As antiretroviral treatment (ART) coverage for people living with HIV (PLHIV) increases, HIV programmes require up-to-date information about evolving HIV risk behaviour and transmission risk, including those with low-level viremia (LLV; >50 to ≤1000 copies/mL), to guide prevention priorities. We aimed to assess differences in sexual risk behaviours, distribution of viral load (VL) and proportion of transmission across PLHIV subgroups. We analysed data from Population-based HIV Impact Assessment surveys in 14 sub-Saharan African countries during 2015-2019. We estimated adjusted prevalence ratios (aPR) of self-reported HIV high-risk behaviour (multiple partners and condomless sex) across cascade stages via generalised estimation equations. We modelled the proportions of transmission from each subgroup using relative self-reported sexual risk, a Hill function for transmission rate by VL, and proportions within cascade stages from surveys and UNAIDS country estimates for 2010-2020. Compared to PLHIV with undetectable VL (≤50 copies/mL), undiagnosed PLHIV (aPR women: 1.28 [95% CI: 1.08-1.52]; men: 1.61 [1.33-1.95]) and men diagnosed but untreated (2.06 [1.52-2.78]) were more likely to self-report high-risk sex. High-risk behaviour was not significantly associated with LLV. Mean VL was similar among undiagnosed, diagnosed but untreated, and on ART but non-suppressed sub-groups. Across surveys, undiagnosed and diagnosed but untreated contributed most to transmission (40-91% and 1-41%, respectively), with less than 1% from those with LLV. Between 2010 and 2020, the proportion of transmission from individuals on ART but non-suppressed increased. In settings with high ART coverage, effective HIV testing, ART linkage, and retention remain priorities to reduce HIV transmission. Persons with LLV are an increasing share of PLHIV but their contribution to HIV transmission was small. Improving suppression among PLHIV on ART with VL ≥1000 copies/mL will become increasingly important.

OBJECTIVE: To assess the national uptake of the Centers for Disease Control and Prevention's (CDC) core elements of antibiotic stewardship in nursing homes from 2016 to 2018 and the effect of infection prevention and control (IPC) hours on the implementation of the core elements. DESIGN: Retrospective, repeated cross-sectional analysis. SETTING: US nursing homes. METHODS: We used the National Healthcare Safety Network (NHSN) Long-Term Care Facility Component annual surveys from 2016 to 2018 to assess nursing home characteristics and percent implementation of the core elements. We used log-binomial regression models to estimate the association between weekly IPC hours and the implementation of all 7 core elements while controlling for confounding by facility characteristics. RESULTS: We included 7,506 surveys from 2016 to 2018. In 2018, 71% of nursing homes reported implementation of all 7 core elements, a 28% increase from 2016. The greatest increases in implementation from 2016 to 2018 were in education (19%), reporting (18%), and drug expertise (15%). In 2018, 71% of nursing homes reported pharmacist involvement in improving antibiotic use, an increase of 27% since 2016. Nursing homes that reported at least 20 hours of IPC activity per week were 14% (95% confidence interval, 7%-20%) more likely to implement all 7 core elements when controlling for facility ownership and affiliation. CONCLUSIONS: Nursing homes reported substantial progress in antibiotic stewardship implementation from 2016 to 2018. Improvements in access to drug expertise, education, and reporting antibiotic use may reflect increased stewardship awareness and resource use among nursing home providers under new regulatory requirements. Nursing home stewardship programs may benefit from increased IPC staff hours.

Residents and staff members of long-term care facilities (LTCFs), because they live and work in congregate settings, are at increased risk for infection with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1,2). In particular, skilled nursing facilities (SNFs), LTCFs that provide skilled nursing care and rehabilitation services for persons with complex medical needs, have been documented settings of COVID-19 outbreaks (3). In addition, residents of LTCFs might be at increased risk for severe outcomes because of their advanced age or the presence of underlying chronic medical conditions (4). As a result, the Advisory Committee on Immunization Practices has recommended that residents and staff members of LTCFs be offered vaccination in the initial COVID-19 vaccine allocation phase (Phase 1a) in the United States (5). In December 2020, CDC launched the Pharmacy Partnership for Long-Term Care Program* to facilitate on-site vaccination of residents and staff members at enrolled LTCFs. To evaluate early receipt of vaccine during the first month of the program, the number of eligible residents and staff members in enrolled SNFs was estimated using resident census data from the National Healthcare Safety Network (NHSN(†)) and staffing data from the Centers for Medicare & Medicaid Services (CMS) Payroll-Based Journal.(§) Among 11,460 SNFs with at least one vaccination clinic during the first month of the program (December 18, 2020-January 17, 2021), an estimated median of 77.8% of residents (interquartile range [IQR] = 61.3%- 93.1%) and a median of 37.5% (IQR = 23.2%- 56.8%) of staff members per facility received ≥1 dose of COVID-19 vaccine through the Pharmacy Partnership for Long-Term Care Program. The program achieved moderately high coverage among residents; however, continued development and implementation of focused communication and outreach strategies are needed to improve vaccination coverage among staff members in SNFs and other long-term care settings.

