Enablers and barriers to post-discharge follow-up among women who have undergone a cesarean section: Experiences from a prospective cohort in rural Rwanda

doi:10.21203/rs.3.rs-1506433/v1

Download PDF

Research Article

Enablers and barriers to post-discharge follow-up among women who have undergone a cesarean section: Experiences from a prospective cohort in rural Rwanda

https://doi.org/10.21203/rs.3.rs-1506433/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background:

Caesarean sections account for roughly one third of all surgical procedures performed in low-income countries. Due to lack of standardised post-discharge follow-up protocols and practices, most of available data are extracted from clinical charts during hospitalization and are thus sub-optimal for answering post-discharge outcomes questions. This study aims to determine enablers and barriers to returning to the hospital after discharge among women who have undergone a c-section at a rural district hospital in Rwanda.

Methods:

Women aged ≥ 18 years who underwent c-section at Kirehe District Hospital in rural Rwanda in the period March to October 2017 were prospectively followed. At discharge, consenting women were given an appointment to return for follow-up on postoperative day 10 (POD 10) (±3days) and provided a voucher to cover transport and compensation for participation to be redeemed on their return. Study participants received a reminder call on the eve of their scheduled appointment. We used a backward stepwise logistic regression, at an α=0.05 significance level, to identify enablers and barriers associated with post-discharge follow-up return.

Results:

Of 586 study participants, the majority were between 21-30 years old (62.6%, n=367), had primary education (70.0%, n=410), were farmers (86.7%, n=508), earned less than 31.8 Euro/month (92.7%, n=543), had a phone contact number (86.4%, n=506), and were discharged by POD3 (73.2% n=423). Of those eligible, 90.4% (n=530) returned for follow-up. The predictors of return were counseling by a female data collector (OR=9.85, 95%CI:1.43-37.59) and receiving a reminder call (OR=16.47, 95%CI:7.07-38.38). Having no insurance reduced the odds of returning to follow-up (OR=0.03, 95%CI:0.03-0.23), and those who spent more than 10.6 Euro for transport to and from the hospital were less likely to return to follow-up (OR=0.14, 95%CI:0.04- 0.50).

Conclusion:

mHealh interventions using calls or notifications can increase the post-discharge follow-up uptake. The reminder calls to patients and counselling by a gender-matching provider had a positive effect on return to care. Further interventions are needed targeting the uninsured and patients facing transportation hardship. Additionally, association between counseling of women patients by a female data collector and greater return to follow-up needs further exploration to optimize counseling procedures.

post-discharge care

global surgery

Sub-Saharan Africa

mHealth

surgical site infections

follow-up

Caesarean sections (c-sections) are the most commonly performed major surgical procedure globally and account for roughly a third of all surgical procedures performed in low-income countries (1). Over the past two decades, there has been an increase in c-section rates from 6.7–19.1% globally, with a more modest increase (6.0%) observed in low-income countries (2). Increased access to c-sections in sub-Saharan Africa (SSA) has been linked to reductions in maternal and neonatal mortality rates [3–5].

However, increased numbers of c-sections have led to more cases of surgical site infections (SSIs) (4), and SSIs introduce a higher morbidity, mortality, and social economic burden on patients and health systems [7–9]. The SSI prevalence and related burden is higher in low-resourced settings (6), such as SSA countries, that report rates of post-caesarean SSI as high as 41.9% (7). Due to the lack of active surveillance and standard follow-up after a patient is discharged from the hospital, these SSI rates are likely underestimated (8) since 60% of SSIs develop after a patient discharge (9).

The World Health Organization (WHO) recommends general postpartum follow-up on day 3 (48–72 hours), between days 7–14, and at six weeks after delivery(10), which could lead to early detection and treatment of complications (11). However, some patients face geographical and financial barriers as they strive to return for follow-up care(12). In their systematic review on factors affecting postpartum follow-up, Adams & Smith found that dependency of women on their husbands, lack of information, absence of complications, unsatisfactory customer care, husband’s education and occupation, and large family size and household income impede the decision to return to care(13) and the report by the Rwandan National Institutes of Statistics (NISR), echoed these findings(14) [18].

