Mapping of anaemia prevalence among pregnant women in Kenya (2016-2019)

Diagnostic accuracy of a non-invasive spot-check hemoglobin meter, Masimo Rad-67® pulse CO-Oximeter®, in detection of anemia in antenatal care settings in Kenya

Background

Point of care hemoglobin meters play key roles in increasing access to anemia screening in antenatal care especially in settings with limited access to laboratories. We aimed to determine the diagnostic accuracy of a non-invasive spot-check hemoglobin (SpHb) meter, Masimo Rad-67® Pulse CO-Oximeter®, in the diagnosis of anemia in pregnant women attending antenatal care clinics in Kilifi, Kenya.

Methods

This was a diagnostic accuracy study that retrospectively evaluated SpHb against a validated reference standard of laboratory assessed hemoglobin (Lab Hb) by a SYSMEX XN-330 automated hematology analyzer. The study was nested within a prospective pregnancy cohort study that recruited unselected pregnant women from antenatal care clinics in two public hospitals in Kilifi County, coastal Kenya. Records with both SpHb and Lab Hb were selected from pregnancy visits between May 2021 and December 2022. Linear regression and Bland-Altman analysis were performed to compare the two tests and diagnostic accuracy parameters obtained for the diagnosis of anemia.

Results

A total of 2,975 records (from 2,203 unique participants), with paired SpHb and Lab Hb were analyzed. Linear regression showed a significant but weak positive correlation, a proportional bias of 0.44 (95% CI 0.41-0.47) and a constant of 7.59 (95% CI 7.30-7.87, p < 0.001). The median bias was 1.70 g/dl, with limits of agreement of -0.80 to 4.20. SpHb tended to be higher than Lab Hb on the low hemoglobin range but lower than Lab Hb on the high hemoglobin range. The sensitivity of SpHb in detecting anemia was 18.66%. Prevalence, specificity, positive predictive value, and negative predictive values were 46.37%, 96.77%, 83.33%, and 57.92% respectively.

Conclusion

Overall, SpHb by Masimo Rad-67® Pulse CO-Oximeter® did not accurately identify pregnant women with anemia and many cases would be missed. We would not recommend its use in antenatal care settings.

Prevalence and factors associated with anaemia among pregnant women attending antenatal care in a district hospital and its feeder community healthcare centre of the Limpopo Province, South Africa

Background

Pregnancy anaemia is a significant public health concern in South Africa (SA), particularly in rural areas, but little is known about its prevalence and risk factors in rural areas. The objective of the study was to determine the prevalence and identify risk factors of pregnancy anaemia in the public health facilities of Limpopo Province (LP), SA.

Methods

A cross-sectional study was conducted among a consecutive sample of 211 pregnant women attending antenatal care at Seshego Hospital and its feeder health centre (May to June 2019). Anaemia was defined as haemoglobin (Hb) <11 g/dL and classified as mild (10-10.9 g/dL), moderate (7-9.9 g/dL) and severe anaemia (<7 g/dL). A multiple logistic regression analysis was used to identify predictors of anaemia.

Results

The mean age of the women was 28.4 ± 5.7 years (range from 18 to 41 years). Over half (52%) had secondary education, 65% were unmarried, 72% were unemployed, 34% were nulliparous, 15% were human immunodeficiency virus (HIV) infected and 67% were in the third trimester. The anaemia prevalence was 18.0% and was significantly associated with parity, HIV status and body mass index (BMI) in a multivariate logistic regression analysis.

Conclusion

This study found that less than one-third of pregnant women were affected by anaemia, associated with parity, HIV infected and BMI. It is essential to promote routine screening for anaemia, health education and prompt treatment of infections to reduce this burden. In addition, further studies on risk factors for anaemia during pregnancy in both urban and rural communities should be conducted to strengthen these findings.

A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Investigating rapid diagnostic testing in Kenya's health system, 2018-2020: validating non-reporting in routine data using a health facility service assessment survey

BACKGROUND

Understanding the availability of rapid diagnostic tests (RDTs) is essential for attaining universal health care and reducing health inequalities. Although routine data helps measure RDT coverage and health access gaps, many healthcare facilities fail to report their monthly diagnostic test data to routine health systems, impacting routine data quality. This study sought to understand whether non-reporting by facilities is due to a lack of diagnostic and/or service provision capacity by triangulating routine and health service assessment survey data in Kenya.

METHODS

Routine facility-level data on RDT administration were sourced from the Kenya health information system for the years 2018-2020. Data on diagnostic capacity (RDT availability) and service provision (screening, diagnosis, and treatment) were obtained from a national health facility assessment conducted in 2018. The two sources were linked and compared obtaining information on 10 RDTs from both sources. The study then assessed reporting in the routine system among facilities with (i) diagnostic capacity only, (ii) both confirmed diagnostic capacity and service provision and (iii) without diagnostic capacity. Analyses were conducted nationally, disaggregated by RDT, facility level and ownership.

RESULTS

Twenty-one per cent (2821) of all facilities expected to report routine diagnostic data in Kenya were included in the triangulation. Most (86%) were primary-level facilities under public ownership (70%). Overall, survey response rates on diagnostic capacity were high (> 70%). Malaria and HIV had the highest response rate (> 96%) and the broadest coverage in diagnostic capacity across facilities (> 76%). Reporting among facilities with diagnostic capacity varied by test, with HIV and malaria having the lowest reporting rates, 58% and 52%, respectively, while the rest ranged between 69% and 85%. Among facilities with both service provision and diagnostic capacity, reporting ranged between 52% and 83% across tests. Public and secondary facilities had the highest reporting rates across all tests. A small proportion of health facilities without diagnostic capacity submitted testing reports in 2018, most of which were primary facilities.

