Abstract
Background.
Timely vaccination maximizes efficacy for preventing infectious diseases. In the absence of national vaccination registries, representative sample survey data hold vital information on vaccination coverage and timeliness. This study characterizes vaccination coverage and timeliness in Tanzania and provides an analytic template to inform contextually relevant interventions and evaluate immunization programs.
Methods.
Cross-sectional data on 6,092 children under age 3 from the 2015–16 Tanzania Demographic and Health Survey were used to examine coverage and timeliness for 14 vaccine doses recommended in the first year of life. The Kaplan-Meier method was used to model time to vaccination. Cox proportional hazard models were used to examine factors associated with timely vaccination.
Results.
Substantial rural-urban disparities in vaccination coverage and timeliness were observed for all vaccines. Across 14 recommended doses, documented coverage ranged from 52% to 79%. Median vaccination delays lasted up to 35 days; gaps were larger among rural than urban children and for later doses in vaccine series. Among rural children, median delays exceeded 35 days for the 3rd doses of the polio, pentavalent, and pneumococcal vaccines. Median delays among urban children were <21 days for all doses. Among rural and urban children, lower maternal education and delivery at home were associated with increased risk of delayed vaccination. In rural settings, less household wealth and greater distance to a health facility were also associated with increased risk of delayed vaccination.
Discussion.
This study highlights persistent gaps in uptake and timeliness of childhood vaccinations in Tanzania and substantial rural-urban disparities. While the results provide an informative situation assessment and outline strategies for identifying unvaccinated children, a national electronic registry is critical for comprehensive assessments of the performance of vaccination programs. The timeliness measure employed in this study—the amount of time children are un- or undervaccinated—may serve as a sensitive performance metric for these programs.
Keywords: Childhood vaccination, Vaccination coverage, Vaccination timeliness, Rural-urban disparities, Tanzania, Demographic and Health Survey
Background
Timely uptake of vaccines is essential to ensure immunity against preventable and potentially deadly infections in children. Age recommendations for vaccine uptake are designed to induce adequate protective immunity, maximize vaccine efficacy, and prevent mortality [1–3]. Consequently, delays in vaccine uptake can leave children susceptible to vaccine-preventable infections. Recent vaccine-preventable outbreaks have occurred in pockets of low immunization, where parents have delayed or refused vaccines for their children [4–6]. Traditional benchmarks for the success of routine childhood vaccination programs rely exclusively on vaccination coverage, i.e., whether a child received a vaccine or not. While such benchmarks include children who never receive vaccines, they may not account for those who receive vaccines late, and therefore, remain at risk during outbreaks.
The World Health Organization (WHO) recommends a vaccination schedule for children from birth through one year of age comprising vaccines against tuberculosis, polio, diphtheria, pertussis, tetanus, hepatitis B, haemophilus influenzae type B (Hib), rotavirus, pneumococcal disease, rubella and measles [7]. Several studies in the literature have highlighted low timeliness in the uptake of these vaccines, particularly in low- and middle-income countries (LMICs) [8–10]. In Tanzania, for example, only 68% of children are estimated to receive all basic vaccines in the first year of life, with low caregiver knowledge and health system challenges noted as barriers to timely vaccination [11–13]. In a study using 2009–10 Demographic and Health Survey (DHS) data, authors reported that 33% of Bacillus Calmette-Guérin (BCG) vaccinations in Tanzania’s Lindi and Mtwara regions were administered more than one month after birth, with a median age at vaccination of 2 weeks [14]. In a comparison of urban Dar es Salaam and rural Morogoro Region, Nadella et al. showed extensive regional variation in the proportion of infants with delayed Diphtheria, Tetanus, and Pertussis (DTP) dose 1 and 3 vaccinations, ranging from 1.8% to 69% [15]. Since delayed vaccination correlates with greater mortality and higher risk of hospitalization from vaccine-preventable diseases and associated complications, efforts to reduce delays are critical for improving child health outcomes [16, 17]. Furthermore, as vaccination delays are associated with non-completion of the recommended vaccination schedule [18], continuous tracking of vaccination timeliness could enable the proactive identification of un- and under-vaccinated infants and pockets of low immunization to focus the efforts of health care infrastructure investments, mass or catch-up immunization campaigns, disease surveillance, and outbreak response.
There are three considerations in identifying and addressing low vaccination timeliness. First, the measurement of timeliness requires reliable documentation of vaccination dates and the date of birth of the child. In many LMICs, these data are documented in paper records at the individual and health facility levels, with only aggregate summaries of vaccine coverage reported to the district health information systems. In these settings, large sample surveys, such as the DHS, constitute valuable sources of information that capture dates of vaccinations from government-issued vaccination cards. While the cross-sectional nature of these data does not permit continuous monitoring of vaccination timeliness, they are nationally representative and may be used to identify pockets of low timeliness from a programmatic perspective.
Second, the measurement of timeliness is complicated by the lack of a consensus definition. In a systematic review of studies of vaccination timeliness, Masters et al. reported that a majority of studies classified vaccines administered within one month of the recommended age of vaccination as timely [9]. The review also reported low consensus regarding the selection of a categorical versus continuous measure of vaccine timeliness. While the categorical measure allows for a representation of the ‘burden’ of unvaccinated children, a continuous measure allows for the estimation of the magnitude and distribution of delays, in terms of the number of unvaccinated days.
Third, the correlates of timely vaccinations can vary widely by geographic context. For instance, in information-rich, high-income settings, vaccine-related concerns such as a spurious link between vaccines and autism lead some parents to delay or ‘space-out’ vaccines [19]. In contrast, in LMICs, service unreliability, missed opportunities for vaccination, and low knowledge of the vaccination schedule among parents are considered primary contributors to delays [20]. Although overarching domains of such correlates are well-documented, the design of interventions to improve vaccination timeliness must be based on the specific, contextually relevant correlates.
In this study, we characterize the rates and correlates of vaccination coverage and timeliness in Tanzania using descriptive and survival-analytic methods and explore rural-urban disparities. We use data from the 2015–16 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2015–16 TDHS-MIS) and represent timeliness using a categorical definition as well as a continuous variable describing the number of unvaccinated days. The goals of this study are to characterize vaccination timeliness in this setting and to develop an analytic and reporting template that may be used in other settings to inform the design, implementation, and evaluation of contextually relevant interventions to improve timeliness. Improving the timeliness of vaccine uptake will reduce windows of time when children are unprotected and may reduce preventable morbidity, health care use, and mortality.
Methods
Methods are described in concordance with the STROBE reporting guidelines for cross-sectional studies [21] (Supplementary Table 1) and the statistical and analytical guidelines checklist for this journal [22].
Ethics statement
This study used non-identifiable public use data and thus is exempt from Institutional Review Board (IRB) review.
Data and study design
This study involves the cross-sectional analysis of data from the 2015–16 TDHS-MIS [13]. Results are representative of children born between 2012 and 2016, under age 3, and alive at the time of the survey. The analysis was not hypothesis driven; instead, this study aimed to explore the rates, magnitude, and correlates of vaccination delays in Tanzania to inform the design and implementation of contextually relevant interventions to improve timeliness.
