estimating the contribution of transmission in primary healthcare clinics to community wide CORD-Papers-2022-06-02 (Version 1)

Title: Estimating the contribution of transmission in primary healthcare clinics to community-wide TB disease incidence and the impact of infection prevention and control interventions in KwaZulu-Natal South Africa
Abstract: BACKGROUND: There is a high risk of Mycobacterium tuberculosis (Mtb) transmission in healthcare facilities in high burden settings. WHO guidelines on tuberculosis (TB) infection prevention and control (IPC) recommend a range of measures to reduce transmission in healthcare settings. These were evaluated primarily based on evidence for their effects on transmission to healthcare workers in hospitals. To estimate the overall impact of IPC interventions it is necessary to also consider their impact on community-wide TB incidence and mortality. METHODS: We developed an individual-based model of Mtb transmission in households primary healthcare (PHC) clinics and all other congregate settings. The model was parameterised using data from a high HIV prevalence community in South Africa including data on social contact by setting by sex age and HIV/antiretroviral therapy status; and data on TB prevalence in clinic attendees and the general population. We estimated the proportion of disease in adults that resulted from transmission in PHC clinics and the impact of a range of IPC interventions in clinics on community-wide TB. RESULTS: We estimate that 7.6% (plausible range 3.9%13.9%) of non-multidrug resistant and multidrug resistant TB in adults resulted directly from transmission in PHC clinics in the community in 2019. The proportion is higher in HIV-positive people at 9.3% (4.8%16.8%) compared with 5.3% (2.7%10.1%) in HIV-negative people. We estimate that IPC interventions could reduce incident TB cases in the community in 20212030 by 3.4%8.0% and deaths by 3.0%7.2%. CONCLUSIONS: A non-trivial proportion of TB results from transmission in clinics in the study community particularly in HIV-positive people. Implementing IPC interventions could lead to moderate reductions in disease burden. We recommend that IPC measures in clinics should be implemented for their benefits to staff and patients but also for their likely effects on TB incidence and mortality in the surrounding community.
Published: 2022-04-08
Journal: BMJ Glob Health
DOI: 10.1136/bmjgh-2021-007136
DOI_URL: http://doi.org/10.1136/bmjgh-2021-007136
Author Name: McCreesh Nicky
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mccreesh_nicky
Author Name: Karat Aaron S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/karat_aaron_s
Author Name: Govender Indira
Author link: https://covid19-data.nist.gov/pid/rest/local/author/govender_indira
Author Name: Baisley Kathy
Author link: https://covid19-data.nist.gov/pid/rest/local/author/baisley_kathy
Author Name: Diaconu Karin
Author link: https://covid19-data.nist.gov/pid/rest/local/author/diaconu_karin
Author Name: Yates Tom A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yates_tom_a
Author Name: Houben Rein MGJ
Author link: https://covid19-data.nist.gov/pid/rest/local/author/houben_rein_mgj
Author Name: Kielmann Karina
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kielmann_karina
Author Name: Grant Alison D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grant_alison_d
Author Name: White Richard
Author link: https://covid19-data.nist.gov/pid/rest/local/author/white_richard
sha: 60c54459a428d36a05202575a7551996a14c8480
license: cc-by
license_url: https://creativecommons.org/licenses/by/4.0/
source_x: Medline; PMC
source_x_url: https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/
pubmed_id: 35396264
pubmed_id_url: https://www.ncbi.nlm.nih.gov/pubmed/35396264
pmcid: PMC8995945
pmcid_url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8995945
url: https://doi.org/10.1136/bmjgh-2021-007136 https://www.ncbi.nlm.nih.gov/pubmed/35396264/
has_full_text: TRUE
Keywords Extracted from Text Content: HIV-negative/unknown Ws = smear+ HIV/ART TB_mortality_rate_smearneg_HIV1 patients Ws = 1 TB_parameter_change_year HIV2_mortality_rate HIVstatus HIV+ people self_cure_rate_HIV0 HIV-test HIV+ ART_intro_year smear-disease TB_mortality_rate_treatment sections treatment_rate_HIV12_late treatment_rate_change_year outside-household adolescents CD4 HIVand Thembisa HIVpeople patient TB_treatment_dropout_rate_MDR reinfection_relative_risk_HIV0 reinfection_relative_risk_HIV1 HIV0 ACH HIV+ART+ spaces HIV-with individuals people HIV1_mortality_rate contact_time_other children Mtb infections People reinfection_relative_risk MDR-TB reinfection_relative_risk_HIV1_late anti-retroviral female smear+ (smear-) disease, UO S3 HIV+ART- treatment_rate_HIV12_early clinic_group self_cure_rate_HIV2 TB_mortality_rate_smearneg_HIV2 transmission_prob birth_rate data=6.55 transmission_prob_early * HIV+ART-people HIV+ART+ people HIV- TB_mortality_rate_treatment_DS reinfection_relative_risk_HIV1_early clinic_rate_switch_prob women reduced_transmission_smearneg contact_time_clinic_m_HIV01_low prop_smearpos_HIV0 self-cure smear+ (smear-) disease. transmission_prob_early HIV+ARTand latently non-MDR-TB develop_tb_reactivation_rate_HIV1_early Mtb model=6.75 TB_mortality_rate_smearpos_HIV2 HIV-and latently upper bounds latently ART_start_rate_change_year men reinfection_relative_risk_HIV2 line
