOODBORNE VIRUS AND RISK BEHAVIOUR SURVEY REPORT 2004,,, AND Prevalence of HIV, hepatitis C, hepatitis B, sexually transmissible infections, and risk behaviours among Australian
Statistics for Engineers Lecture 1 Introduction to Probability Theory
Statistics for Engineers Lecture 1 Introduction to Probability Theory Chong Ma Department of Statistics University of South Carolina chongm@email.sc.edu January 9, 2017 Chong Ma (Statistics, USC) STAT
TECHNICAL APPENDIX WITH ADDITIONAL INFORMATION ON METHODS AND APPENDIX EXHIBITS. Ten health risks in this and the previous study were
Goetzel RZ, Pei X, Tabrizi MJ, Henke RM, Kowlessar N, Nelson CF, Metz RD. Ten modifiable health risk factors are linked to more than one-fifth of employer-employee health care spending. Health Aff (Millwood).
Chapter 8. The analysis of count data. This is page 236 Printer: Opaque this
Chapter 8 The analysis of count data This is page 236 Printer: Opaque this For the most part, this book concerns itself with measurement data and the corresponding analyses based on normal distributions.
Relationship between weather factors and survival of mule deer fawns in the Peace Region of British Columbia
P E A C E R E G I O N T E C H N I C A L R E P O R T Relationship between weather factors and survival of mule deer fawns in the Peace Region of British Columbia by: Nick Baccante and Robert B. Woods Fish
SPACE-TIME ACCESSIBILITY MEASURES FOR EVALUATING MOBILITY-RELATED SOCIAL EXCLUSION OF THE ELDERLY
SPACE-TIME ACCESSIBILITY MEASURES FOR EVALUATING MOBILITY-RELATED SOCIAL EXCLUSION OF THE ELDERLY Izumiyama, Hiroshi Institute of Environmental Studies, The University of Tokyo, Tokyo, Japan Email: izumiyama@ut.t.u-tokyo.ac.jp
University of Lusaka
University of Lusaka BSPH 315 Health Mapping & GIS Topic: Background of GIS Content: 1. Aim of the course 2. What do you know about GIS? 3. Difference between geographic data and geographic information
8/4/2009. Describing Data with Graphs
Describing Data with Graphs 1 A variable is a characteristic that changes or varies over time and/or for different individuals or objects under consideration. Examples: Hair color, white blood cell count,
UK Data Archive Study Number British Social Attitudes, British Social Attitudes 2008 User Guide. 2 Data collection methods...
UK Data Archive Study Number 6390 - British Social Attitudes, 2008 o NatCen National Centre for Social Research British Social Attitudes 2008 User Guide Author: Roger Stafford 1 Overview of the survey...
Applications of GIS in Health Research. West Nile virus
Applications of GIS in Health Research West Nile virus Outline Part 1. Applications of GIS in Health research or spatial epidemiology Disease Mapping Cluster Detection Spatial Exposure Assessment Assessment
DISCRETE PROBABILITY DISTRIBUTIONS
DISCRETE PROBABILITY DISTRIBUTIONS REVIEW OF KEY CONCEPTS SECTION 41 Random Variable A random variable X is a numerically valued quantity that takes on specific values with different probabilities The
Enrollment of Students with Disabilities
Enrollment of Students with Disabilities State legislation, which requires the Board of Higher Education to monitor the participation of specific groups of individuals in public colleges and universities,
Development and Validation of. Statistical and Deterministic Models. Used to Predict Dengue Fever in. Mexico
Development and Validation of Statistical and Deterministic Models Used to Predict Dengue Fever in Mexico A thesis presented by Aditi Hota to the Applied Mathematics Department in partial fulfillment of
The Bowel Screening Pilot: Results of the First 36 Months
The Bowel Screening Pilot: Results of the First 36 Months Deborah Read Mathangi Shanthakumar Barry Borman July 2016 DISCLAIMER The data analysed in this report were supplied to the Environmental Health
Lecture 6: Linear Regression
Lecture 6: Linear Regression Reading: Sections 3.1-3 STATS 202: Data mining and analysis Jonathan Taylor, 10/5 Slide credits: Sergio Bacallado 1 / 30 Simple linear regression Model: y i = β 0 + β 1 x i
On the modelling of immigration and emigration using LIAM2
Federaal Planbureau Economische analyses en vooruitzichten On the modelling of immigration and emigration using LIAM2 November 2015 Gijs Dekkers 1, gd@plan.be Project LIAM2 Abstract this note describes
