Do interviewers moderate the effect of monetary incentives on response rates in household interview surveys?

As citizens around the world become ever more reluctant to respond to survey interview requests, incentives are playing an increasingly important role in maintaining response rates. In face-to-face surveys, interviewers are the key conduit of information about the existence and level of any incentive offered and, therefore, potentially moderate the effectiveness with which an incentive translates nonproductive addresses into interviews. Yet, while the existing literature on the effects of incentives on response rates is substantial, little is currently known about the role of interviewers in determining whether or not incentives are effective. In this article, we apply multilevel models to three different face-to-face interview surveys from the United Kingdom, which vary in their sample designs and incentive levels, to assess whether some interviewers are more successful than others in using incentives to leverage cooperation. Additionally, we link the response outcome data to measures of interviewer characteristics to investigate whether interviewer variability on this dimension is systematically related to level of experience and demographic characteristics. Our results show significant and substantial variability between interviewers in the effectiveness of monetary incentives on the probability of cooperation across all three surveys. However, none of the interviewer characteristics considered are significantly associated with more or less successful interviewers

The epidemiology of tuberculosis in Kenya, a high TB/HIV burden country (2000-2013)

Interest in the epidemiology of TB was triggered by the re-emergence of tuberculosis in the early 1990’s with the advent of HIV and falling economic status of many people which subjected them to poverty. The dual lethal combination of HIV and poverty triggered an unprecedented TB epidemic. In this study, we focused on the period 2000-2013 and all the notified data in Kenya was included. Data on estimates of TB incidence, prevalence and mortality was extracted from the WHO global Tuberculosis database. Data was analysed to produce trends for each of the years and descriptive statistics were calculated. The results showed that there was an average decline of 5% over the last 8 years with the highest decline being reported in the year 2012/13. TB continues to disproportionately affect the male gender with 58% being male and 42% being female. Kenya has made significant efforts to address the burden of HIV among TB patients with cotrimoxazole preventive therapy (CPT) uptake reaching 98% and ART at 74% by the end of 2013. Kenya’s TB epidemic has evolved over time and it has been characterised by a period where there was increase in the TB cases reaching a peak in the year 2007 after which there was a decline which began to accelerate in the year 2011. The gains in the decline of TB could be attributed in part to the outcomes of integrating TB and HIV services and these gains should be sustained. What is equally notable is the clear epidemiologic shift in age indicating reduced transmission in the younger age groups

Spatial temporal modelling of tuberculosis in Kenya using small area estimation

Tuberculosis, a highly infectious disease which is transmitted within and between communities when infected and susceptible individuals interact. Tuberculosis at present is a major public health problem and continues to take toll on the most productive members of the community. An understanding of disease spread dynamics of infectious diseases continues to play a critical role in design of disease control strategies. Modeling of Tuberculosis is useful in understanding disease dynamics as it will guide the importance of basic science as well as public policy, prevention and control of the emerging infectious disease and modeling the spread of the disease. This study sought to establish how long under different frameworks will TB disease recede to extinction. In this study, deterministic and stochastic models for the trends of tuberculosis cases over time in Kenya were developed. Susceptible Infective (SI), Susceptible Infective and Recovered (SIR) and Susceptible Exposed Infective and Recovered (SEIR) models were considered. These models were modified in order to fit the data more precisely (age structure and predisposing factors of the incident cases). The SIR and SEIR model with non-linear incidence rates were further looked at and the stability of their solutions were evaluated. The results indicate that both deterministic and stochastic models can give not only an insight but also an integral description of TB transmission dynamics. Both deterministic and stochastic models fit well to the Kenyan TB epidemic model however with varying time periods. The models show that for deterministic model the number of infected individuals increases dramatically within three years and begins to fall quickly when the transmissible acts are 10 and 15 and falls to close to zero by 15 years but when the transmissible act is 5 the number infected peaks by the 11th year and declines to zero by year 31, while for stochastic models the number infected falls exponentially but when the transmissible acts is 15 the decline is slow and will get to zero by the 53rd year while for 10 transmissible acts to declines to zero by the 18th year. The other transmissible acts (1, 3, 5) decline to zero by the 9th year. From this study we conclude that if the national control program continues with the current interventions it could take them up to the next 31 years to bring the infection numbers to zero if the deterministic model is considered, while in the stochastic model with accelerated interventions and high recovery rate and assuming that there is no change in the risk factors it could take them up to 11 years to bring the infections to zero

