{"_asset":{"@search.score":1,"Name":"Magnitude of screening for gestational diabetes mellitus in an urban setting in Tanzania; a cross-sectional analytic study","AlternateTitleCommaDelimited":null,"Id":"5139","AssetLocationUri":null,"Abstract":"Background: Medical screening detects risk factors for disease or presence of disease in otherwise well persons in order to intervene early and reduce morbidity and mortality. During antenatal care (ANC) it is important to detect conditions that complicate pregnancy, like gestational diabetes mellitus (GDM). Despite international and local guidelines recommending screening for GDM during ANC, there is evidence to suggest that the practice was not being carried out adequately. A major challenge may be lack of consensus on uniform GDM screening and diagnostic guidelines internationally and locally. The primary objective was to determine the magnitude of screening for GDM among women receiving ANC at the Aga Khan Hospital, Dar es Salaam and Muhimbili National Hospital, Dar es Salaam. Secondary objectives were: to determine the methods used by health practitioners to screen for GDM, to determine the magnitude of undiagnosed gestational diabetes mellitus among women attending ANC and factors associated with screening for GDM among these women.\r\n\r\nMethods: A cross-sectional analytical study was done. Data collection was done using pre-tested questionnaires and reviewing antenatal care records. The proportion of women attending ANC who were screened for GDM was determined. The 75 g Oral Glucose Tolerance Test (OGTT) was offered to women who had not been screened after education and consent.\r\n\r\nResults: Only 107 out of 358 (29.9%) had been offered some form of GDM screening. Tests used for GDM screening were random blood sugar (56.8%), fasting blood sugar (32.8%), HbA1C (6%) and 75 g OGTT (3.4%). The uptake of the OGTT was 27%. Of these women the prevalence of GDM was 27.9%. Factors associated with screening for GDM were history of big baby, history of pregnancy induced hypertension and participant awareness of GDM (all p: < 0.05).\r\n\r\nConclusions: Screening for GDM among women attending ANC was lower than the World Health Organization target. Efforts should be directed towards promoting GDM screening, increasing awareness about GDM and developing more effective screening methods.","PublicationOrPresentationDate":"2020-07-23T00:00:00Z","LastModifiedDate":"2021-07-27T18:29:38.85Z","IsDeleted":false,"DigitalObjectIdentifier":"10.1186/s12884-020-03115-3","ContentTypeName":"Research","ParentContentTypeName":"Journal Article","JournalName":"BMC Pregnancy and Childbirth","ContentType":"Journal Article > Research","AvailabilityTypeId":1,"AvailabilityType":"Full Text","JournalIssue":"1","JournalVolume":"20","JournalPages":null,"JournalAbbreviation":"BMC Pregnancy Childbirth","Conference":null,"ConferenceStartDate":null,"ConferenceEndDate":null,"content":"\nRESEARCH ARTICLE Open Access\n\nMagnitude of screening for gestational\ndiabetes mellitus in an urban setting in\nTanzania; a cross-sectional analytic study\nAkampa Mukuve1* , Mariam Noorani2, Ibrahim Sendagire3 and Miriam Mgonja4\n\nAbstract\n\nBackground: Medical screening detects risk factors for disease or presence of disease in otherwise well persons in\norder to intervene early and reduce morbidity and mortality. During antenatal care (ANC) it is important to detect\nconditions that complicate pregnancy, like gestational diabetes mellitus (GDM). Despite international and local\nguidelines recommending screening for GDM during ANC, there is evidence to suggest that the practice was not\nbeing carried out adequately. A major challenge may be lack of consensus on uniform GDM screening and\ndiagnostic guidelines internationally and locally.\nThe primary objective was to determine the magnitude of screening for GDM among women receiving ANC at the\nAga Khan Hospital, Dar es Salaam and Muhimbili National Hospital, Dar es Salaam. Secondary objectives were: to\ndetermine the methods used by health practitioners to screen for GDM, to determine the magnitude of\nundiagnosed gestational diabetes mellitus among women attending ANC and factors associated with screening for\nGDM among these women.\n\nMethods: A cross-sectional analytical study was done. Data collection was done using pre-tested questionnaires\nand reviewing antenatal care records. The proportion of women attending ANC who were screened for GDM was\ndetermined. The 75 g Oral Glucose Tolerance Test (OGTT) was offered to women who had not been screened after\neducation and consent.