COMPARISON OF THE EFFICACY OF DIFFERNT TECHNIQUES FOR ESTIMATING FETAL WEIGT THROUGHOUT PREGNANCY

Estimating fetal weight throughout pregnancy
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Abstract
Fetal weights are a source of concern for pregnant women. This is because any deviations can result in complications with serious consequences to include to intrapartum asphyxia, injuries to the bones and brachial plexus, and so on. The concern is greatest for women in underdeveloped countries and from low-income households since they are unable to access modern medical technologies that would ease accurate determination of fetal weights to evaluate the pregnancy. The concern is heightened by the fact that fetal weight is a function of the gestational age and other demographic factors to include gender and ethnicity. To address this concern, there is a need to identify a method that would effectively and cost-efficiently determine fetal weight with the intention of identifying abnormalities and correcting them. Obviously, the identified method must be able to show that either exceeds 4 kg for extreme weight at birth and 2.5 kg for LFW at birth. Four methods have been proposed as suitable for use in technology deficient settings. These non-technology based tools for assessing fetal weight include tactile assessment, Dawn’s formula, Johnson’s formula and indirect estimation. The conclusion is made that there is a need to evaluate the three non-technology based tools to identify the most accurate tool that is guaranteed to work within the technological restrictions with a high level of accuracy.

Estimating fetal weight throughout pregnancy
Introduction
Both low and excessive fetal weights have been reported at delivery with serious consequences.

In fact, they have been associated with increased risk of complications when giving birth and during the puerperium. The risks are highest for low fetal weight (LFW) since it can cause intrauterine growth restriction or preterm delivery. On the other hand, excessive fetal weights have been linked to intrapartum asphyxia, injuries to the bones and brachial plexus, shoulder dystocia, pelvic floor and birth canal injuries, and postpartum hemorrhage (Basumatary, Bhattacherjee & Borah, 2015; Lighter, 2011). Despite the acknowledged risks associated with abnormal birth weights, there are occasions when the circumstances and situation do not allow for the tests to be carried out. This is particularly true for third world and low-income countries where medical technologies are scarce and income inequalities do not allow the mothers to visit medical facilities to check the fetal weights (Kahan et al., 2014; Sizer, Piche & Whitney, 2011). There is a need to address this concern by proposing a low cost fetal weight assessment tool that makes use of rudimentary technology and can be easily carried out at home.
Situational analysis
LFW is a source of concern in third world countries and among low income countries where cost and technology access factors influence access to modern technologies for evaluating the fetal weight (Aday, 2002). In such cases, the greatest source of concern is LFW that is presented as a situation in which the infant is born before 37 weeks regardless of the birth weight or is born weighing less than 2.5 kg. This would imply that the infant can be designated as having been born with LFW even when his or her weight exceeds 2.5 kg (Davidson, 2010; McLoyd, Hill & Dodge, 2005).
The state of affairs in which pregnant women in third world and low-income communities are indicated as being at high risk of birthing LFW babies is attributed to financial and psychosocial risk factors. These factors make it hard for them to ascertain the accurate fetal weights. The most prominent risk factor is cost associated barriers, which make it difficult for pregnant women to access care owing to an inability to pay for the care (Feldman, 2011; Singh, 2012). In addition to that, some of them mention that they have inadequate medical insurance coverage, which increases their direct health care costs and adds to their financial burden with most of them opting to forego medical checks unless faced with an emergency. In such cases, they are unable to identify abnormal fetal weights with the result that the babies are born with weights outside the standard measures (Stanhope & Lancaster, 2015). Besides that, these populations are exposed to environmental toxicants that affect the fetal weights. This is true for environments with air pollution, lead contamination, alcohol abuse, tobacco use, substance abuse, and partner violence, which inhibit the fetal development in the womb (Greenwald, 2010; Kurjak & Chervenak, 2006).