An application of deterministic and stochastic processes to model evolving epidemiology of tuberculosis in Kenya

Tuberculosis, a highly infectious disease which is transmitted within and between communities when infected and susceptible individuals interact. Tuberculosis at present is a major public health problem and continues to take toll on the most productive members of the community. An understanding of disease spread dynamics of infectious diseases continues to play a critical role in design of disease control strategies. Modeling of Tuberculosis is useful in understanding disease dynamics as it will guide the importance of basic science as well as public policy, prevention and control of the emerging infectious disease and modeling the spread of the disease. This study sought to establish how long under different frameworks will TB disease recede to extinction. In this study, deterministic and stochastic models for the trends of tuberculosis cases over time in Kenya were developed. Susceptible Infective (SI), Susceptible Infective and Recovered (SIR) and Susceptible Exposed Infective and Recovered (SEIR) models were considered. These models were modified in order to fit the data more precisely (age structure and predisposing factors of the incident cases). The SIR and SEIR model with non-linear incidence rates were further looked at and the stability of their solutions were evaluated. The results indicate that both deterministic and stochastic models can give not only an insight but also an integral description of TB transmission dynamics. Both deterministic and stochastic models fit well to the Kenyan TB epidemic model however with varying time periods. The models show that for deterministic model the number of infected individuals increases dramatically within three years and begins to fall quickly when the transmissible acts are 10 and 15 and falls to close to zero by 15 years but when the transmissible act is 5 the number infected peaks by the 11th year and declines to zero by year 31, while for stochastic models the number infected falls exponentially but when the transmissible acts is 15 the decline is slow and will get to zero by the 53rd year while for 10 transmissible acts to declines to zero by the 18th year. The other transmissible acts (1, 3, 5) decline to zero by the 9th year. From this study we conclude that if the national control program continues with the current interventions it could take them up to the next 31 years to bring the infection numbers to zero if the deterministic model is considered, while in the stochastic model with accelerated interventions and high recovery rate and assuming that there is no change in the risk factors it could take them up to 11 years to bring the infections to zero

Pathways to ethnic inequalities in COVID-19 health outcomes in the United Kingdom: A systematic map

Background Marked ethnic inequalities in COVID-19 infection and its consequences have been documented. The aim of this paper is to identify the range and nature of evidence on potential pathways which lead to ethnic inequalities in COVID-19 related health outcomes in the United Kingdom (UK). Methods We searched six bibliographic and five grey literature databases from 1st December 2019 to 23rd February 2022 for research on pathways to ethnic inequalities in COVID-19 health outcomes in the UK. Meta-data were extracted and coded, using a framework informed by a logic model. Open Science Framework Registration: DOI 10.17605/OSF.IO/HZRB7. Results The search returned 10,728 records after excluding duplicates, with 123 included (83% peer-reviewed). Mortality was the most common outcome investigated (N = 79), followed by infection (N = 52). The majority of studies were quantitative (N = 93, 75%), with four qualitative studies (3%), seven academic narrative reviews (6%), nine third sector reports (7%) and five government reports (4%), and four systematic reviews or meta-analyses (3%). There were 78 studies which examined comorbidities as a pathway to mortality, infection, and severe disease. Socioeconomic inequalities (N = 67) were also commonly investigated, with considerable research into neighbourhood infrastructure (N = 38) and occupational risk (N = 28). Few studies examined barriers to healthcare (N = 6) and consequences of infection control measures (N = 10). Only 11% of eligible studies theorised racism to be a driver of inequalities and 10% (typically government/third sector reports and qualitative studies) explored this as a pathway. Conclusion This systematic map identified knowledge clusters that may be amenable to subsequent systematic reviews, and critical gaps in the evidence-base requiring additional primary research. Most studies do not incorporate or conceptualise racism as the fundamental cause of ethnic inequalities and therefore the contribution to literature and policy is limited

Health inequalities at the intersection of multiple social determinants among under five children residing Nairobi urban slums: an application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