Between August 2015 and February 2016, the 2015–16 TDHS-MIS collected data from a representative probability sample of Tanzanian households selected through a two-stage cluster sampling process. The first stage identified 608 enumeration areas from the 2012 Tanzania Population and Housing Census, including up to 37 enumeration areas per administrative region. A household listing operation was carried out in each of the enumeration areas and served as the sampling frame for the second stage. In the second stage, a total of 13,376 households (22 households from each enumeration area) were pre-selected for TDHS-MIS data collection [13, 23]. To prevent bias, non-responding households were not replaced, but approached two to three times to reduce household non-response [13]. Within the 12,536 households surveyed, 7,050 women between the ages of 15 to 49 were mothers of children under age 5 [13]. The TDHS-MIS collected immunization data for 6,092 children who were born between 2012 and 2016, and under age 3 and alive at the time of the survey [13]. Please see the flowchart of the study population and sample size in Supplementary Figure 1. Birth and vaccination dates were extracted from the 2015–16 Tanzania TDHS-MIS public use data: Children’s recode file TZKR7HFL [24].
Study setting
Since 1975, Tanzania has an active routine childhood immunization program [25]. In the last decade, several new vaccines were added to Tanzania’s immunization schedule including the Pneumococcal Conjugate Vaccine (3 dose series, 2013), Rotarix vaccine (2 dose series, 2013), and Measles-Rubella combination vaccine (2 doses, 2014) [11, 26, 27]. With the addition of these vaccines, Tanzania’s immunization schedule now includes 14 doses of 6 different vaccines prior to age 1 year, providing protection against 11 diseases (Supplementary Table 2) [7, 13].
Vaccination coverage
Information on vaccination status was collected from the child’s vaccination card (vaccine uptake and date of administration) and, when written documentation was unavailable, from direct reports from the child’s mother (vaccine uptake only) [13, 28]. In cases where written documentation was unavailable and the mother could not recall whether the child received a particular vaccine, the child was considered unvaccinated. This approach is consistent with methodology outlined in the Guide to DHS Statistics and recent publications [8, 29, 30]. For vaccines that were part of a multi-dose series, gaps in the vaccination history were handled by applying logic defined in the Guide to DHS Statistics that prioritizes the number of doses known to have been administered over the position a specific dose was assigned in the vaccination record [30]. Missing vaccinations (i.e., cases where written documentation was unavailable and the mother could not recall whether the child received a particular vaccine) were assumed to have not been given and, where available, information from subsequent doses were “shifted” to fill in the gap. For example, if there was no record of a child receiving the first (Penta1) dose of the pentavalent vaccine but the second (Penta2) and third (Penta3) doses were recorded, information related to Penta2 was recoded to Penta1 and information related to Penta3 was recoded to Penta2.
Vaccination timeliness
For children with a vaccination card, vaccination timing was determined by comparing the child’s age at vaccine uptake with the recommended age of vaccination. Age at vaccination (measured in days) was calculated as the difference between the documented date of vaccine administration and the child’s date of birth [7]. The child’s date of birth was ascertained via the mother’s self-report. Missing data for the month and year of birth were imputed as outlined in the Guide to DHS Statistics [30]. Cases where the date of vaccine administration was not recorded, only partially recorded, or invalid (e.g., vaccination given before date of birth) resulted in unknown timing and, thus, were excluded from analyses of vaccination timeliness. The percentage of children with written documentation of a vaccination but unknown timing ranged from less than 1% to 4.5%, depending on the vaccine and population of interest. In descriptive analyses, vaccinations were classified as “timely” if received prior to or within 4 weeks (28 days) of the recommended age or “delayed” if received more than 4 weeks beyond the recommended age. In multivariable analyses, time to vaccination was measured continuously.
Statistical analysis
Statistical analyses were implemented in STATA version 16 (StataCorp, College Station, Texas); all analyses accounted for the complex survey design of the TDHS-MIS through the application of sampling weights available in the TDHS-MIS files and appropriate survey commands (e.g., svyset and svy) in STATA [30]. To account for changes in guidelines in January 2013, coverage and timeliness of the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013. Timeliness of the measles vaccine was not assessed for children born after 2014 because, at the time of data collection, most children (83%) born after 2014 were age ineligible for the vaccine. Socio-demographic characteristics were analyzed using descriptive statistics; the statistical significance of differences between rural and urban residents was evaluated using two-tailed Student’s t-tests for continuous and chi-squared tests for categorical variables. Descriptive analyses of vaccination documentation, coverage, and timeliness were restricted to age-eligible children, i.e., the subsample of children who were old enough to receive a particular vaccine dose at the time of the survey.
Time to vaccination was modeled using survival analysis techniques where the event of interest was defined as receipt of a particular vaccine dose, the time scale was measured in days (relative to birth or the recommended age of vaccination), and data were considered right censored if the child had not received the vaccine by the time of the survey. The Kaplan-Meier method was used to obtain age-specific estimates of vaccination coverage and to calculate the median and interquartile range of vaccination delays. Cox proportional hazard models were used to examine factors associated with timely vaccination. The recommended age of vaccination was designated as time zero. If a child was vaccinated at or prior to the recommended age, the time to vaccine initiation was adjusted to immediately after time zero (recoded to 1 × 10−6 days after time zero). Based on a review of the literature [8, 9, 14, 15, 18, 29, 31–44], covariates included: distance to the nearest health facility (kilometers), household wealth (wealth index factor score, rescaled to a mean of 0 and a standard deviation of 1), mother’s age at delivery (years), mother’s education (no education vs. primary school vs. secondary school or higher), birth order (first born vs. later born child), number of antenatal care visits (0–3 vs. 4+ visits or unknown/missing), number of tetanus toxoid (TT) vaccinations received (0 vs. 1, 2, or 3+ doses or unknown/missing), child’s place of delivery (health facility vs. home), and year of birth (2013 vs. 2014 vs. 2015). Models were estimated for the full sample (with a binary indicator for rural residence) and separately for rural and urban children. The proportional hazards assumption was tested by including interactions of the covariates and time.
Results
Table 1 shows key characteristics of 6,092 mothers and their eligible children. In line with the geographic distribution of the population in Tanzania, nearly three quarters of mothers (72.9%) were living in rural areas. Urban children were more likely to live closer to health facilities (1.9 vs. 5.0 km from the nearest health facility, on average; p<0.001) and in wealthier households (1.1 standard deviation above vs 0.4 standard deviations below the mean DHS wealth index score; p<0.001), compared to their rural counterparts. While there was no difference in mothers’ age at delivery by urban versus rural residence (p=0.235), mothers of urban children had higher education (33.5% vs 9.5% with secondary or higher education; p<0.001), greater numbers of antenatal care visits (59.2% vs 38.1% with 4 or more ANC visits; p<0.001) and TT doses (19% vs 8.2% with 3 or more TT doses; p<0.001) compared to mothers of rural children. A significantly higher proportion of urban women than rural women reported on their first child (32.5% vs 23.2%; p<0.001) and had delivered in a health facility (88.2% vs 58.1%; p<0.001). Urban mothers were also marginally more likely to show written documentation of their child’s vaccination history compared to rural mothers (79.6% vs 79.2%; p=0.008).
Table 1.
