The effect of participant nonresponse on HIV prevalence estimates in a population-based survey in two informal settlements in Nairobi city

BACKGROUND: Participant nonresponse in an HIV serosurvey can affect estimates of HIV prevalence. Nonresponse can arise from a participant's refusal to provide a blood sample or the failure to trace a sampled individual. In a serosurvey conducted by the African Population and Health Research Center and Kenya Medical Research Centre in the slums of Nairobi, 43% of sampled individuals did not provide a blood sample. This paper describes selective participation in the serosurvey and estimates bias in HIV prevalence figures. METHODS: The paper uses data derived from an HIV serosurvey nested in an on-going demographic surveillance system. Nonresponse was assessed using logistic regression and multiple imputation methods to impute missing data for HIV status using a set of common variables available for all sampled participants. RESULTS: Age, residence, high mobility, wealth, and ethnicity were independent predictors of a sampled individual not being contacted. Individuals aged 30-34 years, females, individuals from the Kikuyu and Kamba ethnicity, married participants, and residents of Viwandani were all less likely to accept HIV testing when contacted. Although men were less likely to be contacted, those found were more willing to be tested compared to females. The overall observed HIV prevalence was overestimated by 2%. The observed prevalence for male participants was underestimated by about 1% and that for females was overestimated by 3%. These differences were small and did not affect the overall estimate substantially as the observed estimates fell within the confidence limits of the corrected prevalence estimate. CONCLUSIONS: Nonresponse in the HIV serosurvey in the two informal settlements was high, however, the effect on overall prevalence estimate was minimal

The Conundrum of Low COVID-19 Mortality Burden in sub-Saharan Africa: Myth or Reality?

The burden of coronavirus disease (COVID-19) in sub-Saharan Africa (SSA) has been substantially lower compared to other regions of the world. Extensive morbidity and mortality were not observed among countries in SSA during the first wave of the COVID-19 pandemic. To explain this phenomenon, several hypotheses have been formulated, including the low median age of the population in most SSA countries, lack of long-term care facilities, cross-protection from other local coronaviruses, insufficient testing and reporting resulting in an undercounting of COVID-related deaths, genetic risk factors, or the benefit of early lockdowns that were extensive in many SSA countries. Early lockdowns in SSA have been some of the strictest and resulted in devastating economic and social consequences and increased mortality from other health-related problems including maternal deaths. We review the literature and rationale supporting the various hypotheses that have been put forward to account for relatively low hospitalization and death rates for COVID-19 in SSA. We conclude that the strongest evidence would support the demographic age structure with a very low median age as the primary factor in leading to the low mortality seen in the first wave of the pandemic. The impact of new variants of concern in SSA raises the risk of more severe waves. Nevertheless, furthering the understanding of the underlying explanations for the low morbidity and mortality seen across SSA countries may allow the adoption of unique strategies for limiting the spread of COVID-19 without the need for stringent lockdowns

Are slum dwellers at heightened risk of HIV infection than other urban residents? Evidence from population-based HIV prevalence surveys in Kenya

In 2008, the global urban population surpassed the rural population and by 2050 more than 6 billion will be living in urban centres. A growing body of research has reported on poor health outcomes among the urban poor but not much is known about HIV prevalence among this group. A survey of nearly 3000 men and women was conducted in two Nairobi slums in Kenya between 2006 and 2007, where respondents were tested for HIV status. In addition, data from the 2008/2009 Kenya Demographic and Health Survey were used to compare HIV prevalence between slum residents and those living in other urban and rural areas. The results showed strong intra-urban differences. HIV was 12% among slum residents compared with 5% and 6% among non-slum urban and rural residents, respectively. Generally, men had lower HIV prevalence than women although in the slums the gap was narrower. Among women, sexual experience before the age of 15 compared with after 19 years was associated with 62% higher odds of being HIV positive. There was ethnic variation in patterns of HIV infection although the effect depended on the current place of residence

Sero-prevalence and risk factors for hepatitis B virus infection among health care workers in a tertiary hospital in Uganda

