Factors influencing utilization of universal Gestational Diabetes Mellitus screening services among mothers in Sri Lanka: A cross sectional study

DOI: https://doi.org/10.21203/rs.3.rs-2336504/v1

Abstract

Background: Gestational Diabetes Mellitus (GDM) is associated with adverse maternal and perinatal outcomes and increased risk of developing diabetes in later life for both child and mother. Early detection of mothers with risk of GDM is pivotal in preventing such adverse outcomes. Sri Lanka adopted universal GDM screening at two time points in pregnancy (before 12 weeks and between 24 to 28 weeks) since 2014 and its utilization has not been assessed. This study aimed to assess utilization of GDM screening services and associated factors among antenatal mothers in Southern Sri Lanka.

Methods: A cross-sectional study was carried out in a convenient sample of 420 postpartum mothers delivered at three hospitals in Matara district, Sri Lanka. Data were collected using an interviewer-administered questionnaire. A data record sheet was used to collect information on availability of screening services and the logistics to provide services. Data were analysed using SPSS software and Chi square test was used to assess the association between variables.

Results:  Mean age (SD) of the mothers in the sample was 29 (5.2)  and the majority were in their first or second pregnancy. The coverage of first and second screening tests were 91.4% and 94.5% and timeliness were 72.4% and 59.5%, respectively. Median period of amenorrhoea at first and second screening were 10.0 (inter-quartile range: 7.5-12.5) weeks and 28.0 (inter-quartile range: 26.5-29.5) weeks.

A higher utilization was associated with higher maternal education for both screening tests (p=0.021 and p=0.025). Primiparity (p=0.033), nulliparity (p=0.03), planned pregnancy (p=0.00), proximity of nearest laboratory (p=0.02) and having family support (p=0.025) were positively associated with having at least one screening test. Timeliness of screening was associated with performing the test at field clinics for both screening tests (p<0.001 and p=0.007). Being unemployed (p=0.005), planned pregnancy (p=0.023) and availability of logistics at field clinics (p=0.007) were associated with timely performance of at least one screening test.

Conclusions: Though a high utilization of GDM screening was observed among antenatal mothers, measures should be taken to ensure timeliness of screening through improved availability and accessibility of screening facilities. Further studies are recommended to assess service provider’s perspective.

Background

Gestational diabetes mellitus (GDM) is defined as ‘any degree of glucose intolerance with onset or first recognition during pregnancy’ (1). It can lead to maternal hyperglycaemia, which is associated with adverse maternal and perinatal outcomes and increased risk of developing diabetes in later life of the child and mother as well (2). An increasing trend in prevalence of GDM was observed in global context (3) with an estimated global prevalence of 5–25% (4, 5). Highest prevalence is noted in South East Asian Region (SEARO) and more than 90% of the estimated cases are found in low and middle income countries (5) Sri Lanka also shows an increasing trend in prevalence of GDM, where a recent community based study has shown a prevalence of 13.9% (6).

The Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study highlighted the fact that maternal glucose intolerance has shown a liner relationship with adverse perinatal outcomes (2). Thus, early detection and prompt control of maternal glucose level is recommended. Screening is pivotal in prevention of adverse perinatal and long-term outcomes by early detection of mothers with increased risk of developing GDM. International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria has been proposed after reviewing published and unpublished data of HAPO study and other related studies in 2010 (7). Following that most of the technical bodies including American Diabetes Association (ADA) (8), World Health Organization (WHO)(9) and The International Federation of Gynaecology and Obstetrics (FIGO)(10) have recommended IADPSG criteria for GDM screening.

Though scientifically sound and valid screening and diagnostic tests and criteria were available, implementation of screening programmes is affected by client and service-related barriers in low resource settings (11). Lack of trained staff and equipment to perform screening tests and storage and transport issues for collected samples have been highlighted as healthcare system barriers and difficulty in attending clinics in fasting status, late contact with healthcare system and higher distance to primary or specialized care facilities as client related barriers (11). These barriers can reduce screening coverage in such settings leading to inadequate control of maternal hyperglycaemia, thus increasing both maternal and child mortality and morbidity due to adverse outcome of GDM in low- and middle-income countries (4). Therefore, a simple and feasible screening test to be used in universal screening at low resource settings is highly needed to overcome the barriers of screening (12). Diabetes In Pregnancy Study Group in India (DIPSI) introduced a simple method of screening for GDM for low resource settings. They proposed administering 75g of oral glucose challenge to pregnant woman irrespective of fasting state and 2-hour capillary blood glucose value to be checked - a test nominated as non-fasting Glucose Challenge Test (GCT) (13). And it claimed that such simple test can overcome most of the barriers specific to low resource settings highlighted above (13). Further when compared to Oral Glucose Tolerance test (OGTT), GCT showed no statistically significant difference in diagnosing GDM using a cut off at 140mg/dl after 2 hours of glucose intake. (11)