In this analysis we examine through an intersectionality lens how key social determinants of health (SDOH) are associated with health conditions among under-five children (<5y) residing in Nairobi slums, Kenya. We used cross-sectional data collected from Nairobi slums between June and November 2012 to explore how multiple interactions of SDoH shape health inequalities in slums. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) approach. We constructed intersectional strata for each health condition from combinations of significant SDoH obtained using univariate analyses. We then estimated the intersectional effects of health condition in a series of MAIHDA logistic regression models distinguishing between additive and interaction effects. We quantified discriminatory accuracy (DA) of the intersectional strata by means of the variance partitioning coefficient (VPC) and the area under the receiver operating characteristic curve (AUC-ROC). The total participants were 2,199 <5y, with 120 records (5.5%) dropped because health conditions were recorded as “not applicable”. The main outcome variables were three health conditions: 1) whether a child had diarrhea or not, 2) whether a child had fever or not, and 3) whether a child had cough or not in the previous two weeks. We found non-significant intersectional effects for each health condition. The head of household ethnic group was significantly associated with each health condition. We found good DA for diarrhea (VPC = 9.0%, AUC-ROC = 76.6%) an indication of large intersectional effects. However, fever (VPC = 1.9%, AUC-ROC = 66.3%) and cough (VPC = 0.5%, AUC-ROC = 61.8%) had weak DA indicating existence of small intersectional effects. Our study shows pathways for SDoH that affect diarrhea, cough, and fever for <5y living in slums are multiplicative and shared. The findings show that <5y from Luo and Luhya ethnic groups, recent migrants (less than 2 years), and households experiencing CHE are more likely to face worse health outcomes. We recommend relevant stakeholders to develop strategies aimed at identifying these groups for targeted proportionate universalism based on the level of their need

Pathways to ethnic inequalities in COVID-19 health outcomes in the United Kingdom: a systematic map

Background: Marked ethnic inequalities in COVID-19 infection and its consequences have been documented. The aim of this paper is to identify the range and nature of evidence on potential pathways which lead to ethnic inequalities in COVID-19 related health outcomes in the United Kingdom (UK). Methods: We searched six bibliographic and five grey literature databases from 1st December 2019 to 23rd February 2022 for research on pathways to ethnic inequalities in COVID-19 health outcomes in the UK. Meta-data were extracted and coded, using a framework informed by a logic model. Open Science Framework Registration: DOI 10.17605/OSF.IO/HZRB7. Results: The search returned 10,728 records after excluding duplicates, with 123 included (83% peer-reviewed). Mortality was the most common outcome investigated (N = 79), followed by infection (N = 52). The majority of studies were quantitative (N = 93, 75%), with four qualitative studies (3%), seven academic narrative reviews (6%), nine third sector reports (7%) and five government reports (4%), and four systematic reviews or meta-analyses (3%). There were 78 studies which examined comorbidities as a pathway to mortality, infection, and severe disease. Socioeconomic inequalities (N = 67) were also commonly investigated, with considerable research into neighbourhood infrastructure (N = 38) and occupational risk (N = 28). Few studies examined barriers to healthcare (N = 6) and consequences of infection control measures (N = 10). Only 11% of eligible studies theorised racism to be a driver of inequalities and 10% (typically government/third sector reports and qualitative studies) explored this as a pathway. Conclusion: This systematic map identified knowledge clusters that may be amenable to subsequent systematic reviews, and critical gaps in the evidence-base requiring additional primary research. Most studies do not incorporate or conceptualise racism as the fundamental cause of ethnic inequalities and therefore the contribution to literature and policy is limited

Ethnic inequalities in positive SARS-CoV-2 tests, infection prognosis, COVID-19 hospitalisations, and deaths : analysis of two years of a record linked national cohort study in Scotland