BACKGROUND: Hepatitis B virus (HBV) infection is a global public health challenge. Prevalence of current hepatitis B virus infection in the general population in Uganda is about 10%. Health care workers (HCW) have an extra risk of getting infected from their workplace and yet they are not routinely vaccinated against HBV infection. This study aimed at estimating prevalence of hepatitis B virus infection and associated risk factors among health care workers in a tertiary hospital in Uganda. METHODS: Data were obtained from a cross sectional survey conducted in Mulago, a national referral and teaching hospital in Uganda among health care workers in 2003. A proportionate to size random sample was drawn per health care worker category. A structured questionnaire was used to collect data on socio-demographic characteristics and risk factors. ELISA was used to test sera for HBsAg, anti-HBs and total anti-HBc. Descriptive and logistic regression models were used for analysis. RESULTS: Among the 370 participants, the sero-prevalence of current hepatitis B virus infection was 8.1%; while prevalence of life time exposure to hepatitis B virus infection was 48.1%. Prevalence of needle stick injuries and exposure to mucous membranes was 67.8% and 41.0% respectively. Cuts were also common with 31.7% of doctors reporting a cut in a period of one year preceding the survey. Consistent use of gloves was reported by 55.4% of respondents. The laboratory technicians (18.0% of respondents) were the least likely to consistently use gloves. Only 6.2% of respondents were vaccinated against hepatitis B virus infection and 48.9% were susceptible and could potentially be protected through vaccination. Longer duration in service was associated with a lower risk of current infection (OR = 0.13; p value = 0.048). Being a nursing assistant (OR = 17.78; p value = 0.007) or a laboratory technician (OR = 12.23; p value = 0.009) were associated with a higher risk of current hepatitis B virus infection. Laboratory technicians (OR = 3.99; p value = 0.023) and individuals with no training in infection prevention in last five years (OR = 1.85; p value = 0.015) were more likely to have been exposed to hepatitis B virus infection before. CONCLUSIONS: The prevalence of current and life time exposure to hepatitis B virus infection was high. Exposure to potentially infectious body fluids was high and yet only a small percentage of HCW were vaccinated. There is need to vaccinate all health care workers as a matter of policy and ensure a safer work environment

Determinants for HIV testing and counselling in Nairobi urban informal settlements

BACKGROUND: Counselling and testing is important in HIV prevention and care. Majority of people in sub-Saharan Africa do not know their HIV status and are therefore unable to take steps to prevent infection or take up life prolonging anti-retroviral drugs in time if infected. This study aimed at exploring determinants of HIV testing and counselling in two Nairobi informal settlements. METHODS: Data are derived from a cross-sectional survey nested in an ongoing demographic surveillance system. A total of 3,162 individuals responded to the interview and out of these, 82% provided a blood sample which was tested using rapid test kits. The outcome of interest in this paper was HIV testing status in the past categorised as "never tested"; "client-initiated testing and counselling (CITC)" and provider-initiated testing and counselling (PITC). Multinomial logistic regression was used to identify determinants of HIV testing. RESULTS: Approximately 31% of all respondents had ever been tested for HIV through CITC, 22% through PITC and 42% had never been tested but indicated willingness to test. Overall, 62% of females and 38% of males had ever been tested for HIV. Males were less likely to have had CITC (OR = 0.47; p value < 0.001) and also less likely to have had PITC (OR = 0.16; p value < 0.001) compared to females. Individuals aged 20-24 years were more likely to have had either CITC or PITC compared to the other age groups. The divorced/separated/widowed were more likely (OR = 1.65; p value < 0.01) to have had CITC than their married counterparts, while the never married were less likely to have had either CITC or PITC. HIV positive individuals (OR = 1.60; p value < 0.01) and those who refused testing in the survey (OR = 1.39; p value < 0.05) were more likely to have had CITC compared to their HIV negative counterparts. CONCLUSION: Although the proportion of individuals ever tested in the informal settlements is similar to the national average, it remains low compared to that of Nairobi province especially among men. Key determinants of HIV testing and counselling include; gender, age, education level, HIV status and marital status. These factors need to be considered in efforts aimed at increasing participation in HIV testing

Overweight and obesity in urban Africa: A problem of the rich or the poor?

BACKGROUND: Obesity is a well recognized risk factor for various chronic diseases such as cardiovascular diseases, hypertension, and type 2 diabetes mellitus. The aim of this study was to shed light on the patterns of overweight and obesity in sub-Saharan Africa, with special interest in differences between the urban poor and the urban non-poor. The specific goals were to describe trends in overweight and obesity among urban women; and examine how these trends vary by education and household wealth. METHODS: The paper used Demographic and Health Surveys data from seven African countries where two surveys had been carried out with an interval of at least 10 years between them. Among the countries studied, the earliest survey took place in 1992 and the latest in 2005. The dependent variable was body mass index coded as: Not overweight/obese; Overweight; Obese. The key covariates were time lapse between the two surveys; woman's education; and household wealth. Control variables included working status, age, marital status, parity, and country. Multivariate ordered logistic regression in the context of the partial proportional odds model was used. RESULTS: Descriptive results showed that the prevalence of urban overweight/obesity increased by nearly 35% during the period covered. The increase was higher among the poorest (+50%) than among the richest (+7%). Importantly, there was an increase of 45-50% among the non-educated and primary-educated women, compared to a drop of 10% among women with secondary education or higher. In the multivariate analysis, the odds ratio of the variable time lapse was 1.05 (p < 0.01), indicating that the prevalence of overweight/obesity increased by about 5% per year on average in the countries in the study. While the rate of change in urban overweight/obesity did not significantly differ between the poor and the rich, it was substantially higher among the non-educated women than among their educated counterparts. CONCLUSION: Overweight and obesity are on the rise in Africa and might take epidemic proportions in the near future. Like several other public health challenges, overweight and obesity should be tackled and prevented early as envisioned in the WHO Global strategy on diet, physical activity and health