It is important to note that fetal weight is a function of the gestational age and other demographic factors to include gender and ethnicity. Some of the medical publications on fetal weight have subdivided results on the basis of demographic factors to include multiparous versus primiparous gravidas, female versus male genders, and ethnicity. In such cases, standard fetal growth curves are used to estimate an acceptable fetal weight range for the gestation (Davis & LaCour, 2016; Murphy, 2010). Nevertheless, the fetal growth curves usefulness are limited by the fact that they make assumptions that the growth curves were established correctly by matching the right gestation period to the associated gestation. In essence, the growth curves would be difficult to interpret successfully if the gestation is not dated correctly (Alligood, 2014; Friis, 2015). In general, the growth curves apply to the majority of pregnant women who have a good idea of their gestations and well-dated pregnancies (Smith & Parker, 2015).
Still, this approach has a limitation regarding predictive accuracy thereby making it a less than ideal tool for estimating the individual patient’s fetal weight. In fact, the growth curves report a wide range of values for any particular gestational age (Weber & Keller, 2013). Besides that, the growth curves are noted to report extreme deviations and inaccurate figures, particularly seen in cases of growth restriction. Overall, the fetal weight evaluations presuppose that the reference weight range at each gestation age is recognized and proven (Harris, 2015; Rolfes, Pinna & Whitney, 2011). Obviously, the proper reference range can only be established after correctly defining the right gestation age at which birth occurs. This remains an issue because increase in the fetal weight varies with each semester such that the second semester is marked by very rapid increases in weight. These are issues that must be evaluated when developing strategies for estimating the fetus weight since the weight is subjective particularly for areas where access to medical technology is restricted since pregnant women must still ensure that the fetal weight remains healthy (Kliegman et al., 2011).
Techniques for estimating fetal weight without using modern medical technologies
There are a range of medical tools that can be used to determine fetal weights in utero (with some degree of accuracy) without making use of sophisticated medical technologies. In fact, these tools only require assessment skills although they are subject to significant predictive errors. These tools, supposedly based on rudimentary and low-technology tools, are only focused on identifying the two limits of extreme (more than 4 kg) and low birth weight (less than 2.5 kg).
The first non-technology based tool is tactile assessment. It entails the medical personnel observing the pregnancy and making an educated guess about the fetal weight based on experience and knowledge. This is perhaps the most extensive and oldest tool applied by obstetricians and midwives to evaluate fetal weight since it is both costless and convenient. Still, the method is typically associated with large predictive errors since it is subjective and based on each individual’s knowledge and experience. In addition, the risk of errors is increased by other patient specific factors that include gestation time, occurrence of diabetes and obesity, weight gain during pregnancy, maternal weight, height, age and ethnicity, and fetal gender (Sharma et al., 2014).
The second non-technology based tool is Dawn’s formula. In this case, weight is reported in grams and calculated as the product of the uterus’ traverse diameter squared, the uterus’ longitudinal diameter, and 1.44. The only instrument used by the tool is a pelvimeter, with the caveat being that any double abdominal wall thickness exceeding 3 cm is deducted from the traverse diameter and half the excess is deducted from the longitudinal diameter. The third non-technology based tool is Johnson’s formula where is reported in grams and is calculated as a known function X subtracted from the symphysiofundal height in centimeters multiplied by 155. In this case, X is considered 11 when the presenting part is at the +1 station, 12 when it is at the 0 station, and 13 when not engaged (Basumatary, Bhattacherjee & Borah, 2015; Kathiriya, Patil & Patange, 2014; Sowjanya & Lavanya, 2015). The final method is indirect estimation, which presents the fetal weight in grams and calculated as the product of the abdominal diameter in centimeters (measured at the umbilicus level) multiplied by the symphysiofundal height in centimeters (Sharma et al., 2014).
Conclusion
One must accept that fetal weight is a source of distress among the medical fraternity and pregnant women, particularly those from underdeveloped countries and low-income communities. This is because failing to determine fetal weight presents opportunities for complications at birth and during the babies life. In fact, fetal weights outside standard values compromise the child’s ability to develop normally, survive, and lead a healthy life in future. Overall, the reality is that some pregnant women are placed in situations where they are unable to access modern medical technologies and must rely on rudimentary medical tools to determine the fetal weight. Regarding this approach, three methods stand out to include tactile assessment, Dawn’s formula, Johnson’s formula and rapid assessment. The present analysis proposes that the four tools be evaluated to determine the most accurate method. In this respect, there is a need to address the problem of limited access to medical technologies noted for pregnant women by identifying an approach that is guaranteed to work within the technological restrictions with a high level of accuracy.