During the beginning of the coronavirus disease 2019 (COVID-19) pandemic, nursing homes were identified as congregate settings at high risk for outbreaks of COVID-19 (1,2). Their residents also are at higher risk than the general population for morbidity and mortality associated with infection with SARS-CoV-2, the virus that causes COVID-19, in light of the association of severe outcomes with older age and certain underlying medical conditions (1,3). CDC's National Healthcare Safety Network (NHSN) launched nationwide, facility-level COVID-19 nursing home surveillance on April 26, 2020. A federal mandate issued by the Centers for Medicare & Medicaid Services (CMS), required nursing homes to commence enrollment and routine reporting of COVID-19 cases among residents and staff members by May 25, 2020. This report uses the NHSN nursing home COVID-19 data reported during May 25-November 22, 2020, to describe COVID-19 rates among nursing home residents and staff members and compares these with rates in surrounding communities by corresponding U.S. Department of Health and Human Services (HHS) region.* COVID-19 cases among nursing home residents increased during June and July 2020, reaching 11.5 cases per 1,000 resident-weeks (calculated as the total number of occupied beds on the day that weekly data were reported) (week of July 26). By mid-September, rates had declined to 6.3 per 1,000 resident-weeks (week of September 13) before increasing again, reaching 23.2 cases per 1,000 resident-weeks by late November (week of November 22). COVID-19 cases among nursing home staff members also increased during June and July (week of July 26 = 10.9 cases per 1,000 resident-weeks) before declining during August-September (week of September 13 = 6.3 per 1,000 resident-weeks); rates increased by late November (week of November 22 = 21.3 cases per 1,000 resident-weeks). Rates of COVID-19 in the surrounding communities followed similar trends. Increases in community rates might be associated with increases in nursing home COVID-19 incidence, and nursing home mitigation strategies need to include a comprehensive plan to monitor local SARS-CoV-2 transmission and minimize high-risk exposures within facilities.

OBJECTIVE: Antibiotic resistance (AR) is a growing and highly prevalent problem in nursing homes. We describe selected AR phenotypes from pathogens causing urinary tract infections (UTIs) reported by nursing homes to the National Healthcare Safety Network (NHSN). DESIGN: Pathogens and antibiotic susceptibility testing results for UTI events in nursing homes between January 2013 and December 2017 were analyzed. The pathogen distribution and pooled mean proportion of isolates that tested resistant to select antibiotic agents are reported. SETTING AND PARTICIPANTS: US nursing homes voluntarily participating in the Long-Term Care Facility component of the NHSN. RESULTS: Overall, 243 nursing homes reported 1 or more UTIs: 121 (50%) were nonprofit facilities, median bed size was 91 (range: 9-801), and average occupancy was 87%. In total, 6,157 pathogens were reported for 5,485 UTI events. Moreover, 9 pathogens accounted for 90% of all reported UTIs; the 3 most frequently identified were Escherichia coli (41%), Proteus species (14%), and Klebsiella pneumoniae/oxytoca (13%). Among E. coli, fluoroquinolone, and extended-spectrum cephalosporin resistance were most prevalent (50% and 20%, respectively). Although Staphylococcus aureus and Enterococcus faecium represented <5% of pathogens reported, they had the highest rates of resistance (67% methicillin resistant and 60% vancomycin resistant, respectively). Multidrug resistance was most common in Pseudomonas aeruginosa (11%). For the resistant phenotypes we assessed, 36% of all UTIs reported were associated with a resistant pathogen. CONCLUSIONS: This is the first summary of AR among common pathogens causing UTIs reported to NHSN by nursing homes. Improved understanding of the resistance burden among common infections helps inform facility infection prevention and antibiotic stewardship efforts.