In Rwanda, a network of community health workers (CHWs) play a critical role in maternal and neonatal care during the prenatal and postnatal period in their local communities[19], but these CHWs are not trained to support surgical follow-up. Little is known about the post-discharge follow-up of c-section patients, particularly in rural areas where patients face long distances, difficult-to-access terrains, and financial restrictions to access healthcare. As part of our prospective cohort study among women who undergo a c-section in rural Rwanda, we invited women and supported them with transportation vouchers and reminder calls to return to return for a study-specific follow-up visit at postoperative day (POD) 10 (± 3days) at a rural district hospital in Rwanda. Here, we report the enablers and barriers for returning for follow-up to inform strategies for effective postoperative care seeking in this context.

Study setting:

This prospective study included women who underwent c-section between March and October 2017 at Kirehe District Hospital (KDH) in rural Rwanda. KDH is managed by the Rwandan Ministry of Health (RMoH) and receives technical and financial support from Partners In Health/Inshuti Mu Buzima (PIH/IMB), a US-based, non-governmental health organization. At KDH, c-sections are mainly performed by general practitioners (GP) and are the most prevalent surgical intervention. KDH has a full-time obstetrician gynecologist on its staff under PIH/IMB support who provides mentorship to available GPs and manages the most complex obstetric and gynecologic cases.

In the Rwandan health care system, patients present first to the nearest health center for primary health care, basic evaluation, and treatment by nurses. These nurses transfer any cases requiring management by a GP to the district hospitals. District hospitals in Rwanda provide a secondary level of care, including minor and some major surgical interventions, as well as management of surgical emergencies, such as c-sections. These hospitals are staffed with GPs, nurses, midwives, other paramedical health practitioners, and administrative personnel [20]. District hospitals transfer complex cases in need of management by a specialist to tertiary care facilities, mostly in the capital city, Kigali [21].

A woman who undergoes a c-section is admitted to the postpartum ward for monitoring and postoperative care and, if she does not experience complications, is routinely discharged on POD 3. The decision to discharge the patient is made by a GP who completes a discharge form with notes on the patient’s in-hospital management and the plan for post-discharge follow-up. A ward midwife or nurse then counsels the patient and gives discharge instructions, including operative wound care, a follow-up date for her wound dressing change at her nearest health center, details about any post-discharge medications, and neonatal care.

Access to healthcare in this country where 55% of the population is living in poverty [22], is facilitated by the community-based health insurance (CBHI) implemented nationwide. The yearly premiums for this scheme range from 3.2 Euro to 7.6 Euro per head referring to the Ubudehe category. This is the 4-level socio-economic classification system of the population based on household welfare status, whereby Ubudehe category 1 are the poorest and category 4 the wealthiest [23]. Category 1 patients have their entire medical subsidized by the government.

Study design and population:

Data used for this study are a subset of data collected in the larger prospective cohort study where consenting adult women (≥ 18 years of age) who underwent c-section at KDH between March 22nd and October 18th, 2017 were enrolled [24]. Patients from Mahama Refugee Camp were excluded given that their return would depend on the camp management. Patients from outside the KDH catchment area were also excluded given that they were likely to follow-up at their nearest health facility. Further, patients who were still hospitalized by POD 7 or readmitted before their follow-up date were also excluded.

Implementation of the study:

At discharge, enrolled women received discharge counseling detailing the follow-up plan by study trained data collector. Each study participant was given a return date to the study-specific SSI screening clinic at KDH on POD 10 (± 3days). To prevent transport costs from being a financial barrier to study participants returning to KDH, we provided a voucher on discharge that would be redeemed upon return to cover the transport costs and compensate the women’s time participating in the study. To determine transportation fees, we used the guide set by PIH/IMB in Kirehe based on distance from each district zones to the hospital.

The study clinics were held on Tuesdays and Thursdays. Study participants were called on the eve of their scheduled appointment to remind them of their clinic schedule. When a patient missed her first clinic appointment, she was called again and was given a second appointment on the following study clinic day. At the study clinic, data collectors recorded their attendance status and a GP implemented the SSI screening protocol (results of the SSI screening is reported elsewhere) [24, 25]

Data collection:

The study employed five trained data collectors, one female and four males, with clinical backgrounds. The data collectors identified patients who had undergone c-section from the operating room registry and located them in the maternity postoperative room on POD1. They explained the study aims, benefits and risks of participating in the study to eligible participants, and invited them to voluntarily consent to participate in the study. Those who consented were enrolled as study participants. The data collectors administered a structured questionnaire collecting demographic data from enrolled study participants. Clinical data were extracted from medical files. Study participants’ access to a phone was also documented. A patient was confirmed to have access to a phone when she owned one, had a phone available in her household, or she gave the number of a neighbor where she could be reached. All data were entered into REDCap, a secure web application that can support both online or offline data collection [26].

