Estimating waiting times, patient flow, and waiting room occupancy density as part of tuberculosis infection prevention and control research in South African primary health care clinics

Transmission of respiratory pathogens, such as Mycobacterium tuberculosis and severe acute respiratory syndrome coronavirus 2, is more likely during close, prolonged contact and when sharing a poorly ventilated space. Reducing overcrowding of health facilities is a recognised infection prevention and control (IPC) strategy;reliable estimates of waiting times and ‘patient flow' would help guide implementation. As part of the Umoya omuhle study, we aimed to estimate clinic visit duration, time spent indoors versus outdoors, and occupancy density of waiting rooms in clinics in KwaZulu-Natal (KZN) and Western Cape (WC), South Africa. We used unique barcodes to track attendees' movements in 11 clinics, multiple imputation to estimate missing arrival and departure times, and mixed-effects linear regression to examine associations with visit duration. 2,903 attendees were included. Median visit duration was 2 hours 36 minutes (interquartile range [IQR] 01:36–3:43). Longer mean visit times were associated with being female (13.5 minutes longer than males;p<0.001) and attending with a baby (18.8 minutes longer than those without;p<0.01), and shorter mean times with later arrival (14.9 minutes shorter per hour after 0700;p<0.001). Overall, attendees spent more of their time indoors (median 95.6% [IQR 46–100]) than outdoors (2.5% [IQR 0–35]). Attendees at clinics with outdoor waiting areas spent a greater proportion (median 13.7% [IQR 1–75]) of their time outdoors. In two clinics in KZN (no appointment system), occupancy densities of ~2.0 persons/m2 were observed in smaller waiting rooms during busy periods. In one clinic in WC (appointment system, larger waiting areas), occupancy density did not exceed 1.0 persons/m2 despite higher overall attendance. In this study, longer waiting times were associated with early arrival, being female, and attending with a young child. Occupancy of waiting rooms varied substantially between rooms and over the clinic day. Light-touch estimation of occupancy density may help guide interventions to improve patient flow.

Improving Estimates of Social Contact Patterns for Airborne Transmission of Respiratory Pathogens.

Data on social contact patterns are widely used to parameterize age-mixing matrices in mathematical models of infectious diseases. Most studies focus on close contacts only (i.e., persons spoken with face-to-face). This focus may be appropriate for studies of droplet and short-range aerosol transmission but neglects casual or shared air contacts, who may be at risk from airborne transmission. Using data from 2 provinces in South Africa, we estimated age mixing patterns relevant for droplet transmission, nonsaturating airborne transmission, and Mycobacterium tuberculosis transmission, an airborne infection where saturation of household contacts occurs. Estimated contact patterns by age did not vary greatly between the infection types, indicating that widespread use of close contact data may not be resulting in major inaccuracies. However, contact in persons >50 years of age was lower when we considered casual contacts, and therefore the contribution of older age groups to airborne transmission may be overestimated.

Estimating waiting times, patient flow, and waiting room occupancy density as part of tuberculosis infection prevention and control research in South African primary health care clinics.

Transmission of respiratory pathogens, such as Mycobacterium tuberculosis and severe acute respiratory syndrome coronavirus 2, is more likely during close, prolonged contact and when sharing a poorly ventilated space. Reducing overcrowding of health facilities is a recognised infection prevention and control (IPC) strategy; reliable estimates of waiting times and 'patient flow' would help guide implementation. As part of the Umoya omuhle study, we aimed to estimate clinic visit duration, time spent indoors versus outdoors, and occupancy density of waiting rooms in clinics in KwaZulu-Natal (KZN) and Western Cape (WC), South Africa. We used unique barcodes to track attendees' movements in 11 clinics, multiple imputation to estimate missing arrival and departure times, and mixed-effects linear regression to examine associations with visit duration. 2,903 attendees were included. Median visit duration was 2 hours 36 minutes (interquartile range [IQR] 01:36-3:43). Longer mean visit times were associated with being female (13.5 minutes longer than males; p<0.001) and attending with a baby (18.8 minutes longer than those without; p<0.01), and shorter mean times with later arrival (14.9 minutes shorter per hour after 0700; p<0.001). Overall, attendees spent more of their time indoors (median 95.6% [IQR 46-100]) than outdoors (2.5% [IQR 0-35]). Attendees at clinics with outdoor waiting areas spent a greater proportion (median 13.7% [IQR 1-75]) of their time outdoors. In two clinics in KZN (no appointment system), occupancy densities of ~2.0 persons/m2 were observed in smaller waiting rooms during busy periods. In one clinic in WC (appointment system, larger waiting areas), occupancy density did not exceed 1.0 persons/m2 despite higher overall attendance. In this study, longer waiting times were associated with early arrival, being female, and attending with a young child. Occupancy of waiting rooms varied substantially between rooms and over the clinic day. Light-touch estimation of occupancy density may help guide interventions to improve patient flow.

Impact of the Covid-19 epidemic and related social distancing regulations on social contact and SARS-CoV-2 transmission potential in rural South Africa: analysis of repeated cross-sectional surveys.