CONCLUSION

Non-reporting in routine health systems is not always due to a lack of capacity. Further analyses are required to inform other drivers of non-reporting to ensure reliable routine health data.

Prevalence and risk factors of malaria and anaemia and the impact of preventive methods among pregnant women: A case study at the Akatsi South District in Ghana

Aim

This study aimed to ascertain the prevalence and risk factors of malaria and anaemia as well as the impact of preventive methods among pregnant women at the Akatsi South District Hospital of Ghana.

Subjects and methods

A hospital based cross-sectional study using simple random sampling technique was conducted among 200 pregnant women receiving antenatal care and laboratory services at the Akatsi District Hospital from May 2016 to July 2016. A semi-structured questionnaire was administered to obtain participants’ malaria preventive methods in addition to demographic and gestational details. Participants’ hemoglobin and malaria status were assessed using one milliliter (1 ml) whole blood collected from each participant following standard procedures. Factors that produced a p-value of ≤0.2 from the univariate model were included in the final model. Association between potential covariates and the outcomes was assessed using multivariate logistic regression. The Clopper-Pearson test statistic was used to determine the 95% confidence intervals of the outcome variables of interest. We also estimated the population attributable fraction (PAF) of anaemia due to malaria by substituting the adjusted relative risk estimates (RR_i) (using the adjrr command in STATA) of anaemia due to malaria into the category-specific attributable formula. P-values of <0.05 were considered statistically significant.

Results

Prevalence of anaemia in pregnancy (AiP), malaria in pregnancy (MiP) and AiP/MiP comorbidity was 63.5% (95% CI:56.4–70.2), 11.0% (96% CI:7.0–16.2) and 10.5% (95% CI:6.6–15.6) respectively. Prevalence rates of AiP (66.7%) and MiP (18.5%) predominated among pregnant women aged < 20 years. PAF of AiP due to MiP was 34.5% (95% CI:23.8–43.6). High use of IPTp-SP, 64.0% (95% CI:56.9–70.6) and LLIN, 90.0% (95% CI:85.0–93.8) was observed in this study. Only 42.0% (95% CI:35.1–49.2) used repellent. Not being on the IPTp-SP program posed a 11.70 times risk of MiP (95% CI:2.32–58.96; p = 0.003) compared to pregnant women on the IPTp-SP program. Similarly, not sleeping under LLIN posed an 8.07 times risk of MiP (95% CI:1.98–32.2; p = 0.004) compared to pregnant women who slept under LLIN. Meanwhile, being positive for MiP posed a 12.10 times risk (95% CI:1.35–85.06; p = 0.025) of AiP compared to those negative for malaria whereas failure to attend ANC as scheduled posed 6.34 times risk (95% CI:1.81–22.19; p = 0.004) of AiP among the pregnant women studied.

Conclusion

The prevalence of MiP and AiP among pregnant women in the Akatsi South District remains a great concern. High utilization of IPTp-SP and LLIN was observed with a resultant positive effect on malaria prevalence among pregnant women. Improved access to IPTp-SP and LLIN is hence encouraged to help further diminish the risk of malaria infection amongst pregnant women in the District.

Joint spatio-temporal modelling of adverse pregnancy outcomes sharing common risk factors at sub-county level in Kenya, 2016-2019

Background

Adverse pregnancy outcomes jointly account for a high proportion of mortality and morbidity among pregnant women and their infants. Furthermore, the burden attributed to adverse pregnancy outcomes remains high and inadequately characterised due to the intricate interplay of its etiology and shared set of important risk factors. This study sought to quantify and map the underlying risk of multiple adverse pregnancy outcomes in Kenya at sub-county level using a shared component space-time modelling framework.

Methods

Reported sub-county level adverse pregnancy outcomes count from January 2016 – December 2019 were obtained from the Kenyan District Health Information System. A Bayesian hierarchical spatio-temporal model was used to estimate the joint burden of adverse pregnancy outcomes in space (sub-county) and time (year). To improve the precision of our estimates over time and space, information across the outcomes were combined via the shared and the outcome-specific components using a shared component model with spatio-temporal interactions.

Results

Overall, the total number of adverse outcomes in pregnancy increased by 14.2% (95% UI: 14.0–14.5) from 88,816 cases in 2016 to 101,455 cases in 2019. Between 2016 and 2019, the estimated low birth weight rate and the pre-term birth rate were 4.5 (95% UI: 4.4–4.7) and 2.3 (95% UI: 2.2–2.5) per 100 live births. The stillbirth and neonatal death rates were estimated to be 18.7 (95% UI: 18.0–19.4) and 6.9 (95% UI: 6.4–7.4) per 1000 live births. The magnitude of the spatio-temporal variation attributed to shared risk was high for pre-term births, low birth weight, neonatal deaths, stillbirths and neonatal deaths, respectively. The shared risk patterns were dominant in sub-counties located along the Indian ocean coastline, central and western Kenya.

Conclusions

This study demonstrates the usefulness of a Bayesian joint spatio-temporal shared component model in exploiting specific and shared risk of adverse pregnancy outcomes sub-nationally. By identifying sub-counties with elevated risks and data gaps, our estimates not only assert the need for bolstering maternal health programs in the identified high-risk sub-counties but also provides a baseline against which to assess the progress towards the attainment of Sustainable Development Goals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12210-9.