GEOGRAPHY Practice Answer Paper - 2
GEOGRAPHY Practice Answer Paper - 2 Q.1) Select the correct option and rewrite the sentence : 4 1) According to Census 2011, the average population density of India is 382 persons per sq.km. 2) The rate
Analysis of Longitudinal Data. Patrick J. Heagerty PhD Department of Biostatistics University of Washington
Analysis of Longitudinal Data Patrick J Heagerty PhD Department of Biostatistics University of Washington Auckland 8 Session One Outline Examples of longitudinal data Scientific motivation Opportunities
Poole Hospital NHS Foundation Trust. Equality and Diversity Workforce Monitoring Report
Poole Hospital NHS Foundation Trust Equality and Diversity Workforce Monitoring Report 1. INTRODUCTION 1.1 This report provides workforce data relating to the diversity of Trust staff for the period 1
Discrete distribution. Fitting probability models to frequency data. Hypotheses for! 2 test. ! 2 Goodness-of-fit test
Discrete distribution Fitting probability models to frequency data A probability distribution describing a discrete numerical random variable For example,! Number of heads from 10 flips of a coin! Number
MSP Research Note. RDQ Reliability, Validity and Norms
MSP Research Note RDQ Reliability, Validity and Norms Introduction This research note describes the technical properties of the RDQ. Evidence for the reliability and validity of the RDQ is presented against
ECON Interactions and Dummies
ECON 351 - Interactions and Dummies Maggie Jones 1 / 25 Readings Chapter 6: Section on Models with Interaction Terms Chapter 7: Full Chapter 2 / 25 Interaction Terms with Continuous Variables In some regressions
The National Collegiate Athletic Association
2018-2019 NCAA Contacts President Carolyn Stefanco Phone Number: 518-454-5120 Fax Number: stefancc@strose.edu Director of Athletics Catherine C. Haker Phone Number: 518-454-5282 Fax Number: 518-458-5457
BARNUM EFFECT INFULENCE OF SOCIAL DESIRABILITY, BASE RATE AND PERSONALIZATION. Era Jain Y9209
BARNUM EFFECT INFULENCE OF SOCIAL DESIRABILITY, BASE RATE AND PERSONALIZATION Era Jain Y9209 Is this your perfect Astrology Profile? You have a great need for other people to like and admire you. Disciplined
The Globally Harmonized System of Classification and Labelling of Chemicals (GHS) Purpose, scope and application
The Globally Harmonized System of Classification and Labelling of Chemicals (GHS) Purpose, scope and application Purpose of the GHS (1) (a) (b) (c) (d) To enhance the protection of human health and the
Summary and Statistical Report of the 2007 Population and Housing Census Results 1
Summary and Statistical Report of the 2007 Population and Housing Census Results 1 2 Summary and Statistical Report of the 2007 Population and Housing Census Results This document was printed by United
Unit 2 Introduction to Probability
PubHlth 540 Fall 2013 2. Introduction to Probability Page 1 of 55 Unit 2 Introduction to Probability Chance favours only those who know how to court her - Charles Nicolle A weather report statement such
Effect Modification and Interaction
By Sander Greenland Keywords: antagonism, causal coaction, effect-measure modification, effect modification, heterogeneity of effect, interaction, synergism Abstract: This article discusses definitions
KISS Hospital Infection Surveillance System (Krankenhaus-Infektions-Surveillance-System)
(Krankenhaus-Infektions-Surveillance-System) NEO-KISS component Reference Data Prepared: 10 March 2010 National Reference Center for Nosocomial Infection Surveillance Hindenburgdamm 27 12203 Berlin Tel.
Republic of Mozambique NATIONAL INSTITUTE OF METEOROLOGY
Republic of Mozambique NATIONAL INSTITUTE OF METEOROLOGY Climate Information for Improvement of Water Related Disaster Risk Management Experience of Mozambique Mr. Anacleto Duvane National Institute of
PUBLIC EMPLOYEE HAZARDOUS CHEMICAL PROTECTION AND RIGHT TO KNOW ACT O.C.G.A
PUBLIC EMPLOYEE HAZARDOUS CHEMICAL PROTECTION AND RIGHT TO KNOW ACT O.C.G.A. 45-22-2 Georgia s Right to Know Law Federal regulations require that all employees be trained on the Hazard Communications Standard
INTRODUCTION. In March 1998, the tender for project CT.98.EP.04 was awarded to the Department of Medicines Management, Keele University, UK.