In addition to demographic and clinical data, we obtained rainfall data corresponding to the study clinic days from the Rwandan Metrological Agency. This agency uses satellite and has six stations in Kirehe. Each woman had two rainfall data points attached to her study visit. First, we used the data corresponding to the station closest to the participant’s residence on the day of her study follow-up visit. Second, we also collected the rainfall data for the station closest to KDH for the day she was attending the study clinic. These data were used to analyze whether the rain had negative effect on participants’ return on their appointments.

Analysis and statistics:

The primary outcome for this study was the return to the study follow-up visit, defined as coming to the first or the second study clinic appointment. We used descriptive statistics to report study participants demographic and clinical characteristics. We converted Rwandan francs (FRW) to Euro using the 942 FRW/Euro rate, referring to the then average central bank exchange rate [27]. To identify predictors of return to follow-up care, we performed univariable logistic regression to determine variables eligible for a multivariable logistic regression model. The full model included all the variables of study interest. Variables significant at an α = 0.1 significance level were considered for the reduced model. We built the model using backward stepwise selection, stopping when the remaining covariates were significant at α = 0.05 significance level. We report the odds ratios (ORs), 95% confidence intervals (95%CIs) and p-values from the multivariable analysis. All analyses were performed using Stata v15 (College Station, TX: StataCorp LP).

Of 746 women who underwent c-section, 586 (78.6%) were eligible for study enrollment. The majority (62.6%, n = 338) were between 21–30 years, married (43.0%, n = 252), with primary education (70.0%, n = 410), and were insured with CBHI (95.1%, n = 557). Most of the women were farmers (86.7%, n = 508), earned less than 31.8 Euro/month (92.7%, n = 543), and had access to a phone (86.4%, n = 506). Of those with recorded information on transport vouchers, 392 (66.9%) were issued between 5.3 and 10.6 Euro (Table 1). Fetal distress was the leading indication of c-section (32.2%, n = 189) followed by previous scar (29.5%, n = 173). The majority of patients (73.2%, n = 423) were discharged by POD 3. The male data collectors enrolled and consented a majority of the study participants (84.1%, n = 493). Forty-one (7.0%) of study participants were exposed to rain, either at home or KDH, on their first appointment date or, for those who did not attend their first appointment date, on their second appointment date (Table 2).

Table 1

Demographic characteristics of study participants (N = 586)
Variables	Frequency	Percent
Age
20 years and younger	73	12.5
21–30 years old	367	62.6
31–39 years old	128	21.8
40 years and older	18	3.1
Marital status
Single	209	35.7
Married	252	43.0
Living with a partner	119	20.3
Separated (divorced or widowed)	6	1.0
Education level
No education	47	8.0
Primary education	410	70.0
Secondary education or higher	129	22.0
Occupation
Farmer	508	86.7
Employed, trader	43	7.3
Housewives	35	6.0
Type of insurance
No insurance	6	1.0
Community-Based Health Insurance (CBHI)	557	95.1
Private insurance	23	3.9
Monthly household income†
Less than < 31.8 Euro/month	543	92.7
31.8 Euro and above	43	7.3
Does the patient have phone contact?
No	71	12.1
Yes	506	86.4
Missing	9	1.5
Amount of transportation voucher fees
Up to 5.30 Euro†	119		20.3
> 5.30–10.60 Euro	392		66.9
Greater than 10.60 Euro	62		10.6
Missing	13		2.2
†= converted from FRW using the 942 FRW/Euro rate referring to the then average central bank exchange rate.