SARS-CoV-2 seroprevalence in three Kenyan health and demographic surveillance sites, December 2020-May 2021

Background Most of the studies that have informed the public health response to the COVID-19 pandemic in Kenya have relied on samples that are not representative of the general population. We conducted population-based serosurveys at three Health and Demographic Surveillance Systems (HDSSs) to determine the cumulative incidence of infection with SARS-CoV-2. Methods We selected random age-stratified population-based samples at HDSSs in Kisumu, Nairobi and Kilifi, in Kenya. Blood samples were collected from participants between 01 Dec 2020 and 27 May 2021. No participant had received a COVID-19 vaccine. We tested for IgG antibodies to SARS-CoV-2 spike protein using ELISA. Locally-validated assay sensitivity and specificity were 93% (95% CI 88–96%) and 99% (95% CI 98–99.5%), respectively. We adjusted prevalence estimates using classical methods and Bayesian modelling to account for the sampling scheme and assay performance. Results We recruited 2,559 individuals from the three HDSS sites, median age (IQR) 27 (10–78) years and 52% were female. Seroprevalence at all three sites rose steadily during the study period. In Kisumu, Nairobi and Kilifi, seroprevalences (95% CI) at the beginning of the study were 36.0% (28.2–44.4%), 32.4% (23.1–42.4%), and 14.5% (9.1–21%), and respectively; at the end they were 42.0% (34.7–50.0%), 50.2% (39.7–61.1%), and 24.7% (17.5–32.6%), respectively. Seroprevalence was substantially lower among children (&lt;16 years) than among adults at all three sites (p≤0.001). Conclusion By May 2021 in three broadly representative populations of unvaccinated individuals in Kenya, seroprevalence of anti-SARS-CoV-2 IgG was 25–50%. There was wide variation in cumulative incidence by location and age. </jats:sec

Analysis of OpenStreetMap data quality at different stages of a participatory mapping process : evidence from slums in Africa and Asia

This paper examines OpenStreetMap data quality at different stages of a participatory mapping process in seven slums in Africa and Asia. Data were drawn from an OpenStreetMap-based participatory mapping process developed as part of a research project focusing on understanding inequalities in healthcare access of slum residents in the Global South. Descriptive statistics and qualitative analysis were employed to examine the following research question: What is the spatial data quality of collaborative remote mapping achieved by volunteer mappers in morphologically complex urban areas? Findings show that the completeness achieved by remote mapping largely depends on the morphology and characteristics of slums such as building density and rooftop architecture, varying from 84 in the best case, to zero in the most difficult site. The major scientific contribution of this study is to provide evidence on the spatial data quality of remotely mapped data through volunteer mapping efforts in morphologically complex urban areas such as slums; the results could provide insights into how much fieldwork would be needed in what level of complexity and to what extent the involvement of local volunteers in these efforts is required

Global burden of respiratory infections associated with seasonal influenza in children under 5 years in 2018: a systematic review and modelling study