INTRODUCTION In many areas of Europe patterns of drug use are changing. The mechanisms of diffusion are diverse: introduction of new practices by new users, tourism and migration, cross-border contact,
CSAE WPS/2008-06 1998-2001 panel Mitch panel Mitch panel Mitch panel 1998-2001 panel 1998 1999 2001 Poverty 68.7% 74.1% 73.1% 70.6% 63.6% Extreme Poverty 28.6% 32.0% 34.5% 25.9% 25.4%
Spatio-Temporal Threshold Models for Relating UV Exposures and Skin Cancer in the Central United States
Spatio-Temporal Threshold Models for Relating UV Exposures and Skin Cancer in the Central United States Laura A. Hatfield and Bradley P. Carlin Division of Biostatistics School of Public Health University
Group Discriminatory Power of Handwritten Characters
Group Discriminatory Power of Handwritten Characters Catalin I. Tomai, Devika M. Kshirsagar, Sargur N. Srihari CEDAR, Computer Science and Engineering Department State University of New York at Buffalo,
Chapter 9 - Correlation and Regression
Chapter 9 - Correlation and Regression 9. Scatter diagram of percentage of LBW infants (Y) and high-risk fertility rate (X ) in Vermont Health Planning Districts. 9.3 Correlation between percentage of
Data Analysis and Statistical Methods Statistics 651
Data Analysis and Statistical Methods Statistics 651 http://www.stat.tamu.edu/~suhasini/teaching.html Lecture 26 (MWF) Tests and CI based on two proportions Suhasini Subba Rao Comparing proportions in
SCIE1000 Theory and Practice in Science Final Examination, Semester One 2011
1. Researchers propose two models for estimating the weight of accumulated flammable material in a forest, where weight is measured in appropriate units and t is the number of years since the most recent
Research Methods in Political Science I
Research Methods in Political Science I 6. Linear Regression (1) Yuki Yanai School of Law and Graduate School of Law November 11, 2015 1 / 25 Today s Menu 1 Introduction What Is Linear Regression? Some
Supplemental materials for:
Supplemental materials for: Naimer MS, Kwong JC, Bhatia D, et al. The effect of changes in cervical cancer screening guidelines on chlamydia testing. Ann Fam Med. 2017;15(4):329-334. Author Contributions:
Letter STUDENT NUMBER GEOGRAPHY. Written examination. Thursday 16 November 2017
Victorian Certificate of Education 2017 SUPERVISOR TO ATTACH PROCESSING LABEL HERE Letter STUDENT NUMBER GEOGRAPHY Written examination Thursday 16 November 2017 Reading time: 3.00 pm to 3.15 pm (15 minutes)
COMMISSION ON ACCREDITATION 2012 ANNUAL REPORT ONLINE
COMMISSION ON ACCREDITATION 2012 ANNUAL REPORT ONLINE SUMMARY DATA: POSTDOCTORAL PROGRAMS ^Clicking a table title will automatically direct you to that table in this document *Programs that combine two
Vaccine-Preventable Diseases and Immunization Coverage in California in California Vaccine-Preventable
www.getimmunizedca.org www.cdph.ca.gov Vaccine-Preventable Diseases Vaccine-Preventable Diseases and Immunization Coverage in California 2002-07 in California 2000-2005 Vaccine-Preventable Diseases Branch
Roman numeral date generator
Roman numeral date generator The Borg System is 100 % Roman numeral date generator Convert a number into Roman Numerals. I, II, III, IV, V, VI, VII, VIII, IX, X. Number to convert (max. of 10,000). Convert
Scottish Atlas of Variation
Scottish Atlas of Variation User Guide March 2019 Contents Introduction... 3 Accessing the Atlas of Variation... 3 Dashboard... 4 Introduction... 4 Drop-down menus... 5 Explore icon... 5 Information icons...
Integrated Postsecondary Education Data System
University of Nebraska Omaha DigitalCommons@UNO IPEDS Completions Integrated Postsecondary Education Data System 202 Completions 202-3 UNO Office of Institutional Effectiveness University of Nebraska at
ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES. Cox s regression analysis Time dependent explanatory variables
ADVANCED STATISTICAL ANALYSIS OF EPIDEMIOLOGICAL STUDIES Cox s regression analysis Time dependent explanatory variables Henrik Ravn Bandim Health Project, Statens Serum Institut 4 November 2011 1 / 53
Marriage Institutions and Sibling Competition: Online Theory Appendix
Marriage Institutions and Sibling Competition: Online Theory Appendix The One-Daughter Problem Let V 1 (a) be the expected value of the daughter at age a. Let υ1 A (q, a) be the expected value after a
REVISION: POPULATION ECOLOGY 01 OCTOBER 2014
REVISION: POPULATION ECOLOGY 01 OCTOBER 2014 Lesson Description In this lesson we revise: Introduction to Population Ecology What s Happening in the Environment Human Population: Analysis & Predictions