\n\nResults: Only 107 out of 358 (29.9%) had been offered some form of GDM screening. Tests used for GDM\nscreening were random blood sugar (56.8%), fasting blood sugar (32.8%), HbA1C (6%) and 75 g OGTT (3.4%). The\nuptake of the OGTT was 27%. Of these women the prevalence of GDM was 27.9%. Factors associated with\nscreening for GDM were history of big baby, history of pregnancy induced hypertension and participant awareness\nof GDM (all p: < 0.05).\n\nConclusions: Screening for GDM among women attending ANC was lower than the World Health Organization\ntarget. Efforts should be directed towards promoting GDM screening, increasing awareness about GDM and\ndeveloping more effective screening methods.\n\nKeywords: Tanzania, Hyperglycaemia in pregnancy, Screening\n\n© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,\nwhich permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give\nappropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if\nchanges were made. The images or other third party material in this article are included in the article's Creative Commons\nlicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons\nlicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain\npermission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.\nThe Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the\ndata made available in this article, unless otherwise stated in a credit line to the data.\n\n* Correspondence: akampamukuvem@gmail.com\n1Department of Family Medicine, Post Graduate Medical Education, Aga\nKhan University, Dar es Salaam, Tanzania\nFull list of author information is available at the end of the article\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 \nhttps://doi.org/10.1186/s12884-020-03115-3\n\nhttp://crossmark.crossref.org/dialog/?doi=10.1186/s12884-020-03115-3&domain=pdf\nhttp://orcid.org/0000-0002-4414-4300\nhttp://creativecommons.org/licenses/by/4.0/\nhttp://creativecommons.org/publicdomain/zero/1.0/\nmailto:akampamukuvem@gmail.com\n\n\nBackground\nMedical screening identifies apparently healthy people\nwho have a disease or increased risk of disease [1]. The\nstrategy helps clinicians to detect unrecognized symptom-\natic and pre-symptomatic disease. Screening for condi-\ntions therefore enables early diagnosis and intervention\nwhich improves clinical outcomes and reduces costs of\nhealth care.\nGestational diabetes mellitus (GDM) is diabetes that is\n\nfirst diagnosed in the second or third trimester of preg-\nnancy that is not clearly either preexisting type 1 or type\n2 diabetes [2]. GDM is one of the conditions which\nshould be screened for during antenatal care (ANC).\nThis is because GDM increases risk of maternal and fetal\ncomplications during pregnancy [3] and adverse health\noutcomes in the future [4].\nThe prevalence of GDM varies across populations, ran-\n\nging from 10.4 to 25% across the world [5]. Over 90% of\ncases occur in low and middle income countries [6]. Al-\nthough there is a lot of literature on the prevalence, man-\nagement and complications of GDM, little focus has been\nput on how much GDM screening i.e. the magnitude of\nGDM screening is actually done. A study done in Uganda,\nTanzania and Burkina Faso showed that health care\nworkers did not provide all required services during ANC\ndespite good attendance [7]. It is worth also noting that\nguidelines for screening for GDM vary across the globe and\nwithin countries [8]. In Tanzania the national guidelines [9]\nvary from the Tanzania diabetic association [10] guidelines\nand both vary from the World Health Organization\n(WHO) guidelines [3]. Tests used to screen for GDM in\nthese national guidelines include the oral glucose tolerance\ntest (OGTT), random blood sugar (RBS), fasting blood\nsugar (FBS) and glycated haemoglobin (HbA1C).\nLack of vigilance to screen for GDM and varying\n\nguidelines may be responsible for poor GDM screening\npractices in low resource settings as shown in Cameroon\n[11]. From other studies, some factors associated with\nscreening for GDM include: education status/ literacy,\nhusband support, family support, awareness of GDM or\nits risks, health care providers informing the women\nabout the test and help with household work [12], mari-\ntal status, women’s autonomy [13], employment status\n[14], private health insurance status, distance to hospital,\njourney time to health facility, mode of transport and\nability to pay for the service/ financial status [15].\nGiven the significance of the unfavourable pregnancy\n\noutcomes of GDM but paucity of data on how much\nscreening is done for the condition, this study sought to\ndetermine the magnitude of screening for GDM among\nwomen attending ANC at two tertiary hospitals. The\nstudy also determined the tests used for GDM screening\nduring antenatal care, prevalence of undiagnosed GDM\nand factors associated with screening for GDM.