GDM screening had been incorporated to maternal care programme in Sri Lanka since ..... Pregnant mothers with risk factors for developing GDM had been screened at booking visit and all pregnant mothers had been screened between 24–28 weeks of gestation using post-prandial blood sugar test with a cut off threshold of 120mg/dl. (12) However, risk factor-based screening is reported to have low sensitivity, compared to universal screening in several studies conducted in both community and hospital settings. (13) (14) Sri Lanka adopted non-fasting GCT as recommended by DIPSI study as the universal GDM screening tool at field antenatal clinics since 2014. Universal screening of pregnant mothers for GDM before 12 weeks and between 24 to 28 weeks of gestation was incorporated to field maternal care package. All pregnant mothers screened positive with non-fasting GCT were referred for confirmatory test. (14). Nevertheless, there has been no assessment on utilization and factors associated with utilization of screening services at field carried out after the adoption of the new screening test.

Applicability, utilization, and barriers in utilization is of major concern when it comes to GDM screening in developing countries with low resources (12). Sri Lanka has a unique healthcare delivery system with high output with low resources. Therefore, the current study provides an insight on utilization of screening recommendation in a low resource setting and factors associated with it as facilitating factors and barriers. And thus, gives directions, that can be used to strengthen GDM screening in low resource settings, for policy makers and health planners in both global level and low resource settings.

Methods

A cross-sectional study was carried out among mothers delivered at three major hospitals in Matara district in the Southern province of Sri Lanka. These three hospitals account for more than 95% of the deliveries taking place in the district.

Participants

A non-probability convenient sample of 420 mothers delivered in two consecutive months at three main hospitals, that accounted for more than 95% of deliveries taken place in the district, were selected as the study sample. Those who were not registered at field antenatal clinics, had pre-existing diabetes, and delivered at period of amenorrhoea (POA) less than 28 weeks were excluded. The sample size was calculated using the formula suggested by Lwanga & Lemeshow for estimating a population proportion (15). In calculating the sample size, an estimated prevalence of 0.5 and a precision of 0.05 were used at a probability level of 0.05 and 10% allowance was added for non-responses.

Study instruments

Data on socio-demographic factors, access to health care and utilization of screening services were collected using a pre-tested, interviewer-administered questionnaire. A data record sheet was used to collect information on availability of screening services and logistics to provide services at the field clinics. Face, content, and consensual validity of the instruments were ensured through several discussion rounds with public health experts (Consultant Community physicians and experienced field health personnel). Interviews were conducted by the principal investigator at post-partum day one at the hospital and details regarding the screening was traced from the pregnancy records. Data on the availability of logistics were obtained and recorded by inspecting stock ledgers at field health facility.

Statistical analysis

Proportions of mothers underwent screening tests were calculated as screening coverage and timeliness of screening was defined as before 12 weeks for first screening and between 24–28 weeks for second screening. Availability of at least one functioning glucometer and monthly requirement of blood lancets, glucometer strips and 75g glucose sachets in stock ledger was considered as logistics availability. Data was analysed using statistical software and Chi square test was used as the test of significance of associations between variable considering 0.05 as the probability level.

Ethical and administrative clearance

Ethical approval for the study was obtained from the Ethical Review Committee, Faculty of Medicine, University of Ruhuna. Administrative approval was obtained by all relevant authorities (Regional Director of Health Services – Matara, Director / Medical Superintendent of hospitals). Informed written consent was obtained from all mothers prior to data collection. Mothers identified with problems were referred to the obstetrician through ward doctors.

Study was conducted while adhering to the World Medical Association Declaration of Helsinki on ethical principles for medical research involving human subjects.

Results

Four hundred and twenty mothers who delivered during the study period participated in the study (response rate 92.8%), and their socio-demographic characteristics are summarised in Table 1.