Background: Seasonal influenza virus is a common cause of acute lower respiratory infection (ALRI) in young children. In 2008, we estimated that 20 million influenza-virus-associated ALRI and 1 million influenza-virus-associated severe ALRI occurred in children under 5 years globally. Despite this substantial burden, only a few low-income and middle-income countries have adopted routine influenza vaccination policies for children and, where present, these have achieved only low or unknown levels of vaccine uptake. Moreover, the influenza burden might have changed due to the emergence and circulation of influenza A/H1N1pdm09. We aimed to incorporate new data to update estimates of the global number of cases, hospital admissions, and mortality from influenza-virus-associated respiratory infections in children under 5 years in 2018. Methods: We estimated the regional and global burden of influenza-associated respiratory infections in children under 5 years from a systematic review of 100 studies published between Jan 1, 1995, and Dec 31, 2018, and a further 57 high-quality unpublished studies. We adapted the Newcastle-Ottawa Scale to assess the risk of bias. We estimated incidence and hospitalisation rates of influenza-virus-associated respiratory infections by severity, case ascertainment, region, and age. We estimated in-hospital deaths from influenza virus ALRI by combining hospital admissions and in-hospital case-fatality ratios of influenza virus ALRI. We estimated the upper bound of influenza virus-associated ALRI deaths based on the number of in-hospital deaths, US paediatric influenza-associated death data, and population-based childhood all-cause pneumonia mortality data in six sites in low-income and lower-middle-income countries. Findings: In 2018, among children under 5 years globally, there were an estimated 109·5 million influenza virus episodes (uncertainty range [UR] 63·1–190·6), 10·1 million influenza-virus-associated ALRI cases (6·8–15·1); 870 000 influenza-virus-associated ALRI hospital admissions (543 000–1 415 000), 15 300 in-hospital deaths (5800–43 800), and up to 34 800 (13 200–97 200) overall influenza-virus-associated ALRI deaths. Influenza virus accounted for 7% of ALRI cases, 5% of ALRI hospital admissions, and 4% of ALRI deaths in children under 5 years. About 23% of the hospital admissions and 36% of the in-hospital deaths were in infants under 6 months. About 82% of the in-hospital deaths occurred in low-income and lower-middle-income countries. Interpretation: A large proportion of the influenza-associated burden occurs among young infants and in low-income and lower middle-income countries. Our findings provide new and important evidence for maternal and paediatric influenza immunisation, and should inform future immunisation policy particularly in low-income and middle-income countries. Funding: WHO; Bill & Melinda Gates Foundation.Fil: Wang, Xin. University of Edinburgh; Reino UnidoFil: Li, You. University of Edinburgh; Reino UnidoFil: O'Brien, Katherine L.. University Johns Hopkins; Estados UnidosFil: Madhi, Shabir A.. University of the Witwatersrand; SudáfricaFil: Widdowson, Marc Alain. Centers for Disease Control and Prevention; Estados UnidosFil: Byass, Peter. Umea University; SueciaFil: Omer, Saad B.. Yale School Of Public Health; Estados UnidosFil: Abbas, Qalab. Aga Khan University; PakistánFil: Ali, Asad. Aga Khan University; PakistánFil: Amu, Alberta. Dodowa Health Research Centre; GhanaFil: Azziz-Baumgartner, Eduardo. Centers for Disease Control and Prevention; Estados UnidosFil: Bassat, Quique. University Of Barcelona; EspañaFil: Abdullah Brooks, W.. University Johns Hopkins; Estados UnidosFil: Chaves, Sandra S.. Centers for Disease Control and Prevention; Estados UnidosFil: Chung, Alexandria. University of Edinburgh; Reino UnidoFil: Cohen, Cheryl. National Institute For Communicable Diseases; SudáfricaFil: Echavarría, Marcela Silvia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. CEMIC-CONICET. Centro de Educaciones Médicas e Investigaciones Clínicas "Norberto Quirno". CEMIC-CONICET; ArgentinaFil: Fasce, Rodrigo A.. Public Health Institute; ChileFil: Gentile, Angela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Gordon, Aubree. University of Michigan; Estados UnidosFil: Groome, Michelle. University of the Witwatersrand; SudáfricaFil: Heikkinen, Terho. University Of Turku; FinlandiaFil: Hirve, Siddhivinayak. Kem Hospital Research Centre; IndiaFil: Jara, Jorge H.. Universidad del Valle de Guatemala; GuatemalaFil: Katz, Mark A.. Clalit Research Institute; IsraelFil: Khuri Bulos, Najwa. University Of Jordan School Of Medicine; JordaniaFil: Krishnan, Anand. All India Institute Of Medical Sciences; IndiaFil: de Leon, Oscar. Universidad del Valle de Guatemala; GuatemalaFil: Lucero, Marilla G.. Research Institute For Tropical Medicine; FilipinasFil: McCracken, John P.. Universidad del Valle de Guatemala; GuatemalaFil: Mira-Iglesias, Ainara. Fundación Para El Fomento de la Investigación Sanitaria; EspañaFil: Moïsi, Jennifer C.. Agence de Médecine Préventive; FranciaFil: Munywoki, Patrick K.. No especifíca;Fil: Ourohiré, Millogo. No especifíca;Fil: Polack, Fernando Pedro. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Rahi, Manveer. University of Edinburgh; Reino UnidoFil: Rasmussen, Zeba A.. National Institutes Of Health; Estados UnidosFil: Rath, Barbara A.. Vienna Vaccine Safety Initiative; AlemaniaFil: Saha, Samir K.. Child Health Research Foundation; BangladeshFil: Simões, Eric A.F.. University of Colorado; Estados UnidosFil: Sotomayor, Viviana. Ministerio de Salud de Santiago de Chile; ChileFil: Thamthitiwat, Somsak. Thailand Ministry Of Public Health; TailandiaFil: Treurnicht, Florette K.. University of the Witwatersrand; SudáfricaFil: Wamukoya, Marylene. African Population & Health Research Center; KeniaFil: Lay-Myint, Yoshida. Nagasaki University; JapónFil: Zar, Heather J.. University of Cape Town; SudáfricaFil: Campbell, Harry. University of Edinburgh; Reino UnidoFil: Nair, Harish. University of Edinburgh; Reino Unid