\n\nMethods\nDesign and study site\nA cross-sectional analytic study was done at the Aga\nKhan Hospital Dar es Salaam and Muhimbili National\nHospital, Dar es Salaam in Tanzania. The Aga Khan\nHospital is a not for profit, tertiary care facility and the\nbiggest private hospital in Tanzania. Its antenatal care\nclinic attends to averagely 300 women a week. Muhim-\nbili hospital is the national referral hospital providing\ntertiary care services to referral cases and the population\nsurrounding it. Antenatal care attendance in this hos-\npital averages 1000 women per week.\n\nParticipants\nTotal sample size was 358. Sample size was determined\nusing a standard formula on the basis of 22% magnitude\nof screening for GDM from a study in a similar setting\n[11], 20% precision, 80% power, 5% non-response rate\nand type I error of 5%. Each of the 2 study sites provided\nhalf of the total sample. Convenient, consecutive sam-\npling was used to achieve the required sample.\nThe study population comprised of pregnant women\n\nabove 18 years of age, who had already received some\nantenatal care and who were above the gestational age at\nwhich screening for GDM is routinely performed. In-\ncluded were pregnant women attending the antenatal\ncare specialist clinics, aged 18 years and above, posses-\nsion of antenatal card, gestational age of 30 or more\nweeks calculated using menstrual dates or earliest ultra\nsound scan, and who attended a 1st or 2nd trimester\nantenatal care visit at the study site. Women were ex-\ncluded if they had pre-existing self-reported diagnosis of\ndiabetes mellitus, severe illness, mental illness, partici-\npated in another study or were taking corticosteroids,\nantidepressants, diuretics or beta blockers.\n\nData collection\nDuring the routine health talks all women were educated\nabout GDM and given details about the study. Informed,\nwritten and signed consent was obtained from willing\nparticipants. A mini check list confirmed eligibility of\nparticipants.\nData collection tools were an interviewer-administered\n\nquestionnaire and data extraction sheet. Participants\nwere interviewed using a pre-tested, structured question-\nnaire in the language of choice between Kiswahili and\nEnglish. The questionnaire identified patient demo-\ngraphic characteristics, risk factors for GDM, socio-\neconomic factors associated with screening for GDM,\npast obstetric and medical history and tests for glycemic\nstatus and related information regarding screening for\nGDM. Additional information including weight, height,\nbody mass index, blood pressure was obtained and veri-\nfied from the antenatal cards, patient files and laboratory\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 2 of 8\n\n\n\nreports. This information was recorded on the data col-\nlection sheet. Where information from the interview and\npatient records differed, the latter were used.\nWomen were considered screened for GDM if WHO\n\n2013 guidelines [3], Tanzania Diabetic association guide-\nlines [10] or Tanzania ministry of health guidelines 2017\n[9] had been followed. All participants found not to have\nbeen screened for GDM were offered the 75 g OGTT.\nThe glucometer SD GlucoNavii® GDH blood glucose\nmonitoring system and SD BIOSENSOR INC lancets\nwere used. This apparatus had a coefficient of variation\n5% above 5.55 mmol/l and within standard deviation 4\nmg/dl for readings below 5.55 mmol/l. For those with\nmedical insurance, the cost of the test was covered by\nthe insurer. Cash paying clients were offered a dis-\ncounted price at the Aga Khan hospital and it was of-\nfered free of charge for those who could not afford.\nInterpretation of results was according to WHO 2013\nguidelines [3]. Women diagnosed with GDM or pre-\ndiabetic states were informed and referred for appropri-\nate care.\nData were collected on possible determinants of\n\nscreening for GDM which were either socio-economic\nfactors or risk factors of GDM. The socio-economic fac-\ntors included: education status/ literacy, marital status,\nemployment status, women’s autonomy, private health\ninsurance status, distance to hospital, journey time to\nhealth facility, mode of transport, ability to pay for the\nservice/ financial status, husband support, family sup-\nport, obstetric history, parity, awareness of GDM or its\nrisks, health care providers informing the women about\nthe test and help with household work.\nThe risk factors for GDM included: glycosuria, Body\n\nMass Index (BMI) > 25 at time of interview, hyperten-\nsion in current pregnancy, history of chronic hyperten-\nsion, history of pregnancy induced hypertension (PIH),\nhistory of GDM, history of pre-diabetic states, family\nhistory of hypertension, family history of diabetes melli-\ntus, history of multiple pregnancy, history of delivering a\nmacrosomic (≥4 kg of weight) baby, clinical or ultra-\nsound diagnosis of large for gestational age in the on-\ngoing pregnancy, history of excessive weight gain (≥5 kg)\nsince 18 years of age, race, history of pregnancy loss and\ngrand-multiparity (≥ 5 pregnancies).