Extracted Text Content in Record: First 5000 Characters:Respondents were asked if they knew their HIV status. Respondents who reported being HIVpositive were asked if they were on anti-retroviral therapy (ART). Respondent household size was extracted from existing DSA data. Respondents were asked to list all indoor locations visited and transport used on an assigned day in the week before the survey. For each location visited (including their own home), they were asked for further details, including: • What type of location it was (options included 'own home' and 'clinic') • How long they spent there • How many people (adults and children) were there, halfway through the time they were there • How many of those people were children aged <15 years For each use of transport reported, they were asked for further details, including: • What type of transport it was • How long the journey took • How many people (adults and children) were on the vehicle at the start of the trip • How many of those people were children aged <15 years Respondents were also asked for additional details on their clinic visiting behaviour during the six months prior to the interview, including: • The number of days on which they had visited a clinic for their own health in the past six months • The number of days on which they had visited a clinic for on the behalf of someone else (e.g. to collect a prescription) in the past six months, not included any visits that were also made for their own health • The number of days on which they had accompanied someone else to a clinic in the past six months, not including any visits that were also made for their own health and/or on behalf of someone else Finally, respondents were asked when their last visit to a clinic was, and, if it was within the past two years, they were asked for the following information about their last visit: • How long they spent at the clinic • How many people (adults and children) were there, halfway through the time they were there • How many of those people were children aged < 15 Further details of the social contact survey are given in McCreesh et al 1 . For each location visited on the assigned day, adult contact times were calculated as follows. Firstly, the number of adults present was calculated as the reported total number of people present, minus the reported number of children present. If this gave a value less than zero, it was set to missing. The number of adults present was then capped at 100, as above this value, it is unlikely that the respondent had sufficient contact with each adult present to allow transmission. The capped number of adults present was then multiplied by the duration of time that the respondent reported spending in the location, to give the adult contact time. Estimates generated using the data on the respondent's last clinic visit were weighted by the reported number of clinic visits in the past six months. Respondents who reported being HIV-positive were considered to be HIV-positive. Otherwise, respondents were considered to be HIV-negative/unknown. Of the 3090 people sampled for UO, 1723 (56%) were successfully contacted, 298 (10%) were dead or reported to have out-migrated, 1071 (35%) could not be contacted. Of those successfully contacted, 1704 (99%) completed an interview (Table S1 ). Table S3 shows the estimated mean annual number of visits made to clinics, by sex, age, and HIV status, estimated from data on reported clinic visits in the past day, and in the past six months. Overall, there is little difference between the estimates calculated using the data collected using the two different recall durations. The exception to this is the estimates by sex, where there is a large difference in mean annual clinic visits by sex using the six-month recall data, but not the one-day recall data. However, the confidence intervals for the one-day recall estimates contain the estimated values for the six-month recall. As there is no evidence that recall bias has had a large effect on the estimates, the six-month recall data are used to parameterise clinic visiting rates in the model, due to their greater precision. Other locations are defined as indoor locations other than clinics and the respondents' own homes, and transport. Two types of agents were simulated in the model, people and households. The main state variables assigned to people in the model were: • Unique ID -person_ID • Age group -age_group (15-29, 30-49, 50-79) • Sexsex (male, female) • Clinic visiting group -clinic_group (high, low) Households were simulated as agents, for the purpose of grouping people into households with the desired size distributions. Households had the following state variables: Other temporary household-level state variables were used to store information on the disease states of household members when estimating transmission probabilities in the household (see section 'Mtb transmission -Household members') Empirical data were available from the study population on the number of people aged 15+ yea