BACKGROUND: We aimed to determine the prevalence of pulmonary TB amongst the adult population (>/=15 years) in 2016 in Kenya. METHOD: A nationwide cross-sectional survey where participants first underwent TB symptom screening and chest x-ray. Subsequently, participants who reported cough >2weeks and/or had a chest x-ray suggestive of TB, submitted sputum specimen for laboratory examination by smear microscopy, culture and Xpert MTB/RIF. RESULT: The survey identified 305 prevalent TB cases translating to a prevalence of 558 [95%CI 455-662] per 100,000 adult population. The highest disease burden was reported among people aged 25-34 years (716 [95% CI 526-906]), males (809 [(95% CI 656-962]) and those who live in urban areas (760 [95% CI 539-981]). Compared to the reported TB notification rate for Kenya in 2016, the prevalence to notification ratio was 2.5:1. The gap between the survey prevalence and notification rates was highest among males, age groups 25-34, and the older age group of 65 years and above. Only 48% of the of the survey prevalent cases reported cough >2weeks. In addition, only 59% of the identified cases had the four cardinal symptoms for TB (cough >/=2 weeks, fever, night sweat and weight loss. However, 88.2% had an abnormal chest x-ray suggestive of TB. The use of Xpert MTB/RIF identified 77.7% of the cases compared to smear microscopy's 46%. Twenty-one percent of the survey participants with respiratory symptoms reported to have sought prior health care at private clinics and chemists. Among the survey prevalent cases who reported TB related symptoms, 64.9% had not sought any health care prior to the survey. CONCLUSION: This survey established that TB prevalence in Kenya is higher than had been estimated, and about half of the those who fall ill with the disease each year are missed.

BACKGROUND: In 2012, the Centers for Disease Control and Prevention launched the Long-term Care Facility (LTCF) Component of the National Healthcare Safety Network (NHSN) designed for LTCFs to monitor Clostridium difficile infections (CDIs), urinary tract infections (UTIs), infections due to multidrug-resistant organisms, including methicillin-resistant Staphylococcus aureus (MRSA), and infection prevention process measures. METHODS: We describe characteristics and reporting patterns of facilities enrolled in the first 3 years of the surveillance system and rate estimates for CDI, UTI, and MRSA data submitted between 2013 and 2015. RESULTS: From 2013-2015, 279 LTCFs were enrolled and eligible to report to the NHSN with variability in reporting from year to year. Crude rate estimates pooled over these 3 years from reporting facilities were 0.98 incident LTCF-onset CDI cases per 10,000 resident days, 0.59 UTI cases per 1,000 resident days, and 0.10 LTCF-onset MRSA cases per 1,000 resident days. CONCLUSIONS: These initial data demonstrate the capability of the NHSN LTCF Component as a national surveillance system for monitoring infections in LTCFs. Further investigation is needed to understand factors associated with successful enrollment and reporting. As participation increases, data from a larger group of LTCFs will be used to establish national baselines and track prevention goals.

Dengue virus (DENV) and West Nile virus (WNV) are important reemerging arboviruses that are under-recognized in many parts of Africa due to lack of surveillance. As a part of a study on flavivirus, alphavirus, and parasite exposure in coastal Kenya, we measured neutralizing antibody against DENV and, to evaluate assay specificity, WNV in serum samples that tested positive for serum anti-DENV IgG by enzyme-linked immunosorbent assay. Of 830 anti-DENV IgG-positive samples that were tested for neutralizing activity, 488 (58.8%) neutralized DENV and 94 (11.3%) neutralized WNV. Of children ≤ 10 years of age, 23% and 17% had serum neutralizing antibody to DENV and WNV, respectively, indicating that DENV and WNV transmission has occurred in this region within the past decade. The results suggest that ongoing DENV and WNV transmission continues on the coast of Kenya and supports a need for routine arboviral surveillance in the area to detect and respond to future outbreaks.