\n\nData analysis\nData was entered into Microsoft excel 2007 and cleaned.\nThe data was then transferred to SPSS 20 for analysis.\nWe defined “magnitude” of screening for GDM as the\nproportion of participants attending ANC who had re-\nceived any test aimed at screening for GDM. Various\ntests done during screening were categorized and fre-\nquencies and percentages for each calculated and re-\ncorded. The percentage of participants who had not\n\ninitially screened but were diagnosed with GDM by\nOGTT in the study was the magnitude of undiagnosed\nGDM. Socio-economic determinants of screening for\nGDM and risk factors for GDM were evaluated for asso-\nciation with screening for GDM using the chi-square\ntest and associated p-values. Factors found to be statisti-\ncally significant by chi square test were further analyzed\nusing logistic regression for possible association with\nscreening for GDM.\n\nResults\nThe study period was July to September 2018. During\nthe study period July to September 2018, there were\n2250 women who attended specialist ANC at the study\nsites. As shown in Fig. 1, approximately 1500 were eli-\ngible to participate. Women who consented were inter-\nviewed. Six women were excluded. Two had overt\ndiabetes while four had participated in other studies.\nDemographic and obstetric characteristics are summa-\n\nrized in Table 1. The mean age of the participants was\n30.2 years (SD: 4.8; range 20–44). Most of the partici-\npants were between 20 and 30 years, accounting for\n57.6% of the study population. The mean gestational age\nwas 34.9 weeks of gestation (SD = 3.1; range 30–42). Of\nall the participants, 97.2% were black African, while 2.8%\nwere of Asian or of other origins.\n\nMagnitude of screening for GDM\nThe overall magnitude of screening for GDM at both\nhospitals was 29.9% (107 of 358). The magnitude was\n21.5% (38 of 177) at Aga Khan Hospital and 38.1% at\nMuhimbili National Hospital (69 of 181). Of the 251 of\nwomen found not to have been screened for GDM only\n27% (68) accepted the offered OGTT.\n\nTests used to screen for GDM\nOf the 107 participants screened for GDM, 56.8% (66)\nhad done random blood sugar (RBS), 32.8% (38) fasting\nblood sugar (FBS), 6% [7] HbA1C and 3.4% [3] the 75 g\nOral glucose tolerance test (OGTT).\n\nPrevalence of undiagnosed GDM\nThe prevalence of undiagnosed GDM was 27.9% (19 of 68).\n\nFactors associated with screening for GDM\nAs shown in Tables 2 and 3, chi square analysis revealed\nfactors with statistically significant association with\nscreening for GDM to be: awareness about GDM\n(p < 0.001), health worker communication about GDM\nscreening (p < 0.001), partner support during ANC (p =\n0.045), family support during ANC (p 0.049), history of\npregnancy induced hypertension (PIH) (p = 0.022), his-\ntory of delivering macrosomic baby (p = 0.023) and his-\ntory of glycosuria (p = 0.002). Logistic regression analysis\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 3 of 8\n\n\n\n(Table 4) revealed that history of delivering a macroso-\nmic baby (p = < 0.001 OR 2.15 95% CI: 1.12–4.10), his-\ntory of PIH (p = < 0.021 OR 2.34 95% CI: 1.13–4.82) and\nawareness of GDM (p = < 0.001 OR 2.79 95% CI: 1.74–\n4.45) were the only 3 factors associated with screening\nfor GDM.\n\nDiscussion\nIn this study the magnitude of screening for GDM was\nonly 30%. This value is somewhat similar to a Cameroon\nstudy which found the screening level at 22% [11]. Both\nof these values are lower than the WHO target for a\nscreening strategy which is 70% of the target population\n[16]. The study was done in specialist clinics in tertiary\nfacilities, with adequate facilities for screening. In this\nstudy specialist clinics were considered because obstetri-\ncians were expected to be able to address the complexity\nand interpretation of different guidelines and tests for\nGDM better than other cadres. The low screening rate\ncould not be attributed to poor resources or expertise.\nAnother study in the region showed that health workers\nomit certain practices stipulated in ANC guidelines. In-\nadequate screening was attributed to limited resources\nand expertise in the rural settings in that study [7].\n\nThe tests used for screening for GDM in order of fre-\nquency were RBS, FBS, HbA1C and OGTT. The trend\nreflects the ease of performing a particular test. Com-\npared to the OGTT the other tests are easy to use, less\nlabour intensive and more accessible [17]. Random\nblood sugar and HbA1C also do not require fasting [18].\nPerforming these tests is therefore convenient to both\nthe health worker and the pregnant woman [18].