Table 2

Clinical characteristics of study participants (n = 586)
Variables	Frequency	Percent
Co-morbidity
No	571	97.4
Yes	15	2.6
Anaesthesia type
General	14	2.4
Loco-regional	572	97.6
Indication to C-section*
Foetal distress	189	32.3
Previous scar	173	29.5
Prolonged labour	68	11.6
Malpresentation	80	13.7
Obstructed labour	70	12.0
Cord and membrane dystocia	45	7.7
Hypertensive disorders	7	1.2
Uterine pre/rupture	8	1.4
Hypotonic dysfunction	21	3.6
Other indications	13	2.2
Post-operative complications
No	574	98.0
Yes	12	2.0
Total length of stay (LOS)
Within 3 days	338	57.7
More than 3 days	248	42.3
Post-operative LOS
Within 3 days	423	73.2
4–7 days	163	27.8
Duration of post-surgery antibiotic therapy
No post-operative antibiotic	21	3.6
1–3 days	442	75.4
More than 3 days	123	21.0
Counselling data collector
Male data collectors	493	84.1
Female data collector	86	14.7
Missing	7	1.2
Was it raining on the patient's appointment day?
No	545	93.0
Yes	41	7.0
*= Percent total greater that 100% due to possibility of more than 1 indication for a C-section decision

Nearly all participants returned to the study follow-up clinic (90.4%, n = 530), most (85.8%, n = 503) on the date of their first clinic appointment. The phone call reached 72.3% (n = 424) of participants to remind them of either of their appointments. Of those reminded, 93.6% (n = 367) attended their first appointment. Of 194 participants who were not reached by the first reminder phone call, 136 (70.1%) returned to the clinic, all on the first appointment. Of 83 who did not show up at the first visit, 32 (38.6%) were reached by the second reminder call, and 27 of them (84.4%) attended their second appointment. Overall, 56 participants (9.6%) were not reached at all on the phone call and did not attend the clinic (Fig. 1).

In the reduced model of the multivariable analysis, having a female data collector enrolling and counseling study participants was associated with higher return to care compared to male counterparts (OR = 9.85, 95%CI:1.43–37.59). Study participants who were called and reminded of their appointment had 16-times greater odds of returning to their follow-up clinic (95%CI: 7.07–38.38). Study participants who had no insurance were 97% less likely to return to their follow-up clinic as compared to those who had CBHI (OR:0.03, 95%CI: 0.03–0.23). Study participants who were expected to spend more than 10.6 Euro for a round-trip ticket to return to the follow-up clinic were 86% less likely to return (OR = 0.14, 95%CI:0.04–0.50) (Table 3).

Table 3

Enablers and barriers of the return to a follow-up clinic after c-section (multivariable regression model)
	FULL MODEL (n = 509)			REDUCED MODEL (n = 567)
	OR	95% CI	P	OR	95% CI	P
Age
21–30 years old	1
20 years and younger	1.68	(0.60–4.71)	0.324
31–39 years	1.78	(0.69–4.56)	0.231
Marital status
Married	1
Single	0.64	(0.25–1.60)	0.335
Living with a partner	0.58	(0.21–1.56)	0.277
Education level
Primary education	1
No education	0.23	(0.07–0.76)	0.015
Secondary education or higher	1.46	(0.38–5.67)	0.585
Occupation
Farmer	1
Employed, trader	0.98	(0.11–8.82)	0.987
Housewives	2.23	(0.18–28.12)	0.536
Type of insurance
CBHI	1
No insurance	0.04	(0.03–0.41)	0.007	0.03	(0.03–0.23)	0.001
Private insurance	0.18	(0.01–2.63)	0.209	0.73	(0.07–7.23)	0.789
Amount of transportation voucher fees
up to 5.3 Euro	1
> 5.3–10.6 Euro	0.39	(0.12–1.25)	0.113	0.36	(0.13–1.02)	0.055
Greater than 10.6 Euro	0.15	(0.03–0.65)	0.011	0.14	(0.04–0.50)	0.003
Co-morbidity
No	1
Yes	1.49	(0.27–8.20)	0.646
Anesthesia type
General	1
Loco-regional	0.46	(0.41–5.14)	0.529
Duration of postoperative antibiotic therapy
No post-operative antibiotic	1
1–3 days	1.65	(0.27–10.07)	0.589
More than 3 days	1.90	(0.28–12.84)	0.243
Post-operative length of stay
Within 3 days	1
4–7 days	0.51	(0.24–1.11)	0.091
Counseling data collector
Male data collectors	1
Female data collector	10.93	(1.26–45.09)	0.030	9.85	(1.43–37.59)	0.020
Was the patient reminded of her appointment?
No	1
Yes	19.86	(8.11–48.61)	< 0.001	16.47	(7.07–38.38)	< 0.001
Was it raining on the patient's appointment day?
No	1
Yes	1.5	(0.41–5.95)	0.512

In this study, we identified several factors associated with the participants’ return to the study follow-up visit. Counselling before hospital discharge and a reminder call on the eve of the follow-up appointment were associated with higher return to follow-up. In contrast, higher costs of transportation and lack of health insurance were associated with reduced likelihood of return for the follow-up visit.