Characteristics of mothers and their children under age 3, 2015–16 TDHS-MIS
| All | Rural | Urban | ||
|---|---|---|---|---|
| (N=6,092) | (N=4,667; 72.9%) | (N=1,425; 27.1%) | ||
| Mean (sd) | Mean (sd) | Mean (sd) | ||
| or N (%) | or N (%) | or N (%) | p-value | |
| Distance to health facility | ||||
| (km) | 4.15 (4.85) | 4.99 (5.45) | 1.89 (1.82) | <0.001 |
| Wealth index score | ||||
| (sds from mean) | −0.02 (1.01) | −0.44 (0.58) | 1.12 (0.98) | <0.001 |
| Mother’s age at delivery | ||||
| (years) | 26.5 (7.0) | 26.59 (7.40) | 26.28 (5.87) | 0.235 |
| Mother’s education | <0.001 | |||
| None | 1,243 (20.0%) | 1,135 (24.8%) | 108 (7.3%) | |
| Primary | 3,642 (64.0%) | 2,857 (65.7%) | 785 (59.2%) | |
| Secondary | 1,148 (15.0%) | 663 (9.3%) | 485 (30.3%) | |
| Higher | 59 (1.0%) | 12 (0.2%) | 47 (3.2%) | |
| Birth order | <0.001 | |||
| First child | 1,492 (25.7%) | 1,039 (23.2%) | 453 (32.5%) | |
| Second or higher | 4,600 (74.3%) | 3,628 (76.8%) | 972 (67.5%) | |
| Antenatal care visits | <0.001 | |||
| 0–3 | 2,782 (45.0%) | 2,273 (49.8%) | 509 (32.2%) | |
| 4 or more | 2,577 (43.8%) | 1,787 (38.1%) | 790 (59.2%) | |
| Unknown/missing | 733 (11.1%) | 607 (12.1%) | 126 (8.6%) | |
| Tetanus toxoid doses | <0.001 | |||
| 0 | 1,464 (22.8%) | 1,229 (25.7%) | 235 (15.2%) | |
| 1 | 1,346 (21.0%) | 1,072 (22.4%) | 274 (17.4%) | |
| 2 | 1,983 (34.0%) | 1,424 (31.9%) | 559 (39.6%) | |
| 3 or more | 573 (11.1%) | 343 (8.2%) | 230 (19.0%) | |
| Unknown/missing | 726 (11.0%) | 599 (11.9%) | 127 (8.8%) | |
| Child’s place of delivery | <0.001 | |||
| Health facility | 4,002 (66.3%) | 2,738 (58.1%) | 1,264 (88.2%) | |
| Home | 2,090 (33.7%) | 1,929 (41.9%) | 161 (11.8%) | |
| Child’s age at time of survey | 0.284 | |||
| < 1 year | 2,058 (33.7%) | 1,595 (34.0%) | 463 (33.1%) | |
| 1 year | 2,158 (35.8%) | 1,613 (35.1%) | 545 (37.8%) | |
| 2 years | 1,876 (30.5%) | 1,459 (31.0%) | 417 (29.1%) | |
| Vaccination documentation | 0.008 | |||
| Documentation seen | 4,848 (79.3%) | 3,717 (79.2%) | 1,131 (79.6%) | |
| Documentation not seen | 154 (2.4%) | 95 (1.9%) | 59 (3.9%) | |
| No documentation | 1,090 (18.3%) | 855 (18.9%) | 235 (16.5%) | |
| Year of birth | <0.001 | |||
| 2012 | 229 (3.7%) | 141 (2.7%) | 88 (6.4%) | |
| 2013 | 1,856 (30.7%) | 1,439 (31.2%) | 417 (29.3%) | |
| 2014 | 2,146 (35.4%) | 1,607 (34.6%) | 539 (37.6%) | |
| 2015 | 1,821 (29.6%) | 1,443 (30.7%) | 378 (26.5%) | |
| 2016 | 40 (0.6%) | 37 (0.7%) | 3 (0.2%) |
Notes: Means and percentages calculated using weights provided in the TDHS-MIS. Statistical significance of urban-rural differences evaluated using two-tailed Student’s t-tests for continuous variables and chi-squared tests for categorical variables.
km: kilometers; sd: standard deviation; TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Table 2 shows information on coverage and timeliness for 14 vaccine doses recommended by age 1 year, in aggregate and separately for rural and urban children. The data suggest substantial gaps in vaccination documentation, coverage, and timeliness. For nearly one in six children, vaccine uptake was reported by the mother, but administration could not be verified based on vaccination card data. While estimated vaccination coverage (based on written documentation and/or maternal report) for the BCG vaccine exceeded 90%, coverage for the birth dose of the polio vaccine (OPV0) was less than 70%. For vaccines that are recommended as a multi-dose series, rates of coverage and timeliness decreased with each additional dose. Applying the most common definition of timeliness, i.e., administration within one month (28 days) of the recommended age of vaccination, the proportion of children receiving timely pentavalent vaccines, for example, decreased from 82% for the first dose (due 6 weeks after birth) to 69% for the second dose (due 10 weeks after birth) to 57% for the third dose (due 14 weeks after birth). For all vaccine doses, rates of total coverage, rates of documented vaccination coverage, rates of maternal vaccination recall, and rates of vaccination timeliness, were higher among urban children compared to rural children. The decline in rates of vaccination timeliness over the course of multi-dose vaccination series was also greater among rural than urban children. For example, rural children experienced a 27-percentage-point decrease in timeliness between the first and third doses of the OPV vaccination series; the corresponding decrease among urban children was 16 percentage points.
Table 2.
Vaccination coverage and timeliness of administration, 2015–16 TDHS-MIS
| All children | Vaccination Coverage# | Vaccination Timeliness‡ | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Vaccine | N | Documented | Maternal recall | Not covered | Grace period | Unknown | N | Timely | Delayed | Unknown |
| BCG | 6,092 | 76.12% | 16.54% | 5.77% | 1.57% | 0.00% | 4,661 | 64.19% | 33.50% | 2.31% |
| OPV0 | 6,092 | 52.47% | 12.73% | 33.09% | 1.70% | 0.02% | 3,093 | 84.29% | 11.42% | 4.29% |
| OPV1 | 5,820 | 78.45% | 15.71% | 4.67% | 1.16% | 0.02% | 4,584 | 81.07% | 17.11% | 1.83% |
| OPV2 | 5,650 | 74.91% | 14.50% | 8.84% | 1.73% | 0.02% | 4,237 | 68.76% | 29.70% | 1.54% |
| OPV3 | 5,489 | 70.58% | 8.24% | 19.35% | 1.82% | 0.02% | 3,877 | 57.05% | 40.97% | 1.98% |
| Penta1 | 5,820 | 79.44% | 15.43% | 3.96% | 1.13% | 0.04% | 4,631 | 82.31% | 16.66% | 1.03% |
| Penta2 | 5,650 | 76.31% | 15.22% | 6.78% | 1.65% | 0.05% | 4,308 | 69.50% | 29.57% | 0.93% |
| Penta3 | 5,489 | 72.77% | 12.87% | 12.49% | 1.82% | 0.05% | 3,983 | 57.50% | 41.00% | 1.50% |
| PCV1† | 5,591 | 77.05% | 14.88% | 6.63% | 1.17% | 0.27% | 4,331 | 81.74% | 16.81% | 1.45% |
| PCV2† | 5,421 | 73.18% | 14.48% | 10.35% | 1.71% | 0.28% | 3,990 | 69.46% | 29.26% | 1.27% |
| PCV3† | 5,260 | 68.94% | 12.79% | 16.05% | 1.93% | 0.29% | 3,636 | 57.25% | 40.99% | 1.76% |
| Rota1† | 5,591 | 75.57% | 14.79% | 8.06% | 1.19% | 0.41% | 4,250 | 81.09% | 17.49% | 1.42% |
| Rota2† | 5,421 | 71.29% | 14.25% | 12.29% | 1.76% | 0.42% | 3,879 | 67.95% | 30.67% | 1.39% |
| Measles | 4,526 | 66.68% | 18.00% | 13.00% | 2.21% | 0.10% | 2,997 | 62.56% | 34.37% | 3.07% |