\nWhereas the OGTT has better diagnostic accuracy than\nthe other tests, it is more expensive and laborious to\nperform [18]. This possibly is the reason why it is per-\nformed less frequently (3.4%) than other tests. If we were\nto use the generally accepted OGTT as the screening\ntest for GDM, the magnitude of screening of 3.4% is far\nbelow the expected standard of care. In Uganda the up-\ntake of the OGTT was shown to be 75.4% [5]. It would\ntherefore plausible for health workers to offer the OGTT\nmore in this setting.\nThe prevalence of undiagnosed GDM was 28%. Preva-\n\nlence of GDM in a previous Tanzanian study also done\nin an urban setting was 8.4% [19]. Just like in our study\nthe 75 g OGTT was used for diagnosis in that study.\nOur findings may be suggestive of an increasing preva-\nlence of GDM in Tanzanian urban areas. The prevalence\nin our study is somewhat similar to 31.9% in Uganda [5],\n\nFig. 1 Study profile of participants attending Antenatal Care at Muhimbili National Hospital and Aga Khan Hospital Dar es Salaam in 2018\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 4 of 8\n\n\n\n31% in Cameroon [11] and 25.8% in south Africa [20].\nThese findings show a higher burden than the global\nprevalence of 16% [21]. Uptake of the OGTT was 27%\nwhich was similar to the 22% of the Cameroon study.\nHowever the uptake varies from 75.4% found in a\nUgandan study [5] and 55.4% in a South African study\n[20]. These higher uptake rates could be because in\n\nTable 1 Baseline demographic and clinical characteristics of\nparticipants attending Antenatal Care at Muhimbili National\nHospital and Aga Khan Hospital Dar es Salaam in 2018\n\nFrequency\n(Total 358)\n\nPercentage\n(Total 100%)\n\nAge a\n\n18–25 61 17.1\n\n26–30 135 37.9\n\n31–35 113 31.7\n\n36–40 37 10.4\n\n> 40 10 2.8\n\nResidence\n\nIlala 95 26.5\n\nKinondoni 113 31.6\n\nUbungo 68 19.0\n\nTemeke 42 11.7\n\nKigamboni 28 7.8\n\nOutside Dar es salaam 12 3.4\n\nGravidity\n\nPrime Gravida 94 26.3\n\nG2 112 31.3\n\nG3 82 22.9\n\nG4 45 12.6\n\n≥ G5 25 6.9\n\nNumber of GDM risk factors\n\n0 2 0.6\n\n1 20 5.6\n\n2 66 18.4\n\n3 114 31.8\n\n4 79 22.1\n\n≥ 5 77 21.5\n\nNumber of previous ANC visits\n\n1 10 2.8\n\n2 21 5.9\n\n3 46 12.8\n\n4 78 21.8\n\n5 75 20.9\n\n≥ 6 128 35.8\n\nGestational age\n\n30–31.9 66 18.4\n\n32–34.9 103 28.8\n\n35–37.9 114 31.9\n\n38–40.9 70 19.5\n\n≥ 41 5 1.39\n\nKey: ANC Antenatal Care, aData missing for 2 participants\n\nTable 2 Univariable analysis of socio-economic determinants of\nscreening for Gestational Diabetes Mellitus among women\nattending antenatal clinic at Muhimbili National Hospital and\nAga Khan Hospital Dar es Salaam in 2018\n\nFactor Prevalence (%) p-value\n\nEducated > tertiary level 71.8 0.926\n\nMarried 82.4 0.880\n\nIndependent source of income 79.6 0.270\n\nAutonomy 76.0 0.331\n\nMedical insurance 71.2 0.412\n\nPersonal vehicle 27.9 0.179\n\nLive in Ilala 26.5 0.288\n\nHaving help with housework 82.7 0.886\n\nPartner ANC attendance 64.8 0.045\n\nEscorted by family member to ANC 52.8 0.049\n\nAwareness of GDM 45.0 < 0.001\n\nHealth worker communication 27.7 < 0.001\n\nKey: ANC Antenatal care, GDM Gestational Diabetes Mellitus\n\nTable 3 Univariable analysis of risk factors for Gestational\nDiabetes Mellitus among women attending Antenatal care at\nMuhimbili National Hospital and Aga Khan Hospital Dar es\nSalaam in 2018\n\nRisk factor Prevalence (%) p-value\n\nGrand multiparity 6.9 0.709\n\nHistory of GDM 1.1 0.094\n\nHistory of PIH 9.5 0.022\n\nHypertension detected in current pregnancy 2.5 0.707\n\nPre-diabetic state 2.0 0.325\n\nGlycosuria 3.6 0.002\n\nBMI > 25 88.5 0.619\n\nHistory of big baby 12.6 0.023\n\nLGA in current pregnancy 4.5 0.903\n\nExcessive weight gain 65.9 0.281\n\nFamily history of DM 29.6 0.671\n\nFamily history of HT 43.6 0.123\n\nHistory of multiple pregnancy 3.7 0.479\n\nHistory of hypertension 4.5 0.662\n\nHistory of pregnancy loss 27.9 0.587\n\nKey: PIH Pregnancy Induced Hypertension, GDM Gestational Diabetes Mellitus,\nANC Antenatal care, HT Hypertension, LGA Large for Gestational Age, BMI Body\nMass Index\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 5 of 8\n\n\n\nthese studies, the primary objective was to determine the\nprevalence of GDM using the OGTT. In our study we\noffered the OGTT as part of a secondary objective.