Keywords Extracted from PMC Text: MDR-TB Mtb clinics.5 patients people people's MDR People patient clinics-based PHC studies.15 16 non-MDR-TB limited,14 's COVID-19 children HIV IPC locations,8
Extracted PMC Text Content in Record: First 5000 Characters:Tuberculosis (TB) is a major global public health problem, killing an estimated 1.4 million people in 2019.1 There is a high risk of transmission in healthcare facilities in high TB burden settings, evidenced by the elevated rate of TB in healthcare workers.2 Updated WHO guidelines on TB infection prevention and control (IPC) recommend a wide range of measures to reduce transmission in healthcare and institutional settings, ranging from triaging people with TB symptoms to installing ultraviolet germicidal irradiation (UVGI) systems.2 These measures were evaluated and implemented as recommendations in the guidelines primarily based on evidence on their effects on risk to healthcare workers, and in hospitals settings. Protecting healthcare workers should be a key concern of TB control programmes. However, the motivation for, and potential benefits of, IPC interventions in clinics extend beyond the reductions in disease burden among clinic staff. While healthcare workers and other clinic staff are at the highest risk of infection in clinics, due to their longer durations of exposure, the numbers of patients and other clinic attendees are far higher than numbers of staff. It is therefore likely that a large proportion of clinic-acquired TB is in patients and other clinic attendees. As a consequence, it is imperative that the impact on TB incidence in the wider community is considered when estimating the likely impacts of IPC measures. Estimating the contribution of transmission in clinics (or other congregate settings) to overall community-wide disease burden is challenging. Taylor et al used data on ventilation rates and a Wells-Riley approach to estimate a 0.03% risk of infection to patients per clinic visit. This approach is heavily dependent on estimates of mean quanta production rates, however, about which there is considerable uncertainty (their sensitivity analysis gave a wide range of 0.02%–0.35%). Andrews et al also used a Wells-Riley based approach to determine infection risk by location (although not clinics), but removed the dependence on an assigned value for the quanta production rate by using data on contact time in multiple types of location, and calibrating their model to the prevalence of infection by age.3 In this work, we used a social contact data-based approach similar to that adopted by Andrews et al, but used an individual-based model (IBM) that includes HIV/antiretroviral therapy (ART) and TB disease development and resolution, and calibrated the model to overall disease incidence. This allowed us to determine the contribution of primary healthcare (PHC) clinics not only to the incidence of infection, but also to community-wide disease incidence and mortality. This is important for determining the true contribution of clinics-based transmission to disease burden, due to the increased rates of clinic attendance by people at increased risk of progression to disease.4 We also incorporated empirical data on the increased prevalence of TB in PHC clinic attendees compared with the general population, something that acts to amplify transmission in clinics.4 The study community used was the population living in the catchment area of two PHC clinics in KwaZulu-Natal province, South Africa. The IPC interventions we simulated were identified and parameterised through a rigorous multidisciplinary approach. This work forms part of the Umoya omuhle project, that used a whole systems approach to study IPC in primary healthcare facilities in South Africa. As part of the project, system dynamics modelling was used to identify potential IPC interventions that local policy makers and health professionals active at clinic and province levels ranked highly in terms of both feasibility of implementation and perceived likely impact on overall and multidrug resistant (MDR) Mycobacterium tuberculosis (Mtb) transmission in clinics.5 The impact of the interventions on the rate of Mtb transmission to clinic attendees was then estimated using an IBM that simulated the flow of patients through clinics, and ventilation rates and infection risk in clinic waiting areas.6 A social contact survey was conducted in the catchment areas of two primary healthcare clinics in the southern section of the Africa Health Research Institute (AHRI) demographic surveillance area (DSA),7 in March–December 2019. Three thousand and ninety-three adults (aged 18 years and over) were sampled, stratified by local area. Respondents were asked to list all indoor locations visited and transport used on an assigned day in the week before the survey. For each location visited (including their own home) and transport used, they were asked for further details, including the type of location, the duration of time they spent there and the number of other people present. Respondents were also asked the number of times they had visited clinics in the 6 months before the interview, and how long they spent at the clinic and a cross-sectional estimate of
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