A reminder call on the eve of the follow-up appointment was associated with 16-fold higher chance of return. This reflects the role of mobile health technologies in improving the follow-up of surgical patients after their discharge from the hospital. Phone calls have been shown to be feasible and effective in the post-discharge follow-up and care of obstetric patients in Tanzania [28], and has been shown to optimize follow-up in other non-obstetric settings [29–31]. In this study, more than 80% gave phone contact on which they can be reached, consistent with the reported 71% of households owning cell phones in Rwanda [33]. Our reminder calls reached approximately 70% of study participants. This high phone coverage and the effectiveness of the reminder call should be leveraged in follow-up care activities, with automated notifications to remind patients of their appointments a day before their scheduled clinic.

While we attempted to mitigate financial barriers via a transportation voucher, the amount of transportation voucher fees, which served as a proxy of transportation cost from the patient’s home to the hospital, emerged as a significant predictor associated with lower follow-up rates. This implied that the higher the cost of transport, and therefore likely the farther the patient residence, the less likely the patient is to return to follow-up in this rural setting. Further, our study was conducted at a district hospital, the second level of Rwanda healthcare system, which have GPs on its staff; which required participants to travel further distance as compared to distance from home to health centers. Decentralized follow-up of these surgical patients at the nearest health facility may increase follow-up rates; however, as reported in a recent qualitative study [34], health center follow-up is also reported to be both financially and physically burdensome. While our use of vouchers may have offset some of the financial burden, the funding was reimbursed on arrival – a challenge if women could not front the costs – and did not remove physical challenges of travel postpartum and postoperative.

Patients without health insurance had almost no chance to return to the follow-up clinic. Particular to this study clinic, no fees were charged for the service. Yet uninsured patients failed to attend the clinic. For this rural setting, we believe that these were the vulnerable patients particularly from Ubudehe category 2 who are not subsidized by the government, and most prone to significantly have lower adherence to health insurance [35]. We attribute their failure to return to impoverishing out-of-pocket incurred at the hospital. Our findings support available data whereby while health insurance increases health service utilization and provides financial protection, that lack of insurance has negative effects on both [36–38]. This suggests that there is a vicious cycle of lack of insurance leading to impoverishment by healthcare costs, and vice-versa, as demonstrated by other studies where poverty was the root cause of uninsured populations in SSA [39]. This group of patients need more attention from healthcare providers to prevent them from being lost-to-follow-up and should benefit from extension of the available social protection programmes.

Counseling by a female data collector was linked to the higher return to care as compared to her male counterparts. Psychological studies suggest that gender-matching improves agreement and emotional bonds that are associated with treatment compliance and retention[40–42]. However, to our knowledge, no study has assessed the gender-matching aspect when it comes to discharge counseling in the context of surgery in sub-Saharan Africa. Further studies should explore the rationale behind and benefits of that preference.

Notwithstanding, we recognize that additional services such as detailed counseling, reminder call, and transport vouchers provided to the study participants make them unrepresentative of the women who are followed up postoperatively in normal standard of care. Further, according to the Rwandan demographic health survey, only 43% of women benefited from the first visit of standard post-partum care [18]. We suspect that the lost-to-follow-up in post caesarean patients under standard of care may be higher than 10% found in this study and this warrants another study. We believe that our findings would generalize to other women outside of the context of the study if they benefited from the same services since the setting is similar.

This study had some limitations to be considered in interpretation. First, the generalization of results is limited given the population that was part of a larger study whose participants received additional services to encourage their return to follow-up clinic. However, the hypotheses generated by this study are relevant to the general population and we suggest further studies to explore the situation and the impact of those add-on services on the return to follow-up care. Second, there was incomplete data on geographical locations and transportation facilities. However, we used the transport fees, following a compensation structure outlined by PIH/IMB at KDH site, as proxy of distance and transport requirements from home to the hospital. Third, there are likely other possible enablers or barriers not considered, such as patient motivation and husband influence. Finally, this study took place in one location in Rwanda; however, the structure of KDH and care protocols in the district are similar to other facilities in Rwanda and the region.