\nFactors found to be statistically significantly associ-\n\nated with screening were awareness about GDM, his-\ntory of pregnancy induced hypertension (PIH) and\nhistory of delivering a macrosomic baby. Awareness\nof GDM in our study was 45%, which is similar to\n40.3% in a multiethnic Australian study [22] but var-\nies from 17.5% in an Indian study [23]. Similar to the\nfindings in our study, another study in Tamil, India\nshowed that knowledge of GDM promotes GDM\nscreening [12]. People are more inclined to accept or\ndo tests if they are aware about the condition and its\ncomplications. Health literacy enables patients to ac-\ncess and effectively use health information [24], while\nlow health literacy is associated with poor health out-\ncomes [25]. PIH is a mild form of pre-eclampsia and\neclampsia which is the leading direct cause of mater-\nnal death in Tanzania [26]. This could explain why\nclinicians are more vigilant with these women. Deliv-\nering a macrosomic baby is the commonest complica-\ntion of GDM in some settings. For example in a\n\nUgandan study, macrosomia was the only obstetric\ncomplication associated with hyperglycemia in preg-\nnancy [5]. This also explains the possible increased\nvigilance with these women.\n\nConclusions\nIn conclusion, in this study we found a low magnitude\nof screening for GDM (30%). This is inadequate com-\npared to the recommended WHO target of 70%. A sig-\nnificant proportion (28%) of women with GDM had not\nbeen diagnosed with the condition; which could account\nfor significant adverse pregnancy outcomes.\nA majority of participants (over 75%) had attended\n\nANC 4 or more times by the time we evaluated them\nfor screening. Those many contacts with a health\nworker are a missed opportunity to screen for GDM\nand to provide quality, comprehensive ANC services.\nA sizeable proportion of women were not aware of\nGDM. This calls for wider sensitization on GDM.\nUniversal rather than selective screening for GDM\nshould be adopted in this setting given the high\nprevalence of risk factors for GDM. A less cumber-\nsome gold standard test for GDM diagnosis and\n\nTable 4 Multivariate analysis of factors associated with screening for Gestational Diabetes Mellitus among ANC attendees at\nMuhimbili National Hospital and Aga Khan Hospital Dar es Salaam in 2018\n\nCharacteristic Screened for GDM Odds ratio (95% CI) p-value\n\nYes = 107 (N/%) No = 251 (N/%)\n\nGlycosuria\n\nYes 9 (8.4%) 4 (1.6%) 0.96 (0.880–1.053) 0.401\n\nNo 97 (90.7%) 247 (98.4%)\n\nHistory of delivering big baby\n\nYes 20 (18.7%) 25 (10%) 2.15 (1.127–4.101) 0.020\n\nNo 87 (81.3%) 226 (90%)\n\nHistory of PIH\n\nYes 16 (15%) 18 (7.2%) 2.34 (1.131–4.827) 0.021\n\nNo 91 (85%) 233 (92.8%)\n\nAwareness of GDM\n\nYes 67 (62.6%) 94 (37.5%) 2.79 (1.749–4.459) < 0.001\n\nNo 40 (37.4%) 157 (62.5%)\n\nPartner attendance of ANC\n\nYes 77 (72%) 155 (61.8%) 0.96 (0.902–1.038) 0.360\n\nNo 29 (27.1%) 96 (38.2%)\n\nFamily member attendance of ANC\n\nYes 65 (60.7%) 124 (49.4%) 1.57 (0.993–2.504) 0.053\n\nNo 42 (39.3%) 127 (50.6%)\n\nHealth worker communication about GDM tests\n\nYes 91 (85%) 8 (3.2%) 1.03 (0.996–1.068) 0.087\n\nNo 15 (14%) 219 (87.3%)\n\nKey: PIH Pregnancy Induced Hypertension, GDM Gestational Diabetes Mellitus, ANC Antenatal care\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 6 of 8\n\n\n\nscreening needs to be developed to facilitate ease of\nscreening. A study of attitudes and practices of ANC\nproviders would dissect further why adequate screen-\ning for GDM is not conducted. Studies should be\ndone to determine factors associated with poor uptake\nof the OGTT in this setting. Follow up studies should\nbe done on interventions to increase screening for\nGDM.\n\nGeneralizability\nThis study was hospital based and urban based. There-\nfore the study findings may not be generalised to all\nsettings.\n\nLimitations\nIn our study, patients were consecutively sampled. This\nmethod of sampling could have introduced some level of\nbias in our findings. However, we have no reasons to be-\nlieve that manner of selecting our sample would be asso-\nciated with the dependent variable of being screened or\nnot. The principal investigator and the research assis-\ntants were not part of the clinical teams offering ANC\nservices.\nObservational studies, like ours are prone to social de-\n\nsirability phenomenon while getting responses from\nstudy participants. However, the impact of this\nphenomenon on our study was reduced for most of the\ntime by corroborating the participants’ responses with\ndata that was written on the ANC. Our study did not ex-\nplore the specific laboratory methods of performing the\ntests used in GDM screening. However, this could not\nhave affected the study results.\nOur study did not explore effects of health worker\n\nworkload, knowledge and cost of test on the magnitude\nof screening for GDM. However, cost for the tests was\neither covered by insurance, heavily discounted or of-\nfered free of charge by the health services. All ANC was\noffered by obstetricians who are assumed to have ad-\nequate knowledge and similar level of training. We have\nno reason to believe that the study results could be dif-\nferent if these factors were explored.