| Rural children | Vaccination Coverage# | Vaccination Timeliness‡ | ||||||||
| Vaccine | N | Documented | Maternal recall | Not covered | Grace period | Unknown | N | Timely | Delayed | Unknown |
| BCG | 4,667 | 75.40% | 15.55% | 7.22% | 1.83% | 0.00% | 3,558 | 58.30% | 39.47% | 2.24% |
| OPV0 | 4,667 | 46.29% | 11.02% | 40.76% | 1.94% | 0.00% | 2,160 | 82.95% | 12.53% | 4.52% |
| OPV1 | 4,456 | 78.03% | 14.92% | 5.82% | 1.23% | 0.00% | 3,498 | 77.75% | 20.76% | 1.48% |
| OPV2 | 4,331 | 73.61% | 13.67% | 10.72% | 2.01% | 0.00% | 3,205 | 63.28% | 35.46% | 1.27% |
| OPV3 | 4,207 | 68.90% | 8.21% | 20.79% | 2.11% | 0.00% | 2,918 | 50.42% | 47.96% | 1.62% |
| Penta1 | 4,456 | 79.04% | 14.74% | 5.02% | 1.18% | 0.02% | 3,536 | 78.71% | 20.42% | 0.87% |
| Penta2 | 4,331 | 75.21% | 14.10% | 8.71% | 1.96% | 0.02% | 3,266 | 63.90% | 35.33% | 0.77% |
| Penta3 | 4,207 | 71.11% | 11.77% | 14.97% | 2.14% | 0.02% | 3,002 | 51.02% | 47.76% | 1.22% |
| PCV1† | 4,315 | 75.72% | 14.54% | 8.25% | 1.18% | 0.30% | 3,304 | 78.30% | 20.55% | 1.15% |
| PCV2† | 4,190 | 70.96% | 14.05% | 12.66% | 2.02% | 0.31% | 3,017 | 64.10% | 34.85% | 1.05% |
| PCV3† | 4,066 | 66.16% | 12.26% | 19.01% | 2.24% | 0.32% | 2,730 | 50.98% | 47.53% | 1.49% |
| Rota1† | 4,315 | 74.06% | 14.33% | 10.00% | 1.20% | 0.42% | 3,232 | 78.60% | 20.49% | 0.91% |
| Rota2† | 4,190 | 68.80% | 13.51% | 15.17% | 2.08% | 0.44% | 2,919 | 63.69% | 35.25% | 1.07% |
| Measles | 3,448 | 65.54% | 16.75% | 15.57% | 2.03% | 0.11% | 2,261 | 58.56% | 38.73% | 2.71% |
| Urban children | Vaccination Coverage# | Vaccination Timeliness‡ | ||||||||
| Vaccine | N | Documented | Maternal recall | Not covered | Grace period | Unknown | N | Timely | Delayed | Unknown |
| BCG | 1,425 | 78.05% | 19.20% | 1.89% | 0.86% | 0.00% | 1,103 | 79.47% | 18.04% | 2.49% |
| OPV0 | 1,425 | 69.04% | 17.32% | 12.49% | 1.06% | 0.08% | 933 | 86.71% | 9.43% | 3.86% |
| OPV1 | 1,364 | 79.55% | 17.81% | 1.60% | 0.98% | 0.07% | 1,086 | 89.76% | 7.51% | 2.72% |
| OPV2 | 1,319 | 78.40% | 16.73% | 3.81% | 0.98% | 0.07% | 1,032 | 82.57% | 15.20% | 2.23% |
| OPV3 | 1,282 | 75.10% | 8.33% | 15.47% | 1.03% | 0.07% | 959 | 73.45% | 23.67% | 2.88% |
| Penta1 | 1,364 | 80.50% | 17.28% | 1.11% | 0.99% | 0.12% | 1,095 | 91.73% | 6.81% | 1.46% |
| Penta2 | 1,319 | 79.27% | 18.22% | 1.59% | 0.80% | 0.13% | 1,042 | 83.78% | 14.89% | 1.33% |
| Penta3 | 1,282 | 77.27% | 15.82% | 5.82% | 0.97% | 0.13% | 981 | 73.59% | 24.21% | 2.21% |
| PCV1† | 1,276 | 80.74% | 15.82% | 2.10% | 1.14% | 0.19% | 1,027 | 90.70% | 7.06% | 2.24% |
| PCV2† | 1,231 | 79.40% | 15.68% | 3.87% | 0.86% | 0.20% | 973 | 82.89% | 15.27% | 1.83% |
| PCV3† | 1,194 | 76.75% | 14.30% | 7.70% | 1.05% | 0.21% | 906 | 72.47% | 25.09% | 2.44% |
| Rota1† | 1,276 | 79.77% | 16.06% | 2.66% | 1.15% | 0.36% | 1,018 | 87.52% | 9.72% | 2.76% |
| Rota2† | 1,231 | 78.24% | 16.32% | 4.22% | 0.86% | 0.37% | 960 | 78.43% | 19.39% | 2.18% |
| Measles | 1,078 | 69.68% | 21.29% | 6.24% | 2.70% | 0.08% | 736 | 72.47% | 23.55% | 3.98% |
Notes:
Estimates of vaccination coverage are based on a sample of vaccine-eligible children, i.e., children old enough to have received the vaccine. Age-eligible children who are not covered but have not yet aged out of the 28-day window that we use to define timely vaccinations are recorded in the “Grace period” category.
Estimates of vaccination timeliness are based on a sample of children with written documentation of vaccination. Vaccinations are classified as “timely” if received within 4 weeks (28 days) of the recommended age. Vaccination timeliness is considered “unknown” in cases where a dose was marked on the child’s vaccination card, but the date of administration was not recorded, only partially recorded, or invalid.
To account for changes in guidelines in January 2013, coverage and timeliness for the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013. All percentages calculated using weights provided in the TDHS. Sample sizes are unweighted.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Reverse Kaplan-Meier curves in Figure 1 show the cumulative proportion of children vaccinated from birth to age 2 years. Vertical reference lines mark the recommended age of vaccination for each dose. Substantial delays were observed for all doses of all vaccines. While the vast majority of children received the BCG vaccine (due at birth) within the first 4 weeks after birth, the 90% coverage target was not met until 18.4 weeks. Administration of the first doses of the pentavalent, pneumococcal, and rotavirus vaccines (due 6 weeks after birth) was similarly delayed, with 90% coverage reached at 14.9 weeks, 21.1 weeks, and 43.1 weeks, respectively.
Figure 1. Cumulative vaccination coverage, 2015–16 TDHS-MIS.
Notes: Reverse Kaplan-Meier curves show the cumulative proportion of children vaccinated. Vertical lines mark the recommended age of vaccination for each dose. Red horizontal lines highlight the national target of 90% coverage.
Survival data analyzed using weights to account for the complex sampling design of the TDHS-MIS.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Table 3 provides a summary of the length of time children spent unvaccinated (measured as the interval between the recommended age of vaccination and the age of vaccine receipt), in aggregate and separately for rural and urban children. Rural children tended to experience greater delays than urban children. This gap was observed for vaccines recommended at birth and expanded with each additional dose for vaccines that were part of a series. The median delay for the BCG vaccine was 24 days among rural children compared to 5 days among urban children. The median delay for the third (final) dose of the pentavalent vaccine was 37 days among rural children compared to 15 days among urban children. Urban-rural disparities were most striking in comparisons of the upper quartile of vaccine delay. For example, while 75% of urban children received the final dose of the pentavalent vaccine with a delay of 36 days or less, 25% of rural children spent more than 104 days undervaccinated.