Coverage of first and second screening tests was 91.4% and 94.5% respectively. Median POA at first and second screening test was 10.0 weeks (IQR: 7.5–12.5) and 28.0 weeks (IQR: 6.5–29.5) respectively. Timeliness of first and second screening was 72.4% and 59.5% respectively. The first and second screening tests were performed at the field antenatal clinics for 38.3% and 30.3% mothers respectively. And 44.5% of first screening and 59.8% of second screening were done in private laboratories. Availability of logistics to perform non-fasting GCT (glucometer and strips, lancets and 75g glucose sachet) was 22.7% and 11.5% at the time of first and second screening tests, respectively.

Univariate analysis was carried out to identify associations of coverage and timeliness of screening tests with client-related and health service-related characteristics. A higher coverage was positively associated with higher maternal education level for both first and second screening tests (p = 0.021 and 0.025, respectively). Primiparity (p = 0.033), having no living children (p = 0.03) and planned pregnancy (p = 0.00) were associated with a higher coverage of first screening only. Shorter distance to the nearest laboratory (p = 0.02) and having family support during pregnancy (p = 0.025) were associated positively with a higher coverage in second screening (Table 2).

Timeliness of the first and second screening tests was positively associated with performing the screening at field clinics (p = 0.00 and 0.007, respectively). Further, being unemployed during pregnancy (p = 0.005) and planned pregnancy (p = 0.023) were positively associated with timeliness in first screening and availability of logistics at field clinics (p = 0.007) was associated with timeliness of second screening (Table 3).

Discussion

This study was conducted to assess the coverage, timeliness and associated factors of antenatal screening for gestational diabetes mellitus (GDM) among a sample of Sri Lankan pregnant mothers. The participants were recruited from among post-partum mothers to ensure inclusion of mothers who received the entire antenatal care experience at field level. A hospital-based approach was used to extract adequate sample size within a short period of time. As nearly 99% of the deliveries in Matara district were institutional deliveries (16), the study sample could be assumed as sufficiently representative of all the pregnant mothers in the area. Further, the characteristics of the study sample corresponded with national and provincial figures in ethnic distribution and education level favouring for generalizability of results (17).

Despite the higher coverage for both screening tests, a significant percentage of screening tests had been performed at private sector laboratories (44.8% and 59.8%), which exceeds the national average of out-of-pocket expenditure (44%) (18). Though timeliness of the first screening was reasonably good with the median POA at the time of testing being 10 weeks, the second screening test shows poor timeliness, where the median POA at the time of test exceeded the recommended period for second screening.

Positive association of higher education level with health seeking and screening participation has been evident in many studies in developing countries around the globe (19) reinforcing current study findings. Mothers with higher education are more aware of the services and have higher autonomy in households and it can lead to higher utilization of services (20, 21). This fact has been highlighted in both quantitative and qualitative studies in developing countries (19). Moreover, it was well established that those who are having planned pregnancies showed higher utilization and timely attendance for antenatal care (22, 23), as confirmed in the present study. Parity or birth order has been an important determinant of utilization of antenatal care services, and it was highlighted in a systematic review of studies in developing countries (19) and a separate study in South India (24). The current study also showed an inverse association of coverage with parity.

A better timeliness was associated with performance of screening test at field clinics and this might have been due to the strict adherence to the screening guidelines by the field health care workers and the strong performance monitoring system in line with the field health activities. Further, a qualitative study conducted among healthcare workers has pointed out that visiting private clinics during first trimester has contributed to delayed screening and not following recommended guidelines (25)At the same time as evident in previous studies that planned pregnancies are also associated with timely screening (22, 23). Mothers who were not employed during the pregnancy period showed a good timeliness as well going in line with finding from a similar study in South India on utilization of maternal care services (24). It is understandable as most of the health services are only available during office hours, creating barriers for working mothers to attend these health services. Non-availability of logistics might have made it unable to perform screening at field clinic and thus resulted in reduced availability in screening test making screening delayed as highlighted in study on antenatal care service utilization on Kenya (26).

More than 99% of deliveries are institutional deliveries in Sri Lankan context. Therefore, a sample from postpartum mothers form hospitals that accounts for more than 95% of deliveries in the district has given an adequately representative sample and thus contributed to the validity of the study. Interviewer administered questionnaire and data record sheets after observing clinic records and logistic records has added more validity to data eliminating recall bias. However cross-sectional nature of the study might have adversely contributed to temporal relationship of associations.