BACKGROUND: We examine the uptake of HIV Testing and Counselling (HTC) and linkage into care over one year of providing HTC through community and health facility testing modalities among people living in Kibera informal urban settlement in Nairobi Kenya. METHODS: We analyzed program data on health facility-based HIV testing and counselling and community- based testing and counselling approaches for the period starting October 2013 to September 2014. Univariate and bivariate analysis methods were used to compare the two approaches with regard to uptake of HTC and subsequent linkage to care. The exact Confidence Intervals (CI) to the proportions were approximated using simple normal approximation to binomial distribution method. RESULTS: Majority of the 18,591 clients were tested through health facility-based testing approaches 72.5 % (n = 13485) vs those tested through community-based testing comprised 27.5 % (n = 5106). More clients tested at health facilities were reached through Provider Initiated Testing and Counselling PITC 81.7 % (n = 11015) while 18.3 % were reached through Voluntary Counselling and Testing (VCT)/Client Initiated Testing and Counselling (CITC) services. All clients who tested positive during health facility-based testing were successfully linked to care either at the project sites or sites of client choice while not all who tested positive during community based testing were linked to care. The HIV prevalence among all those who were tested for HIV in the program was 5.2 % (n = 52, 95 % CI: 3.9 %-6.8 %). Key study limitation included use of aggregate data to report uptake of HTC through the two testing approaches and not being able to estimate the population in the catchment area likely to test for HIV. CONCLUSION: Health facility-based HTC approach achieved more clients tested for HIV, and this method also resulted in identifying greater numbers of people who were HIV positive in Kibera slum within one year period of testing for HIV compared to community-based HTC approach. Linking HIV positive clients to care proved much easier during health facility- based HTC compared to community- based HTC.

INTRODUCTION: Changes in food production and distribution have increased opportunities for foods contaminated early in the supply chain to be distributed widely, increasing the possibility of multistate outbreaks. In recent decades, surveillance systems for foodborne disease have been improved, allowing officials to more effectively identify related cases and to trace and identify an outbreak's source. MATERIALS AND METHODS: We reviewed multistate foodborne disease outbreaks reported to the Centers for Disease Control and Prevention's Foodborne Disease Outbreak Surveillance System during 1973-2010. We calculated the percentage of multistate foodborne disease outbreaks relative to all foodborne disease outbreaks and described characteristics of multistate outbreaks, including the etiologic agents and implicated foods. RESULTS: Multistate outbreaks accounted for 234 (0.8%) of 27,755 foodborne disease outbreaks, 24,003 (3%) of 700,600 outbreak-associated illnesses, 2839 (10%) of 29,756 outbreak-associated hospitalizations, and 99 (16%) of 628 outbreak-associated deaths. The median annual number of multistate outbreaks increased from 2.5 during 1973-1980 to 13.5 during 2001-2010; the number of multistate outbreak-associated illnesses, hospitalizations, and deaths also increased. Most multistate outbreaks were caused by Salmonella (47%) and Shiga toxin-producing Escherichia coli (26%). Foods most commonly implicated were beef (22%), fruits (13%), and leafy vegetables (13%). CONCLUSIONS: The number of identified and reported multistate foodborne disease outbreaks has increased. Improvements in detection, investigation, and reporting of foodborne disease outbreaks help explain the increasing number of reported multistate outbreaks and the increasing percentage of outbreaks that were multistate. Knowing the etiologic agents and foods responsible for multistate outbreaks can help to identify sources of food contamination so that the safety of the food supply can be improved.

BACKGROUND: Chikungunya virus (CHIKV) and o'nyong nyong virus (ONNV) are mosquito-borne alphaviruses endemic in East Africa that cause acute febrile illness and arthritis. The objectives of this study were to measure the seroprevalence of CHIKV and ONNV in coastal Kenya and link it to demographics and other risk factors. METHODOLOGY: Demographic and exposure questionnaires were administered to 1,848 participants recruited from two village clusters (Milalani-Nganja and Vuga) in 2009. Sera were tested for alphavirus exposure using standardized CHIKV IgG ELISA protocols and confirmed with plaque reduction neutralization tests (PRNT). Logistic regression models were used to determine the variables associated with seropositivity. Weighted K test for global clustering of houses with alphavirus positive participants was performed for distance ranges of 50-1,000 meters, and G* statistic and kernel density mapping were used to identify locations of higher seroprevalence. PRINCIPAL FINDINGS: 486 (26%) participants were seropositive by IgG ELISA. Of 443 PRNT confirmed positives, 25 samples (6%) were CHIKV+, 250 samples (56%) were ONNV+, and 168 samples (38%) had high titers for both. Age was significantly associated with seropositivity (OR 1.01 per year, 95% C.I. 1.00-1.01); however, younger adults were more likely to be seropositive than older adults. Males were less likely to be seropositive (p<0.05; OR 0.79, 95% C.I. 0.64-0.97). Adults who owned a bicycle (p<0.05; OR 1.37, 95% C.I. 1.00-1.85) or motor vehicle (p<0.05; OR 4.64, 95% C.I. 1.19-18.05) were more likely to be seropositive. Spatial analysis demonstrated hotspots of transmission within each village and clustering among local households in Milalani-Nganja, peaking at the 200-500m range. CONCLUSIONS/SIGNIFICANCE: Alphavirus exposure, particularly ONNV exposure, is common in coastal Kenya with ongoing interepidemic transmission of both ONNV and CHIKV. Women and adults were more likely to be seropositive. Household location may be a defining factor for the ecology of alphaviral transmission in this region.