Table 3.
Distribution of vaccination delays among children under age 3, 2015–16 TDHS-MIS
| All Children | Rural Children | Urban Children | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Days unvaccinated by percentiles | Days unvaccinated by percentiles | Days unvaccinated by percentiles | ||||||||||
| Vaccine | 25% | 50% (Median) | 75% | 90% | 25% | 50% (Median) | 75% | 90% | 25% | 50% (Median) | 75% | 90% |
| BCG | 3 | 16 | 49 | 129 | 6 | 24 | 55 | 168 | 1 | 5 | 19 | 51 |
| OPV0† # | 3 | 21 | . | . | 5 | 63 | . | . | 1 | 5 | 28 | . |
| OPV1 | 2 | 8 | 25 | 74 | 3 | 10 | 31 | 103 | 1 | 4 | 11 | 28 |
| OPV2# | 7 | 18 | 47 | 330 | 8 | 23 | 60 | . | 5 | 10 | 23 | 58 |
| OPV3# | 12 | 35 | 161 | . | 15 | 42 | 202 | . | 9 | 18 | 59 | . |
| Penta1 | 2 | 7 | 24 | 62 | 3 | 10 | 30 | 81 | 1 | 4 | 10 | 25 |
| Penta2 | 7 | 17 | 42 | 142 | 8 | 22 | 53 | 234 | 5 | 9 | 21 | 40 |
| Penta3# | 11 | 29 | 83 | . | 14 | 37 | 104 | . | 8 | 15 | 36 | 120 |
| PCV1‡ | 3 | 8 | 28 | 106 | 3 | 11 | 34 | 223 | 1 | 4 | 11 | 28 |
| PCV2# ‡ | 7 | 19 | 50 | . | 8 | 24 | 64 | . | 5 | 10 | 23 | 53 |
| PCV3# ‡ | 12 | 33 | 109 | . | 15 | 41 | 176 | . | 9 | 16 | 39 | 371 |
| Rota1# ‡ | 3 | 9 | 31 | 260 | 3 | 11 | 37 | . | 1 | 5 | 13 | 34 |
| Rota2# ‡ | 7 | 20 | 59 | . | 9 | 26 | 81 | . | 5 | 10 | 28 | 67 |
| Measles# | 8 | 26 | 88 | . | 9 | 32 | 117 | . | 7 | 18 | 36 | 114 |
Notes: Kaplan-Meier estimates of the 25th, 50th, 75th, and 90th percentiles of time to vaccination were adjusted for the recommended age of vaccination. Sample interpretation: 25 percent of children received a BCG vaccination with a delay of 3 or fewer days. Survival data were analyzed using weights to account for the complex sampling design of the TDHS-MIS.
75th percentile of time to vaccination cannot be estimated in cases where fewer than 75% of sample children have received the birth dose of the polio vaccine.
90th percentile of time to vaccination cannot be estimated in cases where <90% of sample children have received the vaccine dose.
To account for changes in guidelines in January 2013, coverage and timeliness for the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Estimates from cox proportional hazard models in Table 4 show several factors associated with time to vaccine initiation. Factors with an adjusted hazard ratio (aHR) greater than 1 (less than 1) can be interpreted as being associated with more timely coverage (delayed coverage). Compared to children in urban settings and children born in health facilities, children in rural settings (aHR: 0.84, 95%CI: 0.76–0.94) and children born at home (aHR: 0.43, 95%CI: 0.37–0.49) were more likely to have delayed BCG vaccination. Even after controlling for home- vs. facility-based delivery, risk of delayed BCG vaccination was found to increase with greater distance to the nearest health facility (aHR: 0.97, 95%CI: 0.96–0.98). In contrast, children from households with greater wealth (aHR: 1.13, 95%CI: 1.07–1.20) and higher maternal education (primary education aHR: 1.13, 95%CI: 1.02–1.24; secondary education aHR: 1.21, 95%CI: 1.04–1.40) had more timely BCG vaccination. Results for other vaccines were similar, though rural residence per se was not found to be a statistically significant predictor of time to the first measles vaccine.
Table 4.
Factors associated with timely vaccination, 2015–16 TDHS-MIS
| BCG | OPV0 | OPV1 | Penta1 | PCV1† | Rota1† | Measles# | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 4,997 | N = 5,194 | N = 5,101 | N = 5,165 | N = 4,984 | N = 4,989 | N = 3,369 | ||||||||
| Risk factor | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI |
| Residence | ||||||||||||||
| Urban | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Rural | 0.84 | (0.76,0.94) | 0.74 | (0.65,0.83) | 0.84 | (0.75,0.94) | 0.85 | (0.76,0.95) | 0.80 | (0.71,0.89) | 0.81 | (0.73,0.90) | 1.02 | (0.88,1.17) |
| Distance to health facility | 0.97 | (0.96,0.98) | 0.96 | (0.95,0.97) | 0.97 | (0.96,0.98) | 0.97 | (0.96,0.98) | 0.97 | (0.96,0.98) | 0.97 | (0.97,0.98) | 0.97 | (0.96,0.98) |
| Wealth index score | 1.13 | (1.07,1.20) | 1.13 | (1.07,1.21) | 1.06 | (1.00,1.13) | 1.09 | (1.03,1.16) | 1.07 | (1.01,1.14) | 1.05 | (0.99,1.11) | 1.14 | (1.06,1.23) |
| Mother’s age at delivery | 1.00 | (1.00,1.01) | 1.00 | (1.00,1.01) | 1.01 | (1.00,1.01) | 1.00 | (1.00,1.01) | 1.01 | (1.00,1.01) | 1.00 | (1.00,1.01) | 1.00 | (0.99,1.01) |
| Mother’s education | ||||||||||||||
| None | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Primary | 1.13 | (1.02,1.24) | 1.05 | (0.93,1.18) | 1.34 | (1.22,1.46) | 1.30 | (1.18,1.42) | 1.29 | (1.17,1.41) | 1.30 | (1.18,1.43) | 1.25 | (1.10,1.42) |
| Secondary or higher | 1.21 | (1.04,1.40) | 1.20 | (1.02,1.41) | 1.56 | (1.36,1.80) | 1.45 | (1.26,1.66) | 1.47 | (1.28,1.69) | 1.45 | (1.26,1.68) | 1.29 | (1.07,1.54) |
| Birth order | ||||||||||||||
| First child | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Second or higher | 0.97 | (0.87,1.07) | 0.92 | (0.82,1.03) | 1.03 | (0.93,1.14) | 1.01 | (0.91,1.11) | 1.03 | (0.93,1.14) | 1.06 | (0.96,1.17) | 0.93 | (0.82,1.05) |
| Antenatal care visits | ||||||||||||||
| 0–3 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 4 or more | 1.01 | (0.93,1.09) | 1.05 | (0.96,1.15) | 0.97 | (0.90,1.04) | 0.97 | (0.90,1.05) | 0.99 | (0.92,1.07) | 1.00 | (0.93,1.08) | 1.02 | (0.93,1.12) |
| Unknown/missing | 0.87 | (0.50,1.51) | 0.88 | (0.46,1.66) | 0.85 | (0.55,1.33) | 0.72 | (0.47,1.12) | 0.74 | (0.45,1.21) | 0.70 | (0.46,1.08) | 1.05 | (0.72,1.52) |
| Tetanus Toxoid doses | ||||||||||||||
| 0 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 1 | 1.01 | (0.91,1.12) | 0.91 | (0.80,1.03) | 1.02 | (0.92,1.12) | 1.02 | (0.92,1.12) | 0.99 | (0.90,1.10) | 1.01 | (0.92,1.12) | 0.91 | (0.80,1.04) |
| 2 | 0.99 | (0.90,1.08) | 0.91 | (0.81,1.02) | 1.02 | (0.93,1.12) | 1.05 | (0.96,1.15) | 1.02 | (0.93,1.12) | 1.05 | (0.95,1.15) | 1.01 | (0.90,1.14) |
| 3 or more | 1.10 | (0.95,1.28) | 1.00 | (0.85,1.17) | 1.07 | (0.91,1.24) | 1.09 | (0.93,1.27) | 1.00 | (0.86,1.16) | 1.08 | (0.93,1.26) | 1.09 | (0.91,1.31) |