Conclusion

This study shows that, the universal GDM screening at field antenatal clinics show a higher coverage, however, its timeliness needs to be improved. Measures should be taken to improve the availability and accessibility for screening facilities. Further studies are recommended for assessing healthcare service provider’s perspectives on utilization of screening services and measures to improve their availability and accessibility.

Abbreviations

ADA               American Diabetes Association 

DIPSI              Diabetes In Pregnancy Study Group in India

FIGO              International Federation of Gynaecology and Obstetrics

GDM               Gestational diabetes mellitus 

GCT                Glucose Challenge Test 

HAPO             Hyperglycaemia and Adverse Pregnancy Outcomes

IADPSG         International Association of Diabetes and Pregnancy Study Group 

IQR                 Inter Quartile Range

POA                Period of Amenorrhoea 

SEAR              South East Asian Region

WHO              World Health Organization 

Declarations

Authors declare that ethical clearance and approval for data collection was obtained from Ethics Review Committee, faculty of Medicine, University of Ruhuna (reference number -17.07.2017:3.1). Informed written consent was obtained from each participant prior to data collection. Study was conducted while adhering to the World Medical Association Declaration of Helsinki on ethical principles for medical research involving human subjects. In the current study consent for publication is not indicated because no individual level data with identification was not collected and included in the manuscript.

The datasets used and/or analysed during the current study are available from the corresponding author on request. 

The authors declare that they have no competing interests. The study has been self-funded by the corresponding author. 

As for author contribution CJW was involved in technical guidance and supervising the research project while GJC did the planning the research project, data collection analysis and report writing. 

Authors acknowledge Regional Director of Health Services – Matara district, Director – District General Hospital Matara, Medical Superintendents – Base Hospital Kamburupitiya and Deniyaya, Consultant obstetrician and gynecologists at all three hospitals, All Medical Officers of Health – Matara district.  

