Immunization Programs

We did all statistical analyses with Stata (version 12.1), accounting for stratification, clustering, and weighting of the sample. We included an additional weight, derived from a logistic regression model, which corrected for unequal probabilities of urine-sample selection, and differential sample response.¹⁹ Generally, before weighting, younger individuals; those who had had same-sex relationships; and those who engaged in high-risk behaviours, such as more partners with whom they had unprotected sex, were more likely to provide a urine sample than were other participants. We present prevalence estimates in women and men, by age group, with 95% CIs, in participants who reported at least one sexual partner over the lifetime. We examined the associations between chlamydia and high-risk HPV and demographic and behavioural variables with logistic regression and present crude odds ratios (ORs) and adjusted ORs (AORs). Multivariable analyses adjusted for two demographic variables (age and area-level deprivation [index of multiple deprivation; IMD])²⁶ and one behavioural factor (number of sexual partners in the past year; a key factor in STI epidemiology and a useful indicator for sexual health-care providers). We considered IMD to be an important predictor and possible confounder, because services and interventions are often commissioned and provided on an area-level basis. We present uptake of interventions by risk factors or target groups, in the relevant age ranges of participants aged 16–44. We compared these findings, when possible, across the three surveys. We estimated the annual rate of chlamydia diagnosis per 100 000 population (an indicator in the Public Health Outcomes Framework for England)²⁷ from self-reported chlamydia diagnoses in the past year in all participants aged 16–24 years living in England. We report coverage of HPV vaccination in women who reported any sexual experience and were eligible for the HPV catch-up immunisation programme (ie, were born between Sept 1, 1990, and Aug 31, 1995). We obtained ethics approval from Oxfordshire Research Ethics Committee A (reference 09/H0604/27). Participants gave written informed consent to anonymised testing, without the return of results, the ethical rationale for which has been previously described.²⁸ (link) Details about the preparation, testing, and quality assurance of urine samples have been published elsewhere.18 (link), 19

In Nepal, the Ministry of Health and Population (MoHP) has implemented GMP for over 20 years as a core child health and nutrition service [15 (link)]. The MoHP aims for monthly contacts for children from birth to two years of age [16 ]. With support from female community health volunteers (FCHVs), trained auxiliary health workers and auxiliary nurse midwives deliver GMP through health facilities and monthly primary health care outreach clinics (PHC ORCs) where they offer additional primary health care services, such as antenatal care, family planning, health education, and counseling. The Expanded Program on Immunization clinics may also be held on the same day (and location) as PHC ORCs.

The questionnaire was a modified version of the questionnaire published as a pre-print by Goodwin et al., (2022) [10 (link)] to establish the psychological factor underpinning vaccine hesitancy. We translated Goodman et al’s (2022) [10 (link)] measures of perceived likelihood of infection, perceived severity of illness, perceived benefits to vaccination, perceived barriers to vaccination, trust in Government and social norms into Arabic. We modified Goodwin et al’s (2022) [10 (link)] measure of vaccine hesitancy to reflect the fact that Iraq had already commenced its vaccination program and Goodwins original measure was designed for administration prior to the start of a vaccination program. Specifically, we added in a questionnaire asking the participant to report their COVID-19 vaccination status. The questionnaire was delivered online via Google forms. The link to information about the study was included in the Facebook post which advertised the study. Participants who clicked on the link in the advert were then asked to read the study information, confirm they were over 18 years of age and consent to the study. Failure to consent or being under 18 years of age prevented participants from accessing the questionnaire. All questions were presented in Arabic. All participants completed questions exploring demographics and their attitudes towards COVID-19 vaccination. Participants also indicated whether they had received any vaccination for COVID-19 (vaccinated), or whether they were unvaccinated (unvaccinated). Those who were unvaccinated also completed a further series of questions exploring their beliefs about their likelihood of infection with COVID-19, their perceptions of the severity of COVID-19 infection, the perceived benefits of vaccination, the perceived barriers to vaccination, subjective norms, and trust in government. The questionnaire took approximately 11 minutes to complete.

For each individual and each day, we calculated the cumulative outdoor travel distance recorded using the ArcGIS Tracker app from their registered household address. This was calculated by summing up the distance (d) computed by the Euclidean distance method (equation 1) between the 2 sequential outdoor GPS records. Considering the accuracy of the GPS records, we set up a 25 m radius buffer zone (the average horizontal accuracy is 25 m) around a participant’s home location. Points that fall within the buffer are considered as at-home travel activities and therefore considered as zero distance in analyses.
The equation to calculate the distance travelled by each participant is as follows:
where d(p_i, p_j) is the Euclidean distance between 2 sequential GPS points (ie, p_i and p_j); the Cartesian coordinates are (p_ix, p_iy) for p_i and (p_jx, p_jy) for p_j. We used the British National Grid as the reference system.
Our statistical analysis was conducted using an interrupted time series, where we used segmented linear regression to estimate the trends in travel patterns, with the first segment estimating the median travel distance for the cohort before vaccination and the second segment estimating the median travel distance for the cohort after vaccination. Therefore, we defined the interruption time point in our analysis as the date of the first vaccination for each individual, with negative days denoting days prior to vaccination and positive days denoting days after vaccination; for each day, we then calculated the median travel distance.
To calculate the travel trajectory before vaccination, we conducted linear regression analysis using data before vaccination to estimate the sample’s median daily travel distance from their home with time (days before vaccination) as the explanatory variable. To calculate the travel trajectory after vaccination, we conducted linear regression analysis using data after vaccination to estimate the sample’s median daily travel distance from their home with time (days after vaccination) as the explanatory variable. For both models, each day represented 1 data point, with the points for each day being the median travel distance of those who submitted readings on that day. The segmented regression equations can be found in equation 2; linear regression was chosen a priori as we expected the limitations on movement to create a stable pattern in movement. Our alternative hypothesis was that after vaccination, we would see an increase in movement that would be expressed if a₂ > a₁ (a slope change) or b₂ > b₁ when a₂ ≥ a₁ (a level change) [27 (link)] in equation 2.
Equation 2 uses segmented linear regression models with model (and subscript) 1 representing the trends before vaccination and model (and subscript) 2 representing the trends after vaccination; y_n represents the estimated median daily travel distance with coefficient a_n, x represents the days since vaccination (negative for model 1 and positive for model 2), and b_n is a constant:
y₁ = a₁x + b₁ for x < 0 (2)y₂ = a₂x + b₂ for x > 0
The UK vaccination program prioritized people by (older) age and clinical risk groups, which, in addition to differences in the socioeconomic backgrounds between those invited and accepting a vaccination, meant that selecting an appropriate control group for this analysis was not feasible.