\n\nSupplementary information\nSupplementary information accompanies this paper at https://doi.org/10.\n1186/s12884-020-03115-3.\n\nAdditional file 1. Questionnaire.\n\nAdditional file 2. Data extraction sheet.\n\nAbbreviations\nANC: Antenatal care; AKU: Aga Khan University; BMI: Body Mass Index;\nCI: Confidence Interval; FBS: Fasting Blood Sugar; GDM: Gestational Diabetes\nMellitus; HbA1C: Glycated Haemoglobin; OGTT: Oral Glucose Tolerance Test;\nOR: Odds Ratio; PIH: Pregnancy Induced Hypertension; RBS: Random Blood\nSugar; WHO: World Health Organization\n\nAcknowledgements\nWe acknowledge the support of the research participants and the staff of\nthe ANC clinics of Aga Khan Hospital Dar es salaam and Muhimbili National\nhospital. We thank Mr. James Orwa, Mr. Kabwama Steven and Dr. Shamira\nPira for their guidance in data analysis.\n\nAuthors’ contributions\nAM conceptualized and designed the study and research questions, carried\nout data collection, data analysis and initial interpretation, and drafted the\nmanuscript. MM was the content supervisor of the project. IS was the\nmethodology supervisor of the project. MN was a supervisor of the project.\nMM, IS and MN reviewed and revised the analysed data and manuscript. All\nauthors read and approved the final manuscript.\n\nFunding\nThe Aga Khan University (AKU) provided funding towards data collection\nand a conducive work station for the authors. AKU had no role in study\ndesign, active data collection, analysis, interpretation of data and writing the\nmanuscript.\n\nAvailability of data and materials\nThe datasets analysed during the study are available from the corresponding\nauthor on reasonable request.\n\nEthics approval and consent to participate\nThis study was conducted in accordance with the ethical standards of the\nHelsinki Declaration of the World Medical Association 1964. The research\nproposal was submitted to the Aga Khan University Research sub-committee\nfor review. Permission to conduct the study was obtained from the Aga\nKhan Hospital Dar es Salaam and Muhimbili National Referral Hospital re-\nsearch ethics review committees. The study was finally approved by the Tan-\nzanian National Medical Research Institute. Participants gave informed,\nwritten and signed consent.\n\nConsent for publication\nNot applicable.\n\nCompeting interests\nThe authors declare that they have no competing interests.\n\nAuthor details\n1Department of Family Medicine, Post Graduate Medical Education, Aga\nKhan University, Dar es Salaam, Tanzania. 2Department of Obstetrics and\nGynecology, Aga Khan University, P.O BOX 38129, Plot 42, Ufukoni Road, Dar\nes Salaam, Tanzania. 3MSF/Epicentre Mbarara Research Centre, Mbarara,\nUganda. 4Department of Pediatrics and Child Health, Aga Khan University,\nDar es Salaam, Tanzania.\n\nReceived: 7 October 2019 Accepted: 16 July 2020\n\nReferences\n1. Armstrong N, Eborall H. The sociology of medical screening: past, present\n\nand future. Sociol Health Illn. 2012;34(2):161–76.\n2. Cefalu WT, Berg EG, Saraco M, Petersen MP, Uelmen S, Robinson S.\n\nClassification and diagnosis of diabetes: standards of medical Care in\nDiabetes-2019. Diabetes Care. 2019;42:S13–28.\n\n3. WHO. Diagnostic Criteria and Classification of Hyperglycemia First Detected\nin Pregnancy. WHO/NMH/MND/132. Geneva: WHO Press; 2013.\n\n4. Shou C, Wei Y-M, Wang C, Yang H-X. Updates in long-term maternal and\nFetal adverse effects of gestational diabetes mellitus. Maternal-Fetal\nMedicine. 2019;1(2):91–4.\n\n5. Nakabuye B, Bahendeka S, Byaruhanga R. Prevalence of hyperglycaemia first\ndetected during pregnancy and subsequent obstetric outcomes at St.\nFrancis Hospital Nsambya. BMC Res Notes. 2017;10(1):174.\n\n6. Mwanri AW, Kinabo J, Ramaiya K, Feskens EJ. Gestational diabetes mellitus\nin sub-Saharan Africa: systematic review and metaregression on prevalence\nand risk factors. Tropical Med Int Health. 2015;20(8):983–1002.\n\n7. Conrad P, Schmid G, Tientrebeogo J, Moses A, Kirenga S, Neuhann F, et al.\nCompliance with focused antenatal care services: do health workers in rural\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 7 of 8\n\nhttps://doi.org/10.1186/s12884-020-03115-3\nhttps://doi.org/10.1186/s12884-020-03115-3\n\n\nBurkina Faso, Uganda and Tanzania perform all ANC procedures? Tropical\nMed Int Health. 2012;17(3):300–7.\n\n8. Agarwal MM. Gestational diabetes mellitus: an update on the current\ninternational diagnostic criteria. World J Diabetes. 2015;6(6):782–91.\n\n9. Ministry of health cd, gender, elderly, and children. Standard treatment\nguidelines and essential medicines list, Tanzania mainland. Dar es\nSalaam2017.\n\n10. Tanzania Diabetic Association MoHSWaTDA. Cardiovascular Disease, Type 2\nDiabetes, Obesity,Cancer, Chronic Obstructive Pulmonary Disease, and\nHyperlipidaemia Care; Case Management Training Modules; NCD Training\nManuals 2014. 