References

  1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2013;36(SUPPL.1):67–74.
  2. The HAPO Study Cooperative Research Group. Associations With Neonatal Anthropometrics. Diabetes. 2009;58(February):453–9.
  3. Ferrara A. Increasing prevalence of gestational diabetes mellitus: A public health perspective. Diabetes Care. 2007;30(SUPPL. 2).
  4. Vince K, Perković P, Matijević R. What is known and what remains unresolved regarding gestational diabetes mellitus (GDM). J Perinat Med [Internet]. 2020 Oct 1 [cited 2022 Oct 7];48(8):757–63. Available from: https://pubmed.ncbi.nlm.nih.gov/32827397/
  5. Guariguata L, Linnenkamp U, Beagley J, Whiting DR, Cho NH. Global estimates of the prevalence of hyperglycaemia in pregnancy. Diabetes Res Clin Pract. 2014;103(2):176–85.
  6. Sudasinghe BH, Ginige PS, Wijeyaratne CN. Prevalence of gestational diabetes mellitus in a Suburban District in Sri Lanka: a population based study. Ceylon Medical Journal. 2016;61(4):149.
  7. International Association of Diabetes and Pregnancy Study Groups Consensus Panel. International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82.
  8. American Diabetes Association. Standards of medical care in diabetes--2011. Diabetes Care. 2011;34 Suppl 1(Supplement_1):S11-61.
  9. World Health Organization. Diagnostic Criteria and Classification of Hyperglycaemia First Detected in Pregnancy. World Health Organization. 2013;1–63.
  10. Hod M, Kapur A, Sacks DA, Hadar E, Agarwal M, Di Renzo GC, et al. The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management, and care. In: International Journal of Gynecology and Obstetrics. 2015. p. S173–211.
  11. Bhavadharini B, Uma R, Saravanan P, Mohan V. Screening and diagnosis of gestational diabetes mellitus – relevance to low and middle income countries. Clin Diabetes Endocrinol [Internet]. 2016;2(1):13. Available from: http://clindiabetesendo.biomedcentral.com/articles/10.1186/s40842-016-0031-y
  12. Nielsen KK, de Courten M, Kapur A. The urgent need for universally applicable simple screening procedures and diagnostic criteria for gestational diabetes mellitus – lessons from projects funded by the World Diabetes Foundation. Glob Health Action [Internet]. 2012 [cited 2022 Oct 11];5. Available from: /pmc/articles/PMC3409336/
  13. Anjalakshi C, Balaji V, Balaji MS, Ashalata S, Suganthi S, Arthi T, et al. A single test procedure to diagnose gestational diabetes mellitus. Acta Diabetol. 2009;46(1):51–4.
  14. Ministry of Health Sri Lanka. Guideline on screening for diabetes during pregnancy. Colombo; 2014.
  15. Lwanga S, Lemeshow S. Sample size determination in health studies: A practical manual, 1991. World Health Organization, Geneva [Internet]. 1991;88. Available from: http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:SAMPLE+SIZE+DETERMINATION+IN+HEALTH+STUDIES:+A+practical+manual#4
  16. Family Health bureau. Annual Report. Colombo: Family health buraeu; 2015.
  17. Department of Census and Statistics Sri Lanka. Census of Population and Housing - 2012 Final Report – Southern Province. In 2012. Available from: http://www.statistics.gov.lk/PopHouSat/CPH2011/Pages/Activities/Reports/Southern.pdf
  18. Ministry of Health Sri Lanka. Sri Lanka National Health Accounts 2013. 2016.
  19. Simkhada B, van Teijlingen ER, Porter M, Simkhada P. Factors affecting the utilization of antenatal care in developing countries: systematic review of the literature. J Adv Nurs [Internet]. 2008 Feb 1 [cited 2022 Nov 1];61(3):244–60. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-2648.2007.04532.x
  20. Erci B. Barriers to utilization of prenatal care services in Turkey. J Nurs Scholarsh [Internet]. 2003 [cited 2022 Nov 3];35(3):269–73. Available from: https://pubmed.ncbi.nlm.nih.gov/14562496/
  21. Nielsen BB, Liljestrand J, Thilsted SH, Joseph A, Hedegaard M. Characteristics of antenatal care attenders in a rural population in Tamil Nadu, South India: a community-based cross-sectional study. Health Soc Care Community [Internet]. 2001 [cited 2022 Nov 3];9(6):327–33. Available from: https://pubmed.ncbi.nlm.nih.gov/11846810/
  22. Dutta M, Shekhar C, Prashad L. Level, Trend and Correlates of Mistimed and Unwanted Pregnancies among Currently Pregnant Ever Married Women in India. PLoS One [Internet]. 2015 Dec 1 [cited 2022 Nov 4];10(12). Available from: https://pubmed.ncbi.nlm.nih.gov/26629813/
  23. Ochako R, Gichuhi W. Pregnancy wantedness, frequency and timing of antenatal care visit among women of childbearing age in Kenya. Reprod Health [Internet]. 2016 May 4 [cited 2022 Nov 4];13(1). Available from: /pmc/articles/PMC4855852/
  24. Navaneetham K, Dharmalingam A. Utilization of maternal health care services in Southern India. Soc Sci Med. 2002 Nov 1;55(10):1849–69.
  25. Chepulis L, Papa V, Morison B, Cassim S, Martis R. Barriers to Screening for Gestational Diabetes Mellitus in New Zealand Following the Introduction of Universal Screening Recommendations. Women’s Health Reports [Internet]. 2022 May 1 [cited 2022 Nov 4];3(1):465. Available from: /pmc/articles/PMC9148651/
  26. Magadi MA, Madise NJ, Rodrigues RN. Frequency and timing of antenatal care in Kenya: Explaining the variations between women of different communities. Soc Sci Med [Internet]. 2000 Aug 15 [cited 2022 Nov 4];51(4):551–61. Available from: https://pubmed.ncbi.nlm.nih.gov/10868670/

Tables

Table 1

Socio-demographic characteristics of study participants (N = 420)

Characteristics of the mothers

Number

Percentage (%)

Nationality

   

Sinhala

398

94.8

Muslim / Tamil

22

5.2

Marital status

   

Legally married

417

99.3

Other

3

0.7

Highest education level attained

   

No schooling or primary education

10

2.4

Secondary education

359

85.4

Tertiary education

51

12.1

Occupation during pregnancy

   

Employed

112

24.3

Not employed

318

75.7

Family type

   

Extended family

259

61.7

Nuclear family

161

38.3

Number of living children

   

No children

166

39.5

1–2

238

56.6

3 or more

16

3.8

Family support

   

Yes

345

82.1

No

75

17.9

Average monthly family income (LKR)

