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Task sharing is a strategy with potential to increase access to effective modern contraceptive methods. This study examines whether community health extension workers (CHEWs) can insert contraceptive implants to the same safety and quality standards as nurse/midwives. We analyze data from 7,691 clients of CHEWs and nurse/midwives who participated in a noninferiority study conducted in Kaduna and Ondo States, Nigeria. Adverse events (AEs) following implant insertions were compared. On the day of insertion AEs were similar among CHEW and nurse/midwife clients—0.5 percent and 0.4 percent, adjusted odds ratio (aOR) 0.92 (95 percent CI 0.38–2.23)—but noninferiority could not be established. At follow-up 6.6 percent of CHEW clients and 2.1 percent of nurse/midwife clients experienced AEs. There was strong evidence of effect modification by State. In the final adjusted model, odds of AEs for CHEW clients in Kaduna was 3.34 (95 percent CI 1.53–7.33) compared to nurse/midwife clients, and 0.72 (95 percent CI 0.19–2.72]) in Ondo. Noninferiority could not be established in either State. Implant expulsions were higher among CHEW clients (142/2987) compared to nurse/midwives (40/3517). Results show the feasibility of training CHEWs to deliver implants in remote rural settings but attention must be given to provider selection, training, supervision, and follow-up to ensure safety and quality of provision.
The study was conducted in public health facilities in two Nigerian states, Kaduna in central northwest Nigeria, and Ondo in the southwest. The north and the south differ culturally, socially, and economically, with the south tending to be richer, and with better health and socioeconomic indicators compared to the north, including maternal, infant and child mortality, education, contraceptive prevalence, distribution of medical schools, and availability of health care providers (Makinde et al. 2018; International Organization for Migration (IOM) 2014). Kaduna State is the third most populous state in Nigeria, with an estimated population of 8.25 million, compared to 4.18 million in Ondo State in 2016. Sixty‐six percent of the population in Kaduna State is under the age of 25, compared to 59 percent in Ondo State (Nigeria Bureau of Statistics 2019). Forty seven percent of women aged 15–49 years in Kaduna State have no education, compared to 7.9 percent in Ondo State. The total fertility rate (TFR) in Kaduna was 5.9 with a mean ideal number of children of 7.2 children, compared to a TFR of 4.1, and a mean ideal number of children of 4.5 in Ondo. In Kaduna 36.5 percent of households live in the poorest two wealth quintiles compared to 21.2 percent in Ondo (National Population Commission (NPC) [Nigeria] and ICF 2019). This was a quasi‐experimental noninferiority study that aimed to compare insertion‐related moderate and severe AEs resulting from insertion of contraceptive implants by CHEWs with nurses and midwives. Random allocation of clients to intervention groups was not possible in this study because clients access their local area clinics. Also, providers could not be randomized because they work at either CHEW‐led or at nurse‐ or midwife‐led public clinics. The methodology and detailed description of the intervention is provided in the published protocol https://doi.org/10.2196/resprot.8721 (Reiss et al. 2018). Briefly, 12 out of 23 local government areas (LGAs) were purposively selected from Kaduna State, and seven of 23 LGAs from Ondo State. LGAs that shared reproductive health interventions funded by the same donor and implemented across several LGAs, as well as those that were geographically hard‐to‐reach were excluded from the sampling frame. Facilities in remaining LGAs were eligible for inclusion if they were CHEW or nurse/midwife led; had not previously provided implants; had provided family planning services for at least three years; had a provider interested in participating in the study who expected to remain at the facility for the 12‐month client recruitment period; and, finally could offer onsite referral, or were within 20 km of a referral facility in case of implant insertion or removal complications. From 657 facilities operating in these LGAs, 93 were eligible for inclusion in the study. Seventy‐seven were primary basic health units (BHUs) or small rural hospitals and 16 were secondary or tertiary centers or hospitals. We aimed to select 60 providers (30 CHEWs and 30 nurse/midwives) from each state. Providers from the 93 facilities were invited to participate in the study if they met the following criteria: they had no prior implant training or provision experience; were resident in the LGA; expected to remain at the health facility for the 12‐month client recruitment period; and lastly, that they had good verbal and written communication skills. In total 119 providers were included in the study. A single provider was recruited from 67 facilities; and two providers from the remaining 26 facilities. This comprised two CHEWs from 15 facilities and two nurse/midwives from 11 facilities. Providers comprised 30 nurses/midwives from each state; and 30 CHEWs from Ondo and 29 from Kaduna State. Providers were trained by 13 supervisors, themselves trained by Marie Stopes Nigeria (MSION) in family planning counselling, insertion and removal of implants, management of AEs, and study procedures. For a full description of training and supervision procedures see Reiss et al. (2018). Two implant brands were initially included in the study: Implanon Classic® (one rod preloaded in trocar) offering three year protection from pregnancy; and Jadelle® (two rods, with separate disposable trocar) offering five year protection. ImplanonNXT® was introduced into Nigeria partway through the study and accounted for 2.6 percent of insertions during the study period. Posttraining, providers went through a facility‐based accreditation process, providing