| Unknown/missing | 1.02 | (0.58,1.80) | 0.84 | (0.44,1.60) | 1.13 | (0.72,1.78) | 1.23 | (0.79,1.92) | 1.16 | (0.71,1.90) | 1.26 | (0.81,1.95) | 0.83 | (0.57,1.22) |
| Child’s place of delivery | ||||||||||||||
| Health facility | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Home | 0.43 | (0.37,0.49) | 0.36 | (0.32,0.41) | 0.76 | (0.70,0.82) | 0.76 | (0.70,0.82) | 0.77 | (0.71,0.83) | 0.74 | (0.68,0.81) | 0.77 | (0.69,0.85) |
| Year of birth | ||||||||||||||
| 2013‡ | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 2014 | 1.17 | (1.07,1.28) | 1.13 | (1.02,1.26) | 1.21 | (1.11,1.33) | 1.16 | (1.06,1.27) | 1.32 | (1.20,1.45) | 1.36 | (1.23,1.49) | 0.92 | (0.84,1.00) |
| 2015‡ | 1.03 | (0.93,1.13) | 1.12 | (1.00,1.25) | 1.18 | (1.07,1.29) | 1.10 | (1.00,1.21) | 1.28 | (1.16,1.41) | 1.34 | (1.22,1.48) | ||
Notes: Adjusted hazard ratios (aHR) and their 95% confidence intervals (CI) obtained from cox proportional hazard models. Survival data analyzed using weights to account for the complex sampling design of the TDHS-MIS. Boldface indicates statistical significance (p<0.05).
To account for changes in guidelines in January 2013, coverage and timeliness for the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013.
Births in 2012 (229) and 2016 (40) were recoded to 2013 and 2015, respectively.
N=1,861 children born in 2015 and 2016 were excluded from the measles model because, at the time of data collection, most of those children in the TDHS-MIS sample were not yet age eligible for the measles vaccine.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Several associations appear to be context specific. In stratified analyses, distance to the nearest health facility was only associated with timely vaccinations among rural children (Table 5). Similarly household wealth was found to be more strongly related to the timing of vaccinations among children in rural settings (Table 5) than in urban settings (Table 6). In contrast, the effect of delivery at home on the timeliness of vaccinations due at birth (BCG, OPV0) was larger among urban children than rural children. For example, the risk of delayed BCG vaccination among urban children born at home (aHR: 0.20, 95%CI: 0.09–0.43) was larger than that of rural children born at home (aHR: 0.48, 95%CI: 0.42–0.54). Vaccination timeliness was generally higher in the 2014 and 2015 birth cohorts than in the earlier cohort.
Table 5.
Factors associated with timely vaccination among rural children, 2015–16 TDHS-MIS
| BCG | OPV0 | OPV1 | Penta1 | PCV1† | Rota1† | Measles# | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 3,876 | N = 4,054 | N = 3,953 | N = 3,994 | N = 3,873 | N = 3,882 | N = 2,583 | ||||||||
| Risk factor | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI |
| Distance to health facility | 0.97 | (0.96,0.98) | 0.96 | (0.94,0.97) | 0.97 | (0.96,0.98) | 0.97 | (0.96,0.98) | 0.97 | (0.96,0.98) | 0.97 | (0.97,0.98) | 0.97 | (0.96,0.98) |
| Wealth index score | 1.20 | (1.11,1.30) | 1.20 | (1.11,1.31) | 1.15 | (1.07,1.23) | 1.23 | (1.15,1.32) | 1.21 | (1.13,1.31) | 1.15 | (1.07,1.24) | 1.17 | (1.06,1.30) |
| Mother’s age at delivery | 1.00 | (1.00,1.01) | 1.00 | (0.99,1.01) | 1.00 | (1.00,1.01) | 1.00 | (1.00,1.01) | 1.01 | (1.00,1.01) | 1.00 | (1.00,1.01) | 0.99 | (0.98,1.00) |
| Mother’s education | ||||||||||||||
| None | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Primary | 1.14 | (1.02,1.27) | 1.03 | (0.91,1.18) | 1.35 | (1.22,1.49) | 1.32 | (1.19,1.45) | 1.27 | (1.15,1.40) | 1.28 | (1.16,1.42) | 1.19 | (1.04,1.36) |
| Secondary or higher | 1.32 | (1.10,1.58) | 1.22 | (1.00,1.49) | 1.47 | (1.25,1.73) | 1.29 | (1.09,1.51) | 1.33 | (1.12,1.56) | 1.45 | (1.23,1.70) | 1.38 | (1.11,1.72) |
| Birth order | ||||||||||||||
| First child | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Second or higher | 0.98 | (0.87,1.11) | 0.94 | (0.81,1.08) | 1.05 | (0.94,1.18) | 1.00 | (0.89,1.12) | 1.03 | (0.92,1.16) | 1.07 | (0.95,1.20) | 0.93 | (0.81,1.08) |
| Antenatal care visits | ||||||||||||||
| 0–3 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 4 or more | 1.00 | (0.91,1.09) | 1.02 | (0.91,1.13) | 1.01 | (0.93,1.10) | 1.01 | (0.93,1.10) | 1.05 | (0.96,1.14) | 1.04 | (0.95,1.13) | 1.03 | (0.93,1.15) |
| Unknown/missing | 0.60 | (0.31,1.18) | 0.59 | (0.26,1.31) | 1.00 | (0.51,1.98) | 0.71 | (0.36,1.42) | 0.78 | (0.33,1.84) | 0.83 | (0.34,2.05) | 1.23 | (0.74,2.07) |
| Tetanus Toxoid doses | ||||||||||||||
| 0 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 1 | 1.01 | (0.90,1.14) | 0.87 | (0.75,1.01) | 1.01 | (0.91,1.12) | 1.01 | (0.91,1.13) | 0.98 | (0.88,1.10) | 1.00 | (0.89,1.12) | 0.87 | (0.75,1.01) |
| 2 | 1.00 | (0.89,1.11) | 0.88 | (0.77,1.01) | 1.04 | (0.94,1.16) | 1.07 | (0.97,1.19) | 1.04 | (0.94,1.16) | 1.08 | (0.97,1.20) | 1.01 | (0.88,1.15) |
| 3 or more | 1.09 | (0.90,1.33) | 1.00 | (0.81,1.22) | 1.03 | (0.85,1.25) | 1.06 | (0.87,1.29) | 0.96 | (0.80,1.16) | 1.07 | (0.90,1.28) | 1.01 | (0.79,1.28) |
| Unknown/missing | 1.51 | (0.77,2.99) | 1.16 | (0.52,2.60) | 1.01 | (0.51,2.01) | 1.31 | (0.65,2.63) | 1.14 | (0.49,2.70) | 1.11 | (0.45,2.75) | 0.70 | (0.41,1.19) |
| Child’s place of delivery | ||||||||||||||
| Health facility | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Home | 0.48 | (0.42,0.54) | 0.38 | (0.33,0.43) | 0.77 | (0.71,0.84) | 0.77 | (0.71,0.84) | 0.78 | (0.71,0.85) | 0.75 | (0.69,0.82) | 0.77 | (0.69,0.86) |
| Year of birth | ||||||||||||||
| 2013‡ | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 2014 | 1.14 | (1.03,1.27) | 1.17 | (1.02,1.34) | 1.27 | (1.14,1.41) | 1.19 | (1.07,1.31) | 1.33 | (1.20,1.49) | 1.42 | (1.27,1.58) | 0.92 | (0.83,1.02) |
| 2015‡ | 1.00 | (0.89,1.12) | 1.11 | (0.97,1.28) | 1.18 | (1.06,1.32) | 1.07 | (0.96,1.19) | 1.22 | (1.09,1.37) | 1.30 | (1.16,1.45) | ||
Notes: Adjusted hazard ratios (aHR) and their 95% confidence intervals (CI) obtained from cox proportional hazard models. Survival data analyzed using weights to account for the complex sampling design of the TDHS-MIS. Boldface indicates statistical significance (p<0.05).