The study found overall high return to follow-up in this study population; participant reminders via phone calls may promote their return for post-discharge follow-up. For those lost-to-follow-up, our findings on the detrimental effects of travel costs support the case for decentralizing follow-up care. Further, patients from rural communities who do not have health insurance represent the population at risk of lost-to-follow-up and so need more support. The association between discharge counseling by the same gender data collector and greater return to follow-up warrants further exploration. While these results are in the context of study-specific follow-up, and may have direct implication for future prospective studies in rural Africa, we believe these lessons learned can inform strategies for effective follow-up care post c-section more broadly.

CBHI

Community-based health insurance

CHW

Community health worker

C-section

Caesarean sections GP:general practitioners

KDH

Kirehe District Hospital

LOS

length of stay

NISR

Rwandan National Institutes of Statistics

PIH/IMB

Partners In Health/Inshuti Mu Buzima

POD

Postoperative day

RNEC

Rwanda National Ethics Committee

SSA

Sub-Saharan Africa

SSI

Surgical site infection

WHO

World Health Organization

Acknowledgements

The authors would like to acknowledge the leadership and staff at KDH and PIH/IMB for their assistance in the implementation of the study, research participants, and the study team. Particular thanks to Kristin Sonderman, Teena Cherian, Irakoze Holy Louise, Bahati Ramadhan, Laban Bikorimana for their diligent work to ensure good quality data collection and data management.

Authors’ contribution

Study concept and design: BH, RR, FK, NT, SJK. Acquisition, analysis or interpretation of data: TN, BH, RR, FK, SJK, EN, JN, MG. Drafting of manuscript: TN, EN, JN, MG. Critical revision of manuscript for important intellectual content: TN, RR, FK, EN, JN, MG, BH, SJK. Statistical analysis: TN, BH. Administrative technical or material support: EN, JN, MG. Study supervision: BH & SJK. All authors read and approved the final manuscript.

Funding

The study was funded by the National Institutes of Health grant number R21EB022368. The funder had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Availability of data and materials

The data that support the findings of this study are available from PIH/IMB but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of PIH/IMB.

Ethics approval and consent to participate

All methods were carried out in accordance with relevant guidelines and regulations. All study participants signed informed consent prior to study enrolment. To protect patients’ privacy, each patient was assigned study ID. Common measures were taken for data protection. Study data were entered directly into a REDCap database using password-protected and encrypted study tablets. This study went through scientific and ethical reviews and was approved by the Rwanda National Ethics Committee (Kigali, Rwanda, No. 848/RNEC/2016) and Partners Human Research Committee (Boston, MA, No 2016P001943/MGH).

Consent for publication

All authors have approved the text for publication. All data from was fully anonymised before analysis, and there are no data from individuals.

Competing interests

The authors declare they have no competing interests.