BACKGROUND: South Africa implemented rapid and strict physical distancing regulations to minimize SARS-CoV-2 epidemic spread. Evidence on the impact of such measures on interpersonal contact in rural and lower-income settings is limited. METHODS: We compared population-representative social contact surveys conducted in the same rural KwaZulu-Natal location once in 2019 and twice in mid-2020. Respondents reported characteristics of physical and conversational ('close interaction') contacts over 24 hours. We built age-mixing matrices and estimated the proportional change in the SARS-CoV-2 reproduction number (R0). Respondents also reported counts of others present at locations visited and transport used, from which we evaluated change in potential exposure to airborne infection due to shared indoor space ('shared air'). RESULTS: Respondents in March-December 2019 (n = 1704) reported a mean of 7.4 close interaction contacts and 196 shared air person-hours beyond their homes. Respondents in June-July 2020 (n = 216), as the epidemic peaked locally, reported 4.1 close interaction contacts and 21 shared air person-hours outside their home, with significant declines in others' homes and public spaces. Adults aged over 50 had fewer close contacts with others over 50, but little change in contact with 15-29 year olds, reflecting ongoing contact within multigenerational households. We estimate potential R0 fell by 42% (95% plausible range 14-59%) between 2019 and June-July 2020. CONCLUSIONS: Extra-household social contact fell substantially following imposition of Covid-19 distancing regulations in rural South Africa. Ongoing contact within intergenerational households highlighted a potential limitation of social distancing measures in protecting older adults.

Estimating ventilation rates in rooms with varying occupancy levels: Relevance for reducing transmission risk of airborne pathogens.

BACKGROUND: In light of the role that airborne transmission plays in the spread of SARS-CoV-2, as well as the ongoing high global mortality from well-known airborne diseases such as tuberculosis and measles, there is an urgent need for practical ways of identifying congregate spaces where low ventilation levels contribute to high transmission risk. Poorly ventilated clinic spaces in particular may be high risk, due to the presence of both infectious and susceptible people. While relatively simple approaches to estimating ventilation rates exist, the approaches most frequently used in epidemiology cannot be used where occupancy varies, and so cannot be reliably applied in many of the types of spaces where they are most needed. METHODS: The aim of this study was to demonstrate the use of a non-steady state method to estimate the absolute ventilation rate, which can be applied in rooms where occupancy levels vary. We used data from a room in a primary healthcare clinic in a high TB and HIV prevalence setting, comprising indoor and outdoor carbon dioxide measurements and head counts (by age), taken over time. Two approaches were compared: approach 1 using a simple linear regression model and approach 2 using an ordinary differential equation model. RESULTS: The absolute ventilation rate, Q, using approach 1 was 2407 l/s [95% CI: 1632-3181] and Q from approach 2 was 2743 l/s [95% CI: 2139-4429]. CONCLUSIONS: We demonstrate two methods that can be used to estimate ventilation rate in busy congregate settings, such as clinic waiting rooms. Both approaches produced comparable results, however the simple linear regression method has the advantage of not requiring room volume measurements. These methods can be used to identify poorly-ventilated spaces, allowing measures to be taken to reduce the airborne transmission of pathogens such as Mycobacterium tuberculosis, measles, and SARS-CoV-2.

The impact of COVID-19 control measures on social contacts and transmission in Kenyan informal settlements.

BACKGROUND: Many low- and middle-income countries have implemented control measures against coronavirus disease 2019 (COVID-19). However, it is not clear to what extent these measures explain the low numbers of recorded COVID-19 cases and deaths in Africa. One of the main aims of control measures is to reduce respiratory pathogen transmission through direct contact with others. In this study, we collect contact data from residents of informal settlements around Nairobi, Kenya, to assess if control measures have changed contact patterns, and estimate the impact of changes on the basic reproduction number (R0). METHODS: We conducted a social contact survey with 213 residents of five informal settlements around Nairobi in early May 2020, 4 weeks after the Kenyan government introduced enhanced physical distancing measures and a curfew between 7 pm and 5 am. Respondents were asked to report all direct physical and non-physical contacts made the previous day, alongside a questionnaire asking about the social and economic impact of COVID-19 and control measures. We examined contact patterns by demographic factors, including socioeconomic status. We described the impact of COVID-19 and control measures on income and food security. We compared contact patterns during control measures to patterns from non-pandemic periods to estimate the change in R0. RESULTS: We estimate that control measures reduced physical contacts by 62% and non-physical contacts by either 63% or 67%, depending on the pre-COVID-19 comparison matrix used. Masks were worn by at least one person in 92% of contacts. Respondents in the poorest socioeconomic quintile reported 1.5 times more contacts than those in the richest. Eighty-six percent of respondents reported a total or partial loss of income due to COVID-19, and 74% reported eating less or skipping meals due to having too little money for food. CONCLUSION: COVID-19 control measures have had a large impact on direct contacts and therefore transmission, but have also caused considerable economic and food insecurity. Reductions in R0 are consistent with the comparatively low epidemic growth in Kenya and other sub-Saharan African countries that implemented similar, early control measures. However, negative and inequitable impacts on economic and food security may mean control measures are not sustainable in the longer term.