To account for changes in guidelines in January 2013, coverage and timeliness for the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013.
Births in 2012 (229) and 2016 (40) were recoded to 2013 and 2015, respectively.
N=1,861 children born in 2015 and 2016 were excluded from the measles model because, at the time of data collection, most of those children in the TDHS-MIS sample were not yet age eligible for the measles vaccine.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Table 6.
Factors associated with timely vaccination among urban children, 2015–16 TDHS-MIS
| BCG | OPV0 | OPV1 | Penta1 | PCV1† | Rota1† | Measles# | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| N = 1,121 | N = 1,140 | N = 1,148 | N = 1,171 | N = 1,111 | N = 1,107 | N = 786 | ||||||||
| Risk factor | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI | aHR | 95% CI |
| Distance to health facility | 0.99 | (0.96,1.02) | 1.00 | (0.97,1.03) | 1.03 | (0.99,1.07) | 1.03 | (0.99,1.06) | 1.03 | (0.99,1.06) | 1.01 | (0.98,1.05) | 1.02 | (0.97,1.06) |
| Wealth index score | 1.07 | (0.99,1.16) | 1.10 | (1.01,1.21) | 1.04 | (0.96,1.13) | 1.04 | (0.96,1.13) | 1.02 | (0.94,1.10) | 1.00 | (0.92,1.09) | 1.12 | (1.01,1.24) |
| Mother’s age at delivery | 1.01 | (1.00,1.02) | 1.01 | (1.00,1.02) | 1.01 | (1.00,1.02) | 1.00 | (0.99,1.02) | 1.01 | (1.00,1.02) | 1.01 | (1.00,1.02) | 1.02 | (1.00,1.03) |
| Mother’s education | ||||||||||||||
| None | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Primary | 0.88 | (0.69,1.13) | 1.06 | (0.77,1.45) | 1.24 | (0.97,1.59) | 1.16 | (0.91,1.49) | 1.41 | (1.07,1.86) | 1.41 | (1.06,1.86) | 1.62 | (1.08,2.44) |
| Secondary or higher | 0.90 | (0.67,1.19) | 1.20 | (0.85,1.69) | 1.48 | (1.12,1.97) | 1.38 | (1.05,1.82) | 1.65 | (1.21,2.25) | 1.51 | (1.10,2.07) | 1.48 | (0.94,2.31) |
| Birth order | ||||||||||||||
| First child | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Second or higher | 0.91 | (0.78,1.08) | 0.89 | (0.74,1.07) | 0.96 | (0.79,1.16) | 0.98 | (0.82,1.18) | 0.95 | (0.80,1.14) | 0.96 | (0.80,1.16) | 0.87 | (0.70,1.07) |
| Antenatal care visits | ||||||||||||||
| 0–3 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 4 or more | 1.04 | (0.90,1.19) | 1.12 | (0.95,1.31) | 0.86 | (0.74,0.99) | 0.89 | (0.77,1.03) | 0.86 | (0.75,0.99) | 0.93 | (0.80,1.07) | 0.99 | (0.84,1.18) |
| Unknown/missing | 2.18 | (1.03,4.62) | 3.84 | (2.38,6.19) | 0.64 | (0.42,0.96) | 0.63 | (0.42,0.95) | 0.57 | (0.37,0.86) | 0.57 | (0.39,0.82) | 0.79 | (0.46,1.35) |
| Tetanus Toxoid doses | ||||||||||||||
| 0 | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 1 | 0.96 | (0.77,1.20) | 1.04 | (0.82,1.33) | 1.02 | (0.82,1.28) | 1.02 | (0.81,1.29) | 1.02 | (0.81,1.29) | 1.03 | (0.81,1.30) | 1.09 | (0.81,1.47) |
| 2 | 0.91 | (0.75,1.10) | 0.99 | (0.80,1.24) | 0.93 | (0.77,1.14) | 0.95 | (0.78,1.15) | 0.93 | (0.76,1.13) | 0.91 | (0.75,1.12) | 1.07 | (0.82,1.40) |
| 3 or more | 1.04 | (0.83,1.31) | 1.03 | (0.80,1.34) | 1.06 | (0.82,1.36) | 1.10 | (0.86,1.41) | 1.01 | (0.78,1.31) | 1.04 | (0.79,1.37) | 1.28 | (0.95,1.73) |
| Unknown/missing | 0.34 | (0.16,0.74) | 0.23 | (0.13,0.41) | 1.22 | (0.80,1.86) | 1.13 | (0.73,1.74) | 1.22 | (0.79,1.90) | 1.34 | (0.88,2.03) | 1.21 | (0.67,2.16) |
| Child’s place of delivery | ||||||||||||||
| Health facility | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| Home | 0.20 | (0.09,0.43) | 0.19 | (0.07,0.50) | 0.69 | (0.54,0.89) | 0.70 | (0.54,0.90) | 0.76 | (0.58,1.00) | 0.74 | (0.56,0.97) | 0.71 | (0.51,0.99) |
| Year of birth | ||||||||||||||
| 2013‡ | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | Ref. | |||||||
| 2014 | 1.21 | (1.04,1.41) | 1.06 | (0.89,1.27) | 1.12 | (0.95,1.32) | 1.11 | (0.94,1.30) | 1.28 | (1.08,1.52) | 1.25 | (1.05,1.49) | 0.90 | (0.76,1.06) |
| 2015‡ | 1.08 | (0.92,1.27) | 1.11 | (0.92,1.35) | 1.15 | (0.95,1.39) | 1.15 | (0.96,1.38) | 1.36 | (1.12,1.64) | 1.48 | (1.22,1.80) | ||
Notes: Adjusted hazard ratios (aHR) and their 95% confidence intervals (CI) obtained from cox proportional hazard models. Survival data analyzed using weights to account for the complex sampling design of the TDHS-MIS. Boldface indicates statistical significance (p<0.05).
To account for changes in guidelines in January 2013, coverage and timeliness for the pneumococcal (PCV1-PCV3) and rotavirus (Rota1-Rota2) vaccine series were not assessed for children born prior to 2013.