Funding: Economics and Social Research Council (ESRC) ES/W000849/1, Medical Research Council (MRC) MC_UU_00022/2, Scottish Government Chief Scientist Office SPHSU17.BACKGROUND: This study aims to estimate ethnic inequalities in risk for positive SARS-CoV-2 tests, COVID-19 hospitalisations and deaths over time in Scotland. METHODS: We conducted a population-based cohort study where the 2011 Scottish Census was linked to health records. We included all individuals≥16 years living in Scotland on 1 March 2020. The study period was from 1 March 2020 to 17 April 2022. Self-reported ethnic group was taken from the census and Cox proportional hazard models estimated HRs for positive SARS-CoV-2 tests, hospitalisations and deaths, adjusted for age, sex and health board. We also conducted separate analyses for each of the four waves of COVID-19 to assess changes in risk over time. FINDINGS: Of the 4 358 339 individuals analysed, 1 093 234 positive SARS-CoV-2 tests, 37 437 hospitalisations and 14 158 deaths occurred. The risk of COVID-19 hospitalisation or death among ethnic minority groups was often higher for White Gypsy/Traveller (HR 2.21, 95% CI (1.61 to 3.06)) and Pakistani 2.09 (1.90 to 2.29) groups compared with the white Scottish group. The risk of COVID-19 hospitalisation or death following confirmed positive SARS-CoV-2 test was particularly higher for White Gypsy/Traveller 2.55 (1.81-3.58), Pakistani 1.75 (1.59-1.73) and African 1.61 (1.28-2.03) individuals relative to white Scottish individuals. However, the risk of COVID-19-related death following hospitalisation did not differ. The risk of COVID-19 outcomes for ethnic minority groups was higher in the first three waves compared with the fourth wave. INTERPRETATION: Most ethnic minority groups were at increased risk of adverse COVID-19 outcomes in Scotland, especially White Gypsy/Traveller and Pakistani groups. Ethnic inequalities persisted following community infection but not following hospitalisation, suggesting differences in hospital treatment did not substantially contribute to ethnic inequalities.Publisher PDFPeer reviewe

Comparing cross-sectional and longitudinal approaches to Tuberculosis Patient Cost Surveys using Nepalese data.

The World Health Organization has supported the development of national tuberculosis (TB) patient cost surveys to quantify the socio-economic impact of TB in high-burden countries. However, methodological differences in study design (e.g. cross-sectional vs longitudinal) can generate different estimates making the design and impact evaluation of socioeconomic protection strategies difficult. The objective of the study was to compare the socio-economic impacts of TB estimated by applying cross-sectional or longitudinal data collections in Nepal. We analysed data from a longitudinal costing survey (patients interviewed at three-time points) conducted between April 2018 and October 2019. We calculated both mean and median costs from patients interviewed during the intensive (cross-sectional 1) and continuation phases of treatment (cross-sectional 2). We then compared costs, the prevalence of catastrophic costs and the socio-economic impact of TB generated by each approach. There were significant differences in the costs and social impacts calculated by each approach. The median total cost (intensive plus continuation phases) was significantly higher for the longitudinal compared to cross-sectional 2 (US$119.42 vs 91.63, P < 0.001). The prevalence of food insecurity, social exclusion and patients feeling poorer or much poorer were all significantly higher applying a longitudinal approach. In conclusion, the longitudinal design captured important aspects of costs and socioeconomic impacts which were missed by applying a cross-sectional approach. If a cross-sectional approach is applied due to resource constraints, our data suggest the start of the continuation phase is the optimal timing for a single interview. Further research to optimize methodologies to report patient incurred expenditure during TB diagnosis and treatment is needed

Comparing cross-sectional and longitudinal approaches to Tuberculosis Patient Cost Surveys using Nepalese data : Tuberculosis cost survey approaches

The World Health Organization has supported the development of national tuberculosis (TB) patient cost surveys to quantify the socio-economic impact of TB in high-burden countries. However, methodological differences in study design (e.g. cross-sectional vs longitudinal) can generate different estimates making the design and impact evaluation of socioeconomic protection strategies difficult. The objective of the study was to compare the socioeconomic impacts of TB estimated by applying cross-sectional or longitudinal data collections in Nepal. We analysed data from a longitudinal costing survey (patients interviewed at three-time points) conducted between April 2018 and October 2019. We calculated both mean and median costs from patients interviewed during the intensive (cross-sectional 1) and continuation phases of treatment (cross-sectional 2). We then compared costs, the prevalence of catastrophic costs and the socio-economic impact of TB generated by each approach. There were significant differences in the costs and social impacts calculated by each approach. The median total cost (intensive plus continuation phases) was significantly higher for the longitudinal compared to cross-sectional 2 (US$119.42 vs 91.63,