Weiser TG, Haynes AB, Molina G, Lipsitz SR, Esquivel MM, Uribe-Leitz T, et al. Size and distribution of the global volume of surgery in 2012. Bull World Health Organ. 2016;94(3):201-209F.
Betran AP, Ye J, Moller A-B, Zhang J, Gulmezoglu AM, Torloni MR. The Increasing Trend in Caesarean Section Rates: Global, Regional and National Estimates: 1990–2014. PLoS One. 2016;11(2):e0148343.
Collaborators GBD 2015 MM. Global, regional, and national levels of maternal mortality, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1775–812.
Molina G, Esquivel MM, Uribe-Leitz T, Lipsitz SR, Azad T, Shah N, et al. Avoidable maternal and neonatal deaths associated with improving access to caesarean delivery in countries with low caesarean delivery rates: an ecological modelling analysis. Lancet. 2015;385 Suppl:S33.
Thomas S, Meadows J, McQueen KAK. Access to Cesarean Section Will Reduce Maternal Mortality in Low-Income Countries: A Mathematical Model. World J Surg. 2016 Jul;40(7):1537–41.
Zuarez-Easton S, Zafran N, Garmi G, Salim R. Postcesarean wound infection: prevalence, impact, prevention, and management challenges. Int J Womens Health. 2017;9:81–8.
Findeisen A, Arefian H, Doenst T, Hagel S, Pletz MW, Hartmann M, et al. Economic burden of surgical site infections in patients undergoing cardiac surgery. Eur J cardio-thoracic Surg Off J Eur Assoc Cardio-thoracic Surg. 2019;55(3):494–500.
Jenks PJ, Laurent M, McQuarry S, Watkins R. Clinical and economic burden of surgical site infection (SSI) and predicted financial consequences of elimination of SSI from an English hospital. J Hosp Infect. 2014; 86(1):24–33
Sullivan E, Gupta A, Cook CH. Cost and Consequences of Surgical Site Infections: A Call to Arms. Surg Infect. 2017;18(4):451–4.
Allegranzi B, Bagheri Nejad S, Combescure C, Graafmans W, Attar H, Donaldson L, et al. Burden of endemic health-care-associated infection in developing countries: systematic review and meta-analysis. Lancet. 2011;377(9761):228–41.
Sway A, Nthumba P, Solomkin J, Tarchini G, Gibbs R, Ren Y, Wanyoro A. Burden of surgical site infection following cesarean section in sub-Saharan Africa: a narrative review. Int J Womens Health. 2019;11:309–318..
De Oliveira AC, Carvalho DV. Evaluation of underreported surgical site infection evidenced by post-discharge surveillance. Rev Lat Am Enfermagem. 2007;15(5):992–7.
Woelber E, Schrick EJ, Gessner BD, Evans HL. Proportion of Surgical Site Infections Occurring after Hospital Discharge: A Systematic Review. Surg Infect. 2016;17(5):510–9.
USAID. Ending Preventable Maternal Mortality: USAID Maternal Health Vision for Action. USAID Matern Heal Vis Action. 2015;(January 2015). https://www.usaid.gov/sites/default/files/documents/1864/MCHVision.pdf. Accessed 13 July 2017.
Tsai PS, Hsu CS, Fan YC, Huang CJ. General anaesthesia is associated with increased risk of surgical site infection after Caesarean delivery compared with neuraxial anaesthesia: A population-based study. Br J Anaesth. 2011;107(5):757–61.
Grimes CE, Bowman KG, Dodgion CM, Lavy CBD. Systematic Review of Barriers to Surgical Care in Low-Income and Middle-Income Countries. World J Surg. 2011;35:941–50.
Adams YJ, Smith BA. Integrative Review of Factors That Affect the Use of Postpartum Care Services in Developing Countries. J Obstet Gynecol neonatal Nurs JOGNN. 2018; 47(3):371–84.
National Institute of Statistics of Rwanda, Ministry of Health (MoH), ICF International. Rwanda Demographic and Health Survey 2014-15. Rockville, Maryland, USA:NISR, MOH, and ICF International.; 2015.
Mugeni C, Levine AC, Munyaneza RM, Mulindahabi E, Cockrell HC, Glavis-Bloom J, et al. Nationwide implementation of integrated community case management of childhood illness in Rwanda. Glob Heal Sci Pract. 2014;2(3):328–41.
Government of Rwanda, Ministry of Health. Service packages for health facilities at different levels of service delivery. 2011 p. 34–7.
Government of Rwanda, Ministry of Health. HSSP III Rwanda Health Sector Strategic Plan 2012–2018. Kigali, Rwanda; 2012.
World Bank. The World Bank In Rwanda: overview. 2018. http://www.worldbank.org/en/country/rwanda/overview. Accessed 28 September 2018.
Ezeanya-Esiobu, Chika. The rise of homegrown ideas andgrassroots voices: New directions in social policy in Rwanda, UNRISD Working Paper, 2017; No.2017-6, United Nations Research Institute for Social Development (UNRISD), Geneva
Cherian T, Hedt-Gauthier B, Nkurunziza T, Sonderman K, Gruendl MA, Nihiwacu E, et al. Diagnosing Post-Cesarean Surgical Site Infections in Rural Rwanda: Development, Validation, and Field Testing of a Screening Algorithm for Use by Community Health Workers. Surg Infect (Larchmt). 2020;21(7):613–620.