The number of US outbreaks caused by nonpasteurized milk increased from 30 during 2007-2009 to 51 during 2010-2012. Most outbreaks were caused by Campylobacter spp. (77%) and by nonpasteurized milk purchased from states in which nonpasteurized milk sale was legal (81%). Regulations to prevent distribution of nonpasteurized milk should be enforced.

BACKGROUND: Although health care-associated infections are an important cause of morbidity and mortality worldwide, the epidemiology and etiology of respiratory health care-associated infections (rHAIs) have not been documented in Kenya. In 2010, the Ministry of Health, Kenya Medical Research Institute, and Centers for Disease Control and Prevention initiated surveillance for rHAIs at 3 hospitals. METHODS: At each hospital, we surveyed intensive care units (ICUs), pediatric wards, and medical wards to identify patients with rHAIs, defined as any hospital-onset (≥3 days after admission) fever (≥38 degrees C) or hypothermia (<35 degrees C) with concurrent signs or symptoms of acute respiratory infection. Nasopharyngeal and oropharyngeal specimens were collected from these patients and tested by real-time reverse transcription polymerase chain reaction for influenza and 7 other viruses. RESULTS: From April 2010-September 2012, of the 379 rHAI cases, 60.7% were men and 57.3% were children <18 years old. The overall incidence of rHAIs was 9.2 per 10,000 patient days, with the highest incidence in the ICUs. Of all specimens analyzed, 45.7% had at least 1 respiratory virus detected; 92.2% of all positive viral specimens were identified in patients <18 years old. CONCLUSION: We identified rHAIs in all ward types under surveillance in Kenyan hospitals. Viruses may have a substantial role in these infections, particularly among pediatric populations. Further research is needed to refine case definitions and understand rHAIs in ICUs.

INTRODUCTION: The interstate commerce of unpasteurized fluid milk, also known as raw milk, is illegal in the United States, and intrastate sales are regulated independently by each state. However, U.S. Food and Drug Administration regulations allow the interstate sale of certain types of cheeses made from unpasteurized milk if specific aging requirements are met. We describe characteristics of these outbreaks, including differences between outbreaks linked to cheese made from pasteurized or unpasteurized milk. METHODS: We reviewed reports of outbreaks submitted to the Foodborne Disease Outbreak Surveillance System during 1998-2011 in which cheese was implicated as the vehicle. We describe characteristics of these outbreaks, including differences between outbreaks linked to cheese made from pasteurized versus unpasteurized milk. RESULTS: During 1998-2011, 90 outbreaks attributed to cheese were reported; 38 (42%) were due to cheese made with unpasteurized milk, 44 (49%) to cheese made with pasteurized milk, and the pasteurization status was not reported for the other eight (9%). The most common cheese-pathogen pairs were unpasteurized queso fresco or other Mexican-style cheese and Salmonella (10 outbreaks), and pasteurized queso fresco or other Mexican-style cheese and Listeria (6 outbreaks). The cheese was imported from Mexico in 38% of outbreaks caused by cheese made with unpasteurized milk. In at least five outbreaks, all due to cheese made from unpasteurized milk, the outbreak report noted that the cheese was produced or sold illegally. Outbreaks caused by cheese made from pasteurized milk occurred most commonly (64%) in restaurant, delis, or banquet settings where cross-contamination was the most common contributing factor. CONCLUSIONS: In addition to using pasteurized milk to make cheese, interventions to improve the safety of cheese include limiting illegal importation of cheese, strict sanitation and microbiologic monitoring in cheese-making facilities, and controls to limit food worker contamination.