   

Less than 30, 000

103

24.5

30,000–44,999

159

37.9

45,000–60,000

82

19.5

More than 60,000

76

18.1

Parity

   

1

137

32.6

2

153

36.4

3 or more

130

31.0

Planned pregnancy

   

Yes

356

84.8

No

64

15.2

Distance to the nearest hospital with specialized care

   

< 30 min

174

41.4

30 min – 1 hour

180

42.9

> 1 hour

66

15.7

Distance to nearest laboratory

   

< 30 min

385

91.7

=>30 min

35

8.3

*Living together/ married but separated

Table 2

Factors (client) associated with coverage of screening tests

Characteristic

First screening test

Second screening

Coverage (N = 420)

No (%)

P– value

Timeliness (N = 384)

No (%)

P -

value

Coverage (N = 416)

No (%)

P – value

Timeliness (N = 393)

No (%)

P -

value

Highest education level attained

AL and above

161(95.3)

0.021

121(75.2)

0.304

161(97.6)

0.025

98(60.9)

0.655

Below AL

223(88.8)

157(70.4)

232(92.4)

 

136(58.9)

 

Employed during pregnancy

Yes

92(90.2)

0.609

56(60.9)

0.005

95(93.1)

0.609

173(38.1)

0.337

No

292(91.8)

222(76.0)

298(94.9)

 

61(64.2)

 

Family type

Extended family

237(91.5)

0.943

169(71.3)

0.545

245(95.0)

0.576

146(59.6)

0.979

Nuclear family

147(91.3)

 

109(74.1)

 

148(93.7)

 

88(59.5)

 

Living children

Yes

224(88.2)

0.003

156(69.6)

0.153

240(94.9)

0.664

149(62.1)

0.199

No

160(96.4)

 

122(76.3)

 

153(93.9)

 

85(55.6)

 

Family support

Yes

319(92.5)

.104

230(72.1)

.774

328(95.6)

0.025

199(60.7)

0.306

No

65(86.7)

 

48(73.9)

 

65(89.0)

 

35(53.9)

 

Average monthly family income (LKR)

Below median a

194(92.4)

0.486

140(72.2)

0.919

195(94.2)

0.812

104(53.3)

0.919

Median and above

190(90.5)

 

138(72.6)

 

198(94.7)

 

130(65.7)

 

Parity

Primiparous

131(95.6)

0.033

102(77.9)

0.085

126(94.0)

0.786

70(55.6)

0.269

Multiparous

253(89.4)

 

176(69.6)

 

267(94.7)

 

164(61.4)

 

Planned pregnancy

Yes

335(94.1)

0.000

249(74.3)

0.023

335(95.2)

0.143

204(60.9)

0.189

No

49 (76.5)

 

29(59.2)

 

58(90.6)

 

30(51.8)

 
a 39,000 LKR

Table 3

Factors (health service) associated with coverage of screening tests

Characteristic

First screening test (N = 420)

Second screening (N = 416)

Coverage (N = 420)

No (%)

P– value

Timeliness (N = 384)

No (%)

P -

value

Coverage (N = 416)

No (%)

P – value

Timeliness (N = 393)

No (%)

P -

value

Distance to nearest hospital with specialized care

< 30min

160(92.0)

0.746

112(70.0)

0.375

162(94.7)

0.843

95(58.6)

0.761

> 30min

224(91.1)

 

166(74.1)

 

231(94.3)

 

139(60.2)

 

Distance to nearest laboratory

< 30min

352(91.4)

1.00

256(72.7)

0.630

364(95.5)

0.002

218(59.9)

0.618

> 30min

32(91.4)

 

22(68.7)

 

29(82.9)

 

16(55.2)

 

Place of screening test

Field

   

125 (85.0)

0.00

   

83(69.7)

0.007

Other

   

153(64.6)

     

151(55.1)

 

Availability of PHM*

Yes

360(91.4)

1.00

264(73.3)

0.613

341(94.5)

. 229

204(59.8)

1.00

No

21(91.3)

 

14(76.7)

 

49(94.2)

 

29(59.2)

 

Availability of logistics*

Yes

73(85.9)

.140

52(71.2)

0.766

41(95.3)

1.000

33(80.5)

0.002

No

266(91.7)

 

196(73.7)

 

311(93.9)

 

173(55.6)

 
* Availability data for mothers from other districts was not collected