2014. p. 52–56.\n\n11. Djomhou M. Maternal hyperglycemia during labor and related immediate\npost-partum maternal and perinatal outcomes at the Yaoundé Central\nHospital, Cameroon. J Health Population Nutrit. 2016;35:28.\n\n12. Karoline Kragelund Nielsen TR, Anil Kapur, Peter Damm. Factors influencing\ntimely initiation and completion of gestational diabetes mellitus screening\nand diagnosis - a qualitative study from Tamil Nadu, India. 2017;17:255.\n\n13. Nisar N, White F. Factors affecting utilization of antenatal care among\nreproductive age group women (15-49 years) in an urban squatter\nsettlement of Karachi. J Pakistan Med Assoc. 2003;53(2):47–53.\n\n14. Akowuah JA, Agyei-Baffour P, Awunyo-Vitor D. Determinants of antenatal\nhealthcare utilisation by pregnant women in third trimester in peri-urban\nGhana. J Trop Med. 2018;2018.\n\n15. Dea AO. Screening uptake rates and the clinical and cost effectiveness of\nscreening for gestational diabetes mellitus in primary versus secondary care:\nstudy. 2014;15:27.\n\n16. Organization WH. Cancer control: knowledge into action. WHO guide for\neffective programmes: prevention. Manila: World Health Organization; 2007.\n\n17. Bhavadharini B, Mahalakshmi MM, Maheswari K, Kalaiyarasi G, Anjana RM,\nDeepa M, et al. Use of capillary blood glucose for screening for gestational\ndiabetes mellitus in resource-constrained settings. Acta Diabetol. 2016;53(1):\n91–7.\n\n18. Trujillo J, Vigo A, Reichelt A, Duncan B, Schmidt M. Fasting plasma glucose\nto avoid a full OGTT in the diagnosis of gestational diabetes. Diabetes Res\nClin Pract. 2014;105(3):322–6.\n\n19. Mwanri AW, Kinabo J, Ramaiya K, Feskens EJ. Prevalence of gestational\ndiabetes mellitus in urban and rural Tanzania. Diabetes Res Clin Pract. 2014;\n103(1):71–8.\n\n20. Adams S, Rheeder P. Screening for gestational diabetes mellitus in a south\nAfrican population: prevalence, comparison of diagnostic criteria and the\nrole of risk factors. S Afr Med J. 2017;107(6):523–7.\n\n21. Hod M, Kapur A, Sacks DA, Hadar E, Agarwal M, Di Renzo GC, et al. The\nInternational Federation of Gynecology and Obstetrics (FIGO) initiative on\ngestational diabetes mellitus: a pragmatic guide for diagnosis, management,\nand care. Int J Gynecol Obstet. 2015;131:S173–211.\n\n22. Carolan M, Steele C, Margetts H. Knowledge of gestational diabetes among\na multi-ethnic cohort in Australia. Midwifery. 2010;26(6):579–88.\n\n23. Shriraam V, Rani MA, Sathiyasekaran B, Mahadevan S. Awareness of\ngestational diabetes mellitus among antenatal women in a primary health\ncenter in South India. Indian J Endocrinol Metabolism. 2013;17(1):146.\n\n24. Singh SG, Aiken J. The effect of health literacy level on health outcomes in\npatients with diabetes at a type v health Centre in Western Jamaica. Int J\nNurs Sci. 2017;4(3):266–70.\n\n25. Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Crotty K. Low health\nliteracy and health outcomes: an updated systematic review. Ann Intern\nMed. 2011;155(2):97–107.\n\n26. Pembe AB, Paulo C, D'Mello BS, van Roosmalen J. Maternal mortality at\nMuhimbili National Hospital in Dar-es-salaam, Tanzania in the year 2011.\nBMC Pregnancy Childbirth. 2014;14:320.\n\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in\npublished maps and institutional affiliations.\n\nMukuve et al. BMC Pregnancy and Childbirth (2020) 20:418 Page 8 of 8\n\n\n\tAbstract\n\tBackground\n\tMethods\n\tResults\n\tConclusions\n\n\tBackground\n\tMethods\n\tDesign and study site\n\tParticipants\n\tData collection\n\tData analysis\n\n\tResults\n\tMagnitude of screening for GDM\n\tTests used to screen for GDM\n\tPrevalence of undiagnosed GDM\n\tFactors associated with screening for GDM\n\n\tDiscussion\n\tConclusions\n\tGeneralizability\n\tLimitations\n\n\tSupplementary information\n\tAbbreviations\n\tAcknowledgements\n\tAuthors’ contributions\n\tFunding\n\tAvailability of data and materials\n\tEthics approval and consent to participate\n\tConsent for publication\n\tCompeting interests\n\tAuthor details\n\tReferences\n\tPublisher’s Note\n\n","UrlSlug":"magnitude-screening-gestational-diabetes-mellitus-urban-setting-tanzania-cross-sectional-analytic-study","collections":[],"countries":[],"asset_languages":[{"AlternateTitle":null,"language":[{"Id":1,"Name":"English"}],"contentTranslation":[],"contentLocation":[]}],"creators":[{"Name":"Noorani M","Id":27005,"ac":[{"Order":1}]},{"Name":"Sendagire I","Id":27006,"ac":[{"Order":2}]},{"Name":"Mgonja M","Id":27007,"ac":[{"Order":3}]},{"Name":"Mukuve A","Id":29076,"ac":[{"Order":0}]}],"keywords":[{"Name":"non-communicable disease","Id":10},{"Name":"filovirus","Id":81}]}}

Science Portal

Magnitude of screening for gestational diabetes mellitus in an urban setting in Tanzania; a cross-sectional analytic study