implant insertions and removals under supervision. After five successful supervised insertions and two successful supervised removals of both Jadelle® and either Implanon Classic® or ImplanonNXT®, providers were qualified to insert and remove implants without clinical supervision. Postaccreditation, each provider received visits every two to three weeks from their study supervisor, supported by a MSION clinical supervisor, a quarterly visit from the State Ministry of Health, and a bi‐annual visit from the Federal Ministry of Health (FMoH) and a study principal investigator. During these visits providers received study updates and, if required, additional training. MSION also implemented demand generation activities around each facility, including advocacy with local stakeholders and engagement of mobilizers (health promoters) to help promote and publicize service availability. Between November 30, 2015 and November 30, 2016, all clients attending the selected facilities, aged 18–49 years, and who voluntarily chose an implant following comprehensive contraceptive counselling were invited to participate in the study. Written informed consent was obtained from all study participants by the provider. This included consent for follow‐up at the clinic or by telephone. Immediately following implant insertion, the provider completed a structured questionnaire to record participants’ demographic and background characteristics, and experience of any insertion‐related clinical AE during and immediately postinsertion (Table 1). All AEs, including those classified as more minor events or side effects were recorded. AEs were categorized into one of three levels: (1) Minor (side effect): The client experiences some level of discomfort that only requires resting or minimum level of medical intervention such as taking pain medication; (2) Moderate (complication): The client experiences frequent or more severe level of discomfort that requires a medical intervention and/or expulsion of implant resulting in risk of unintended pregnancy; (3) Major/critical (complication): Major injury leading to long‐term incapacity/disability and requires hospitalization and/or results in fatality, or expulsion of implant resulting in pregnancy. Implant insertion‐related AEs recorded on day of insertion and at follow‐up Women were invited to return for follow‐up two weeks postinsertion. At follow‐up, they were reconsented to ensure they remained willing to participate, and providers completed a structured questionnaire to record all AEs experienced since insertion. Women who did not return to the clinic were followed‐up by phone by the provider, with up to three attempts made. Clinical supervisors visited every provider within the first month following training (accreditation), then again one, two, three, and six months post‐accreditation to assess the quality of implant insertions. Visits lasted one to two days, during which all implant insertions were observed and data on quality recorded (Reiss et al. 2018). They used a 28‐item checklist (Online Appendix T1) to record competence in preinsertion counselling, preinsertion preparation, insertion technique, postinsertion procedures, and counselling. An overall score of 28/28 was defined as high quality. Supervisors also conducted client satisfaction exit interviews among a subsample of participants. Women were considered highly satisfied if they rated seven aspects of care as “good” or “very good” (Online Appendix T2). We had two primary outcomes for this study: (1) insertion‐related moderate or severe AEs at the time of insertion, and (2) insertion‐related moderate or severe AEs overall, measured at the time of insertion and at follow‐up. Secondary outcomes were (1) quality of implant insertions observed by clinic supervisors, and (2) client satisfaction with implant insertion measured through client exit interviews. This was a noninferiority study designed to assess whether the proportion of insertion‐related moderate/severe AEs among CHEW clients was not higher than a specified amount than the proportion of moderate/sever AEs among nurse/midwife clients. Insertion‐related AEs are rare and based on outcome data from clinical trials (Reiss et al. 2018; Meirik et al. 2013), and agreement of the research team, we assumed a base rate of 0.5 percent for moderate/severe AEs among clients of nurse/midwives on the day of insertion. The noninferiority margin, or predetermined benchmark of acceptable difference between the two groups for the day of implant insertion was set at 0.5 percent. In other words, we consider that CHEWs are noninferior to nurses/midwives if the upper confidence bound of the difference in AE rates (CHEWS – nurse/midwives) is not higher than 0.5 percent. In the absence of any data on moderate/severe insertion related AEs at follow‐up, we assumed a base rate of 1 percent at follow‐up, with a noninferiority margin of 1 percent. We combined data from the day of insertion and follow‐up to give a total complications score. The target sample size required to measure moderate/severe AEs on the day of insertion assuming a noninferiority margin of 0.5 percent, 80 percent power, 95 percent confidence, a design effect of 1.5 (for clustering by provider), and 10 percent incomplete records was 8,125. The target sample size for the same outcome at follow‐up, assuming a noninferiority margin of 1 percent and a loss to follow‐up of 20 percent, was 4,410. To assess the quality of insertions, we assumed 80 percent would be rated good based on Marie Stopes International’s (MSI’s) previous quality audits with nurses in multiple countries, with a noninferiority margin of 10 percent. We assumed that the noninferiority margin referred to a difference in proportions, meaning that if the lower 95 percent confidence limit for the difference in proportions is −0.10 or greater, then we can conclude that CHEWs are noninferior to nurse/midwives in terms of quality. We used