Nkurunziza T, Kateera F, Sonderman K, Gruendl M, Nihiwacu E, Ramadhan B,et al. Prevalence and predictors of surgical site infections: a prospective cohort study of women undergoing caesarean section at a rural district hospital in Rwanda. British Journal of Surgery. 2019; 106: e121–e128
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.
National Bank of Rwanda. Exchange Rate.Historical data. https://www.bnr.rw/currency/exchange-rate/?tx_bnrcurrencymanager_master%5Baction%5D=archive&tx_bnrcurrencymanager_master%5Bcontroller%5D=Currency&cHash=9b3b8a3170a02e5876e4a1be17720fec. Accessed 13 June 2017.
Nguhuni B, De Nardo P, Gentilotti E, Chaula Z, Damian C, Mencarini P, et al. Reliability and validity of using telephone calls for post-discharge surveillance of surgical site infection following caesarean section at a tertiary hospital in Tanzania. Antimicrob Resist Infect Control. 2017;6:43.
Hurt K, Walker R, Campbell J, Egede L. mHealth Interventions in Low and Middle-Income Countries: A Systematic Review. Glob J Health Sci. 2016;8(9):54429.
Karageorgos G, Andreadis I, Psychas K, Mourkousis G, Kiourti A, Lazzi G, et al. The Promise of Mobile Technologies for the Health Care System in the Developing World: A Systematic Review. IEEE Rev Biomed Eng. 2019;12:100–22.
Malherbe LF. Cataract surgery and non-attendance: RCT to determine the effect of a SMS reminder system and financial impact in a developing country [Internet]. University of Cape Town; 2017. http://hdl.handle.net/11427/27434. Accessed 4 October 2017.
Von Allmen RS, Tinner C, Schmidli J, Tevaearai HT, Dick F. Randomized controlled comparison of cross-sectional survey approaches to optimize follow-up completeness in clinical studies. Puebla I, editor. PLoS One. 2019; 14(3):e0213822.
National Institute of Statistics of Rwanda. Rwanda Poverty Profile Report, 2016/17 [Internet]. Kigali, Rwanda; 2018. https://www.statistics.gov.rw/publication/eicv-5-rwanda-poverty-profile-report-201617 Accessed 30 March 2021.
Powell BL, Nkurunziza T, Kateera F, Dusabe L, Kubwimana M, Koch R, et al. Synthesizing postpartum care pathways, facilitators, and barriers for women after cesarean section: a qualitative analysis from rural Rwanda. Journal of Global Health Reports. 2021;5:e2021043. doi:10.29392/001c.23615
Mukangendo M, Nzayirambaho M, Hitimana R, Yamuragiye A. Factors Contributing to Low Adherence to Community-Based Health Insurance in Rural Nyanza District, Southern Rwanda. J Environ Public Health. 2018; 2018: 2624591
Spaan E, Mathijssen J, Tromp N, McBain F, Have AT, Baltussen R. The impact of health insurance in Africa and Asia: a systematic review. Bulletin of the World Health Organization. 2012;90:685–92.
Atake EH. Does the type of health insurance enrollment affect provider choice, utilization and health care expenditures? BMC Health Serv Res. 2020 Nov 3;20(1):1003
Kullgren JT, McLaughlin CG. Beyond affordability: the impact of nonfinancial barriers on access for uninsured adults in three diverse communities. J Community Health. 2010;35(3):240–248
Atake, EH. Health shocks in Sub-Saharan Africa: are the poor and uninsured households more vulnerable?. Health Econ Rev 8, 26 (2018). https://doi.org/10.1186/s13561-018-0210-x
Bhati KS. Effect of Client-Therapist Gender Match on the Therapeutic Relationship: An Exploratory Analysis. Psychol Rep. 2014;115(2):565–83.
Behn A, Davanzo A, Errázuriz P. Client and therapist match on gender, age, and income: Does match within the therapeutic dyad predict early growth in the therapeutic alliance? J Clin Psychol. 2018;74(9):1403–21.
Landes SJ, Burton JR, King KM, Sullivan BF. Women’s preference of therapist based on sex of therapist and presenting problem: An analog study. Couns Psychol Q. 2013;26(3–4):330–42.

No competing interests reported.

Supplementarymaterials.docx

Download PDF

Editorial decision: Major revision
29 Apr, 2022
Reviews received at journal
26 Apr, 2022
Reviewers agreed at journal
16 Apr, 2022
Reviewers invited by journal
13 Apr, 2022
Editor assigned by journal
05 Apr, 2022
Editor invited by journal
05 Apr, 2022
Submission checks completed at journal
05 Apr, 2022
First submitted to journal
30 Mar, 2022

You are reading this latest preprint version

Enablers and barriers to post-discharge follow-up among women who have undergone a cesarean section: Experiences from a prospective cohort in rural Rwanda

Status:

Version 1

Abstract

Figures

Introduction

Methods

Study setting:

Study design and population:

Implementation of the study:

Data collection:

Analysis and statistics:

Results

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1