References
Aday, L. (2002). At Risk in America: The health and health care needs of vulnerable populations in the United States (2nd ed.). San Francisco, CA: Jossey-Bass Inc.
Alligood, M. (2014). Nursing Theorists and their Work (8th ed.). St. Louis: MO: Mosby.
Basumatary, J., Bhattacherjee, A. & Borah, S. (2015). Estimation of Fetal Weight by Johnson’s Formula and Its Correlation with Actual Birth Weight. Scholars Journal of Applied Medical Sciences, 3(7B), 2552-2557.
Davidson, S. (2010). Still Broken: Understanding the U.S. Health Care System. Stanford, CA: Stanford University Press.
Davis, N. & LaCour, M. (2016). Foundations of Health Information Management. Amsterdam: Elsevier Health Sciences.Feldman, A. (2011). Understanding Health Care Reform: Bridging the gap between myth and reality. Boca Raton, FL: CRC Press.
Friis, R. (2015). Occupational Health and Safety for the 21st Century. Burlington, MA: Jones & Bartlett Learning.
Greenwald, H. (2010). Health Care in the United States: Organization, management, and policy. San Francisco, CA: Jossey-Bass.
Harris, M. (2015). Handbook of Home Health Care Administration (6th ed.). Burlington, MA: Jones & Bartlett Learning.
Kahan, S., Gielen, A., Fagan, P. & Green, L. (2014). Health Behavior Change in Populations. Baltimore, MD: John Hopkins University Press.
Kathiriya, D., Patil, Y. & Patange, R. (2014). Comparative Study of Various Methods of Fetal Weight Estimation at Term Pregnancy. International Journal of Recent Trends in Science And Technology, 9(3), 453-456.
Kliegman, R., Behrman, R., Jenson, H. & Stanton, B. (2011). Nelson Textbook of Pediatrics (19th ed.). Philadelphia, PA: Saunders Elsevier.
Kurjak, A. & Chervenak, F. (2006). Textbook of Perinatal Medicine (2nd ed.). Oxon: Informa Healthcare.Lighter, D. (2011). Basics of Health Care Performance Improvement. Burlington, MA: Jones & Bartlett Publishers.McLoyd, V. Hill, N. & Dodge, K. (2005). African American Family Life: Ecological and Cultural Diversity. New York, NY: The Guilford Press.
Murphy, J. (2010). The Educator’s Handbook for Understanding and Closing Achievement Gaps. Thousand Oaks, CA: Corwin.Rolfes, S., Pinna, K. & Whitney, E. (2011). Understanding Normal and Clinical Nutrition. Boston, MA: Cengage Learning.
Sharma, N., Srinivasan, J., Sagayaraj, B. & Lal, D. (2014). Foetal weight estimation methods: Clinical, sonographic and MRI imaging. International Journal of Scientific and Research Publications, 4(1), 1-5.
Singh, N. (2012). Nursing: The ultimate study guide. New York, NY: Springer Publishing Company.
Sizer, F., Piche, L. & Whitney, E. (2011). Nutrition: Concepts and controversies. Boston, MA: Cengage Learning.
Smith, M. & Parker, M. (2015). Nursing Theories and Nursing Practice (4th ed.). Philadelphia, PA: F. A. Davis Company.
Sowjanya, R. & Lavanya, S. (2015). Comparative Study of Clinical Assessment of Fetal Weight Estimation Using Johnson’s Formula and Ultrasonographic Assessment Using Hadlock’s Formula at or near Term. IOSR Journal of Dental and Medical Sciences, 14(4), 20-23.
Stanhope, M. & Lancaster, J. (2015). Public Health Nursing: Population-centered health care in the community (9th ed.). St. Louis, MO: Elsevier.Weber, J. & Kelley, J. (2013). Health Assessment in Nursing (5th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.