Stata software version 15 (StataCorp 2017) for the statistical analyses. All analyses were adjusted for clustering by provider. Outcomes were analyzed using Generalized Estimating Equations (GEE) models where nested models were compared using Wald tests. Covariates were excluded from the models where they were not statistically significant at the 5 percent level and when data were too sparse to support more complicated models. Effect modification was examined for several key covariates (state, facility type, implant type, in‐study insertion experience). Due to a large variation in the timing of follow‐up visits (which were beyond the study team’s direct control), we extended the follow‐up interval from two weeks to include data up to 75 days postinsertion. For the primary outcomes, noninferiority was assessed by modeling odds ratios (OR) rather than risk differences. Since the AEs rates were so low the odds is a very close approximation to the risk difference. Assuming a prevalence of moderate/severe AEs among nurses/midwives of 0.5 percent on the day of insertion, and a risk difference of no more than 0.5 percent, the corresponding noninferiority margin expressed as an odds ratio is 2.01. Assuming prevalence of moderate/severe AEs of 1 percent by day 14 (follow‐up), the corresponding odds ratio margin is 2.02. For the secondary outcome—quality of insertions—proportions were within the range where it is reasonable to assume linearity and so difference in proportions was modeled directly using GEE rather than converting to odds ratios. We examined background characteristics and differences between CHEW clients and nurse/midwife clients among all those with complete primary outcome data for the day of insertion. Simple proportions and frequencies for categorical variables and means for continuous variables are shown. We then compared insertion‐related AEs among clients of each provider at the time of insertion, and overall. We present proportions reporting specific AEs as well as an overall AE prevalence for each provider, with unadjusted odds ratios and 95 percent confidence intervals (CIs) for comparison. For the multivariate analysis, we investigated several potential confounders. Contributions to the model fit were assessed using Wald tests. Variables were included in the model if the p‐value was <0.05, or if there was evidence of confounding whereby the estimated effect (nurses/midwives vs. CHEWs) changed by 10 percent or more. Provider level co‐variates were state, rural location of facility, facility type (BHU/small hospital vs. tertiary hospital), number of in‐study insertions conducted by provider, and other provider present at facility. Individual client characteristics were age, number of living children, education, marital status, previous family planning use, brand of implant, distance travelled, residential location, household drinking water source (proxy for household poverty), and employment status. We conducted stratified analyses to assess potential effect modification by selected individual and contextual characteristics listed above. Effect modification was considered statistically significant if the interaction term p‐value was <0.05. We found some effect modification by state and adjusted the final model. Models were sensitive to choice of variables, likely due to the small number of AEs. The multivariate analyses to assess insertion‐related AEs occurring on (1) the day of insertion and (2) overall, (on the day of insertion and at follow‐up), were conducted on all observations with complete data for covariates. On the day of insertion, 98 observations or 1.3 percent of observations from each cadre (53 nurse/midwife clients; 45 CHEW clients) had missing data for at least one of the variables included in the final model (implant brand, number of previous in‐study insertions, and employment status) and were excluded from the analysis. Excluded observations did not include any AEs. For the combined outcome, there were no missing observations. We conducted four sensitivity analyses (Online Appendix T4 and T5) by repeating the primary analyses while excluding or including selected subgroups to determine if they had an inordinate effect on the estimated measure of effect, and noninferiority margin. Three of these sensitivity analyses were predetermined, and one was data driven. The latter excluded outlier providers, or those with relatively high rates of AEs; defined as a provider with more than 10 AEs, or above the 25 percentile for AEs, with more than five expulsions, or above the 25 percentile for expulsions. We conducted three sensitivity analyses to investigate how best to address issues with missing or incomplete data, and these included (1) exclusion of observations with low‐quality insertions; (2) exclusion of observations with low‐quality follow‐up dates; (3) inclusion of observations with less than half missing outcome components, and inclusion of observations with any missing outcome components. During study implementation supervisors visited all providers every two to three weeks to provide study and clinical support. Study participants were encouraged to return to the facility if they had concerns or experienced any AEs. The study followed standard FMoH/MSION management and reporting protocols requiring immediate reporting to the clinical services manager of severe and moderate AEs, and within 24 hours for minor events. The clinical services manager was responsible for ensuring effective management of AEs, and that all AEs were reported to the study manager. Severe AEs were reported to MSI's Medical Development Team in London within 24 hours. Ethical approval was obtained from MSI's Ethics Committee, National Health Research Ethics Committee of Nigeria and the Population Council Institutional Review Board. This study is registered with ClinicalTrials.gov, number {"type":"clinical-trial","attrs":{"text":"NCT03088722","term_id":"NCT03088722"}}NCT03088722.