Births in 2012 (229) and 2016 (40) were recoded to 2013 and 2015, respectively.
N=1,861 children born in 2015 and 2016 were excluded from the measles model because, at the time of data collection, most of those children in the TDHS-MIS sample were not yet age eligible for the measles vaccine.
TDHS-MIS: Tanzania Demographic and Health Survey and Malaria Indicator Survey.
Discussion
The study findings highlight persistent gaps in coverage, timeliness, and documentation of vaccinations among Tanzanian children, as well as substantial rural-urban disparities. Our analysis shows that for OPV, Penta, and PCV, documented coverage for rural children dropped to approximately 70 percent by the third dose; the proportion of children receiving timely vaccinations dropped to approximately 50 percent. The lowest rates of documented coverage among children under age 3 were observed for the OPV0 (52.5%) and measles (66.7%) vaccinations. Among those with documented vaccinations, the largest delays in vaccine initiation were observed for the BCG (median: 16 days, IQR: 3–49 days) and measles (median: 26 days, IQR: 8–88 days) vaccines. On aggregate the delays are substantial: The earliest 90% coverage target is reached for Penta1, with a delay of 62 days; coverage targets for follow-up vaccinations within multi-dose series were only reached with delays over 4 months, or not at all. For nearly all vaccines, rates of documented vaccination coverage and timeliness were lower for rural children compared to urban children. Rural children also tended to spend longer periods of time unvaccinated. The findings of our study are consistent with other publications in the literature, which suggest delayed vaccine uptake in Tanzania generally, and in rural areas specifically [11, 14, 15, 45].
The correlates of delayed vaccination in our study overlap with the larger framework of factors that influence vaccine uptake, which are published in the World Health Organization’s Epidemiology of the Unimmunized Child report [46]. Higher maternal education and delivery at a health facility were consistently associated with reduced risk of delayed vaccination. In rural settings, higher socioeconomic status (greater household wealth) and proximity to the nearest health facility were also associated with earlier vaccination. Home births were associated with substantially higher risk of delayed vaccination. This finding was consistent across all vaccines and in both rural and urban settings. These findings may be indicative of persistent barriers to maternal and child health care across the reproductive, maternal, newborn and child health (RMNCH) care continuum and suggest that children whose mothers overcome barriers to facility-based delivery are less at risk for delayed coverage. Given known associations between socio-economic status, access to care, and place of delivery, the inclusion of in-facility delivery in the model alongside other variables may have attenuated estimates of the effect of sociodemographic and access characteristics on the timeliness of vaccinations, especially in rural areas and for vaccinations recommended at birth [32]. Poor healthcare access and missed opportunities for vaccination have been highlighted by other studies in Tanzania, as well as in recent work done by the authors [11, 47]. While the effect of delivery at home on the timeliness of vaccinations due at birth was larger among urban children than rural children, home births are relatively rare for urban mothers (11.8%). Further research is needed to understand the reasons behind these differences, in particular, whether low timeliness of the BCG and OPV0 doses in urban children delivered at home is linked to unobserved characteristics of mothers such as vaccine hesitancy, beliefs, or distrust.
The contributions of our study result primarily from the use of a nationally representative dataset and the use of both a categorical and continuous measure of timeliness. To our knowledge, this is the first analysis examining the timeliness of routine childhood vaccine uptake using 2015–16 TDHS-MIS data. Comparing coverage and timeliness of vaccine uptake in rural versus urban children allowed us to identify contextual barriers that could inform the fine-tuning of vaccination services in these settings. For instance, our data suggest that timeliness of vaccine uptake among rural children may be improved by increasing access to services, especially for mothers who are less wealthy and those who live further away from healthcare facilities. Our findings also suggest that lower maternal education and home-based delivery may serve as useful indicators in screening tools aiming to proactively identify children vulnerable to no or delayed vaccinations. While the data for this study are based on a cross-sectional survey, variation in the timing of birth and vaccination due dates provide a glimpse at temporal changes, with indications that the likelihood of non-coverage and late vaccinations decreased between 2013 and 2015. Improvements in the timeliness of vaccine uptake for most vaccines mirror programmatic improvements implemented by the Ministry of Health, Community Development, Gender, Elderly and Children (MoHCDEC) and the national Immunization and Vaccine Development (IVD) program. The continuous measure of vaccination timeliness employed in this study, the number of un- or under-vaccinated days, appears to provide a valuable and sensitive performance metric for Tanzania’s vaccination program. Our findings also provide important baseline information for future comparisons of uptake and timeliness of the PCV and rotavirus vaccines, which were introduced just prior to the 2015–16 TDHS-MIS.
This study has several limitations. First, as described earlier, there were changes in the recommended vaccination schedule. Rates of adoption of the new schedule may have varied across different areas of Tanzania; and the introduction of new vaccines may have resulted in temporal programmatic changes impacting the timeliness of vaccine uptake [25, 48]. Second, there is a lack of clarity in the literature on what constitutes a meaningful delay in vaccine uptake. We followed other studies in the literature that used a 28-day period following the due date as the window of timeliness, however, the continuous measures do not include this grace period. As continuous measures of timeliness may be more informative and comparable than categorical measures [9] we present medians, interquartile ranges, and the 90 percent coverage target to characterize the magnitude and distribution of delays.
Other limitations are related to data completeness and quality. Children without written vaccination documentation and whose mother could not recall whether the child received a particular vaccine were considered unvaccinated. Therefore, some children could be falsely classified as unvaccinated which may underestimate vaccination coverage. We were unable to include children without vaccination records in our analysis of timeliness. There may be inherent differences in families who have versus who do not have documentation of vaccinations [9]. Inaccuracies in data entry may impact our calculations of vaccination coverage and timeliness. Tanzania is in the process of deploying a national immunization registry [49]. Availability of electronic registry data may mitigate some of the concerns with data availability and quality in future analyses, particularly if the registry also includes information on unvaccinated children. Once reliable data are available, coverage and timeliness can be modeled jointly to provide more nuanced data to policy makers. However, since administrative data tend to contain only very limited information on sociodemographic characteristics and other potential correlates of vaccination coverage and timeliness, there will continue to be an important role for data from representative sample surveys such as the DHS.
Conclusion
The study findings highlight persistent gaps in uptake, timeliness, and documentation of vaccinations among rural and urban children in Tanzania. The definitions and methods used in this study can serve as an analytic template that can be applied to data from vaccination registries and any surveys that collect dates of birth and vaccinations, including DHS data from other countries, to allow for nuanced assessments of temporal changes and cross- and within-country variation in vaccination coverage and timeliness. The continuous timeliness measure employed in this study—the number of unvaccinated days—may serve as a sensitive performance metric for immunization programs.
Supplementary Material
Highlights.
Gaps in vaccination documentation, coverage, and timeliness persist in Tanzania
Rural children are more likely to experience vaccination delays
Rural children experience longer vaccination delays than urban children
Delays in individual doses are compounded for multi-dose vaccines
The number of unvaccinated days is a sensitive performance metric for vaccination programs
Funding
This study was supported by a grant from the Maternal, Adolescent and Child Health (MACH) working group of the Duke Global Health Institute. The development of algorithms to define timeliness was supported by the Fogarty International Center of the National Institutes of Health under Award Number R21TW010262. LV receives funding from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number KL2TR002554. The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, or writing of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Footnotes
Competing interests
None to declare
Ethical approval
This study used non-identifiable public use data and thus is exempt from IRB review.
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